research methodology part 2

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• Published: 26 August 2024

The impact of surge capacity enhancement training for nursing managers on hospital disaster preparedness and response: an action research study

• Alireza Shafiei 1 ,
• Narges Arsalani 2 ,
• Mehdi Beyrami Jam 3 &
• Hamid Reza Khankeh   ORCID: orcid.org/0000-0002-9532-5646 4  

BMC Emergency Medicine volume  24 , Article number:  153 ( 2024 ) Cite this article

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Introduction

Hospitals as the main providers of healthcare services play an essential role in the management of disasters and emergencies. Nurses are one of the important and influential elements in increasing the surge capacity of hospitals. Accordingly, the present study aimed to assess the effect of surge capacity enhancement training for nursing managers on hospital disaster preparedness and response.

All nursing managers employed at Motahari Hospital in Tehran took part in this interventional pre- and post-test action research study. Ultimately, a total of 20 nursing managers were chosen through a census method and underwent training in hospital capacity fluctuations. The Iranian version of the “Hospital Emergency Response Checklist” was used to measure hospital disaster preparedness and response before and after the intervention.

The overall hospital disaster preparedness and response score was 184 (medium level) before the intervention and 216 (high level) after the intervention. The intervention was effective in improving the dimensions of hospital disaster preparedness, including “command and control”, “triage”, “human resources”, “communication”, “surge capacity”, “logistics and supply”, “safety and security”, and “recovery”, but had not much impact on the “continuity of essential services” component.

The research demonstrated that enhancing the disaster preparedness of hospitals can be achieved by training nursing managers using an action research approach. Encouraging their active participation in identifying deficiencies, problems, and weaknesses related to surge capacity, and promoting the adoption and implementation of suitable strategies, can enhance overall hospital disaster preparedness.

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Hospitals, as the main providers of healthcare services, play an essential role in managing and reducing the suffering of injured people in emergencies and disasters [ 26 ]. Most of the definitive, life-saving and emergency care for injured people are carried out in hospitals. Therefore, the preparedness of hospitals is essential in moderating and decreasing the negative health consequences of disasters [ 29] . From an international perspective, the Sendai Framework for Disaster Risk Reduction 2015–2030 and World Health Organization (WHO), highlights the need for disaster preparedness and risk reduction measures in hospitals [ 30 , 31 ]. Based on WHO, the preparedness and well-trained hospital personnel is the main factor in minimizing the casualties and damages resulting from disasters. Therefore, assessing and improving hospitals’ capacity and preparedness for disasters is a crucial first step toward effective disaster response and achieving the objectives outlined in the Sendai Framework 2015–2030 [ 30 , 32 ].

In Iran, efforts to enhance hospitals’ disaster preparedness began in the winter of 2009 with the creation of the National Hospital Disaster Preparedness Plan (NHDPP) by the Health Research Center on Disasters at the University of Social Welfare and Rehabilitation Sciences. This initiative, serving as a national guideline, received backing from the Secretariat of the Disaster Health Working Group in the Ministry of Health and was communicated to all hospitals across the country [ 1 ]. Furthermore, in the third phase of Iran’s hospital accreditation program, criteria for disaster risk management were added in the form of seven standards and thirty-seven measurements, directly addressing the hospital’s preparedness and response to emergencies and disasters [ 2 ].

To effectively address disasters, a hospital needs a thorough preparedness strategy, necessary tools, equipment, sufficient space, skilled staff, and, in essence, enough surge capacity [ 33 ]. Surge capacity refers to the ability to acquire additional resources during a disaster or emergency. It is the ability to provide quickly the usual functions beyond the increased demand for experienced staff, medical care, and social health services. Surge capacity has three core components including staff, stuff, and structures [ 3 ].

Nurses are one of the major groups of healthcare providers in hospitals(staff) [ 4 ]. They have the most contact with patients and provide the most care [ 5 ]. Along with other disaster management teams, they also play crucial roles in planning, education and training, response, and recovery for hospital disaster preparedness [ 6 , 7 ].

Experiences have shown that training and exercises before the occurrence of disasters can significantly increase the ability of people to face critical situations such as natural disasters [ 4 , 6 ]. Therefore, providing effective disaster training for nurses has a crucial role in increasing hospital preparedness and capacity for response to disasters. Previous studies have demonstrated inadequate training for nurses on preparedness and response to emergencies and disasters [ 2 , 4 , 5 , 6 ]. Moreover, despite numerous investigations assessing the preparedness of Iranian hospitals for disasters [ 8 , 9 , 10 ], to the best of our knowledge, only a limited number of interventional studies have explored the impact of disaster training for nurses on enhancing hospital disaster preparedness in Iran. Hence, recognizing the crucial contributions of nurses to the development of hospital capacity, this research aimed to examine the effects of training of surge capacity enhancement for the nursing managers on the emergency and disaster preparedness of Motahari Hospital in Iran.

Study design and settings

The current investigation utilized a pretest-posttest interventional design, conducted at Shahid Motahari Burn Hospital, affiliated with Iran University of Medical Sciences in Tehran, Iran. This hospital is the first and only main and specialized center providing medical services to burn patients in the center of the country and plays an essential role in the management of the injured during disasters and emergencies, especially fires.

Population and sampling

Aligned with the study’s goals, we employed a census sampling method to select all nursing managers at Shahid Motahari Hospital in Tehran. The eligibility criteria encompassed individuals within the nursing profession, such as nursing managers, supervisors, and head nurses, who held a minimum of a bachelor’s degree and possessed a minimum of one year of managerial experience. Those who expressed unwillingness to participate in the study were excluded.

The data was collected using the Persian version of the Hospital Emergency Response Checklist developed by Khankeh et al. (2013) [ 34 ]. The checklist was used to estimate the current state of preparedness of hospitals and healthcare centers. The original version of this tool was formulated by the World Health Organization [ 35 ]. The checklist measures 9 key components including command and control (7 items), triage (10 items), human resources (15 items), communication (9 items), surge capacity (13 items), logistics and supply management (10 items), safety and security (10 items), continuity of essential services (8 items) and post-disaster recovery (8 items). The reliability and validity of the Persian version of the tool have been confirmed by Karimian et al. (2013) [ 14 ]. They confirmed the validity of the tool (CVI = 0.86) and its reliability with Cronbach’s alpha of 0.83. The items in the checklist are rated on a 3-point scale (1 = due for review, 2 = in progress, and 3 = completed).

Moreover, the hospital surge capacity guideline was used to examine the current situation, weaknesses, problems, and target actions and develop a hospital surge capacity training program. This guidance was formulated by the Health in Emergency and Disaster Research Center at the University of Social Welfare and Rehabilitation Sciences and approved and disseminated by the Iranian Ministry of Health [ 34 ].

Intervention

This intervention study adopted a participatory action research approach as the participants were involved in problem identification and intervention to improve the process. Research in action is a type of study used by people to change unfavorable situations into relatively favorable situations and finally improve procedures in their workplace [ 11 ]. Action research is a type of study that attempts to learn and understand purposeful interventions meant to bring about desired changes in the organizational environment [ 12 ]. Action research simultaneously promotes problem-solving and expands scientific knowledge, as well as strengthens the skills of research participants [ 13 ].

In general, in action research, participants are involved in all stages of the research, from identifying the problem and collecting the data to planning, implementation, and evaluation. The engagement of participants in all stages of the research will encourage their participation in the research procedure and make them interested in the research topic [ 7 ].

This study adopted Streubert Speziale and Carpenter’s five-step action research method [ 7 ]. These steps include (1) defining the problem (explaining the current situation), (2) collecting, analyzing, and interpreting data, (3) planning, (4) implementing, and (5) evaluating. In this research, nurses actively engaged in elucidating the issue, gathering and analyzing data related to hospital surge capacity, devising and executing capacity-enhancing strategies based on their training, and assessing these measures to enhance hospital disaster preparedness and response.

To collect the data, the required permits were obtained from the hospital managers and officials. Besides, some instructions about the research procedure and data gathering were provided in a briefing session for the participants. The researcher and the participants made the required arrangements and plans for conducting the training intervention. In the next step, the items on the instruments (the Hospital Emergency Response Checklist) were completed by the participants(pre-test). When completing the checklist, the officials and managers of the hospital were also interviewed to better identify the problems and challenges related to the surge capacity. After that, topics and concepts related to increasing surge capacity and hospital disaster preparedness were taught to the participants during a two-day workshop, and they did round table exercises. Following the National Hospital Emergency Preparedness and Response Instructions [ 1 ], the content of the workshop included hospital risk and hazard assessment, incident command system, early warning system, response plan, and enhancing hospital capacity in response to emergencies and disasters with emphasis on solving problems and weaknesses identified in the pre-intervention stage. After completing the training workshop, the participants were given a six-month opportunity to carry out interventions and transfer the training to other staff and nurses. During this period, the participants and other members of the disaster risk management committee attended meetings held every two weeks. In these meetings, the necessary actions for the next two weeks were set, and the officials to manage each action were specified. In addition, in each meeting, the extent to which the goals of the previous meeting were achieved and the reasons for not fulfilling them were discussed. Finally, the items in the Hospital Emergency Response Checklist were completed for the second time (post-test) and the collected data was analyzed.

Ethical considerations

To comply with ethical protocols, this research project was approved with the code of ethics of the Ethics Committee of the University of Rehabilitation Sciences and Social Health. Moreover, informed consent was obtained from all the participants. The participants completed the checklists anonymously and, they were assured that their participation was voluntary and had no impact on their evaluation procedure.

The participants in this study were 20 nursing managers and supervisors at Motahari Burn Hospital in Iran. The study participants had an average age of 38 years (30 to 52 years old) and an average work experience of 16 years (4 to 25 years). Most of the participants were female (15 persons), married (18 persons), had a bachelor’s degree (12 persons), and had served in managerial positions (9 persons). Table No. 1 Shows other demographic characteristics of the participants. The surge capacity enhancement strategies that were recognized and put into practice by the participants throughout the study(6 months) included: 1- Executing a memorandum with retired personnel and reactivating them when necessary, Executing a memorandum with the Iran University of Medical Sciences to hire students if needed, drafting instructions for requesting staff from the relevant authorities such as the Emergency Operations Center (EOC) of the Ministry of Health, in the realm of enhancing “staff” capacity. 2- Preparing and reserving medications and essential equipment for a minimum duration of 72 h, signing a memorandum with other hospitals and nearby health centers to provide equipment in emergencies, and also creating more water storage volume to be used in emergencies and disasters, in the realm of enhancing “stuff” capacity. 3- Identifying suitable non-clinical and clinical spaces in the Motahhari Hospital to place beds and admit patients during disasters and emergencies, concluding an agreement with a school near the hospital to provide physical space for the hospital, creating a new rehabilitation department in the hospital, enlarging the space of the emergency department in the realm of increasing “space” capacity. And, 4- developing plans and instructions necessary to manage the risk of emergencies and disasters, doing training and practice in the hospital, in the realm of enhancing “system” capacity. The data showed that hospital disaster preparedness was at an average level (184) before the intervention and reached the optimal level (216) after the intervention. Also, the results also demonstrated that, except for “continuity of essential services”, the intervention improved the hospital’s disaster preparedness score across all dimensions. Most notably, the intervention enhanced “surge capacity” by 10 units and “staff” by 6 units. For detailed information on the intervention’s effects on hospital preparedness dimensions, please refer to Table No. 2 .

This study aimed to examine how providing action research training to nursing managers enhances surge capacity and contributes to improving hospital disaster preparedness. Many hospitals may face numerous challenges due to inadequate preparedness in the face of disasters and the increased demand for healthcare services [ 36 , 37 ]. The results of this study indicated that implementing the surge capacity enhancement intervention for nursing managers and officials led to a 32-unit improvement in disaster preparedness at Motahari Hospital. This improvement was expected because surge capacity is one of the most important components of hospital disaster preparedness and response.

Regarding the impact of the intervention on enhancing hospital disaster preparedness, various studies have been conducted in Iran, each employing distinct approaches to bolster preparedness.

In a study conducted by Karimiyan et al. (2013), it was found that hospital preparedness training aligned with the national plan significantly enhanced the hospital’s preparedness to address emergencies and disasters [ 14 ]. Delshad et al. (2015) showed early warning system training improved the preparedness of Motahari Hospital in emergencies and disasters [ 15 ]. Also, Salawati et al. (2014) in another study, examined the effect of teaching and applying non-structural hospital safety principles for nurses on the preparedness of medical departments of several private and public hospitals in Tehran during disasters [ 16 ]. The findings indicated that the safety score of two non-structural and functional parts of the hospital safety index increased after the intervention. The authors concluded that teaching and applying non-structural safety principles to nurses improves hospital safety and preparedness [ 16 ].

Like numerous other hospitals in Iran [ 17 , 18 , 19 ], Motahari Hospital’s disaster preparedness status was assessed as moderate before the intervention. Nevertheless, some studies have indicated inadequacies in the preparedness level of the examined hospital. For example, both the investigation conducted by Hekmatkhah et al. [ 20 ] and that of Ojaghi et al. [ 21 ] revealed insufficient preparedness in the hospitals under examination.

The current study demonstrated that enhancing the hospital’s response capacity and hospital’s disaster preparedness across various components can be achieved through capacity-building training for nursing managers through action research. The greatest effect of the intervention in this study was on “surge capacity” and the “human resource” dimension(staff). This outcome can be primarily attributed to instructing the hospital surge capacity-building principles for participants in the training workshop. Additionally, due to steps were taken to augment capacity in terms of “human resources”, “medication, and equipment”. Two studies conducted in Iran have identified a shortage of human resources and equipment as a primary factor contributing to the limited preparedness of hospitals in dealing with disasters [ 22 , 23 ]. In this research, the re-employment of retired employees and the use of university students were among the most important strategies that were adopted to increase the hospital capacity and preparedness in the human resource dimension. Similarly, Dowlati et al. (2021) reported that the preparation of a list of employers from other hospitals and medical centers, including clinics and health students, is one of the most important strategies to increase the capacity of hospital staff to respond to chemical, biological, and nuclear hazards and disasters [ 38 ].

The results of this study show that the intervention improved the hospital preparedness scores in the “triage” and “command and control” dimensions. In this context, the educational intervention on triage by Rahmati and colleagues enhances the preparedness of the emergency department, as highlighted in their study [ 24 ]. Also, Delshad et al. conducted a study where actions such as designating an external location for triage and formulating a strategy for the postponement of elective surgeries contributed to an improvement in the hospital preparedness score [ 15 ].

The results of this study emphasize that enhancing hospital preparedness can be achieved through conducting a needs assessment, recognizing gaps within the organization as identified by study participants, and effectively communicating and raising awareness among hospital managers. In this context, Karimian et al. (2013) underscored the importance of providing additional training for officials, managers, and hospital staff concerning emergency preparedness and response in hospitals [ 14 ].

The data in the present study indicated the intervention had a smaller impact on the components of “continuity of essential services”, “logistics and supply”, and “safety and security” compared to other components of hospital preparedness. Perhaps one of the main reasons was the restricted timeframe of the study and limited financial resources to carry out capacity-building and preparedness measures in these dimensions. As stated earlier, measures to increase the surge capacity and improve preparedness were formulated and followed up during the meetings of the emergency and disaster risk committees. Since these meetings were held every two weeks, the 6-month timeframe of the study did not leave an opportunity to carry out measures to improve the mentioned components. Furthermore, the limited financial resources can be considered one of the main reasons for not carrying out the actions planned by the committee. The findings of the “logistics” and “essential services” are consistent with the findings of the study by Ingrassia et al. (2016). This study showed that hospital preparedness in these dimensions was poor [ 25 ]. The findings concerning the " logistics and supply” as well as the “countiniuty of essential services “dimensions in this research align with the outcomes observed in Ingrassia et al.‘s (2016) study, highlighting the inadequate preparedness of the hospital in these aspects [ 25 ].

Limitations

The study was constrained by a limited duration of 6 months and insufficient financial resources, restricting the ability to implement further measures to enhance hospital preparedness. Future investigations could overcome these limitations by extending the study period to at least one year and ensuring adequate financial resources. Furthermore, as this study solely assessed the impact of the intervention on the disaster preparedness level of a single hospital, statistical analysis could not be conducted due to the absence of mean and standard deviation data. The alterations were solely presented descriptively.

This study examined the effect of surge capacity training using an action research plan on disaster preparedness and response at Shahid Motahari Hospital in Tehran. The results showed that surge capacity enhancement training for nursing managers and officials increased their sensitivity to the importance of hospital emergency preparedness and response. Furthermore, their proactive involvement in recognizing capacities, deficiencies, problems, and weaknesses with appropriate tools and taking measures to address them can improve hospital emergency preparedness and response. The findings indicated that senior managers within the hospital can instigate changes through the provision of financial backing and the implementation of mandatory protocols.

Data availability

The datasets that were used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to express their acknowledgments to the staff at the Department of Postgraduate Studies in the University of Social Welfare and Rehabilitation Sciences and appreciate the sincere cooperation of hospital managers, officials, and staff of Shahid Motahhari Hospital for their contributions to conducting this research project.

This study was conducted as part of a master’s thesis at the University of Social Welfare and Rehabilitation Sciences.

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Alireza Shafiei

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Narges Arsalani

Department of Pre-Hospital Medical Emergencies, School of Paramedical, Qazvin University of Medical Sciences, Qazvin, Iran

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ASH, HKH design of the study, MB, ASH and NA collect and analysed the data and ASH, MB, HKH preparation of the manuscript.

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Shafiei, A., Arsalani, N., Beyrami Jam, M. et al. The impact of surge capacity enhancement training for nursing managers on hospital disaster preparedness and response: an action research study. BMC Emerg Med 24 , 153 (2024). https://doi.org/10.1186/s12873-024-00930-1

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• Published: 22 August 2024

Spatial and temporal changes of ecosystem service value and its influencing mechanism in the Yangtze River Delta urban agglomeration

• Yugui Lu 1 , 2 ,
• Jiacong Wang 3 &
• Xiaokun Jiang 1  

Scientific Reports volume  14 , Article number:  19476 ( 2024 ) Cite this article

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• Ecosystem services
• Environmental economics
• Sustainability

As the mainstream and trend of urban development in China, deeply exploring the spatiotemporal patterns and influencing mechanisms of ecosystem service value in the Yangtze River Delta urban agglomeration is of great significance for achieving sustainable development goals in urban agglomerations. This paper uses the normalized difference vegetation index and net primary productivity as dynamic adjustment factors to measure the ecosystem service value of the Yangtze River Delta urban agglomeration and analyze its spatiotemporal evolution characteristics. Furthermore, a panel quantile regression model is constructed to explore the response differences of ecosystem service value at different levels to various influencing factors. The results show that: (1) From 2006 to 2020, the ecosystem service value of the Yangtze River Delta urban agglomeration decreased by 37.086 billion yuan, with high-value areas mainly concentrated in the southern part of the urban agglomeration. (2) The value structure of various land type ecosystems and primary ecosystem sub-services in the Yangtze River Delta urban agglomeration is stable. (3) The number of grid units with reduced ecosystem service value is continuously increasing, mainly distributed in the eastern coastal areas. (4) The degree of interference of various types of land on ecosystem service value varies, and the response of ecosystem service value at different levels to the same influencing factor also shows heterogeneity. In summary, exploring the spatiotemporal patterns of ecosystem service value in the Yangtze River Delta urban agglomeration and analyzing its influencing mechanisms is conducive to adjusting the intensity of human utilization and protection methods of ecosystems, which is of great significance for enhancing the value of ecosystem products in urban agglomerations.

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Unveiling the dynamics of urbanization and ecosystem services: insights from the Su-Xi-Chang Region, China

Introduction.

Ecosystems play a crucial role in providing humans with essential biological resources, including food and raw materials. Additionally, they also provide vital services for climate regulation, hydrology management, gas regulation, and waste disposal from human activities 1 , serve as a fundamental cornerstone for ecological security, human living environments, and sustainable economic and social development 2 . According to the United Nations Environment Programme's 2021 Annual Report, humanity still faces a significant journey in addressing the global crisis and improving the “human-earth relationship”. Promoting harmony between people and nature has become increasingly crucial. There is a global consensus on the importance of protecting and enhancing the ecology to ensure a healthy living environment for humans 3 . The conflict between the degradation of ecosystem service capacity and the growing demands of human beings is intensifying. The increasingly complex relationship between humans and land has brought the research on Ecosystem service value (ESV) to the forefront of the academic community 4 , 5 , 6 . China's urbanization level continues to rise due to rapid economic development in recent years 7 , 8 , 9 . However, this trend is accompanied by the occupation of significant amounts of arable land, wetlands, and forestland 10 , 11 . Changes in ecological land use patterns pose a serious threat to the balance of urban ecological environments 12 . Serve as the primary form and crucial driver of urbanization in China, urban agglomeration experience frequent changes in ecological land patterns during urbanization and exhibit a significant positive spatial spillover effect on urban land urbanization. This implies that an increase in the urbanization level of central cities will drive a corresponding increase in the urbanization level of adjacent cities 13 . As a new form of urbanization, the construction of urban agglomerations not only promotes regional economic development but also has a profound impact on the local ESV 14 . The Yangtze River Delta (YRD) urban agglomeration, known as the most economically developed and highly urbanized region in China 15 . The rapid urban expansion and economic growth have attracted widespread attention 16 . The significant increase in urbanization level is accompanied by a substantial consumption of land and water resources, placing enormous pressure and challenges on the ecological environment, which is critical to the region's sustainable development and the quality of life for its residents 17 . The unique position of the YRD urban agglomeration, along with its core role in the economic pattern of China and even the world, provides an ideal case for studying the spatiotemporal pattern evolution and influence mechanisms of ESV in rapidly urbanizing areas.

The concept of ESV was first proposed by Daily (1997) and gives a numerical indicator based on monetary units, which is a quantitative estimate of the capacity of ecosystem services 18 . Costanza et al. 19 first summarized the functional types of ecosystem services and quantified the global natural asset value, laying the foundation for quantitative estimation of ESV. Since then, academics have conducted extensive research on ecosystem services. In terms of calculating ESV, the approach is based on two main dimensions: economic value and material quality. From the economic value per spective, there are the equivalent factor method 20 and the remote sensing model method 21 respectively. The material quality assessment 22 method is the perspective based on the material quality. In addition, some researches have also evaluated regional ESV by Meta-regression using the results of the available literature 23 , which is a rigorous statistical approach with strong objectivity 24 . Li et al. 25 conducted an assessment of China's terrestrial ESV from the perspective of economic value, finding a downward trend over time in the country's terrestrial ESV. Additionally, they observed spatial heterogeneity in ESV changes. Chen et al. 26 assessed the ESV of the urban agglomeration in the middle reaches of the Yangtze River and found that forests contributed the largest proportion to the ESV, primarily providing hydrological regulation services. They also noted that the ESV in urban areas and plains was significantly lower than in other regions. Hou et al. 27 discovered that the ESV of Xi’an in China increased along the urban–rural gradient. Scholars have extensively studied the temporal and spatial characteristics of ESV at various administrative scales, which could aid regional ecosystem management. However, to manage different ecosystems precisely, assessments need to be conducted on the ESV of specific ecosystems. Song et al. 28 found that in the wetlands of the Northeast China region, the reduction in natural wetland area was mainly converted to farmland, resulting in an overall increase in ESV. Kibria et al. 29 . conducted research on Cambodia's national forest park and identified that the value of this forest ecosystem was underestimated and faced threats from population pressure and illegal extraction.

The assessment of ESV is a way to identify ecological problems and is not sufficient for optimal regional ecosystem service management. Therefore, it is particularly important to study the influence mechanism of ESV in order to solve regional ecological problems. The research methods on exploring the impact mechanism of ESV are mainly divided into whether to consider the spatial correlation of variables. To be specific, the methods that consider the spatial correlation of variables include geographic weighted regression 30 , 31 and geographic detector 32 . The methods that do not consider the spatial correlation of variables mainly include grey correlation analysis 33 and logistic regression 34 . It has been shown that the factors affecting ESV can be summarized as both natural environmental and human factors 35 with natural factors as direct drivers and human factors as indirect drivers 36 . Natural factors mainly include vegetation growth, climate conditions and terrain conditions 37 . Vegetation can affect ESV by maintaining soil, sequestration of carbon and other ecological services 38 . For example, climatic factors represented by thermal and water conditions (temperature, rainfall) 39 , topographic relief, elevation 40 , and so on, mainly affect ESV by influencing vegetation growth. Human factors include land use, population density and economic density 41 . With the development of cities, population agglomeration and urban expansion 42 have changed the pattern of regional land use 43 as well as the accompanying socio-economic activities 44 , which indirectly affect ESV by generating pollutants 45 . Natural and human factors are intertwined, and human beings will selectively and systematically develop land use according to natural conditions 46 . However, inappropriate over-exploitation 47 and excessive population density 48 can exceed the carrying capacity of the ecosystem for human activities, resulting in serious consequences such as environmental damage (soil erosion, desertification) and ecological collapse (For example, the ecosystem service capacity is declining, and the service capacity of food provision, climate regulation and hydrological regulation is insufficient). Therefore, the investigation of ESV impact mechanisms can help humans improve the efficiency of ecosystem management and contribute to the improvement of human-land relations. Therefore, the exploration of ESV impact mechanisms can help humans to decode regional ecological problems, improve the efficiency of ecosystem management, as well as contribute to the improvement of human-earth relations.

In summary, there is an abundance of research results on the estimation of ESV and its influencing mechanisms, which lays a solid theoretical and methodological foundation for this study. By taking the Yangtze River Delta urban agglomeration as the entry point, this paper will analyze the characteristics and influencing mechanisms of ESV’s spatiotemporal pattern evolution. This can provide insight for good ecosystem management in urban agglomeration across China. Additionally, it offers theoretical guidance for promoting sustainable socio-economic development and is crucial for formulating effective ecological conservation and urban planning policies, contributing to the sustainable development of the region. It also has the potential to point out new paths for the synergistic coexistence of economic development and ecological environment protection in other regions globally. The possible contributions of this paper include: (1) Calculating ESV by subdividing grid units and analyzing the ESV spatiotemporal pattern evolution from multiple perspectives, which is beneficial for more refined ecosystem management. (2) Using normalized difference vegetation index (NDVI) and net primary productivity (NPP) as dynamic adjustment factors in the ESV accounting, making the assessment results more consistent with regional characteristics. (3) Building a panel quantile regression model and utilizing “segmented effects” to reveal the underlying mechanisms of ESV influence in depth, emphasizing the heterogeneity of various influencing factors at different ESV levels on its dynamic changing trend, providing a new perspective for the study of ESV influence mechanisms.

Study area and data sources

Study area overview.

The YRD urban agglomeration is located in the downstream region of the Yangtze River in China, with a total area of 211,700 km 2 , occupying approximately 2.2% of China's land area (Fig.  1 ). It is composed of 26 cities including Shanghai, Nanjing, Hangzhou, and Hefei. The YRD urban agglomeration is low-lying in the south and high in the north, with an elevation below 10 m and scattered remnants of some solitary mountains. The YRD urban agglomeration is the region with the highest density of river network in China, with a predominantly subtropical monsoon climate and a significant increase in average annual temperature and average annual maximum and minimum temperatures in recent years. The YRD urban agglomeration has maintained high growth since 2006. According to the data of the municipal statistics bureau, the average urbanization rate of the YRD urban agglomeration reached 75.01% by 2020. As a core region, the YRD urban agglomeration had a GDP of over 20 trillion yuan in 2020, concentrating one-fourth of China's total economic output within its 2.2% regional area.

Source : Based on the standard map GS(2019)1825 from the Standard Map Service website of the Ministry of Natural Resources of China ( http://bzdt.ch.mnr.gov.cn/ ), it was clipped and produced using Arcgis10.2 software. The base map boundaries have not been modified, same as below.

Location map of the Yangtze River Delta urban agglomeration.

Data sources

Since China proposed in 2006 to take urban agglomeration as the main form of advancing urbanization, this study chooses the period from 2006 to 2020 to investigate the spatiotemporal pattern evolution of ESV since the construction of urban agglomeration. The data used in this paper are mainly divided into two categories: remote sensing data and regional statistical data, the specific types and sources of which are shown in Table 1 . All remote sensing data are processed through geographical operations such as mask extraction, resampling, grid projection, and spatial statistics using ARCGIS 10.2 software. To meet the needs of this study and based on the current standards of “Land Use Status Classification” issued by the Ministry of Land and Resources of China in 2017, the land use types are reclassified into six categories: farmland, forest, grassland, water bodies, desert, and construction land. This classification provides the input data for calculating the ESV of the YRD urban agglomeration. To improve the spatial differentiation resolution and the reliability of quantile regression, this paper studies the spatiotemporal pattern evolution of ESV in the YRD urban agglomeration at a grid scale. Previous research experience suggests that if the grid assessment unit is close to the resolution of the land use data, i.e., the unit is too small, it will destroy the relative consistency of the adjacent units 49 ; while if the grid assessment unit is close to the scale of administrative divisions, i.e., the unit is too large, it will greatly ignore the spatial differences of ESV 50 . Taking into account the actual situation of the study area and after repeated experiments, this paper divides the YRD urban agglomeration into 5 km × 5 km grids, resulting in 8764 basic assessment units for related research on ESV in the Yangtze River Delta urban agglomeration.

Research methodology

This paper unfolds around the research approach of "posing questions—analyzing problems—solving problems," primarily focusing on analyzing the spatio-temporal pattern evolution and its influencing mechanisms of the ESV of the YRD urban agglomeration from 2006 to 2020. Consequently, it proposes effective ecological conservation and urban planning policies to promote regional sustainable development. The research framework is shown in Fig.  2 .

Research framework.

Remote sensing quantitative ESV accounting model

Building upon the previous work, Xie et al. 52 derived an ESV equivalent factor table specific to China through a combination of literature research, expert knowledge, and model operation. In this study, we adopt this table as the fundamental basis for calculating the ESV of the YRD urban agglomeration 52 . Additionally, taking into account the influence of social and economic factors, we estimated the average market value of grain yield per unit area in the YRD urban agglomeration. This was done by calculating the arithmetic average of the sown area and yield of major crops, such as corn, wheat, and rice, in Shanghai, Zhejiang, Jiangsu, and Anhui provinces within the YRD urban agglomeration for the years 2006, 2011, 2016, and 2020. We also considered the annual prices in the Chinese agricultural market for the year 2020. This paper conducted a preliminary adjustment of the ESV coefficients for the YRD urban agglomeration based on the definition of the ESV equivalent factor. Specifically, this adjustment assumes that one seventh of the average market value of grain yield in the YRD urban agglomeration for a given year represents the economic value of one equivalent factor 53 . The calculation formula is shown in the followed equation:

Let \(V\) represent the economic value of one equivalent factor. Here, \(i\) refers to the crop type, while \(n\) represents the total number of crops \(n=3\) . The planting area of the \(i\) th crop is denoted as \({s}_{i}\) , the annual average market price as \({p}_{i}\) , and the yield per unit area as \(q\) . The total planted area of the \(n\) crops is represented by \(m\) . Additionally, \({V}_{j}\) represents the value coefficient of ecosystem service for category \(j\) , and \({\beta }_{j}\) represents the current value of type \(j\) ecosystem services. The calculated market value of grain output per unit area in the YRD urban agglomeration is 17317.02 yuan/hm 2 . Consequently, using Eq. ( 1 ), the economic value of per unit equivalent in the YRD urban agglomeration is determined to be 2473.86 yuan/hm 2 . The ESV coefficients per unit area of the YRD urban agglomeration were derived from Eq. ( 2 ) (Table 2 ).

The same land use type will serve significantly different ecosystem services due to the difference of vegetation density in each land use type 54 . In addition, the net carbon sequestration by green plants in different regions through photosynthesis represents the carbon sequestration capacity of surface vegetation as well as the differences in regional ecosystem service capacity 55 . Therefore, this paper uses the NDVI to reflect the plant growth status in the region and the NPP of vegetation to reflect the carbon sequestration capacity of regional vegetation, and revises the ESV of the study area to make the evaluation results more in line with regional characteristics and ensure the credibility of the results. Finally, the formulas for calculating the ESV for each land category, each sub-service, and the ESV per unit area for each city are obtained:

Here, \({EVS}_{i}\) represents the ecosystem services value of the i-th city, and the ESV value of the YRD urban agglomeration is the cumulative value of ESV of the 26 cities in the region; \({ESV}_{im}\) represents the ESV of the mth land type of the ith city; \({ESV}_{ik}\) represents the value of the kth type of sub-service in the ith city. \({AESV}_{i}\) represents the ESV per unit area of the \(i\) th city, reflecting the abundance of natural capital in the city. \({S}_{mn}\) represents the area of the \(m\) th type of ecosystem in the \(n\) th grid unit, \({V}_{mk}\) represents the value coefficient of the \(k\) th ecosystem service of the \(m\) th type of ecosystem; \({R}_{n}\) is the dynamic adjustment factor of ESV calculated based on the spatiotemporal variation of NDVI and NPP for the \(n\) th grid unit. m represents the type of ecosystem, and since the ESV coefficient of construction land is 0, this paper does not consider the ESV of construction land, so \(a=5\) ; k represents the type of ecosystem service, \(b=9\) ; \(n\) represents the grid unit number, and the value of \(c\) is determined based on the size of the area of each city region, with a total of 8764 grid units in the YRD urban agglomeration.

The formula for the dynamic adjustment factor \({R}_{n}\) is:

In the above equation,

In the equation, \({NPP}_{mean}\) and \({f}_{mean}\) are the mean values of \(NPP\) and vegetation cover \(f\) ; \({NPP}_{n}\) and \({f}_{n}\) are the \(NPP\) and \(f\) of the \(n\) th grid unit; the calculation of vegetation cover \(f\) is obtained using the pixel-based binary model, where \({NDVI}_{veg}\) and \({NDVI}_{mean}\) represent the \(NDVI\) values of pixels that are completely covered by vegetation and pixels that have no vegetation cover, respectively. These values can be replaced by the maximum and minimum \(NDVI\) values of grid units within the region. Other variables are the same as above.

Cold hotspot analysis model

The cold-hot spot analysis method is used to identify high and low value areas in different spatial locations, that is, to explore the spatial distribution characteristics of hotspots and cold spots of ESV ecological resilience 56 . The calculation formula is as follows:

Equation ( 7 ) is normalized.

where \({W}_{ij}\) is the spatial weight matrix. \({X}_{i}\) and \({X}_{j}\) represent ESV of \(i\) and \(j\) , respectively. \(E\left({G}_{i}^{*}\right)\) and \(Var({G}_{i}^{*})\) are the mathematical expectation and coefficient of variation of \({G}_{i}^{*}\) , respectively. If \(Z({G}_{i}^{*})\) is significantly positive, it indicates that the ESV around this area is high and it belong to the hotspot area. If \(Z({G}_{i}^{*})\) is significantly negative, it indicates that the ESV around this area is low and it belong to the cold pot area.

Land use intensity index

Land use intensity reflects the degree of human interference on land resources and becomes an important driving force of environmental change. Different land use types represent the characteristics of different degrees of land use, which is an important indicator of sustainable land use 57 . According to Chen 58 ,this paper assigns values to the intensity of human use of various types of land and calculates the land use intensity index for each research unit. The specific values are: 4 for construction land; 3 for farmland; 2 for forestland, grassland and water area; 1 for desert. The calculation formula is as follows.

where \({LUI}_{j}\) represents the land use intensity index of the \(j\) th grid cell. \({S}_{i}\) is the area of category \(i\) land use type. \({D}_{i}\) is the assigned value of land use intensity of category \(i\) .

Contribution rate of ESV change

The data form of land use types could not be used in the panel quantile regression model. However, considering its most direct and dominant influence on the ESV 59 , the contribution of ecosystem services of each type of land is applied in this paper to analyze the interference extent of land use types with changes in the ESV. The contribution rate of ESV change can reveal the main influencing factors of ESV change in each city of the YRD urban agglomeration. It signifies the proportion of ESV change for different land use types in each city to the total ESV change of the city during the study period 60 , with the following formula:

where \({F}_{i}\) is the contribution rate of category \(i\) land to the change in ESV of urban agglomeration during the study period. \(i\) is the land use type. \(\Delta {ESV}_{i}\) is the change in ESV of the \(i\) th land during the study period.

Variable selection

The selection of influencing factors variables is based on the above review and analysis of influencing factors of ESV. The intensity of human activities on land use indirectly affects the natural ecosystem. In this paper, LUI was used as a variable to influence the ESV. Average annual rainfall (AAR) and average annual temperature (AAT) were selected as climate factor variables affecting the value of ecosystem services. Population density (PD) and economic density are the main socio-economic factors, which can reflect the overall characteristics of the development and utilization level of regional land resources. PD is considered to be the main driver of land use change 61 . Economic density reflects land use efficiency and regional social production development intensity 62 . Both of them affect the ESV by influencing the utilization of land resources. Therefore, the two variables were selected as the socio-economic factors affecting the ESV. Under certain conditions, night light brightness (NLB) can be used as a proxy variable of GDP, so this paper uses the regional average NLB as a proxy variable of economic density 63 . The topographic factors do not vary significantly over the years and are affected by the deficiencies of the year of monitoring data, which could not meet the data conditions of the panel regression. Therefore, topographic factors are not included in this paper.

Panel quantile regression model

The panel quantile regression model has the advantages of both the panel model and the quantile regression model, providing more information and weaker collinearity between variables. Moreover, it has strong resistance to estimation in the presence of non-normal distribution or outliers, and can provide more complete information and clearer explanation of the heterogeneity of the response of independent variables under different distributions of dependent variables 64 . This method differs from the traditional least squares regression as it minimizes the weighted absolute residuals to fit conditions at various quantiles, thereby revealing the effect of the independent variables on the dependent variable across different distributions 65 . By modeling different quantiles, it is possible to analyze the effect of the independent variables at various quantile values of the dependent variable, which helps to unveil the variable sensitivity and response patterns under different conditions 66 . Panel data has both time and cross-sectional properties, greatly increasing the sample size of data, and can improve the reliability and accuracy of regression results. Due to the large amount of data and non-normal distribution with outliers in the study of ESV impact mechanisms at the grid scale, this paper overcomes the non-normal distribution and outlier problems based on the characteristics of the data, using panel data of 8764 grid units for four periods in 2006, 2011, 2016, and 2020, and selects the panel quantile regression model to explore the “segmentation effect” of the response of ESV in the YRD urban agglomeration to various influencing factors. The commonly used panel regression models include fixed effects and random effects. The Hausman test shows that the fixed effects model is more suitable for this study. The basic model is as follows:

where \({ESV}_{kt}\) is the explained variable; \(c\) is the constant term; \(\alpha\) is the regression coefficient; \({LUI}_{kt}\) is the land use intensity index; \({AAR}_{kt}\) is the average annual rainfall; \({AAT}_{kt}\) is the average annual temperature; \({PD}_{kt}\) is the population density; \({NLB}_{kt}\) is the night light brightness; The value of \({\mu }_{k}\) does not change over time as an individual effect; \({\tau }_{kt}\) is the disturbance term.

Panel fixed effects model studies the influence of each explanatory variable \(X\) on the conditional expectation \(E(ESV|X)\) of \({ESV}_{kt}\) . This is a mean reversion 67 . In the case of asymmetric distribution of \(ESV|X\) , the regression results only reflect part of the relationship between \(X\) and \(ESV\) . Quantile regression model can be used to explore the linear relationship between \(X\) and \(ESVESV\) quantiles, which can depict the conditional distribution in more detail. Therefore, regression results that are not affected by extreme values are more robust 68 . The corresponding regression coefficient estimates can be obtained for \({\varvec{X}}\) and \({\varvec{ESV}}\) at different quartiles. To sum up, this paper uses panel quantile regression model to study the influence mechanism of ESV. The model design is as follows:

where \({\varvec{\beta}}\) is the quantile, \({\varvec{\beta}} \in \left( {0,1} \right)\) ; \({\varvec{Z}}_{{{\varvec{ESV}}_{{{\varvec{kt}}}} }}\) is the \({\varvec{\beta}}\) conditional quantile of ESV; \({\varvec{c}}\left( {\varvec{\beta}} \right)\) is the constant term at the \({\varvec{\beta}}\) quartile; \({\varvec{\alpha}}\left( {\varvec{\beta}} \right)\) is the influence coefficient at the quartile of \({\varvec{\beta}}\) ; \({\varvec{\mu}}_{{\varvec{k}}} \left( {\varvec{\beta}} \right)\) is the individual effect at the quartile of \({\varvec{\beta}}\) , and other variables are the same as above.

Results analysis

Spatial and temporal patterns of esv evolution in yrd urban agglomeration.

According to Table 2 and Eq. ( 4 ), the ESV of different land categories in each city were measured by grid cells using the raster calculator tool of ARCGIS, and then summed up using the spatial statistics tool to obtain the ESV of each land category in the YRD urban agglomeration (Table 3 ). The total ESV in the YRD urban agglomeration was 8220.23, 8189.37, 8016.77 and 784.937 billion yuan in 2006, 2011, 2016 and 2020 respectively, showing a monotonically decreasing trend, but the average annual decline rate gradually stabilized. According to the land use data, the construction land of the YRD urban agglomeration that does not generate ESV has expanded by 9343.18 km 2 since 2006. The area of land types generating ESV have all decreased, leading to a decline in ESV and degradation of ecosystem service capacity in the YRD urban agglomeration. It can be seen that the spatial urbanization of the YRD urban agglomeration has brought enormous pressure to the construction of ecological civilization, and the contradiction between the degradation of ecosystem service capacity and the increasing human demand has become increasingly intense. The structure of ESV in the YRD urban agglomeration is stable, mainly dominated by forestland, farmland and water area. These three together generate more than 98% of the overall value, and their respective proportion do not change significantly over time. During this study period, the average annual decline rate of ESV generated by farmland and desert ecosystems was gradually decreasing, while that generated by grassland and water area ecosystems was gradually increasing. Stress on grassland and water area ecosystems in the YRD urban agglomeration is gradually increasing. Therefore, efforts to protect grassland and water area ecosystems should be strengthened.

Similarly, according to Table 2 and Eq. ( 5 ), the value of each sub-service for each city is obtained, and then aggregated to yield the overall value of each ecosystem sub-service for the YRD urban agglomeration. (Table 4 ). Overall, the value structure of Tier 1 subservices is stable, mainly providing adjustment services and support services, which together account for more than 82% of the overall value, with insignificant changes over time. Ecosystems regulate the pollution caused by human activities and maintain the stable operation of ecosystems through hydrological regulation, climate regulation, soil conservation and biodiversity maintenance. Among them, the capacity of supply and support service has been continuously degraded, however, the degradation rate has slowed down and the trend has been controlled since 2016. The value of regulating services and cultural services is also declining, and their average annual decline rate is expanding. The ability of ecosystems to play a role in gas regulation, hydrological regulation, climate regulation, and provision of aesthetic landscapes is continuously degrading, which may lead to a series of ecological and environmental problems in the YRD urban agglomeration, such as intensified climate change, water pollution, air pollution, ultimately resulting in the degradation of the living environment.

By combining Eqs. ( 3 ) and ( 6 ), the per unit area ESV for each city can be calculated. To visually analyze the composition of the ESV and the level of natural capital abundance in each city, a dual y-axis graph can be drawn as shown in Fig.  3 . The composition of ESV for each city is represented by a bar chart, with different colors indicating the ESV of different land categories, and their values corresponding to the y-axis on the left, unit is billion yuan. The per unit area ESV for each city is represented by a line graph, with its values corresponding to the y-axis on the right, unit is ten thousand yuan /km 2 . At the same time, since the ESV generated by grasslands and deserts accounts for a very small proportion, it is not easy to show it in the bar chart. Therefore, we take Figure a as an example and zoom in on it to illustrate the ESV generated by grasslands and deserts. Overall, the ESV and ESV per unit area in Hangzhou are the highest, which are 97.082 billion yuan and 5.7764 million yuan /km 2 respectively by 2020. Hangzhou has the strongest ecosystem service capacity and the highest degree of natural capital abundance. Although Zhoushan City has the lowest ESV, it has the highest ESV per unit area of 4.1546 million yuan/km 2 , indicating a high degree of natural capital abundance. During the study period (2006–2020), only Anqing city had a positive growth trend of ESV, while the rest of cities had a negative growth. The cities of Shanghai, Zhoushan, Changzhou, Suzhou, Jiaxing and Zhenjiang suffered serious degradation of ecosystem service capacity, and their ESV decreased by more than 10% per year compared with 2006. According to data from the regional statistical bureaus, the level of urbanization in these cities has increased significantly since 2006, which easily shows that rapidly urbanizing areas inevitably come at the cost of destroying ecosystem services. According to the analysis of land category, the urban ESV was mainly generated by farmland, forestland and water area ecosystems, while grassland and desert ecosystems ESV accounted for a small proportion. Anqing, Nantong, Hefei, Chuzhou and Yancheng, as the main food producers in the YRD urban agglomeration, have high values of farmland ecosystem services.

Yangtze River Delta urban agglomeration ecosystem service value and its structure.

The ESV of the grid cells of the YRD urban agglomeration were precisely spatially located, and its spatio-temporal evolution characteristics were analyzed (Fig.  4 ). For ease of analysis, classification is carried out at intervals of 40 million yuan. The numbers in parentheses in the legend represent the quantity of grid cells, and the same applies below. Firstly, overall, ESV in the YRD urban agglomeration presents the characteristics of spatial imbalance and time instability. The spatial divergence between high and low values of ESV is obvious, and the north–south divergence of ESV becomes more obvious as time goes on. The number of high-value grid cells gradually decreases and the number of low-value grid cells gradually increases over time, and the low-value space gradually spreads to the south of the YRD urban agglomeration. Second, counting statistical analysis of grid cells with different value levels. The number of grid cells with ESV less than 40 million yuan is increasing year by year, while the number of grids with ESV greater than 80 million RMB is decreasing year by year, and there are constantly new grid cells with decreasing ESV. Third, looking at the spatial distribution of grid units with different value levels across cities. The low-value spatial units with ESV less than 80 million yuan are mainly distributed in the northern cities of the YRD urban agglomeration. The low-value space is spread out from the center of Shanghai, Hefei, Nanjing and Hangzhou. High-value spaces worth more than 160 million yuan are mainly distributed in the southern cities of the urban agglomeration, forming a solid ecological barrier for the YRD urban agglomeration.

Spatial and temporal pattern evolution of ecosystem service value in Yangtze River Delta urban agglomeration.

The spatial agglomeration characteristics of ESV in the YRD urban agglomeration from 2006 to 2020 were further explored through a cold hotspot analysis model (Fig.  5 ). Here, “HS” stands for hotspots, “CS” stands for coldspots, and “99”, “95”, “90” respectively represent significance levels of 99%, 95%, and 90%. “NS” indicates non-significant. Counting statistical analysis shows that half of the regional ESV spatial agglomeration characteristics are not significant from 2006 to 2020, and the insignificant areas are on the rise. The number and spatial distribution of agglomeration areas are more stable, and the number of high-value agglomeration areas is higher than the number of low-value agglomeration areas. High-value agglomerations are mainly located in the eastern coastal regions, while low-value agglomerations are mainly located in the northeastern part of the YRD urban agglomeration and the eastern coastal region. The high cold spot areas spatially overlap with the central city areas with high urbanization levels. It can be found from Fig.  5 that the cold spot region in northeast China, namely the agglomeration area of low ESV value, decreases year by year and transforms from the cold spot region to the insignificant region, indicating that the ecosystem service capacity of this region has changed significantly and gradually transformed from contiguous distribution to discrete distribution. Figures  3 and 4 both show that the ESV of the eastern coast and the northern region along the Yangtze River in the YRD urban agglomeration is relatively low, which has become the frontier position of urbanization construction. The southern region of the urban agglomeration builds the logistics support area of the YRD urban agglomeration.

Spatial agglomeration characteristics of ecosystem service value in Yangtze River Delta urban agglomeration.

In the same way, spatial positioning was carried out for the grid cells with ESV changes in the YRD urban agglomeration (Fig.  6 ). Analysis of their spatial and temporal pattern evolution enables accurate monitoring to identify the changing status of ESV in the region. The spatial units of ESV changes in each research stage have obvious spatial differentiation and are discretely distributed in urban agglomeration. From Fig.  6 a, b, and c, the number of grid cells with increasing (decreasing) ESV decreases (increases) with time. The percentage of the number of areas with decreasing ESV in each specific phase is 61.86%, 79.01%, and 82.98%, showing a gradual increasing trend. From Fig.  6 d, the spatial percentage of ESV reduced is 82.78%, which mainly concentrated in the contiguous areas centered on Shanghai, Nanjing, Suzhou and Hangzhou, as well as the central part of Jinhua and the central part of Hefei. The spatial proportion of units with a increase in ESV is only 17.22%, which is much smaller than the number of units with a decrease in ESV. The above analysis further explains the reasons for the decrease of ESV in the YRD urban agglomeration from a spatial perspective, and the spatial positioning of the units with changed ESV can provide a reference for local governments to make decisions on targeted protection of regional ecology.

Spatial and temporal evolution of ecosystem service value changes in the Yangtze River Delta urban agglomeration.

Degree of disturbance to changes in ESVs by land type

According to Eq. ( 12 ), the contribution rates of ESV changes in various land categories in each city to the overall ESV change are calculated, thereby revealing the main influencing factors of ESV change in the cities of the YRD urban agglomeration (Fig. 7 ). The overall impact of each land ecosystem on ESV change in the same city varies from period to period; the impact of each land on ESV change in different cities in the same period also varies. In this paper, the contribution rate of all types of land to ESV change exceeding 50% is defined as the main influencing factor of ESV change in the city. During the study period, the farmland ecosystem was identified as the primary influencing factor for ESV variations in Taizhou, Wuhu, Jinhua, Xuancheng, Suzhou, Anqing, and Shaoxing. Forestland ecosystem was identified as the primary influencing factor for ESV variations in Chizhou, Nantong, Nanjing, Taizhou, Chuzhou, Yangzhou, Zhenjiang, Changzhou, Hefei and Ma 'anshan. Water area ecosystem was identified as the primary influencing factor for ESV variations in Tongling, Shanghai, Wuxi and Yancheng. From Fig.  6 , it can be observed that the degree of interference from agricultural ecosystems on ESV shows an increasing trend in Ma'anshan, Huzhou, and Jinhua. The disturbance degree of forestland ecosystem to ESV in Xuancheng City and Changzhou city also showed an increasing trend. The disturbance of water area ecosystems to ESV changes in the majority of cities showed an oscillating upward or strict upward trend, and even the disturbance to ESV changes in Shaoxing, Zhoushan, Xuancheng, Taizhou, Tongling and Wuxi cities has reached 90% during 2016–2020. Based on the above results, local governments should strengthen the monitoring and management of various kinds of ecosystems to ensure the stability of regional ecosystems and enhance the ability of regional ecosystem services.

Contribution rate of ecosystem service value change in the Yangtze River Delta urban agglomeration.

Segmentation effect of influencing factors of ESV

Quantile regression can identify the degree of response of different levels of ESV to each influencing factor, i.e., the "segmentation effect" of various influencing factors on different levels of ESV, which can enable us to understand the influence mechanism of ESV in a deeper level. To precisely identify this "segmentation effect", nine quartiles from 0.1 to 0.9 are selected in the model for regression, and the results reveal the changes in the elasticity coefficients of each influencing factor in the conditional distribution of ESV in the YRD urban agglomeration (Table 5 ). The baseline regression results show that LUI, AAR, AAT, PD, and NLB which represents economic density, have significant effects on ESV in the YRD urban agglomeration. The regression coefficients of LUI and AAR are significantly negative, indicating that land development and rainfall have a significant constraining effect on the enhancement of ecosystem service capacity in the YRD urban agglomeration. The regression coefficients of AAT, PD and NLB, which represent economic activities, are significantly positive, indicating that the heat conditions provided by high temperature for plant growth, human activities and appropriate economic activities have obvious positive promoting effects on the improvement of ecological environment and the promotion of ESV capacity.

The following analyzes the "segmentation effect" of the impact factors on the influence of ESV at different levels. First, observing the quantile regression results for LUI, the regression coefficients for each quantile are significantly negative, indicating that the reduction in ESV is coerced by the exploitation of land at different ESV levels. The absolute value of the regression coefficient at the 0.1 quantile was significantly smaller than the rest of the quantile, indicating that areas with low levels of ESV were less stressed by land development at the same land use intensity. Second, the regression coefficients of AAR at different quartiles are significantly negative, probably due to the sufficient water resources and rainfall in the YRD urban agglomeration itself, which implies that increased rainfall will instead reduce ESV 69 . The overall stress effect on ESVs in the YRD urban agglomeration was characterized by an "inverted U-shape", with the 0.5 quantile as the boundary. At the 0.1 to 0.5 quantile, the lower the ESV, the stronger the stress effect of rainfall on the region, while the result was opposite at the 0.5 to 0.9 quantile. Third, the regression coefficients of AAT were not significant at the 0.6, 0.7 and 0.8 quartiles, while the rest of the quartiles were significantly positive. The promotion effect of average temperature on ESV showed a U-shaped characteristic of marginal decrease at first and then marginal increase, with a cut-off at the 0.6 quantile. Fourth, the regression coefficients of both PD and NLB were insignificant only at the 0.1 quantile, and the rest of the quantile regression coefficients were significantly positive at the 1% level and monotonically increasing with quantile. This indicates that in the natural context, if land is not developed and utilized, certain population and economic activities in the region may provide aesthetic landscape value by improving the cultural services of the ecosystem, and thus enhancing ecosystem services.

Ecosystem provides important ecological resources for human production and life, serves as a bridge and link for the harmonious development of man and nature 69 . Ecological civilization construction is the fundamental plan for China's development, and the contradiction between the degradation of ecosystem services and the ever-increasing needs of human beings is becoming increasingly intense. The quantitative accounting of ecosystem service capacity of urban agglomeration in monetary form and the spatial identification of ESV change status translate ecological and environmental issues into economic issues that is easily understandable for the public 70 , which helps the general public to understand ecological issues intuitively and aids in the monitoring of regional ecosystem dynamics 71 . The ESV was divided into different levels by quantile, and the heterogeneity of the dynamic change trend of the same influencing factor under different ESV levels was emphasized, so as to obtain a more comprehensive explanation of ESV in the affected region. It can also provide reference for local governments to manage regional ecosystems, solve ecological problems, and target policies. This article takes the YRD urban agglomeration as the research object, divides the study area into detailed grid units, and revises the ESV assessment model again with NDVI and NPP. It analyzes the spatiotemporal pattern evolution of the ESV in the YRD urban agglomeration from different perspectives. Finally, it applies panel quantile regression to analyze the mechanisms influencing ESV. The following discussions are conducted:

regarding the discussion of research methods

Costanza et al. accounted for the value of global ecosystem services and natural capital 19 , which quantified the measurement of ESV, but their results also contain biases and are not entirely applicable to ESV research in China. Xie Gaodi and others, based on Costanza et al.'s research, derived ESV equivalent factor tables suitable for China, which this article uses as the basis for measuring the ESV of the YRD urban agglomeration 52 . Based on this, the economic value per equivalent unit was first revised using the yield, planting area, and market prices of the main crops in the YRD urban agglomeration. Then, on the basis of previous work using NDVI to reflect the vegetation growth status within a region 72 , NPP was added to reflect the carbon sequestration capacity of regional vegetation for a second revision. The revised model with added NPP can measure ecosystem productivity more accurately. More importantly, NPP directly reflects the ability of ecosystems to convert CO2 into biomass, which can to some extent reflect the carbon sink potential of the region. This is of great significance in the assessment of regional ESV under China's "dual carbon" goals and conforms to the development concept that "lucid waters and lush mountains are invaluable assets." The ESV assessment model, after two rounds of revisions, can ensure that the ESV assessment results conform to regional characteristics and the characteristics of the era. It can provide decision-making references for local governments to manage regional ecosystems, solve ecological problems, and target policies. Additionally, this paper uses the panel quantile regression model to explore the mechanisms influencing ESV. Previous studies often used geodetectors 73 , geographic weighted regression models 74 , piecewise linear regression 75 , etc., to examine the mechanisms influencing ESV, ignoring the differential response levels of different ESV levels to influencing factors. This paper effectively solves this problem by using the panel quantile regression model. By dividing ESV into different levels using quantiles, it emphasizes the heterogeneity in the dynamic trends of the same influencing factor at different ESV levels, leading to a more comprehensive and scientific understanding of regional ESV influences, providing a new perspective for the study of ESV mechanisms.

In terms of the discussion of research findings

From 2006 to 2020, the ESV of the YRD urban agglomeration continuously declined, which is consistent with the results of Ding et al. 76 , confirming the reliability of the calculations in this study 77 . Moreover, the grid cells with decreased ESV have primarily expanded from coastal cities to inland areas and from urban built-up areas to the periphery. This indicates that rapid urbanization and high-intensity land development are the main reasons for the ESV decline in the YRD urban agglomeration 78 . According to the ESV grid distribution map, cities such as Hangzhou and Jinhua in the southern part of the urban agglomeration have higher ESVs, because the dominant land use type in the southern part is forest land. The study finds that the ESV of forests accounts for a relatively large proportion, which explains the higher ESV in the cities of the southern urban agglomeration. This also shows that the ESV of cities is closely related to their natural resource endowment, and during the practice of ecological civilization construction, cities should carry out targeted restoration and protection based on their local natural resource endowment. Observing the degree of disturbance of each land type on ESV changes, it's apparent that farmland, forest, and water ecosystems are the main influencing factors of ESV changes in most cities. This also suggests that in the process of ESV decline, a considerable amount of these ecosystems have been destroyed 79 . In the future, protection and restoration of the ecosystems of farmland, forests, and water areas should be strengthened in all cities, and the boundaries of urban development should be controlled 80 . Especially in shrinking cities, land development should be stopped or restricted, and existing built-up areas should be used rationally for urbanization construction 81 . The segmented effects of each influencing factor on ESV suggest that areas with higher ESV levels have a stronger negative response to land use intensity. This might be because these areas, with high ESV, initially lacked land use and development, and once they undergo land use and development, the damage to ESV is extremely severe 82 . This also tells us that when developing land, we should as much as possible adopt a contiguous development model instead of an enclave-style development model.

Future research prospects

There are also some limitations in this paper. Firstly, the accounting model of ESV has room for improvement, and the dynamic factors of spatial and temporal changes are not selected comprehensively enough, and future studies can consider more human economy, regional natural conditions and other factors. Secondly, due to the limitations of data availability and method models, the research on the influence mechanism of ESV is not comprehensive enough. For example, soil conditions, light and other factors are not considered in the analysis. In the future, new research methods can be actively explored to carry out more completed research on these aspects.

Conclusions and policy recommendations

Based on the multi-source remote sensing data, this paper combines the equivalent factor method and the quantitative remote sensing method, and integrates the NDVI and NPP factors that represent the regional habitat quality with spatial and temporal changes, to measure the ESV of the YRD urban agglomeration at the grid scale and analyze its spatial agglomeration and spatio-temporal evolution characteristics. The contribution rate model was used to explore the interference of each land use type on ESV changes. A panel quantile regression model is used to reveal the degree of difference in the response of different spatial regions of ESV to the influencing factors at different levels. The following conclusions were obtained.

First, from 2006 to 2020, the ESV of the YRD urban agglomeration continuously decreased, with a total reduction of 37.086 billion yuan. The ESV declines were more severe in Shanghai, Zhoushan, Changzhou, Suzhou, Jiaxing, and Zhenjiang.

Second, the value structure of various land-type ecosystems and primary ecosystem services in the YRD urban agglomeration is stable. The ESV is mainly generated from farmland, forest land, and water bodies, primarily providing regulation services and support services.

Third, the number of grid cells with reduced ESV is continuously increasing, mainly distributed in the eastern coastal areas. From 2006 to 2020, over 80% of the grid cells experienced a decrease in ESV, primarily located in contiguous areas centered around Shanghai, Nanjing, Suzhou, and Hangzhou.

Fourth, the degree of disturbance to ESV varies among different land types, and there is heterogeneity in the response of ESV levels to the same influencing factors. Farmland, forest land, and water bodies have a greater degree of disturbance to the ESV of the urban agglomeration. LUI exerts more stress on areas with higher ESV levels, while population and economic activities within the region have a greater promoting effect on these high ESV areas. AAR and AAT have stress and promoting effects on the ESV of the YRD urban agglomeration, respectively, exhibiting "inverted U-shaped" and "U-shaped" characteristics.

Policy recommendations

In this paper, through the study of ESV in the YRD urban agglomeration, the following policy recommendations are obtained.

Firstly, under the background of integrated urban agglomeration construction, it is crucial to establish an integrated ecosystem management mechanism centered on urban agglomerations. Facing the severe fact that many cities are experiencing a decline in ESV, it is important for urban agglomerations to coordinate and jointly demarcate ecological land use red lines with local natural resource departments that are adapted to the sustainable economic and social development of the urban agglomeration, especially the red lines for construction land use. The same applies to other urban agglomerations; they need to establish urban development boundaries and ecological land use red lines, clearly defining areas that are off-limits to development and the boundaries of ecological resources that can be used sustainably. This can ensure that the pursuit of economic development does not excessively sacrifice the health and service capacity of ecosystems.

Secondly, in the urbanization construction of urban agglomerations, the concept "to protect in development, and to develop in protection" should be practiced. Implementing ecosystem protection measures such as "compensation for occupation"—where for every piece of ecological land occupied by urbanization construction, measures such as artificial afforestation and lake creation are taken to maintain the total ESV. Actively construct urban green spaces to achieve ecological urbanization. Promoting rooftop greening, city parks, green belts, and other urban greening projects can not only enhance the ecological functions of cities but also improve residents' quality of life and the city's attractiveness.

Thirdly, the management of forest, water, and farmland ecosystems should be highly emphasized. Since the ecosystem services of the YRD urban agglomeration are primarily provided by forests, water, and farmlands, increased protection and restoration of these ecosystems is necessary. Scientific management methods and restoration technologies, such as ecological agricultural practices and wetland restoration, can be applied to enhance the natural resilience and service functions of these ecosystems. This is critical for increasing the regional ecosystem service capacity and ensuring the self-regulation ability of the regional ecosystem, maintaining stability while meeting human demands.

Fourthly, it is essential to improve the land-use efficiency of urbanized areas. According to the exploration of factors affecting ESV, in any area, land development behavior greatly damages the ecosystem service capacity. Therefore, during the urbanization process, contiguous development should be adopted to avoid “enclave-style” development as much as possible and reduce the damage to ecologically vulnerable areas. Optimize the city layout and land use to ensure that development activities are coordinated with ecological protection goals. Promote land-intensive use and contiguous development strategies to minimize the negative impact of urban expansion on ecosystems.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Conceptualization, Y.L., X.J.; methodology, X.J., J.W.; software, X.J.; validation, Y.L., J.W.; formal analysis, X.J, J.W.; investigation, X.J., Y.L.; resources, Y.L.; data gathering, X.J.; writing—original draft preparation, Y.L.; writing—review and editing, J.W.; visualization, Y.L.; supervision, Y.L.; project administration, X.J.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

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Lu, Y., Wang, J. & Jiang, X. Spatial and temporal changes of ecosystem service value and its influencing mechanism in the Yangtze River Delta urban agglomeration. Sci Rep 14 , 19476 (2024). https://doi.org/10.1038/s41598-024-70248-2

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Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule

This page provides guidance about methods and approaches to achieve de-identification in accordance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule. The guidance explains and answers questions regarding the two methods that can be used to satisfy the Privacy Rule’s de-identification standard: Expert Determination and Safe Harbor 1 .  This guidance is intended to assist covered entities to understand what is de-identification, the general process by which de-identified information is created, and the options available for performing de-identification.

In developing this guidance, the Office for Civil Rights (OCR) solicited input from stakeholders with practical, technical and policy experience in de-identification.  OCR convened stakeholders at a workshop consisting of multiple panel sessions held March 8-9, 2010, in Washington, DC. Each panel addressed a specific topic related to the Privacy Rule’s de-identification methodologies and policies. The workshop was open to the public and each panel was followed by a question and answer period.  Read more on the Workshop on the HIPAA Privacy Rule's De-Identification Standard. Read the Full Guidance .

1.1 Protected Health Information 1.2 Covered Entities, Business Associates, and PHI 1.3 De-identification and its Rationale 1.4 The De-identification Standard 1.5 Preparation for De-identification

Guidance on Satisfying the Expert Determination Method

2.1 Have expert determinations been applied outside of the health field? 2.2 Who is an “expert?” 2.3 What is an acceptable level of identification risk for an expert determination? 2.4 How long is an expert determination valid for a given data set? 2.5 Can an expert derive multiple solutions from the same data set for a recipient? 2.6 How do experts assess the risk of identification of information? 2.7 What are the approaches by which an expert assesses the risk that health information can be identified? 2.8 What are the approaches by which an expert mitigates the risk of identification of an individual in health information? 2.9 Can an Expert determine a code derived from PHI is de-identified? 2.10 Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Expert Determination Method?

Guidance on Satisfying the Safe Harbor Method

3.1 When can ZIP codes be included in de-identified information? 3.2 May parts or derivatives of any of the listed identifiers be disclosed consistent with the Safe Harbor Method? 3.3 What are examples of dates that are not permitted according to the Safe Harbor Method? 3.4 Can dates associated with test measures for a patient be reported in accordance with Safe Harbor? 3.5 What constitutes “any other unique identifying number, characteristic, or code” with respect to the Safe Harbor method of the Privacy Rule? 3.6 What is “actual knowledge” that the remaining information could be used either alone or in combination with other information to identify an individual who is a subject of the information? 3.7 If a covered entity knows of specific studies about methods to re-identify health information or use de-identified health information alone or in combination with other information to identify an individual, does this necessarily mean a covered entity has actual knowledge under the Safe Harbor method? 3.8 Must a covered entity suppress all personal names, such as physician names, from health information for it to be designated as de-identified? 3.9 Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Safe Harbor Method? 3.10 Must a covered entity remove protected health information from free text fields to satisfy the Safe Harbor Method?

Glossary of Terms

Protected health information.

The HIPAA Privacy Rule protects most “individually identifiable health information” held or transmitted by a covered entity or its business associate, in any form or medium, whether electronic, on paper, or oral. The Privacy Rule calls this information protected health information (PHI) 2 . Protected health information is information, including demographic information, which relates to:

• the individual’s past, present, or future physical or mental health or condition,
• the provision of health care to the individual, or
• the past, present, or future payment for the provision of health care to the individual, and that identifies the individual or for which there is a reasonable basis to believe can be used to identify the individual. Protected health information includes many common identifiers (e.g., name, address, birth date, Social Security Number) when they can be associated with the health information listed above.

For example, a medical record, laboratory report, or hospital bill would be PHI because each document would contain a patient’s name and/or other identifying information associated with the health data content.

By contrast, a health plan report that only noted the average age of health plan members was 45 years would not be PHI because that information, although developed by aggregating information from individual plan member records, does not identify any individual plan members and there is no reasonable basis to believe that it could be used to identify an individual.

The relationship with health information is fundamental.  Identifying information alone, such as personal names, residential addresses, or phone numbers, would not necessarily be designated as PHI.  For instance, if such information was reported as part of a publicly accessible data source, such as a phone book, then this information would not be PHI because it is not related to heath data (see above).  If such information was listed with health condition, health care provision or payment data, such as an indication that the individual was treated at a certain clinic, then this information would be PHI.

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Covered Entities, Business Associates, and PHI

In general, the protections of the Privacy Rule apply to information held by covered entities and their business associates.  HIPAA defines a covered entity as 1) a health care provider that conducts certain standard administrative and financial transactions in electronic form; 2) a health care clearinghouse; or 3) a health plan. 3   A business associate is a person or entity (other than a member of the covered entity’s workforce) that performs certain functions or activities on behalf of, or provides certain services to, a covered entity that involve the use or disclosure of protected health information. A covered entity may use a business associate to de-identify PHI on its behalf only to the extent such activity is authorized by their business associate agreement.

See the OCR website https://www.hhs.gov/ocr/privacy/ for detailed information about the Privacy Rule and how it protects the privacy of health information.

De-identification and its Rationale

The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources.  The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.

The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information.  However, in recognition of the potential utility of health information even when it is not individually identifiable, §164.502(d) of the Privacy Rule permits a covered entity or its business associate to create information that is not individually identifiable by following the de-identification standard and implementation specifications in §164.514(a)-(b).  These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual. 4 As discussed below, the Privacy Rule provides two de-identification methods: 1) a formal determination by a qualified expert; or 2) the removal of specified individual identifiers as well as absence of actual knowledge by the covered entity that the remaining information could be used alone or in combination with other information to identify the individual.

Both methods, even when properly applied, yield de-identified data that retains some risk of identification.  Although the risk is very small, it is not zero, and there is a possibility that de-identified data could be linked back to the identity of the patient to which it corresponds.

Regardless of the method by which de-identification is achieved, the Privacy Rule does not restrict the use or disclosure of de-identified health information, as it is no longer considered protected health information.

The De-identification Standard

Section 164.514(a) of the HIPAA Privacy Rule provides the standard for de-identification of protected health information.  Under this standard, health information is not individually identifiable if it does not identify an individual and if the covered entity has no reasonable basis to believe it can be used to identify an individual.

§ 164.514 Other requirements relating to uses and disclosures of protected health information. (a) Standard: de-identification of protected health information. Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually identifiable health information.

Sections 164.514(b) and(c) of the Privacy Rule contain the implementation specifications that a covered entity must follow to meet the de-identification standard. As summarized in Figure 1, the Privacy Rule provides two methods by which health information can be designated as de-identified.

Figure 1. Two methods to achieve de-identification in accordance with the HIPAA Privacy Rule.

The first is the “Expert Determination” method:

(b) Implementation specifications: requirements for de-identification of protected health information. A covered entity may determine that health information is not individually identifiable health information only if: (1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: (i) Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and (ii) Documents the methods and results of the analysis that justify such determination; or

The second is the “Safe Harbor” method:

(2)(i) The following identifiers of the individual or of relatives, employers, or household members of the individual, are removed:

(B) All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP code, and their equivalent geocodes, except for the initial three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a ZIP code for all such geographic units containing 20,000 or fewer people is changed to 000

(C) All elements of dates (except year) for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older

(D) Telephone numbers

(L) Vehicle identifiers and serial numbers, including license plate numbers

(E) Fax numbers

(M) Device identifiers and serial numbers

(F) Email addresses

(N) Web Universal Resource Locators (URLs)

(G) Social security numbers

(O) Internet Protocol (IP) addresses

(H) Medical record numbers

(P) Biometric identifiers, including finger and voice prints

(I) Health plan beneficiary numbers

(Q) Full-face photographs and any comparable images

(J) Account numbers

(R) Any other unique identifying number, characteristic, or code, except as permitted by paragraph (c) of this section [Paragraph (c) is presented below in the section “Re-identification”]; and

(K) Certificate/license numbers

(ii) The covered entity does not have actual knowledge that the information could be used alone or in combination with other information to identify an individual who is a subject of the information.

Satisfying either method would demonstrate that a covered entity has met the standard in §164.514(a) above.  De-identified health information created following these methods is no longer protected by the Privacy Rule because it does not fall within the definition of PHI.  Of course, de-identification leads to information loss which may limit the usefulness of the resulting health information in certain circumstances. As described in the forthcoming sections, covered entities may wish to select de-identification strategies that minimize such loss.

Re-identification

The implementation specifications further provide direction with respect to re-identification , specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity.

If a covered entity or business associate successfully undertook an effort to identify the subject of de-identified information it maintained, the health information now related to a specific individual would again be protected by the Privacy Rule, as it would meet the definition of PHI.  Disclosure of a code or other means of record identification designed to enable coded or otherwise de-identified information to be re-identified is also considered a disclosure of PHI.

(c) Implementation specifications: re-identification. A covered entity may assign a code or other means of record identification to allow information de-identified under this section to be re-identified by the covered entity, provided that: (1) Derivation. The code or other means of record identification is not derived from or related to information about the individual and is not otherwise capable of being translated so as to identify the individual; and (2) Security. The covered entity does not use or disclose the code or other means of record identification for any other purpose, and does not disclose the mechanism for re-identification.

Preparation for De-identification

The importance of documentation for which values in health data correspond to PHI, as well as the systems that manage PHI, for the de-identification process cannot be overstated.  Esoteric notation, such as acronyms whose meaning are known to only a select few employees of a covered entity, and incomplete description may lead those overseeing a de-identification procedure to unnecessarily redact information or to fail to redact when necessary.  When sufficient documentation is provided, it is straightforward to redact the appropriate fields.  See section 3.10 for a more complete discussion.

In the following two sections, we address questions regarding the Expert Determination method (Section 2) and the Safe Harbor method (Section 3).

In §164.514(b), the Expert Determination method for de-identification is defined as follows:

 (1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: (i) Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and (ii) Documents the methods and results of the analysis that justify such determination

Have expert determinations been applied outside of the health field?

Yes. The notion of expert certification is not unique to the health care field.  Professional scientists and statisticians in various fields routinely determine and accordingly mitigate risk prior to sharing data. The field of statistical disclosure limitation, for instance, has been developed within government statistical agencies, such as the Bureau of the Census, and applied to protect numerous types of data. 5

Who is an “expert?”

There is no specific professional degree or certification program for designating who is an expert at rendering health information de-identified.  Relevant expertise may be gained through various routes of education and experience. Experts may be found in the statistical, mathematical, or other scientific domains.  From an enforcement perspective, OCR would review the relevant professional experience and academic or other training of the expert used by the covered entity, as well as actual experience of the expert using health information de-identification methodologies.

What is an acceptable level of identification risk for an expert determination?

There is no explicit numerical level of identification risk that is deemed to universally meet the “very small” level indicated by the method.  The ability of a recipient of information to identify an individual (i.e., subject of the information) is dependent on many factors, which an expert will need to take into account while assessing the risk from a data set.  This is because the risk of identification that has been determined for one particular data set in the context of a specific environment may not be appropriate for the same data set in a different environment or a different data set in the same environment.  As a result, an expert will define an acceptable “very small” risk based on the ability of an anticipated recipient to identify an individual.  This issue is addressed in further depth in Section 2.6.

How long is an expert determination valid for a given data set?

The Privacy Rule does not explicitly require that an expiration date be attached to the determination that a data set, or the method that generated such a data set, is de-identified information.  However, experts have recognized that technology, social conditions, and the availability of information changes over time.  Consequently, certain de-identification practitioners use the approach of time-limited certifications.  In this sense, the expert will assess the expected change of computational capability, as well as access to various data sources, and then determine an appropriate timeframe within which the health information will be considered reasonably protected from identification of an individual.

Information that had previously been de-identified may still be adequately de-identified when the certification limit has been reached.  When the certification timeframe reaches its conclusion, it does not imply that the data which has already been disseminated is no longer sufficiently protected in accordance with the de-identification standard.  Covered entities will need to have an expert examine whether future releases of the data to the same recipient (e.g., monthly reporting) should be subject to additional or different de-identification processes consistent with current conditions to reach the very low risk requirement.

Can an expert derive multiple solutions from the same data set for a recipient?

Yes.  Experts may design multiple solutions, each of which is tailored to the covered entity’s expectations regarding information reasonably available to the anticipated recipient of the data set.  In such cases, the expert must take care to ensure that the data sets cannot be combined to compromise the protections set in place through the mitigation strategy. (Of course, the expert must also reduce the risk that the data sets could be combined with prior versions of the de-identified dataset or with other publically available datasets to identify an individual.) For instance, an expert may derive one data set that contains detailed geocodes and generalized aged values (e.g., 5-year age ranges) and another data set that contains generalized geocodes (e.g., only the first two digits) and fine-grained age (e.g., days from birth).  The expert may certify a covered entity to share both data sets after determining that the two data sets could not be merged to individually identify a patient.  This certification may be based on a technical proof regarding the inability to merge such data sets.  Alternatively, the expert also could require additional safeguards through a data use agreement.

How do experts assess the risk of identification of information?

No single universal solution addresses all privacy and identifiability issues. Rather, a combination of technical and policy procedures are often applied to the de-identification task. OCR does not require a particular process for an expert to use to reach a determination that the risk of identification is very small.  However, the Rule does require that the methods and results of the analysis that justify the determination be documented and made available to OCR upon request. The following information is meant to provide covered entities with a general understanding of the de-identification process applied by an expert.  It does not provide sufficient detail in statistical or scientific methods to serve as a substitute for working with an expert in de-identification.

A general workflow for expert determination is depicted in Figure 2. Stakeholder input suggests that the determination of identification risk can be a process that consists of a series of steps.  First, the expert will evaluate the extent to which the health information can (or cannot) be identified by the anticipated recipients.  Second, the expert often will provide guidance to the covered entity or business associate on which statistical or scientific methods can be applied to the health information to mitigate the anticipated risk.  The expert will then execute such methods as deemed acceptable by the covered entity or business associate data managers, i.e., the officials responsible for the design and operations of the covered entity’s information systems.  Finally, the expert will evaluate the identifiability of the resulting health information to confirm that the risk is no more than very small when disclosed to the anticipated recipients.  Stakeholder input suggests that a process may require several iterations until the expert and data managers agree upon an acceptable solution. Regardless of the process or methods employed, the information must meet the very small risk specification requirement.

Figure 2.  Process for expert determination of de-Identification.

Data managers and administrators working with an expert to consider the risk of identification of a particular set of health information can look to the principles summarized in Table 1 for assistance. 6   These principles build on those defined by the Federal Committee on Statistical Methodology (which was referenced in the original publication of the Privacy Rule). 7 The table describes principles for considering the identification risk of health information. The principles should serve as a starting point for reasoning and are not meant to serve as a definitive list. In the process, experts are advised to consider how data sources that are available to a recipient of health information (e.g., computer systems that contain information about patients) could be utilized for identification of an individual. 8

Table 1. Principles used by experts in the determination of the identifiability of health information.

When evaluating identification risk, an expert often considers the degree to which a data set can be “linked” to a data source that reveals the identity of the corresponding individuals.  Linkage is a process that requires the satisfaction of certain conditions.  The first condition is that the de-identified data are unique or “distinguishing.”  It should be recognized, however, that the ability to distinguish data is, by itself, insufficient to compromise the corresponding patient’s privacy.  This is because of a second condition, which is the need for a naming data source, such as a publicly available voter registration database (see Section 2.6).  Without such a data source, there is no way to definitively link the de-identified health information to the corresponding patient. Finally, for the third condition, we need a mechanism to relate the de-identified and identified data sources. Inability to design such a relational mechanism would hamper a third party’s ability to achieve success to no better than random assignment of de-identified data and named individuals. The lack of a readily available naming data source does not imply that data are sufficiently protected from future identification, but it does indicate that it is harder to re-identify an individual, or group of individuals, given the data sources at hand. 

Example Scenario Imagine that a covered entity is considering sharing the information in the table to the left in Figure 3. This table is devoid of explicit identifiers, such as personal names and Social Security Numbers.  The information in this table is distinguishing, such that each row is unique on the combination of demographics (i.e., Age , ZIP Code , and Gender ).  Beyond this data, there exists a voter registration data source, which contains personal names, as well as demographics (i.e., Birthdate , ZIP Code , and Gender ), which are also distinguishing.  Linkage between the records in the tables is possible through the demographics.  Notice, however, that the first record in the covered entity’s table is not linked because the patient is not yet old enough to vote.

Figure 3.  Linking two data sources to identity diagnoses.

Thus, an important aspect of identification risk assessment is the route by which health information can be linked to naming sources or sensitive knowledge can be inferred. A higher risk “feature” is one that is found in many places and is publicly available. These are features that could be exploited by anyone who receives the information.  For instance, patient demographics could be classified as high-risk features.  In contrast, lower risk features are those that do not appear in public records or are less readily available.  For instance, clinical features, such as blood pressure, or temporal dependencies between events within a hospital (e.g., minutes between dispensation of pharmaceuticals) may uniquely characterize a patient in a hospital population, but the data sources to which such information could be linked to identify a patient are accessible to a much smaller set of people. 

Example Scenario An expert is asked to assess the identifiability of a patient’s demographics.  First, the expert will determine if the demographics are independently replicable .  Features such as birth date and gender are strongly independently replicable—the individual will always have the same birth date -- whereas ZIP code of residence is less so because an individual may relocate.  Second, the expert will determine which data sources that contain the individual’s identification also contain the demographics in question.  In this case, the expert may determine that public records, such as birth, death, and marriage registries, are the most likely data sources to be leveraged for identification.  Third, the expert will determine if the specific information to be disclosed is distinguishable .  At this point, the expert may determine that certain combinations of values (e.g., Asian males born in January of 1915 and living in a particular 5-digit ZIP code) are unique, whereas others (e.g., white females born in March of 1972 and living in a different 5-digit ZIP code) are never unique.  Finally, the expert will determine if the data sources that could be used in the identification process are readily accessible , which may differ by region.  For instance, voter registration registries are free in the state of North Carolina, but cost over $15,000 in the state of Wisconsin.  Thus, data shared in the former state may be deemed more risky than data shared in the latter. 12

What are the approaches by which an expert assesses the risk that health information can be identified?

The de-identification standard does not mandate a particular method for assessing risk.

A qualified expert may apply generally accepted statistical or scientific principles to compute the likelihood that a record in a data set is expected to be unique, or linkable to only one person, within the population to which it is being compared. Figure 4 provides a visualization of this concept. 13 This figure illustrates a situation in which the records in a data set are not a proper subset of the population for whom identified information is known.  This could occur, for instance, if the data set includes patients over one year-old but the population to which it is compared includes data on people over 18 years old (e.g., registered voters).

The computation of population uniques can be achieved in numerous ways, such as through the approaches outlined in published literature. 14 , 15   For instance, if an expert is attempting to assess if the combination of a patient’s race, age, and geographic region of residence is unique, the expert may use population statistics published by the U.S. Census Bureau to assist in this estimation.  In instances when population statistics are unavailable or unknown, the expert may calculate and rely on the statistics derived from the data set.  This is because a record can only be linked between the data set and the population to which it is being compared if it is unique in both.  Thus, by relying on the statistics derived from the data set, the expert will make a conservative estimate regarding the uniqueness of records. 

Example Scenario Imagine a covered entity has a data set in which there is one 25 year old male from a certain geographic region in the United States.  In truth, there are five 25 year old males in the geographic region in question (i.e., the population).  Unfortunately, there is no readily available data source to inform an expert about the number of 25 year old males in this geographic region.

By inspecting the data set, it is clear to the expert that there is at least one 25 year old male in the population, but the expert does not know if there are more.  So, without any additional knowledge, the expert assumes there are no more, such that the record in the data set is unique.  Based on this observation, the expert recommends removing this record from the data set.  In doing so, the expert has made a conservative decision with respect to the uniqueness of the record.

In the previous example, the expert provided a solution (i.e., removing a record from a dataset) to achieve de-identification, but this is one of many possible solutions that an expert could offer.  In practice, an expert may provide the covered entity with multiple alternative strategies, based on scientific or statistical principles, to mitigate risk.

Figure 4. Relationship between uniques in the data set and the broader population, as well as the degree to which linkage can be achieved.

The expert may consider different measures of “risk,” depending on the concern of the organization looking to disclose information.  The expert will attempt to determine which record in the data set is the most vulnerable to identification.  However, in certain instances, the expert may not know which particular record to be disclosed will be most vulnerable for identification purposes.  In this case, the expert may attempt to compute risk from several different perspectives. 

What are the approaches by which an expert mitigates the risk of identification of an individual in health information?

The Privacy Rule does not require a particular approach to mitigate, or reduce to very small, identification risk.  The following provides a survey of potential approaches.  An expert may find all or only one appropriate for a particular project, or may use another method entirely.

If an expert determines that the risk of identification is greater than very small, the expert may modify the information to mitigate the identification risk to that level, as required by the de-identification standard. In general, the expert will adjust certain features or values in the data to ensure that unique, identifiable elements no longer, or are not expected to, exist.  Some of the methods described below have been reviewed by the Federal Committee on Statistical Methodology 16 , which was referenced in the original preamble guidance to the Privacy Rule de-identification standard and recently revised.

Several broad classes of methods can be applied to protect data.  An overarching common goal of such approaches is to balance disclosure risk against data utility. 17   If one approach results in very small identity disclosure risk but also a set of data with little utility, another approach can be considered.  However, data utility does not determine when the de-identification standard of the Privacy Rule has been met.

Table 2 illustrates the application of such methods. In this example, we refer to columns as “features” about patients (e.g., Age and Gender) and rows as “records” of patients (e.g., the first and second rows correspond to records on two different patients).

Table 2. An example of protected health information.

A first class of identification risk mitigation methods corresponds to suppression techniques. These methods remove or eliminate certain features about the data prior to dissemination.  Suppression of an entire feature may be performed if a substantial quantity of records is considered as too risky (e.g., removal of the ZIP Code feature).  Suppression may also be performed on individual records, deleting records entirely if they are deemed too risky to share.  This can occur when a record is clearly very distinguishing (e.g., the only individual within a county that makes over $500,000 per year).   Alternatively, suppression of specific values within a record may be performed, such as when a particular value is deemed too risky (e.g., “President of the local university”, or ages or ZIP codes that may be unique).  Table 3 illustrates this last type of suppression by showing how specific values of features in Table 2 might be suppressed (i.e., black shaded cells).

Table 3. A version of Table 2 with suppressed patient values.

A second class of methods that can be applied for risk mitigation are based on generalization (sometimes referred to as abbreviation) of the information.  These methods transform data into more abstract representations.  For instance, a five-digit ZIP Code may be generalized to a four-digit ZIP Code, which in turn may be generalized to a three-digit ZIP Code, and onward so as to disclose data with lesser degrees of granularity.  Similarly, the age of a patient may be generalized from one- to five-year age groups. Table 4 illustrates how generalization (i.e., gray shaded cells) might be applied to the information in Table 2.

Table 4. A version of Table 2 with generalized patient values.

A third class of methods that can be applied for risk mitigation corresponds to perturbation .  In this case, specific values are replaced with equally specific, but different, values.  For instance, a patient’s age may be reported as a random value within a 5-year window of the actual age.  Table 5 illustrates how perturbation (i.e., gray shaded cells) might be applied to Table 2.  Notice that every age is within +/- 2 years of the original age.  Similarly, the final digit in each ZIP Code is within +/- 3 of the original ZIP Code.

Table 5. A version of Table 2 with randomized patient values.

In practice, perturbation is performed to maintain statistical properties about the original data, such as mean or variance.

The application of a method from one class does not necessarily preclude the application of a method from another class.  For instance, it is common to apply generalization and suppression to the same data set.

Using such methods, the expert will prove that the likelihood an undesirable event (e.g., future identification of an individual) will occur is very small.  For instance, one example of a data protection model that has been applied to health information is the k -anonymity principle. 18 , 19   In this model, “ k ” refers to the number of people to which each disclosed record must correspond.  In practice, this correspondence is assessed using the features that could be reasonably applied by a recipient to identify a patient.  Table 6 illustrates an application of generalization and suppression methods to achieve 2-anonymity with respect to the Age, Gender, and ZIP Code columns in Table 2.  The first two rows (i.e., shaded light gray) and last two rows (i.e., shaded dark gray) correspond to patient records with the same combination of generalized and suppressed values for Age, Gender, and ZIP Code.  Notice that Gender has been suppressed completely (i.e., black shaded cell).

Table 6, as well as a value of k equal to 2, is meant to serve as a simple example for illustrative purposes only.  Various state and federal agencies define policies regarding small cell counts (i.e., the number of people corresponding to the same combination of features) when sharing tabular, or summary, data. 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27   However, OCR does not designate a universal value for k that covered entities should apply to protect health information in accordance with the de-identification standard.  The value for k should be set at a level that is appropriate to mitigate risk of identification by the anticipated recipient of the data set. 28

Table 6. A version of Table 2 that is 2-anonymized.

As can be seen, there are many different disclosure risk reduction techniques that can be applied to health information. However, it should be noted that there is no particular method that is universally the best option for every covered entity and health information set.  Each method has benefits and drawbacks with respect to expected applications of the health information, which will be distinct for each covered entity and each intended recipient.  The determination of which method is most appropriate for the information will be assessed by the expert on a case-by-case basis and will be guided by input of the covered entity.

Finally, as noted in the preamble to the Privacy Rule, the expert may also consider the technique of limiting distribution of records through a data use agreement or restricted access agreement in which the recipient agrees to limits on who can use or receive the data, or agrees not to attempt identification of the subjects.  Of course, the specific details of such an agreement are left to the discretion of the expert and covered entity.

Can an Expert determine a code derived from PHI is de-identified?

There has been confusion about what constitutes a code and how it relates to PHI.  For clarification, our guidance is similar to that provided by the National Institutes of Standards and Technology (NIST) 29 , which states:

“ De-identified information can be re-identified (rendered distinguishable) by using a code, algorithm, or pseudonym that is assigned to individual records.  The code, algorithm, or pseudonym should not be derived from other related information* about the individual, and the means of re-identification should only be known by authorized parties and not disclosed to anyone without the authority to re-identify records.  A common de-identification technique for obscuring PII [Personally Identifiable Information] is to use a one-way cryptographic function, also known as a hash function, on the PII.

*This is not intended to exclude the application of cryptographic hash functions to the information.”

In line with this guidance from NIST, a covered entity may disclose codes derived from PHI as part of a de-identified data set if an expert determines that the data meets the de-identification requirements at §164.514(b)(1).  The re-identification provision in §164.514(c) does not preclude the transformation of PHI into values derived by cryptographic hash functions using the expert determination method, provided the keys associated with such functions are not disclosed, including to the recipients of the de-identified information.

Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Expert Determination Method?

No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified.  However, a covered entity may require the recipient of de-identified information to enter into a data use agreement to access files with known disclosure risk, such as is required for release of a limited data set under the Privacy Rule.  This agreement may contain a number of clauses designed to protect the data, such as prohibiting re-identification. 30 Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Expert Determination Method. Further information about data use agreements can be found on the OCR website. 31   Covered entities may make their own assessments whether such additional oversight is appropriate.

In §164.514(b), the Safe Harbor method for de-identification is defined as follows:

(R) Any other unique identifying number, characteristic, or code, except as permitted by paragraph (c) of this section; and

When can ZIP codes be included in de-identified information?

Covered entities may include the first three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20,000 people; or (2) the initial three digits of a ZIP code for all such geographic units containing 20,000 or fewer people is changed to 000. This means that the initial three digits of ZIP codes may be included in de-identified information except when the ZIP codes contain the initial three digits listed in the Table below.  In those cases, the first three digits must be listed as 000.

OCR published a final rule on August 14, 2002, that modified certain standards in the Privacy Rule.  The preamble to this final rule identified the initial three digits of ZIP codes, or ZIP code tabulation areas (ZCTAs), that must change to 000 for release. 67 FR 53182, 53233-53234 (Aug. 14, 2002)).

Utilizing 2000 Census data, the following three-digit ZCTAs have a population of 20,000 or fewer persons. To produce a de-identified data set utilizing the safe harbor method, all records with three-digit ZIP codes corresponding to these three-digit ZCTAs must have the ZIP code changed to 000. Covered entities should not, however, rely upon this listing or the one found in the August 14, 2002 regulation if more current data has been published .

The 17 restricted ZIP codes are:

The Department notes that these three-digit ZIP codes are based on the five-digit ZIP Code Tabulation Areas created by the Census Bureau for the 2000 Census. This new methodology also is briefly described below, as it will likely be of interest to all users of data tabulated by ZIP code. The Census Bureau will not be producing data files containing U.S. Postal Service ZIP codes either as part of the Census 2000 product series or as a post Census 2000 product. However, due to the public’s interest in having statistics tabulated by ZIP code, the Census Bureau has created a new statistical area called the Zip Code Tabulation Area (ZCTA) for Census 2000. The ZCTAs were designed to overcome the operational difficulties of creating a well-defined ZIP code area by using Census blocks (and the addresses found in them) as the basis for the ZCTAs. In the past, there has been no correlation between ZIP codes and Census Bureau geography. Zip codes can cross State, place, county, census tract, block group, and census block boundaries. The geographic designations the Census Bureau uses to tabulate data are relatively stable over time. For instance, census tracts are only defined every ten years. In contrast, ZIP codes can change more frequently. Because of the ill-defined nature of ZIP code boundaries, the Census Bureau has no file (crosswalk) showing the relationship between US Census Bureau geography and U.S. Postal Service ZIP codes.

ZCTAs are generalized area representations of U.S. Postal Service (USPS) ZIP code service areas. Simply put, each one is built by aggregating the Census 2000 blocks, whose addresses use a given ZIP code, into a ZCTA which gets that ZIP code assigned as its ZCTA code. They represent the majority USPS five-digit ZIP code found in a given area. For those areas where it is difficult to determine the prevailing five-digit ZIP code, the higher-level three-digit ZIP code is used for the ZCTA code. For further information, go to: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html

The Bureau of the Census provides information regarding population density in the United States.  Covered entities are expected to rely on the most current publicly available Bureau of Census data regarding ZIP codes. This information can be downloaded from, or queried at, the American Fact Finder website (http://factfinder.census.gov).  As of the publication of this guidance, the information can be extracted from the detailed tables of the “Census 2000 Summary File 1 (SF 1) 100-Percent Data” files under the “Decennial Census” section of the website. The information is derived from the Decennial Census and was last updated in 2000.  It is expected that the Census Bureau will make data available from the 2010 Decennial Census in the near future.  This guidance will be updated when the Census makes new information available.

May parts or derivatives of any of the listed identifiers be disclosed consistent with the Safe Harbor Method?

No.  For example, a data set that contained patient initials, or the last four digits of a Social Security number, would not meet the requirement of the Safe Harbor method for de-identification.

What are examples of dates that are not permitted according to the Safe Harbor Method?

Elements of dates that are not permitted for disclosure include the day, month, and any other information that is more specific than the year of an event.  For instance, the date “January 1, 2009” could not be reported at this level of detail. However, it could be reported in a de-identified data set as “2009”.

Many records contain dates of service or other events that imply age.  Ages that are explicitly stated, or implied, as over 89 years old must be recoded as 90 or above.  For example, if the patient’s year of birth is 1910 and the year of healthcare service is reported as 2010, then in the de-identified data set the year of birth should be reported as “on or before 1920.”  Otherwise, a recipient of the data set would learn that the age of the patient is approximately 100.

Can dates associated with test measures for a patient be reported in accordance with Safe Harbor?

No. Dates associated with test measures, such as those derived from a laboratory report, are directly related to a specific individual and relate to the provision of health care. Such dates are protected health information.  As a result, no element of a date (except as described in 3.3. above) may be reported to adhere to Safe Harbor. 

What constitutes “any other unique identifying number, characteristic, or code” with respect to the Safe Harbor method of the Privacy Rule?

This category corresponds to any unique features that are not explicitly enumerated in the Safe Harbor list (A-Q), but could be used to identify a particular individual.  Thus, a covered entity must ensure that a data set stripped of the explicitly enumerated identifiers also does not contain any of these unique features.  The following are examples of such features:

Identifying Number There are many potential identifying numbers.  For example, the preamble to the Privacy Rule at 65 FR 82462, 82712 (Dec. 28, 2000) noted that “Clinical trial record numbers are included in the general category of ‘any other unique identifying number, characteristic, or code.’

Identifying Code A code corresponds to a value that is derived from a non-secure encoding mechanism.  For instance, a code derived from a secure hash function without a secret key (e.g., “salt”) would be considered an identifying element.  This is because the resulting value would be susceptible to compromise by the recipient of such data. As another example, an increasing quantity of electronic medical record and electronic prescribing systems assign and embed barcodes into patient records and their medications.  These barcodes are often designed to be unique for each patient, or event in a patient’s record, and thus can be easily applied for tracking purposes.  See the discussion of re-identification.

Identifying Characteristic A characteristic may be anything that distinguishes an individual and allows for identification.  For example, a unique identifying characteristic could be the occupation of a patient, if it was listed in a record as “current President of State University.”

Many questions have been received regarding what constitutes “any other unique identifying number, characteristic or code” in the Safe Harbor approach, §164.514(b)(2)(i)(R), above.  Generally, a code or other means of record identification that is derived from PHI would have to be removed from data de-identified following the safe harbor method.  To clarify what must be removed under (R), the implementation specifications at §164.514(c) provide an exception with respect to “re-identification” by the covered entity.  The objective of the paragraph is to permit covered entities to assign certain types of codes or other record identification to the de-identified information so that it may be re-identified by the covered entity at some later date. Such codes or other means of record identification assigned by the covered entity are not considered direct identifiers that must be removed under (R) if the covered entity follows the directions provided in §164.514(c).

What is “actual knowledge” that the remaining information could be used either alone or in combination with other information to identify an individual who is a subject of the information?

In the context of the Safe Harbor method, actual knowledge means clear and direct knowledge that the remaining information could be used, either alone or in combination with other information, to identify an individual who is a subject of the information.  This means that a covered entity has actual knowledge if it concludes that the remaining information could be used to identify the individual.  The covered entity, in other words, is aware that the information is not actually de-identified information.

The following examples illustrate when a covered entity would fail to meet the “actual knowledge” provision.

Example 1: Revealing Occupation Imagine a covered entity was aware that the occupation of a patient was listed in a record as “former president of the State University.”  This information in combination with almost any additional data – like age or state of residence – would clearly lead to an identification of the patient.  In this example, a covered entity would not satisfy the de-identification standard by simply removing the enumerated identifiers in §164.514(b)(2)(i) because the risk of identification is of a nature and degree that a covered entity must have concluded that the information could identify the patient.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method unless the covered entity made a sufficient good faith effort to remove the ‘‘occupation’’ field from the patient record.

Example 2: Clear Familial Relation Imagine a covered entity was aware that the anticipated recipient, a researcher who is an employee of the covered entity, had a family member in the data (e.g., spouse, parent, child, or sibling). In addition, the covered entity was aware that the data would provide sufficient context for the employee to recognize the relative.  For instance, the details of a complicated series of procedures, such as a primary surgery followed by a set of follow-up surgeries and examinations, for a person of a certain age and gender, might permit the recipient to comprehend that the data pertains to his or her relative’s case.  In this situation, the risk of identification is of a nature and degree that the covered entity must have concluded that the recipient could clearly and directly identify the individual in the data.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

Example 3: Publicized Clinical Event Rare clinical events may facilitate identification in a clear and direct manner.  For instance, imagine the information in a patient record revealed that a patient gave birth to an unusually large number of children at the same time.  During the year of this event, it is highly possible that this occurred for only one individual in the hospital (and perhaps the country).  As a result, the event was reported in the popular media, and the covered entity was aware of this media exposure.  In this case, the risk of identification is of a nature and degree that the covered entity must have concluded that the individual subject of the information could be identified by a recipient of the data.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

Example 4: Knowledge of a Recipient’s Ability Imagine a covered entity was told that the anticipated recipient of the data has a table or algorithm that can be used to identify the information, or a readily available mechanism to determine a patient’s identity.  In this situation, the covered entity has actual knowledge because it was informed outright that the recipient can identify a patient, unless it subsequently received information confirming that the recipient does not in fact have a means to identify a patient.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

If a covered entity knows of specific studies about methods to re-identify health information or use de-identified health information alone or in combination with other information to identify an individual, does this necessarily mean a covered entity has actual knowledge under the Safe Harbor method?

No.  Much has been written about the capabilities of researchers with certain analytic and quantitative capacities to combine information in particular ways to identify health information. 32 , 33 , 34 , 35   A covered entity may be aware of studies about methods to identify remaining information or using de-identified information alone or in combination with other information to identify an individual.  However, a covered entity’s mere knowledge of these studies and methods, by itself, does not mean it has “actual knowledge” that these methods would be used with the data it is disclosing.  OCR does not expect a covered entity to presume such capacities of all potential recipients of de-identified data.  This would not be consistent with the intent of the Safe Harbor method, which was to provide covered entities with a simple method to determine if the information is adequately de-identified.

Must a covered entity suppress all personal names, such as physician names, from health information for it to be designated as de-identified?

No. Only names of the individuals associated with the corresponding health information (i.e., the subjects of the records) and of their relatives, employers, and household members must be suppressed.  There is no explicit requirement to remove the names of providers or workforce members of the covered entity or business associate.  At the same time, there is also no requirement to retain such information in a de-identified data set.

Beyond the removal of names related to the patient, the covered entity would need to consider whether additional personal names contained in the data should be suppressed to meet the actual knowledge specification.  Additionally, other laws or confidentiality concerns may support the suppression of this information.

Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Safe Harbor Method?

No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified.  However, nothing prevents a covered entity from asking a recipient of de-identified information to enter into a data use agreement, such as is required for release of a limited data set under the Privacy Rule.  This agreement may prohibit re-identification. Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Safe Harbor method. Further information about data use agreements can be found on the OCR website. 36   Covered entities may make their own assessments whether such additional oversight is appropriate.

Must a covered entity remove protected health information from free text fields to satisfy the Safe Harbor Method?

PHI may exist in different types of data in a multitude of forms and formats in a covered entity.  This data may reside in highly structured database tables, such as billing records. Yet, it may also be stored in a wide range of documents with less structure and written in natural language, such as discharge summaries, progress notes, and laboratory test interpretations.  These documents may vary with respect to the consistency and the format employed by the covered entity.

The de-identification standard makes no distinction between data entered into standardized fields and information entered as free text (i.e., structured and unstructured text) -- an identifier listed in the Safe Harbor standard must be removed regardless of its location in a record if it is recognizable as an identifier.

Whether additional information must be removed falls under the actual knowledge provision; the extent to which the covered entity has actual knowledge that residual information could be used to individually identify a patient. Clinical narratives in which a physician documents the history and/or lifestyle of a patient are information rich and may provide context that readily allows for patient identification.

Medical records are comprised of a wide range of structured and unstructured (also known as “free text”) documents.  In structured documents, it is relatively clear which fields contain the identifiers that must be removed following the Safe Harbor method.  For instance, it is simple to discern when a feature is a name or a Social Security Number, provided that the fields are appropriately labeled.  However, many researchers have observed that identifiers in medical information are not always clearly labeled. 37 . 38 As such, in some electronic health record systems it may be difficult to discern what a particular term or phrase corresponds to (e.g., is 5/97 a date or a ratio?).  It also is important to document when fields are derived from the Safe Harbor listed identifiers.  For instance, if a field corresponds to the first initials of names, then this derivation should be noted.  De-identification is more efficient and effective when data managers explicitly document when a feature or value pertains to identifiers.  Health Level 7 (HL7) and the International Standards Organization (ISO) publish best practices in documentation and standards that covered entities may consult in this process.

Example Scenario 1 The free text field of a patient’s medical record notes that the patient is the Executive Vice President of the state university.  The covered entity must remove this information.

Example Scenario 2 The intake notes for a new patient include the stand-alone notation, “Newark, NJ.”  It is not clear whether this relates to the patient’s address, the location of the patient’s previous health care provider, the location of the patient’s recent auto collision, or some other point.  The phrase may be retained in the data.

Glossary of terms used in Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule.  Note: some of these terms are paraphrased from the regulatory text; please see the HIPAA Rules for actual definitions.

Read the Full Guidance

Comments & Suggestions

In an effort to make this guidance a useful tool for HIPAA covered entities and business associates, we welcome and appreciate your sending us any feedback or suggestions to improve this guidance. You may submit a comment by sending an e-mail to [email protected] . 

Read more on the Workshop on the HIPAA Privacy Rule's De-Identification Standard

Acknowledgements

OCR gratefully acknowledges the significant contributions made by Bradley Malin, PhD, to the development of this guidance, through both organizing the 2010 workshop and synthesizing the concepts and perspectives in the document itself.  OCR also thanks the 2010 workshop panelists for generously providing their expertise and recommendations to the Department.

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Shots - Health News

The new covid shot is now available. here's what you need to know.

New COVID Vaccines

A new round of COVID-19 vaccines will be rolled out soon. Scott Olson/Getty Images hide caption

It’s that time of year again.

New COVID-19 shots are now available all over the country.

That comes after the Food and Drug Administration last week greenlighted the two updated vaccines, which are aimed at helping protect people from the latest strains of the virus.

The arrival of the new shots may come as a relief to those who’ve tried to dodge a summer surge in cases, fueled by the FLiRT variants.

Whether or not you decide to rush out and get the vaccine could depend on a few factors, including when you last had COVID-19 and your underlying risk of getting seriously ill.

Here’s what you need to know:

Is COVID endemic yet? Yep, says the CDC. Here's what that means

What exactly are these new shots.

The Pfizer-BioNTech and Moderna vaccines rely on the same mRNA technology as the earlier versions of the vaccine, but they now target the KP.2 variant – a member of the omicron family that rose to prominence over the summer.

As many of us know by now, the virus continues evolving to better evade our immune defense, which means regularly updating the vaccines to keep up with the latest strain.

It turns out the KP.2 variant has already been overtaken by newer variants. Because those are also descendants of omicron, the hope is that the new vaccines are close enough matches that they can still boost immunity and protect people in the coming months – ideally reducing the chances of a big winter wave.

“The vaccine is not intended to be perfect. It’s not going to absolutely prevent COVID-19," Dr. Peter Marks from the FDA told NPR in an interview.

"But if we can prevent people from getting serious cases that end up in emergency rooms, hospitals or worse — dead — that’s what we’re trying to do with these vaccines.”

On average across all age groups, the new vaccines should cut the risk of having COVID-19 by 60% to 70% and reduce the risk of getting seriously ill by 80% to 90% during the three to four months after receiving the shot, Marks says.

A third vaccine is also expected to get the FDA’s stamp of approval soon.

That one, made by Novavax, is based on older technology (not mRNA), and targets an earlier strain of the virus, called JN.1.

Who should get them?

The FDA gave the OK for anyone ages 6 months and older to get one of the new shots. The Centers for Disease Control and Prevention is recommending the vaccines for those age groups.

“In my opinion, everyone should get one of the new vaccines,” says Dr. George Diaz , chief of medicine at Providence Regional Medical Center Everett and a spokesperson for the Infectious Disease Society of America.

That said, it’s most important for those at high risk of becoming seriously ill from COVID-19, namely those over the age of 65 or who have other underlying health problems like a weakened immune system.

Studies suggest getting vaccinated can also reduce the risk of long COVID, Diaz adds.

While anyone can get a shot, Dr. Paul Offit says not everyone necessarily needs another one.

“Anyone who wants to get this vaccine should get it,” says Offit, a vaccine expert at the University of Pennsylvania and Children's Hospital of Philadelphia who advises the FDA.

The vaccine does lessen your chance of getting a mild or moderate infection for about four to six months and to “some extent lessens your chances of spreading the virus,” he says.

But the calculation could be different for younger people who may have enough immunity from previous COVID shots and infections that they’re already protected from getting very sick.

“Were I a 35-year-old healthy adult who’d already had several doses of vaccine and one or two natural infections, I wouldn’t feel compelled to get it,” he says.

And regardless of the public health advice, it’s far from clear how many people will want one of the new shots. Only about 22% of eligible adults got one of the last ones.

Should I get the shot now? Or wait?

That’s a personal judgment call.

Marks suggests most people get vaccinated sooner rather than later because there’s an ongoing surge in COVID cases and the current vaccine is a “reasonably close match” to the current strain that’s circulating.

“Right now we’re in a wave, so you’d like to get protection against what’s going on right now,” Marks says. “You’re probably going to get the most benefit.”

However, it would be wise to hold off if you had COVID-19 over the summer.

People should wait at least two or three months since their last bout, or their last shot, in order to maximize the chances of getting the best protection from this new vaccine, says Marks.

Some people may want to get vaccinated later in September or October if they are primarily concerned about fending off COVID during a potential winter surge and staying healthy over the holiday season.

“This [protection] is not like something that suddenly cuts off at three or four months,” says Marks, “It’s just that the immunity will decrease with time.”

Where can I find the shots? Do I have to pay?

All the major pharmacy chains, including CVS, Rite Aid and Walmart, say the shots should be available at all their stores this week.

Insured people can get vaccinated for free if they get their shot from an in-network provider. But it won’t necessarily be free for those without health coverage.

A federal program that paid for the vaccines for uninsured adults expired. The uninsured may be able to still get the shots for free at some places, such as federally-funded health clinics.

“In the public health community we’re very concerned about how they will access protection,” says Dr. Kelly Moore , who runs Immunize.org , an advocacy group.

“We know that the people who are uninsured are the least likely to be able to afford becoming ill – missing work, staying home from school.”

Can I double up and get the COVID and flu shots at the same time?

Yes, health officials say it’s perfectly safe to get both shots at the same time. In fact, officials are recommending that, especially if that makes it more likely that people will get vaccinated because it’s more convenient.

What about kids? Can they get the same shots?

Yes, children can get the same vaccines that adults receive. But kids get different doses and may need more than one dose, depending on their age and whether they’ve been vaccinated before. They may also need to get their shots from a pediatrician.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

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What Is Research Methodology?

I f you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Learn More About Methodology

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Research Limitations 101: What You Need To Know

Learn everything you need to know about research limitations (AKA limitations of the study). Includes practical examples from real studies.

In Vivo Coding 101: Full Explainer With Examples

Learn about in vivo coding, a popular qualitative coding technique ideal for studies where the nuances of language are central to the aims.

Process Coding 101: Full Explainer With Examples

Learn about process coding, a popular qualitative coding technique ideal for studies exploring processes, actions and changes over time.

Qualitative Coding 101: Inductive, Deductive & Hybrid Coding

Inductive, Deductive & Abductive Coding Qualitative Coding Approaches Explained...

📄 FREE TEMPLATES

Research Topic Ideation

Proposal Writing

Literature Review

Methodology & Analysis

Academic Writing

Referencing & Citing

Apps, Tools & Tricks

The Grad Coach Podcast

199 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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• Knowledge Base
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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Prevent plagiarism, run a free check.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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CHAPTER 2 RESEARCH METHODOLOGY

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Randel D Estacio

The purpose of this study is to evaluate the acceptability of the proposed instructional material, the learning assessment tool in Physics 1 (Mechanics), and to investigate its effect in teaching the subject. The design of this study is a combination of descriptive and experimental designs. A total of thirty three (33) experts and instructors in Physics from selected local colleges and universities in Metro Manila evaluated the proposed instructional material and learning assessment tool. In order to determine the effect of the proposed instructional material and learning assessment tool in Physics 1 (Mechanics) a one group pretest-posttest was employed and a total of 50 freshmen Industrial Engineering students of the Quezon City Polytechnic University (QCPU) served as the experimental group. Lessons in Physics 1 (Mechanics) were presented according to the outcomes based learning approach and the proposed instructional material and learning assessment tool were utilized. An instructional material and learning assessment tool were developed based from the results of validity, reliability, and item analysis of the achievement test in Physics 1 (Mechanics). The acceptability of the proposed instructional material and assessment of learning tool as assessed by the experts and faculty in Physics respondents revealed the following findings: As to Objective. It revealed that the objectives found in the proposed instructional material and learning assessment tool in Physics were highly acceptable as a result by its mean of 4.16. As to Content. The content of the proposed instructional material and learning assessment tool in Physics was highly acceptable having a mean of 4.42 as assessed by the experts and faculty in Physics. As to Usefulness. The study revealed that the usefulness of the proposed instructional material and learning assessment tool in Physics subject was highly acceptable with a mean of 4.45 as assessed by the experts and faculty in the field. As for Clarity. Both expert and faculty agreed that when it comes to clarity, the proposed instructional material and learning assessment tool in Physics were highly acceptable with a mean of 4.43. As to Presentation. The mean response of the expert and faculty in Physics was 4.44 and indicates that the presentation of the lessons in the proposed instructional material and assessment of learning tool was highly acceptable. As to Evaluation. The study found out that the evaluation of the proposed instructional material in a form of concept cartoon was highly accepted by the experts and faculty in Physics as supported by a mean of 4.49. As to Language and Style. Experts and faculty members in the field of Physics assessed the language and style of the proposed instructional-material and assessment of learning tool as very highly acceptable having a mean of 4.46. The study revealed that there is a statistically significant difference in the performance in the post-test of students who were taught with the use of the proposed instructional material and assessment of learning tool as compared to those who did not, p(98)=8.9174, p<.05. When the result of pre-test and post-test of each group was compared, statistically significant difference was found, p(49)=12.9769, p<.05 (control group) and p(49)=22.9071, p<.05 (experimental group). This implies that the proposed instructional material and assessment of learning tool in Physics 1 (Mechanics) greatly affect the performance of students in the class; the result also signifies that students were able to learn the lesson easily if it was presented by means of picture diagrams. The study claims and reaffirms that the findings of other researches that concept cartoon when used as formative assessment can improve the performance and achievement of students in difficult subjects like Physics.

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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• How It Works

Research Methods

Chapter 2 introduction.

Maybe you have already gained some experience in doing research, for example in your bachelor studies, or as part of your work.

The challenge in conducting academic research at masters level, is that it is multi-faceted.

The types of activities are:

• Finding and reviewing literature on your research topic;
• Designing a research project that will answer your research questions;
• Collecting relevant data from one or more sources;
• Analyzing the data, statistically or otherwise, and
• Writing up and presenting your findings.

Some researchers are strong on some parts but weak on others.

We do not require perfection. But we do require high quality.

Going through all stages of the research project, with the guidance of your supervisor, is a learning process.

The journey is hard at times, but in the end your thesis is considered an academic publication, and we want you to be proud of what you have achieved!

Probably the biggest challenge is, where to begin?

• What will be your topic?
• And once you have selected a topic, what are the questions that you want to answer, and how?

In the first chapter of the book, you will find several views on the nature and scope of business research.

Since a study in business administration derives its relevance from its application to real-life situations, an MBA typically falls in the grey area between applied research and basic research.

The focus of applied research is on finding solutions to problems, and on improving (y)our understanding of existing theories of management.

Applied research that makes use of existing theories, often leads to amendments or refinements of these theories. That is, the applied research feeds back to basic research.

In the early stages of your research, you will feel like you are running around in circles.

You start with an idea for a research topic. Then, after reading literature on the topic, you will revise or refine your idea. And start reading again with a clearer focus ...

A thesis research/project typically consists of two main stages.

The first stage is the research proposal .

Once the research proposal has been approved, you can start with the data collection, analysis and write-up (including conclusions and recommendations).

Stage 1, the research proposal consists of he first three chapters of the commonly used five-chapter structure :

• Chapter 1: Introduction
• An introduction to the topic.
• The research questions that you want to answer (and/or hypotheses that you want to test).
• A note on why the research is of academic and/or professional relevance.
• Chapter 2: Literature
• A review of relevant literature on the topic.
• Chapter 3: Methodology

The methodology is at the core of your research. Here, you define how you are going to do the research. What data will be collected, and how?

Your data should allow you to answer your research questions. In the research proposal, you will also provide answers to the questions when and how much . Is it feasible to conduct the research within the given time-frame (say, 3-6 months for a typical master thesis)? And do you have the resources to collect and analyze the data?

In stage 2 you collect and analyze the data, and write the conclusions.

• Chapter 4: Data Analysis and Findings
• Chapter 5: Summary, Conclusions and Recommendations

This video gives a nice overview of the elements of writing a thesis.

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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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• Chapter Five: Qualitative Data (Part 2)

Qualitative Data Gathering Research Designs

In their search for understanding communication phenomena, researchers have multiple qualitative methods from which to choose. Depending on a variety of factors (such as the nature of the research question, access to participants, time and resource commitments, etc.), researchers may select one or more of the following methods:

• ethnography
• in-depth field interviews
• focus group interviews
• the collection of narratives. 

Selecting the appropriate method for data collection is a vital component of the research process. Regardless of the method selected, researchers must reconcile the established traditions of the methodology with the specific requirements of the group or individuals participating in the research. We now discuss how to plan and implement your qualitative study. This section begins with topic selection and research focus and then proceeds to a discussion of each of the different qualitative methods.

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)
• Chapter Seven: Presenting Your Results

Identifying the Research Setting, Research Group, and Research Focus

Researchers have several choices when deciding how to proceed with a qualitative study. Some studies may begin with a specific communication concept, such as family communication. Researchers then begin to identify potential study participants. On other occasions, a researcher might be interested in a specific setting, such as a tattoo parlor. Gaining access to that setting to see what interesting communication concepts emerge would be very helpful. In both cases, research proceeds inductively, and conclusions emerge from the carefully gathered data. Regardless of how the initial inspiration strikes, the subsequent steps in the procedure follow a similar pattern. The following chart demonstrates the typical qualitative data gathering process:

Selecting a topic and narrowing the research focus.  During the earliest phases of the qualitative research process, researchers are tasked with identifying a focus for their study. Like all research, the individuals conducting the study are often drawn to those communication phenomena that are of the most interest to them. Perhaps a researcher has a friend or a family member who recently met his or her spouse through an on-line dating service and the researcher becomes interested in understanding how on-line dating develops. An initial research question might be, “What are the normative behaviors regarding on-line courtship?” From this point, it is important for the researcher to develop a rationale for the research. Sometimes, as in the case of on-line dating, the rationale is self-evident. As the number of people who participate in on-line dating continues to grow, it becomes an important and useful social activity to investigate. Regardless of whether or not the utility of the study seems self-evident, the researcher has an obligation to demonstrate the relevance of his or her study rationale through a review of the existing literature.

The literature review is an important component of any carefully designed research study. Although existing theory typically guides the more deductive approach of quantitative research, there are several differences regarding the more inductive, qualitative literature review. In qualitative research, the literature review is not completely finalized before data collection begins. In fact, in many cases, the literature review proceeds alongside the interviews or observations in which the researcher may be engaged. In the previous example regarding on-line courtship, the researcher would likely construct a literature review based on articles that examine on-line dating behaviors, as well as articles that examine off-line dating behaviors. However, if during the process of interviewing participants, several interviewees discuss the importance of having friends who accepted and encouraged their on-line dating attempts, the importance of a concept like  social support  might emerge. The qualitative researcher, well into the process of interviewing, might gather existing literature on social support and then ask questions regarding social support in future interviews. In some cases, the literature review continues to grow and develop as the data is being collected.

Another difference, though to a lesser degree, is that qualitative researchers often conduct a broad, rather than a deep, literature review—at least initially. The broad approach familiarizes the researcher with multiple topics that seem related to his or her research purpose. However, given that the specific data, rather than general theory, guides the entire qualitative process, the researcher can choose to analyze more deeply those topics that begin to emerge from the interaction with, or observation of, the participants. It is imperative that researchers are responsive to data collected over the course of the qualitative project so that they can augment or de-emphasize segments of the literature review as needed.

Choosing the appropriate methodology and accessing the setting or participants.  Although you can choose from many acceptable qualitative methods, including case study analysis, autoethnography, or qualitative content analysis, this chapter will focus on four common methods of collecting qualitative data: ethnography, interviewing, focus group interviewing, and narrative inquiry. Each of these methods, including their strengths and limitations, will be discussed in more detail later in this section. Choosing the appropriate method is based on a variety of factors including the nature and scope of your research question or questions, access to study participants, and researcher training and familiarity with potential methods, to name a few. As with any type of scholarly research, the law of the hammer need not apply. The law of the hammer states that if the only tool available is a hammer, then every problem will resemble a nail. If a researcher is trained and comfortable with conducting focus groups, but the best method of data collection for answering a specific research question is ethnographic research, then the researcher needs to take the necessary amount of time familiarizing himself or herself with the steps of an ethnography rather than forcing the question to conform to a focus group format. As always, the research purpose should guide the methodology, rather than the methodology guiding the purpose of the research.  

Access and trust are fundamental elements of successful data collection in qualitative research. A researcher must be able to access the research setting or interview participants in order to gather data. If the researcher is unable to gain access, it is possible that the study will have to be abandoned or significantly altered. In the case of Tom Hall’s research into government secrecy and its effects on democracy several years ago, he attempted to interview the members of the U.S. Senate and the House of Representatives who served on the Senate and House intelligence committees, respectively. Of the nearly thirty members serving at the time (2003-2004), he was not able to gain access to a single congressperson. In fact, only a handful responded to his numerous attempts at contact. Due to this lack of access, he had to shift his project entirely to a textual analysis of public documents, such as Executive Orders, Congressional Research Reports, and Department of Energy documents, in order to proceed with the research. This lack of interview access changed the general purpose of the research project and significantly altered the research questions he sought to answer. Needless to say, flexibility is an important attribute for a qualitative researcher.

In many cases, whether or not a group or organization provides access stems from whether or not the members of that organization trust the researcher. Imagine your suspicion if an individual appeared at your place of employment one day asking questions and writing information in a notebook. Just because a supervisor or gatekeeper has granted access to a researcher does not mean that all of the members of the organization or group are ready to trust the motivations of the researcher. Trust is person specific. The key to collecting solid data is for those individuals who are being observed or interviewed to understand why you are there and how you plan to use the data. If an interviewee does not trust your motivations, it is highly unlikely that s/he will be forthcoming in her/his responses—if s/he chooses to participate at all. While access to a group often relies on gaining the permission of a gatekeeper or other organizational leader, trust develops over time.

Examples of the early stages of qualitative research.  Sally is interested in studying a successful student organization on her campus. She realizes that PRSSA is an award-winning public relations organization on campus, so she contacts the faculty advisor and the current student leaders in the organization. Sally must gain the consent of the faculty advisor and other student leaders in order to begin her research on PRSSA. Sally decides to immerse herself as fully as possible with PRSSA—attending meetings, interviewing members, and observing committees, among other activities. Over the course of her research, all of Sally’s data collection efforts will be focused on this one organization. Although the initial approval of the faculty advisor and the student leaders of the organization are vital to gain access, it is important to remember that Sally will still need to gain consent from any other organization members who participate in her study.

Another example involves Bob who is also interested in studying reasons for participation in student organizations on his campus. Rather than focusing on a single organization, he decides to interview multiple participants in multiple organizations. Over the course of several weeks or months, Bob interviews numerous individuals and learns why they chose to involve themselves in student organizations. Bob uses a snowball or volunteer sample in order to recruit participants for his study. While there is no single faculty advisor or organization leader needed to gain access to these groups, Bob will still have to acquire individual consent from all of the participants in his study.

The early stages of a qualitative project are crucial for providing the foundations for a credible study. Developing the research purpose, examining the existing literature, selecting an appropriate method, and gaining access to and the trust of participants are all necessary steps.  

Qualitative Data Collection

In this section the authors discuss each of the more common qualitative methods, including the purpose, the steps involved in the particular method, and the strengths and limitations of the method. An extended example of each method is provided before moving on to the next method. The methods discussed include: ethnography, interviewing, focus group interviewing, and narrative interviewing.

Ethnography . This method is the total immersion of the researcher into the research setting. The roots of ethnography lie in cultural anthropology, which is when researchers attempt to fully understand the culture of a group by integrating themselves into the culture under investigation. Some communication scholars, such as Lawrence Wieder (1999), consider ethnography to be the main qualitative method. Although observation is the central practice of ethnographic research, a researcher may employ multiple other qualitative methodologies during the course of the project. Ethnography is the method of choice when a researcher decides to study the participants within their natural environment. Therefore, a study examining the communication patterns of college wrestlers would involve extensive interaction with a college wrestling team.

Traditional ethnography concerns a researcher or group of researchers studying the activities of a specific group or culture Over the last twenty years, an additional type of ethnographic research has emerged, where a person tells the story of some experience within their own life, with a scholarly purpose. This practice of autoethnography has become more popular in the past few years, and its validation as a legitimate form of knowledge development has also increased. Autoethnography places the researcher at the center of the investigation by directing the analysis towards the researcher’s role in the natural setting. By definition, an autoethnographer is a complete participant in his or her research, and is engaged in a full scale, in-depth, critical analysis on his or her life as it is being lived. A college athlete might detail his or her experiences as a member of a team—critically exploring his or her assimilation and identification with the group or chronicling the personal difficulties of competing at the highest level athletically. Whatever the specific focus of research, the data is drawn from detailed accounts of a person’s own experiences.  

The purpose of ethnographic research . Given though the individual goals vary from one ethnographic project to another, the overall purpose of the research remains the same: to accurately capture the social activities of the group or organization being studied. Frequently, the researcher does not have clearly defined research questions, preferring instead to capture, through observation and interviews, the social practices of the organization members. The purpose is to experience the participants in their natural setting and interpret those experiences accurately, in order to develop a better understanding of the interaction processes of the social group. Remember that one of the overarching goals of qualitative research is developing an understanding of an event, phenomenon, communicative practice, or cultural group. Ethnography is one of the ways that a researcher might accomplish this goal.

The steps in the ethnographic process . You should follow four distinct steps when conducting ethnographic research.

• Identify the research site. In ethnographic research, the principle investigator is often attracted to a particular group or organization. The person conducting the study may already be a member of the group (emic) he or she desires to research. An example of this would be when an individual who is a member of a book club decides to research the group in order to understand the group better. An etic approach might involve a graduate student who is interested in high school coaching selecting an athletic team to observe. Observation is a necessary component of ethnographic research. The full range of observation techniques are available to the researcher—from complete participant to complete observer.
• Gain access to the organization. Gaining access to an organization is vital to ethnography. Simply put, without access, ethnography is impossible. In order for the research to proceed, investigators must gain access to the organization or group. Gaining access relies on building trust with gatekeepers, informants, and other key organizational personnel. For example, if a researcher wanted to study the cultural climate of a Communication Studies Department, he or she might first begin by approaching the head of the department for approval. In this example, the department head might act as a gatekeeper—permitting or denying the research. However, even with the consent and trust of the department head, successful data collection and a successful research project are not assured. The researcher would likely need to approach each of the professors and staff in order to gain their consent to be observed during meetings and classes and office activities. While the consent of the department head may be enough to gain access to the data site, the success of the data collection portion of the project relies on earning the trust of as many of the organizational members as possible. 
• Collect and analyze data. During this stage, the real work begins. Researchers immerse themselves in the natural environment of the participants, collecting copious field notes and analyzing the data frequently. This is by far the most time consuming portion of the research process. At a minimum, most ethnographers spend six months engaged in this portion of the research and spending one or two years immersed in a group is not uncommon.
• Leave the field of investigation. When subsequent observations are no longer producing new data, the researcher is ready to wrap up the project and begin writing the final report. There are considerations when leaving the field. For one, the researcher may want to schedule additional meetings—focus groups or interviews—after the conclusion of the observation-based data gathering, in order to have the participants validate the findings or in order to follow up on interest areas not revealed through observation. The researcher should maintain the same high levels of trust that were so important during the initiation of the research project.

Recording observations using field notes . Field notes are a vital tool of the ethnographer. They are a written record of the researcher’s observations during his or her time in the field. Researchers would not be capable of remembering everything that they see during their observations, therefore keeping a notebook detailing the various activities of the community in which they are immersed is critical. Field notes are comprised of detailed records of observations as they occur. Over the course of compiling field notes, probative analysis also develops. Observers write down tentative and initial questions or conceptual possibilities alongside the field notes as they are collected. These are often referred to as “theoretical asides.” As observations and field notes grow in number, conceptual and theoretical elements may be combined and reflected upon in greater detail. More in-depth writing, focusing on explicit connections among and between the asides, are referred to as “observer commentaries” and represent a more sustained analytic treatment of the evidence collected. When the writer develops, in paragraph form, a rough draft explicating tentative themes, these writings are known as “in-process memos.”   

Strengths and limitations of ethnographic research . Ethnographic research has several strengths. First, ethnographic research observes the participants in their natural setting. In fact, to borrow terminology popular in quantitative social science, one would say that ethnographic research is ecologically valid. Rather than relying on a self-reported survey where someone reveals how they would act in a conflict situation in the workplace or relying on an experimental design where participants might role-play a workplace conflict in an artificial setting, ethnographers observe the participants in their natural environment. Second, ethnographic researcher stems from the thick, rich descriptions of the social actions of the group under investigation. Imagine the details collected by a researcher who is able to capture and describe group behavior as it unfolds and then follows up with informal interviews in order to gain the perspectives of the participants. This high level of detail is not something that survey data can replicate. Third, due to the extended observation periods in the natural setting and the cultural immersion that occurs with this method, ethnographers are able to gain deep understanding of the social activities of the group or cultural being observed.

Ethnographic research is not without its limitations. First, perhaps its most significant limitation stems from the fact that although one is able to develop an in-depth understanding of a group or culture, that understanding comes at the cost of generalizability. Just because a researcher can understand the social actions of one group or culture at a particular point in time does not mean that the knowledge derived from this understanding is relatable to other groups or cultures, which could then limit the applicability of the research. Second, sheer time and resource commitment required of a researcher to immerse herself and fully understand a culture is a limit. As noted previously, two years is not an exception but is, in many cases, normative. Finally, there exists the possibility that the researcher begins to over-identify with his or her research subjects. One example of this might involve a researcher deliberately leaving out accurate, but negative information about research subjects because the researcher does not want any aspect of the group to be seen in a negative light. Conversely, a researcher may deliberately exaggerate some characteristics to paint the group in a more desirable light. This over-identification is often referred to as  going native , and it seriously jeopardizes the credibility of a study.  

Sample ethnography . If one wants to fully understand the process of ethnographic research, one should identify a research environment, initiate an ethnographic study, and begin data collection and analysis. To facilitate understanding of the ethnographic process, we describe Susan Weinstein’s (2007) study to clarify the process of ethnographic research. Weinstein (2007) spent three years gathering field observations, collecting written artifacts, and conducting informal interviews while studying how “nine low-income, African-American and Latino urban youths” wrote about gender and sexuality through “poetry, prose, and rap lyrics” (p. 28). In her own words, Weinstein states,

In this single paragraph, Weinstein details many of the aspects vital to her study—the locations and settings where data was collected, the length of time spent conducting the research, information about the participants, consent gained, and efforts towards respondent validation. Respondent validation involves the researcher reviewing her tentative conclusions with the study participants in order to elicit their feedback regarding her interpretation of events. Weinstein’s research was guided by her belief that imaginative writing served as an outlet for the identity construction of urban youths. She found that gender and sexuality materialized in often contradictory ways in the writings of the youths and concluded those contradictory writings to be indicative of the complexity of the methods regarding these topics that youths receive from their social environment, such as family and friends and popular culture.

Ethnographic research is a challenging, yet rewarding, scholarly endeavor and a method to be used when one wants to develop as comprehensive and in-depth an understanding of a social environment as possible.

In-depth interviewing . In-depth interviewing is a qualitative method that fully situates the interviewee in the role of providing information to the interviewer. According to Lindlof and Taylor (2002), an interview is “an event in which one person (the interviewer) encourages others to freely articulate their interests and experiences” (p. 170). Ethnography may be a technique that is unfamiliar to many people, but interviewing is a process that most people have encountered at one time or another. A high degree of flexibility and variation characterizes the interview process. Qualitative interviewing can range from very structured formal interviewing to the loosely structured field interviewing that accompanies ethnographic research. This section begins with a discussion of the basic characteristics and goals of interviewing, followed by the steps for conducting an interview and the strengths and limitations of this method. We conclude with an exploration of a study relying on interviewing as the primary method.

Interview goals and characteristics . Qualitative research is inherently subjective. Qualitative research relies on its participants sharing their subjective understanding of certain experiences. Interviewing allows research participants to share their unique perspectives. Therefore, a primary goal of interviewing is for the research participant to answer the questions of the researcher. In essence, interviewing is the process of asking questions and receiving answers. Researchers want their interviewees to provide information about events or experiences that occur separate from the interview setting. Answers may take the form of stories or explanations. Interviews enable the researcher to understand the context and language forms of the social actors. Additionally, interviews allow the researcher to investigate events that he or she would otherwise not be able to access, such as closed meetings, past events, etc. The goal is to get the best possible data that will enable the researcher to successfully answer his or her research question or to provide an in-depth understanding of the social processes under investigation.

Regardless of the formality of the interview structure, an important characteristic of a sound interview is establishing a conversational tone during the interview process. Because trust is such a vital component of an interview, the interviewer should adopt a style that puts the interviewee at ease, and a conversational interview tone often goes a long way towards making the interviewee relaxed. According to Denscombe (2010), other important characteristics of the interview process include being cognizant of the feelings of the interviewee and the ability of the interviewer to tolerate silences. In day-to-day conversations silence is usually not tolerated very well, but when one is asking another to reflect on an issue, time is sometimes essential to allow the interviewee to think through an issue and to feel compelled to dig deeper in this analysis.

Time is another important element of the interview process. Interviews should last a reasonable amount of time. A researcher should not expect a participant to be willing to devote more than an hour of time for an interview, except in the most extreme of cases. Thirty minutes to one hour is considered a reasonable expectation for an interview. However, for some research questions, an even shorter interview may be enough.

Several ethical considerations confront a researcher during the interview process. As with all research involving human participants, gaining the consent of the participants is an obligation of the researcher. The researcher will also want to make sure that the interviewee understands how the researcher plans to use the information and how confidentiality will be ensured. It is common for the researcher to use pseudonyms for the interview subjects in order to mask their identities. A final ethical consideration is for the researcher to be aware of the potential for certain questions to lead the interviewee in a specific direction—questions posed in such a way that they elicit a particular answer from the participant. It is advantageous for the interviewer if the interviewee consents to an audio or video recording of the interview. Recording the interview can ensure the accuracy of the participant’s statements. With or without a recording, it is suggested that interviews be conducted in pairs—this way, one person can conduct and moderate the interview, while the other takes detailed notes regarding the interaction. Of course, the decision to use a second interviewer depends on the comfort level and consent of the participant.

Appropriate types of interview questions . Close-ended, yes or no, type questions are necessary from time to time during a qualitative interview, such as when gathering demographic information about your participants to write your methods section, but open-ended questions are recommended because they provide the interviewee with greater freedom in responding and offer the interviewer more data for analysis. Open-ended questions follow two common formats—non-directive questions and directive questions.

Directive questions  are specific questions designed to discover specific responses. Examples of directive questions would be, “Tell me what kind of professor Tom Hall is” or “How does Tom Hall’s teaching style differ from April Chatham-Carpenter’s teaching style.” In both cases, the interviewer is asking the participant to address a specific point. Compare and contrast questions, as well as Devil’s Advocate questions, are examples of directive questions.

Consider the following  non-directive question  examples. “Tell me about a time when you experienced conflict in the workplace” or “Tell me a little about yourself.” In these examples, the interviewee is free to take the focus of the question in the direction of his or her choosing, and while that response will clearly contain specific elements, it begins with a more general approach than the directive questions.

Also, it is important to remember that not every question must specifically address the research purpose. For example, in order to increase comfort and gain the trust of a participant in the early stages of the interview, the researcher might consider asking a general question like, “Would you please tell me about yourself?” This allows the participant to grow comfortable sharing information with the researcher.

Finally, during the interview process, it is essential that the researcher is skilled at asking probing questions when more specific information is desired, asking clarifying questions when the information provided is unclear, or asking validating questions when the researcher wants to ensure that he or she interpreted the response of the participant correctly.

Steps of conducting an interview .   Five steps will help you through the process.

• Identify the purpose of your study and design the interview guide. The researcher needs to make sure that interviews are the appropriate methodology to employ in answering his or her research focus. If interviews are the appropriate data collection format, then the researcher needs to design a guide for how the interview will proceed. (The focus here is on research where interviews will serve as the primary data collection method and therefore are more formally structured than they might be during the impromptu interviewing that occurs during ethnographic research). A guide is just that, a guide to conducting the interview. It is a means for the researcher to organize his or her thoughts in order to ensure consistent approaches are taken across all interviews. However, it is just a guide and the researcher can add to it, take from it, or restructure questions as the need arises during an individual interview or over the course of multiple interviews.
• Identify the participants of the study and arrange times to conduct the interviews. If the researcher has selected a particular place or location to conduct his or her study, then the selection of the participants is likely a straightforward process. If, however, the researcher seeks to interview people who share common characteristics, rather than a physical location, selection of participants will likely rely on volunteer, snowball, or purposive sampling techniques. For example, suppose a researcher is interested in studying people who read comic books. In the process of identifying the participants, the researcher could gain access to a local comic book store and ask patrons if they are willing to participate in interviews, or the researcher could find one or two people who read comic books, interview them, and then ask them to help identify other potential participants (i.e., snowball sampling).
• Introductions . Ask broad questions to increase the comfort level of the participants. In some cases a broad, introductory question might take the form of “Tell me about some of your communication strengths.” Questions of this type are known as biography questions and are designed to establish a conversational and comfortable tone for the remainder of the interview.
• Research focus questions . As the interview is underway, the researcher turns to those questions to which he or she specifically seeks answers. This is the main portion of the interview, and is where the researcher’s skills at asking probing and follow-up questions help him or her collect the desired information. As the interview moves from one topic to the next, it is helpful for the researcher to summarize the previous topic and solicit feedback from the interviewee before shifting to the next topic.
• Concluding the interview . It is common during this portion of the interview for the researcher to summarize main points in order to seek validation from the interviewee regarding the researcher’s interpretation of the responses. It is also common for the interviewer to ask the participant if he or she has any questions for the researcher. Also, always thank the participant for his or her time.
• Transcribe the interview. In order to accurately collect the data from the interviews, it is often necessary to transcribe the interviews. The transcription process can take as long, if not longer, than the interview process itself. It is essential that the researcher accurately portray the statements of the participants. Transcriptions also convert the data into a format that is often easier to analyze and interpret than the rough notes taken during the interview process.
• Analyze and interpret the data. The final step of the interview process involves analyzing and interpreting the data. Once the interviews have been transcribed, the researcher sorts back through the data in order to identify themes and common elements among the responses. Obviously, the researcher needs to keep the original research purpose firmly in mind when interpreting the data. The practice of analysis and interpretation is similar across the various qualitative methods, so a more lengthy discussion of analysis and interpretation will be presented in the final segment of this chapter.

Strengths and limitations of interviewing . Like so many other qualitative methods, one of the strengths of interview data is, quite simply, the depth of the information gathered by the researcher. Over the course of the interview, the researcher is able to probe, refocus, and follow-up on the various responses from the participant. Because of these characteristics, rich, detailed information is the product of qualitative interviewing. Another advantage stems from the flexibility of the interview process, and this flexibility also contributes to the depth of the information. Although you will likely follow an interview guide, the process itself is not as concrete as survey research questions. The researcher can adjust to the responses during the interview and guide the interview in the direction necessary to speak to the overall research agenda. It is also possible to augment and excise questions following interviews. For example, if the first three interviewees all talk about a specific event, it alerts the researcher to the importance of this event. In preparation for the subsequent interviews, the researcher can include a question to make sure that the previously mentioned event is addressed in future interviews. Interviews also allow for the acquisition of the participants’ subjective interpretation of the events being discussed. This is data in the actual words of the participants. The researcher does not rely on previously established categories but rather on the words and experiences of the interviewees. Finally, in many cases, interviews are the only means of discovering information about events that have already taken place. Interviews allow the researcher to uncover information that otherwise would not be available to him or her.

As far as the limitations of qualitative interviewing go, there are several that are worth mentioning. The sheer amount of time involved in setting up, conducting, transcribing, and analyzing interviews is daunting for many researchers. A one-hour interview may take three times that long to transcribe, particularly without the aid of electronic transcribing devices. In many cases, the interviewee may wander off-course during the interview process. The researcher has to balance the comfort level of the participant with the researcher’s need to gather relevant data. In some cases, brief meandering may be necessary in order to maintain a comfortable conversational flow between the interviewer and interviewee. There are, of course, other concerns. It is one thing to focus the interviewee on answering a specific question, it is quite another to deliberately lead the interviewee to a specific response desired by the researcher. Researchers must be fully aware of the impact and influence that they have on the entire interview process. As is always the case with qualitative data, the data collection instrument is the researcher; therefore, the limits of the researcher will affect the entire process.

Interview study example.

Cohen, M., & Avanzino, S. (2010). We are people first: Framing organizational assimilation  experiences of the physically disabled using co-cultural theory.  Communication Studies ,  61 (3), 272-303.

Cohen and Avanzino examined “how organizational members with disabilities experience and manage organizational assimilation in the workplace” (p. 272). They conducted interviews with 24 individuals with physical disabilities. The researchers employed snowball and purposive sampling to identify participants and “sixteen interviews were conducted face-to-face and eight took place over the telephone due to distance and time constraints” (pp. 280-281). From the twenty-four interviews, 140 pages of transcriptions were produced. From the data, Cohen and Avanzino uncovered eight concepts and two themes, and identified aspects of the difficult process of workplace assimilation, as well as various techniques employed by the study participants to successfully negotiate workplace assimilation.

All of the elements of qualitative interviewing are present here: a general research question focused on understanding rather than prediction and control; a small, purposely selected group of participants; and an in-depth analysis of the participants’ responses to develop themes and facilitate understanding of the assimilation process.

Research methods are not mutually exclusive, and although this study primarily used interviewing, the authors note that they also engaged in observer-participant activities. In fact, researchers often triangulate their methods, combining the next two methods to be discussed—focus groups and narrative interviewing—in conjunction with ethnographic research or qualitative interviewing to strengthen the quality of the study.

Focus group interviewing . The fundamental difference between interviewing and focus group interviewing is that focus group interviewing is designed to allow multiple participants to interact with one another. Regular interviewing often occurs one-on-one; focus groups often bring together 6-12 participants in order to gather data as they interact with one another. Many people are familiar with marketing or political focus groups designed to uncover people’s attitudes towards a particular product, political figure, or idea, but fewer people are familiar with scholarly focus groups. Research focus groups may be similar in number of participants and duration of the interaction (90-120 minutes) to these others types of focus groups, but their purpose is not to gauge attitudes about a brand or political figure. Much like a regular interview, a focus group interview is designed to elicit information from the participants but is arranged in such a way that the participants are able to openly engage in discussion with the other participants. This experience, while often difficult to moderate, can provide a wealth of data. This section includes a discussion of focus group characteristics, moderator concerns, steps in focus group interviewing, and strengths and limitations. It concludes with a look at research employing focus group methodology.

Characteristics of focus groups . In addition to the number of participants and the length of time required to conduct a focus group, other important characteristics distinguish focus groups. Focus groups allow the researcher to interview several people at once in a format that resembles a purposeful discussion. Focus groups allow researchers to gather information from a group of people in a single setting. Some of the characteristics shared with regular interviews include the designing of an interview guide and involving two researchers for the process (one to moderate and another to take notes). In the case of the interview guide, it should be clearly developed but will likely not be as lengthy as the guide for a one-on-one interview. Because focus groups allow the participants to interact with one another, a few questions by the moderator may be all the prompting needed to elicit discussion. Due to the collaborative nature of focus groups, the moderator may only need to initiate the discussion and then can spend the majority of his or her time managing and focusing the ensuing discussion rather than constantly interjecting new questions. In some cases, the interactions among the group may emphasize points of agreement, as several of the participants add on to a topic, idea, or event that has been introduced. On other occasions, the group interaction may result in points of contention, enabling the researcher to see where participants have very differing perspectives on the research questions and topic.  

Moderator concerns . The moderator’s job is much more difficult in a focus group than it is in a one-on-one interview. A focus group moderator has to manage multiple personalities rather than a single personality. It is still important to establish a conversational tone among the participants, but it is also necessary to pay close attention to the various personalities of the group. Is one person dominating the discussion? Are two people ganging up on another member? Are tensions running high among the group? Is one person overly shy and unwilling to open up? These are just a few of the concerns, characteristics, and mannerisms that a focus group moderator may need to address over the course of the focus group interview. Practice is the best tool for learning when to allow disagreements and when to cool discussion down before arguments develop. Remember, the express disagreement that stems from constructive conflict is very different from the hostility associated with destructive group conflict. The focus group moderator must also insure that the group remains focused and does not wander too far from the original intent of the moderator’s topic or question. The moderator should also refrain from displaying any bias over the course of the focus group.

Steps of conducting a focus group . Some of the standard steps of designing and conducting a focus group are as follows:

• Identify the purpose of your study and confirm that focus groups will be the most useful method for your study. Researchers will want to consider their overall research focus and then construct a list of questions that will provide the best opportunity to elicit relevant responses from the participants. You should have a clear purpose to the focus group questions, but you also need to have the flexibility to adapt as the situation merits.
• Recruit participants. Once the researcher has decided which participant attributes are essential to the study, he or she needs to initiate the process of recruiting participants. Once again, snowball sampling and purposive sampling are often the most useful methods of recruitment. Even though a desired number for a focus group is between 6-12, the researcher should select a number that he or she feels capable of moderating. Also, just because people say that they will show up does not mean that they will. It is better to have too many people show up for your focus group session, and have to turn a few people away, than to have too few people show up. It is acceptable to over-recruit by a person or two.
• Introduce purpose of the group, participants, and explain the expectations and ground rules for the discussion . The first segment of the focus group should begin with the researcher/moderator explaining the goals and purpose of the focus group, followed by a presentation of the ground rules and discussion expectations. This is a good time to express the desire for respectful conversation and equal sharing of information. It is also a good time to allow the participants to introduce each other if they are not familiar with one another.
• Ask questions and moderate the ensuing discussion . Questions should be open-ended and may be either directive or non-directive depending on the needs of the researcher. It is during this segment that most of the moderator’s skills will be put to the test as he or she strives to keep the group on task, sharing equally, and clarifying points of contention or agreement.
• Conclude the focus group . Similar to one-on-one interviewing, the moderator should allow time for the participants to clarify or elaborate on any of their previous statements. Participants should also have the opportunity to ask questions of the moderator. Finally, make sure to thank the participants for taking the time to be a part of the focus group.
• Transcribe the focus group data. One of the challenges of focus group research is clearly differentiating the various participants, who in some cases will talk over or interrupt one another. This is one of the reasons that it is good to have a moderator, a note taker, and multiple recording devices (to catch all the voices) throughout the focus group. Transcribing data of this sort can be a time consuming process. When it comes time to analyze and interpret the data, detailed and accurate transcriptions are a necessity.
• Analyze and interpret the data. Once the notes have been transcribed, the researcher sorts through the data in order to identify themes and common elements among the responses. Obviously, the researcher needs to keep the original research purpose firmly in mind when interpreting the data. The practice of analysis and interpretation is similar across the various qualitative methods, so a lengthy discussion of analysis and interpretation will be presented in the final segment of this chapter.

Strengths and limitations of research focus groups . Obviously, the greatest strength of focus group methodology is the interplay among the various focus group participants. The healthy give and take among the participants serves as a fruitful generator of data. In fact, the primary reason for selecting focus groups over one-on-one interviews is so that the researcher can record several people interacting regarding the same topic. Provided that all of the subjects of the focus group participate, the researcher can gather a multitude of opinions and ideas on similar topics. Another advantage is that you can collect a relatively large amount of data in a brief period of time. In the time that it might take to conduct two individual interviews, the researcher can conduct a focus group with 10-12 participants. Although the depth of the data as compared to individual interviews may suffer, the breadth of the data and the discussion of common topics are extremely beneficial.

There are limitations to focus groups, several of which have already been highlighted. A skilled moderator is a necessity or the group can quickly get off task and produce information that does not relate or address the original purpose of the research. There also is the potential that overly dominant group members will lead the discussion in ways that might not represent the feelings of the remainder of the group. Finally, group members might either go along to get along or deliberately disagree (playing Devil’s Advocate)—neither of which will lead to the most accurate data. According to Morgan (1997), there is always the possibility that the moderator has overly influenced the groups. Focus groups do not take place in the natural environment; they are artificially constructed. This, in turn, impacts the ecological validity of the research.

Focus group example .

Hundley, H. L., & Shyles, L. (2010). US teenagers’ perceptions and awareness of digital  technology: A focus group approach.  New Media & Society ,  12 (3), 417-433.

Hundley and Shyles conducted focus groups with 80 middle and high school teenagers. “The chief objective of this research was to further our understanding of what young people think about digital devices and the functions they serve in their lives” (p. 417). A total of eleven focus groups were conducted with five to nine students in each group. Hundley and Shyles utilized both formally structured and semi-structured focus group interview protocols. The formally structured segment consisted of specifically prepared questions, while the semi-structured portions “allowed students to speak freely, elaborate, ask questions and join in group discussions” (p. 419). According to the authors:

From the focus groups, Hundley and Shyles were able to identify several common themes among the 80 participants. Themes included high level of awareness regarding the various types of technology, a lack of awareness regarding amount of time actually spent using the technology (nearly all underestimated time spent), awareness that digital devices help them socialize, and the risk of having personal information available online. Hundley and Shyles found that their research was consistent with the existing literature regarding teens and technology.

Autoethnography  is a relatively new qualitative research method that is generating a great deal of interest. It is based in ethnography, meaning that it, too, is an attempt to understand and describe the insiders’ cultural perspectives—i.e., how insiders construct their world view/culture. Like ethnography, it is also holistic and naturalistic, rather than trying to isolate what is studied and control it. Finally, like ethnography, it requires some degree of participant observation, but in this case, the observer may the reader, not just the researcher.

While there is no one definition of autoethnography, it is the study of some aspect of culture from the author’s personal experience and perspective. Examples of topics where authors have shared their personal experience through this method include surviving breast cancer, an eating disorder, depression, being of multi-ethnic identities,  the process of transitioning from woman to man as a transgender, and much more. The researcher is the subject of study, the key informant. The method is similar to narrative data collection. The researcher/author tells h/his story on a topic or issue the person feels warrants others to learn about from an insider view. The author role is reversed from being a researcher first and then an author to being a story teller first. If the story is not compelling, the research effort has failed.

Unlike other qualitative research that focuses on description, the goal in autoethnography is not just to describe but to evoke feeling and deeper understanding. This takes the view of knowledge as being an embodied experience, not just observation. To do so, the researcher must make h/himself vulnerable, sharing a great deal of self-disclosure and demonstrating a great deal of self-reflexivity.

Nevertheless, autoethnographers still value systematic methods used in other research methods:

If you imagine research methods as on a continuum, quantitative laboratory research would be on one end of the spectrum, and autoethnography would be at the other end of the spectrum, followed by performance studies and other more artistic ways of knowing. Because this method requires a unique set of writing skills, we do not cover it in full here, but offer websites for those who might like to learn more.

For an overview:  http://www.qualitative-research.net/index.php/fqs/article/view/1589/3095

For a focus on analytic, rather than evocative autoethnography:  http://web.media.mit.edu/~kbrennan/mas790/02/Anderson,%20Analytic%20autoethnography.pdf   For an example of autoethnographic research as researcher ethics:  http://jrp.icaap.org/index.php/jrp/article/view/213/183

To perform autoethnography:  http://eppl604.wmwikis.net/file/view/spry.pdf

Media portrayal of what autoethnography is:  http://www.youtube.com/watch?v=pb50nPHgI04

Data Analysis: Interpreting Results

Collecting information is only the first of two parts in the research process. In qualitative research  how  one interprets the results is particularly salient. Recall that the world-view or epistemological approach of qualitative research is the assumption that multiple meanings are always possible and present and that meaning is created, it is perceptual, and influenced by context. It is not just observed, nor is it considered universal or objective. Thus, in the second part of the qualitative research process the researcher purposefully and explicitly  makes meaning  of the study by applying methods to reveal patterns in the data. As discussed in chapter one, making meaning is what people do when they interact every day. They negotiate and construct perspectives through the exchange of verbal and nonverbal cues. In research, the author makes meaning through a negotiation with the verbal and nonverbal messages or data collected. The researcher’s charge is to make every effort to make fair and insightful sense of what might currently be a large pile of data.

As this description suggests, because there is room for multiple interpretations, the researcher has a responsibility to analyze the data or information gathered in a highly  systematic  fashion, drawing from previous methodologists’ recommendations and guidelines. To be systematic means the researcher cannot simply focus on data that is the most convenient, or anecdotal and examine it in a half-hazard fashion. Ideally, the researcher must make every attempt to consider all the information even though it would be impossible and not useful for the researcher to include all the information in the final report of the study.

Common Steps in Qualitative Data Analysis

Analysis methods for qualitative research have several commonalities. Recall that in all qualitative research, the data is words and behaviors, not numbers. It may be in the form of artifacts such as newspaper clippings or diaries from the field of study, field notes you have taken, transcriptions of one-to-one interviews, focus groups, and/or participant’s more naturally occurring conversations. In the analysis phase, the researcher’s job is to select analysis tools  that seem to be a good fit for the type of data being examined and the objectives of the study and then to review all the data in a consistent fashion. From this, the researcher seeks to synthesize the material by organizing it into categories and then identifying connections among the many categories that will make visible a more narrow, manageable focus to make sense of the information. 

There is a variety of methods to conduct qualitative data analysis. Some are more complex, others more accessible, but it is helpful to remember that at their essence, the meaning making process for all of them is to look for patterns -- themes and patterns among the themes by “comparing and contrasting parts of the data” in a series of steps (Keyton, 2011, p. 62). Thus, before reviewing specific types of qualitative methods, we are able to identify basic steps common to all of the methods.

According to qualitative communication scholars, Thomas Lindlof and Bryan Taylor (2002), the overall analysis process involves three basic tasks: “data management, data reduction and conceptual development” (p. 211). In data management, tools to code and categorize data are used to help the researcher gain some control or order over what could be an endless volume of information. From there it becomes easier for the researcher to see that some data may be more central to understanding the results than other data. Data reduction is then when the researcher begins to assign prioritized value to the data, reducing the size of the focus of analysis for a closer look. This does not mean any information is to be thrown away. Remember qualitative researchers believe meaning is created in context. Although it is necessary practically to try to distinguish what is most salient to focus on, the other information may in time provide a rich context for more complex, subtle interpretations. The researcher will want to later return to this secondary data for such considerations. Conceptual development should emerge from accomplishing the prior two tasks. This is where and how the concepts and themes -- or meanings in the study emerge. They are grounded in the data from the specific social context of study as well as influenced by the researcher’s review of previous research and qualitative theory. Together the tasks of data collection, data management, data reduction and conceptual development emulate the  inductive  nature of qualitative research. The researcher studies communication in a smaller, more specific context, gathers extensive data, organizes it and reduces it even further to smaller kernels of knowledge, and then returns the kernels to the larger social and theoretical contexts for further thought about the significance of these interactions in everyday life.    

In applying Lindlof & Taylor’s three steps for qualitative analysis, we break down the process further, adding two additional steps to make the process more explicit. We add an introductory step called data immersion and a concluding step called evaluating results. While we present them as if they are discrete steps, the reader should know researchers begin to think about these steps before all the data is collected and may return to the data collection phase after conducting preliminary analyses. The reader should also be forewarned if you read other descriptions of this process the steps may be divided a bit differently or labeled differently. We tried to do what was most streamlined. In the end, the various descriptions are really about the same basic process.

Member checks add to the credibility of a study. They better assure the participants’ perspectives are respected and that the interpretations are grounded in their perspectives (Lincoln & Guba, 1985). Useful results from member checks are not just results that say, “yes, this is good,” or “you made a mistake in the spelling of my name,” but rather feedback that helps the researcher see the findings through the participants’ eyes, thus extending the depth and breadth of interpretations and resulting theory proposed. Another value added from this method is that it gives some ownership of the results back to the participants. They are empowered to edit and add their input (Lincoln & Guba, 1985).

For example, in a study of African-American women’s self-esteem, DeFrancisco and Chatham-Carpenter (2000) conducted member checks in recognition of their position as White women interpreting the stories of women of color. The meetings caused the researchers to reframe one of the primary themes taken from the study to focusing on the effects of racism on the women’s self-esteem to focusing on the demand for respect. The shift in words from the researcher’s (racism) to the participants’ (respect) may have seemed small, but the former framed the participants as powerless victims of a racist society, the latter framed them as strong women demanding fair treatment.

• Data Immersion: Get to know your entire data set closely. This includes the participants’ contributions as well as your field notes during data collection. A  close-read , includes reading and rereading the data set multiple times, line-by-line, perhaps in different orders (Lindlof & Taylor, 2002). There is nothing that can be substituted for building a close awareness of what you have gathered. You will likely gain a new insight every time you review the material. You need a view of the whole before you can begin to sort the data. As you do so, look for holes – is there anything you need to go back to the field of study to gather? Use a concept we discuss in more depth later called  reflexivity  (Ellis, 2004). Critically monitor and write down your observations, reactions and feelings as you process the information collected, they may influence the directions you take next. The instructions for taking good field notes described under data collection above will be helpful here, as well. It is a good idea to make a complete copy of your data and archive it in case the copy you work from becomes damaged or lost.

The chunk is called a  unit of analysis or unit of data.  Identifying one’s unit of analysis is necessary in qualitative as well as quantitative data. It helps to define what level or size of data the researcher is focusing on – to identify the boundaries of individual units of data being considered. The unit of analysis is the smallest level of the data from which the researcher sorts the data and thus making meaning. The goal is to have the same or similar size for each chunk of data. The size depends on the goals of the study. For example, if a researcher is studying how people use conversation to create and maintain their identities, the researcher’s unit of analysis is typically a person’s turn taken in a conversation. But in most theme/categorization analysis of qualitative data, the unit of analysis can be more flexible. It can be a word, a phrase, a complete sentence, a conceptual theme, a nonverbal expression, a communication episode or event such as episodes from reality television, full texts, such as speeches, and more. Since the size may vary, you may find recommendations from researchers Yvonna Lincoln and Egon Guba (1985) helpful for identifying your unit of analysis. They suggest the unit have two criteria: the first is noted above – that it be the smallest piece of recognizable information, meaning that it does not require other contextual information to be understood. Second, the piece of information should be heuristic, meaning it should help with understanding. It should help address the research question or objective of study.

For example, imagine you are studying how young adults perform their identity by what they choose to post on their Facebook or another social media site. Your unit of analysis will likely be each verbal or nonverbal indicator about the participants’ identity. So, if a person posted, “I’m a 21 year old woman who loves to laugh, but I am dead serious about the type of man I want in my life.” The following is how the sentence might be broken into individual units, or pieces of information about her identity: “I’m a 21 year old/ woman/ who loves to laugh,/ but I am dead serious/ about the type of man I want in my life/.” Thus, from just one sentence, the research can glean 5 units of data representing 5 different categories or themes: age, gender, personality, goal (looking for a mate), and sexual orientation (heterosexual).  

Once the unit of analysis is determined, the researcher can chunk up all the data into units and begin  coding  the data – organizing it into groups under labels. A  coding scheme , is a sort of shorthand device to label and separate the data (Landlof & Taylor, 2002). When finished, the codes will help to reveal categories of concepts or themes and codes make it possible to retrieve the data more easily for further analysis.

So, for the example of social identity in Facebook above, as you review the data collected from other participants’ sites and your interviews with them, imagine you notice a tendency in the descriptors that seem to refer to the participant’s demographics: gender/sex, sexual orientation, age, etc. These demographics may each become a category where you will compile the individual instances that demonstrate that category. A post from a person who identifies as male describing how he lifts weights and strives to attain muscle mass might be categorized under gender, and specifically masculinity. A person who posts a message about being a women who competes in a race for wheel-chair users might be categorized under physical abilities, etc.

The code or coding scheme is to look for demographics, the resulting categories or themes are gender/sex, sexual orientation, age, etc. In the study of coming-out narratives, the code is degree of agency, the categories are the specific degrees noted in the data. In the study of relational conversation, questions are the code, the specific functions of questions are the categories. Coding helps to identify whole families of categories and sub-categories. They help the researcher not only sort, but display the data in visual ways to make the meaning more apparent.

We offer two cautions regarding oversimplifying this process. First, while you may see preliminary codes and categories emerge from the data, the final ones may not be the same. It is important to keep one’s mind open to considering alternative or extended coding systems otherwise one may only see what s/he wants to see. While there are multiple ways to organize data and endless sizes or levels of latter categorization possible, there will likely be a way that makes sense to you. There is also no set number of categories required. You should have as many categories as it takes to account for all the data possible. In the end, you should feel the categories do a good job of representing the data as a whole, both in terms of its similarities and in terms of its differences (Lincoln & Guba, 1985).

Second, a warning about labels. Qualitative researcher use the terms coding schemes and categorizing to refer to the process of organizing data in qualitative analysis, but they do not always use the two terms in the same way. Some researchers use the terms interchangeably. Others claim there are important distinctions between the two terms. We follow Lindlof and Taylor (2002) who argue researchers first  code  the data into groups and then look for categories (how the groups connect in terms of concepts, themes, constructs). Codes are descriptive of the data groupings, whereas categories and themes are more interpretive – they tell what overall meaning the researcher makes of the groupings. Themes and category systems are the result of coding (Saldana, 2009). For example, a code may be chunking the data by participant demographics, which results in a categorization system of data that emerged according to race/ethnicity, social class, age, and gender/sex. A theme for the category of race/ethnicity might be “participants identify with homogeneous others” or “participants celebrate multicultural identities.” Each are conclusions or themes drawn from data within the category of race/ethnicity.

To further assist you in identifying codes and categories, two other researchers proposed a list of questions you might find useful (Lofland & Lofland, 1995).

Tips for coding and categorizing data:

• What is this? What does it represent?
• What is this an example of?
• What do I see going on here? What are people doing? What is happening? What kind of events are at issue here?
• If something exists, what are its types?
• How often does something occur?
• How big, strong, or intense is something?
• Is there a process, a cycle, or phases to the topic of study?
• Does one thing influence another?
• Do people use the interaction in specific ways?

(See also Sociologist Graham Gibbs’ lecture, “What is coding for?”  http://www.youtube.com/watch?v=5xM-9yuBhMc )

Data Reduction: If the data set is relatively small or the initial categories seem particularly salient, the researcher may not need to proceed to this step of refining the focus of analysis. However when conducting extensive ethnographic research it is particularly necessary to reduce the focus of analysis to a size and framework the researcher can better manage. Framing or  frame analysis  is selecting a focus or lens from which to analyze and present the data. When doing so, the researcher acknowledges that multiple frames are possible and that one’s unique perspective may influence how one positions and interprets data. One has to make choices and this fact should be documented and rational provided for the choices made. For example, one’s roles in society may influence the frame taken in analysis: a student, a psychology major, a sociology major, a communication scholar, a parent, a single person, etc. (Grbich, 2007). The goal is to develop in-depth quality in the final analysis, not quantity. Thus the researcher may begin to identify primary categories or themes and secondary ones that may or may not end up in the final report.

If we return to the example of studying participants’ identity construction on social media, the researcher may decide reporting people tend to describe themselves according to demographics is not very insightful or useful. Perhaps the comments suggest a particularly interesting connection among the participants’ comments about their gender, sex, and sexual orientation, and that given the researcher’s review of previous research and/or training, the researcher decides this focus would make a more useful contribution to the state of knowledge. The researcher is then framing the study around themes or categories tied to gender, sex and sexual orientation. Other demographic information such as race, ethnicity and age may be related in the participant comments and serve as a secondary level of themes or categories to be explored.

Throughout the analysis process, but certainly while conducting data reduction, the researcher should be looking for  exemplars  – examples such as quotes from the data that vividly illustrate the themes and categories proposed. The examples should emulate the boundaries or characteristics the researcher has used to distinguish the themes or categories. They should make the themes or categories come alive for the reader (Ryles, as summarized by Geertz, 1973). These are what will be presented in writing the report to represent the qualitative data reviewed.

Conceptual Development: The analysis process would not be very meaningful if the researcher stopped after creating a list of categories. The researcher now needs to determine what meaning to make of the list. In this step the researcher attempts to integrate the categories to consider what they mean when examined together. The researcher looks at how they might relate to or influence each other (Lindlof & Taylor, 2002). This phase is a  meta-analysis  – an examination of the examination of data – the creation of codes, categories and themes created in the previous steps. In grounded theory, described later, this is called  axial coding , but the process and goal is the same across methods – attempting to connect the codes, categories and/or themes.

The process used is basically to repeat the same steps but on a higher level of analysis: review all the codes, categories or themes (instead of the individual instances of each) in comparison to each other and attempt to understand how they relate to each other. It is as if the researcher is constructing a framework or umbrella in which to locate the individual codes, categories and themes. There are many ways researchers may attempt to do so. The manual methods suggested previously for color coding, creating grids or other visual depictions of the categories and themes can help make the connections visible. Researchers may see that the categories or themes fit well with an existing theory and so place them within that theoretical context, they may see that together the parts suggest a new theory or conceptualization of the issue being studied, they may see a metaphor emerge that helps to explain how the parts fit together, etc.

• Triangulation : As defined in the first part of this chapter, triangulation is approaching the study from multiple perspectives to enhance the rigor or integrity of the results of the study. It can include using multiple methods of data collection, gathering multiple sources of data, and for analysis it can include having multiple researchers or coders for the data, and/or drawing from multiple academic disciplines to frame the study. The idea is that if shared meanings emerge from multiple directions of data collection and analysis, those meanings are likely more sound. Sociologist Norman Denzin (2006) proposed this method to directly refute the criticism that because multiple interpretations are possible in qualitative research, the findings proposed from a study are unreliable or invalid. 
• Representativeness:  The researcher should be sure there are multiple exemplars – specific examples of quotes or behaviors that support each theme or conclusion formed. If the researcher cannot come up with enough strong examples, the claim is likely not strong enough to be warranted.
• Member check:  After preliminary or final analyses, the researcher shares the interpretations with participant members of the study to solicit feedback. The sharing can be in the form of face-to-face interviews, focus groups, or written data summaries. The goal is for the researcher to find out if the interpretations rendered ring true to the participants. Do they feel the results reflect their lived experiences? The researcher may want to use a detailed standard list of feedback questions or ask for a more holistic reaction to the research summary. After obtaining input, the researcher implements the information gathered to rethink the findings and/or cite as support for the claims in the study.
• Transparency:  Transparency in research is the expectation that researchers will make the entire process of their work explicit, openly sharing the process of meaning construction with the readers (Hiles, 2008; Seale, Gobo, Gurbrium & Silverman, 2004). This criterion emerged out of a need, as previous qualitative and quantitative research was often not transparent, in part due to page limits and related costs for academic journal publications. Thus research conclusions and what academia comes to call knowledge appeared as if from a vacuum, free of individual decision-making and perceptual influences that are always present in human endeavor, and prevented others from testing out the results further. Transparency is an ethical consideration. When researchers clearly document the steps taken in data collection and analysis and share these with the reader, s/he should be able to better understand and visualize how the conclusions were formed. If this clarity is not present, the value of the study and the researcher’s integrity may come into question. 
• Reflexivity:  An effort to examine how one’s own thoughts, feelings, and behaviors might intermingle with phases of the research process (Bochner & Ellis, 1992). Reflexivity is the recognition that the researcher is a part of what is being studied. The researcher’s unique cultural lens will necessarily affect the research process. Taking the time to place oneself and one’s values and possible biases under examination better assure this inevitable influence is not abused and that not only the researcher, but the participants know the nature of the researcher’s likely influence on the study. Careful field notes and keeping a diary or log during the analysis process will help the researcher examine this criterion.
• Grounded Theory –  Grounded Theory is actually one of the individual analysis methods described below. Its mention here speaks to the pervasive influence of grounded theory on the general process of all qualitative research. The key point regarding using grounded theory as a criterion to assess rigor and integrity is that the researcher should refrain from using her/his words to label themes/categories, and rely wherever possible on the words and images conveyed by the participants. The example above on studying self-esteem from African American women’s perspective illustrates why this is so important. The two White women researchers had initially inadvertently assigned their own label for a theme as racism rather than “respect” which the participants used.

These five steps represent the general process of conducting qualitative analyses. What follows is information about the specific analysis methods most commonly used: thematic analysis, grounded theory, and content analysis. We will also briefly introduce the reader to two methods that focus more specifically on  how  the verbal and nonverbal messages under study were constructed – discourse analysis and conversation analysis.

Thematic Analysis .  This is the most general, easily accessible method of data coding or categorizing. The reason this method is more accessible is that the rules for the general process of analyzing data as described above are more relaxed. This does not mean thematic analysis is unsystematic however. The researcher still defines the unit of analysis and data is coded first to organize it. From the categories the researcher looks for a theme within each category and then an umbrella theme or themes that might connect the individual themes. The concluding themes should best reflect the data as a whole.

In a comparison of grounded theory and thematic analysis, Mohammed Ibrahim Alhojailan (2012) concluded thematic analysis is a comprehensive method, just as is grounded theory, however “It provides flexibility for approaching research patterns in two ways, i.e. inductive and deductive” (p. 39). In its purist sense, grounded theory requires data to be inductive only. In thematic analysis, the themes may be based on information gathered from interviews and previous research and theory. And, the data does not have to be collected at one time. “This makes the process of thematic analysis more appropriate for analyzing the data when the researcher’s aim is to extract information to determine the relationship between variables and to compare different sets of evidence that pertain to different situations in the same study” (p. 39). 

Characteristics of Thematic Analysis:  Theme analysis is often used to study texts, both written and transcribed oral texts such as interviews or focus group discussions. The themes can come from the research questions and objectives guiding the study, from previous research or theory presented in the literature review, from the researcher’s standpoint as a certain gender and sex or ethnicity, social role, etc., from the data itself gathered in the study, or what is most common is from a combination of pre-existing influences and meanings that emerge from the data.  “This makes the process of thematic analysis more appropriate for analyzing the data when the research’s aim is to extract information to determine the relationship between variables and to compare different sets of evidence that pertain to different situations in [the] same study”   Alhojailan (2012, p. 39).  

Steps of Conducting a Thematic Analysis:  While the overall steps are as described above for qualitative data analysis, in thematic analysis steps 3, 4 and 5 take on a particular process. The researcher must answer the question: When do comments or behaviors become a theme? Literally anything could be claimed as a theme, so what makes a given researcher’s claims acceptable? While the comment or behavior needs to occur repeatedly in the data, counting repetition alone is not enough. Interpersonal Communication scholar William Owen (1984) suggests the researcher will know s/he has a theme when it meets the following criteria: recurrence, repetition, and forcefulness.

• Recurrence means that at least two parts of the data have the same meaning, although the meaning may be expressed in different words. The researcher looks for a pattern in the relational or underlying meaning of a message.
• Repetition is about frequency – that the same key words, phrases or sentences are mentioned again.
• Forcefulness refers to the degree of emphasis conveyed in the message. Does the way an idea is said or written suggest it is important to the speaker? This can be through vocal inflection, timing, volume, and/or emphasis placed on words or phrases in written or oral form.

While Owen would require that the data meet all three of these criteria, it may not always be possible to do so. What is important is that the researcher show evidence in the text to support her/his claims, and that they speak to the underlying meanings being expressed. There will always be other meanings in a message; the researcher’s job is to best assure the themes selected seem primary, rather than secondary. 

Owen offers an example from an analysis of a daily log a female college student was asked to make about her relationships. This one is about a high school friend. The short passage reveals all three criteria simultaneously:

Day One: She is an ideal friend. I haven’t really known her for very long, but it seems like we jumped into the middle of a relationship. I feel like I’ve known her forever.  Day Three: That night we burnt chocolate-chip cookies and drank white wine, and for the  first  time since Bud died (her brother), I had someone I could relate to. Day Five:  Special  is the word-of-the-day.

The thematic concept Owen claims is relational uniqueness. The references to “ideal friend,” jumped into the middle of a relationship,” “feel I’ve known her forever,” I had someone I could relate to,” “special is the word-of-the-day,” “It’s like Debbie and I share a secret,” “she is so special, we are so special!” “We have the perfect relationship,” all suggest the theme of uniqueness. Recurrence is apparent as is repetitious, with the word “special” being used three times in one entry. Forcefulness is also apparent with the italicizes used as in “for the  first  time,” and “special.” Comparing the current relationship to the comfort of her relationship with her brother who has now died is quite powerful.  (For an online lecture on the steps of theme analysis, see sociologist Graham Gibbs, University of Huddersfield, UK ).

Strengths and Limitations of Thematic Analysis . Because this method is a general and relatively simple (but not fast) process, it is the most widely used method for analyzing qualitative data. It is a good choice for novice qualitative researchers because the process is easy to understand. It mirrors the social cognition perceptual process we humans engage in every day: organizing countless types and sizes of data into categories to make sense of the world around us. Thematic analysis can be less time consuming than other methods for qualitative data analysis, although to be done well, it still requires repeated reviews of all the data and a willingness to sort and resort data according to appropriate themes. And, it can be applied to analyzing all sorts of data from looking for themes in artifacts, such as news stories about an event, to looking for themes across interviewees’ comments about a particular topic, to looking for themes in  how  individuals who share a cultural identity tend to exhibit similar communicative behaviors or categories. It also works when there is a large volume of data, which is not as easy to do with other qualitative methods. These strengths of the method also become limitations. Because it is so general, it is sometimes criticized for not being systematic enough. While not a failure of the method, some researchers using thematic analysis fail to fully account for their thinking that went into creating the themes claimed as results of the study. A limitation of the method itself is that the meanings of human behavior are interdependent, not independent of other being or the physical and social context. Thematic analysis calls for sorting data into discrete categories, whereby the same comment cannot be placed under two themes. Yet, as the example below will likely suggest to the reader, comments or behaviors often seem connected and could be placed in multiple categories. The themes are almost always interdependent, but the researcher may not acknowledge this. And so, research using thematic analysis is more easily open to criticism regarding the relevance of the themes identified and the bias or hidden agenda of the researcher’s choice in framing the data within the selected themes.       

Example of Thematic Analysis:

Pohl, Gayle, & DeFrancisco, Victoria. (2006). Teaching through crisis.  The International Journal of Diversity in Organisations, [sic]  Communities & Nations

In a study of why some college instructors addressed in their classrooms the events of the airplane bombings in the U.S. on 9/11 of 2001, their comments suggested the following themes: they felt they had no choice but to address the event; they needed to make sense of the events both for themselves and their students; and they felt the events fit their course content (Pohl & DeFrancisco, 2006). Below are sample comments that when sorted suggested the three themes.

• Because I felt I had to both for myself and for my students. The event was too large and had too many ramifications to ignore. I knew that it was going to be an issue that we would be dealing with for a long time.
• I really wrestled with the decision. It seemed that it would be impossible, if not completely inhuman, to try to ignore.
• Both the students and I seemed to need to talk about it and explore the events surrounding 9/11.
• It was a historic, painful, and socially critical event.
• A lot of people, including students, were experiencing a variety of strong emotions. I think it’s important to open discussion for those who want to talk and those who might benefit from listening to others’ ideas. Our students look to us for guidance – we should provide it.
• I teach in the state of New York. During the days and weeks that followed 9/11, a few members of my classes were mobilized as part of the National Guard. Other members were “absent” mentally or physically because they had found out, or hadn’t found out, about whether their family members and dear friends were alive. These issues were so in our faces” that I believed I had an obligation to incorporate the events in my teaching. Furthermore, when I suggested to my Comm. Theory class that we carry on with the projected daily schedule, they decided they WANTED to learn about theory in order to make sense of what was happening.
• I taught on the day of the attacks, and students needed some help in giving meaning to what happened.
• My students seemed paralyzed by the events of 9/11. We incorporated these events because no one else seemed to be talking about them. There was a tremendous need to address what was happening so students could begin to see beyond the events of the day.
• The events occurred shortly before my first class met, and as a relational scholar, I don’t feel we should ignore the things that affect our everyday lives in our teaching. I feel it is most powerful to use our lives in applying communication principles. As it was, we were about to discuss confirmation and disconfirmation and it seemed to me that these events could exemplify those concepts on a larger than interpersonal scale.
• Our college is just 70 miles from New York City. Some had family or friends in the World Trade Center area. My class met on September 12. We were all stunned. A Communication Ethics class will always address such issues as news media decisions to transmit graphic images. These questions were poignantly present, and the arguments on either side very forcefully available to us.
• I teach Epidemiology, human diseases, and environmental health. Terrorism and biological, nuclear, and chemical weapons issues are all topics that have to be dealt with in these courses. Unfortunately, now I realize that I have to make sure I teach these topics because a well-trained responder might save lives in the future. It’s sad I even have to deal with these issue.
• After teaching a course on “Minority Images in American Media” for several years, and regularly emphasizing the manner in which the media--- especially the news media – rely on stereotypes of Arabs and others from the Middle East as terrorists, it was immediately evident to me that I needed to spend more time addressing this issue. Also, I teach a course on Visual Communication and, for several years, had difficult convincing my students that visual images communicate much more powerfully and immediately and effectively than words do. September 11 made that argument much easier to convey with the four-day bombardment of images over and over again, so including issues from Sept. 11 in my lesson plan was an obvious choice.

Together, the four themes suggested a pattern or over-arching theme among the responses – the instructors felt a need to use the course experience to work through the crisis with their students. Some did this in a more formal way by applying relevant course concepts to help critically analyze the contexts surrounding the events, others more basically sought to create a safe space for them and their students to sort through their mixed emotions.  

Grounded Theory Method. The word “theory” in this method of data analysis might be a bit confusing initially. People tend to think of theory and methods as separate, but in this groundbreaking methodology first proposed by sociologists Barney Glaser and Anselm Strauss (1967) the relationship between theory and method are more overtly celebrated. The approach has been refined and is widely practiced today in qualitative research. It is not simply a method for categorizing data, it is a method for using data categorization to reveal underlying theory (and the word  theory  can be used here in its most general sense, as an attempt to explain some phenomena). The resulting theory can be in the form of themes, or other attempts to explain deeper levels of meaning for what is going on in the interactions of study, referred to as  substantive theory , or the result can be  formal theory  that will be used to advance the larger conceptual field of sociological inquiry (Glaser & Strauss, 1967). The idea is that theory can be and is most useful when it is developed from observation, rather than the other way around as done in quantitative research where the theory dictates the direction of a study. Thus Grounded Theory calls for an inductive approach to building theory from the ground up – based on research observation and analysis.

Unique characteristics.  As noted above, the most unique characteristic about grounded theory is that the meanings should emerge from the data itself – from the words and behaviors of the participants. Thus, it is an excellent extension of the ethnographic approach where researchers work to honor the words and perceptions of the participants. The researcher must work to resist letting h/his perspective overly influence the meanings derived from the data. Because of this characteristic, grounded theory has become a criterion for assessing a researcher’s ethics in qualitative research and for assessing the value of the results of the study.  Such criteria demand  a highly systematic and rigorous process of data coding and meaning development.

Steps in conducting.  Grounded Theory is also referred to as  open coding  or the  constant comparative process  of letting the meanings open-up -- emerge from the data, rather than imposing predetermined codes, from previous research, theory, and/or the research question(s). The terms open coding and constant comparative are actually what researchers do in steps 2-5 of the qualitative research process. They will be described further below, as they are the heart of what makes grounded theory a unique qualitative approach.

Step one (data immersion):  The researcher begins with an exhaustive review of the data, trying to capture as much of it as possible.

Step two (data management):    Open coding  is the initial, unrestrictive coding of data” before the researcher knows what the final categories or themes will be (Lindlof & Taylor, 2002, p. 219).  This step still requires defining the smallest unit of analysis and tracking each one closely. The researcher tries to ignore previous theory and research and let the meanings emerge through a process of  constantly comparing  or putting each piece of the data next to the previously coded data to look for similarities and/or differences, letting the meanings emerge piece by piece.

Open coding is a creative process. You can use a pencil, highlighter, post-it notes, or the computer to block quotes and other data and move them around as you begin to see patterns emerge. Each piece of data or instance is compared to the previous ones already categorized to see where the new piece of data belongs. Does it belong in a current category, or does it suggest a new category is warranted? You are looking for what makes sense regarding ways to organize the data. Through repeated reviews of the data the distinguishing characteristics of each category and the coding scheme will emerge.

In vivo coding.  This type of coding is done at the same time as open coding, it is a more specific type of open or grounded coding where the actual names of the codes come directly from what the participants say. The idea is to avoid the researcher imposing her/his work view as reflected in her/his labels for codes and themes. Instead the labels reflect the insider participant perspectives – it is using their words. Thus it is a preferred, more carefully grounded coding system, but it is not always possible to attain. For example, while the researcher may be quite sure the participants are engaging in a great deal of face-saving strategies, as in the case of inappropriate behaviors and creates a coding scheme for these, the participants may not realize or want to verbally recognize those behaviors.

Step three (data reduction):  Eventually the number of new categories will diminish and you, as the researcher can begin to consider whether you have reached the point of what is called  theoretical saturation  (Glaser & Strauss, 1967) .  When no new categories are emerging and the categories you have created seem stable, the researcher can assume no new data is needed for now, and the analyses may be sufficient. However, if the results do not seem very insightful, rendering for example new, unique interpretations, the researcher may still decide to return to the field to collect more information.

Step four (conceptual development):  When the open coding is completed (at least for now), the researcher moves to conduct what is called  axial coding , basically the same general step four described previously. Think of an axis – as identifying a common framework on which the categories or themes can be located. In this step the emergent or grounded theory becomes more overt. The researcher examines how the categories created might relate to each other by conducting a metacoding – codes that attempt to connect the categories, remembering these codes must also be grounded in the data.

A useful tool to test one’s metacoding is called  negative case analysis  (Lincoln & Guba, 1985) .  The basic idea behind negative case analysis is that if the researcher’s emergent theory from the axial coding can account for a specific case, incident, person, or comment that does not seem to fit with the other data codes or categories, then the resulting theory is stronger. Sometimes researchers will return to the field to find a negative case to test their emergent theory conclusions. In this view, the negative case is not to be feared as something that will hurt the researcher’s study, but rather a piece of information that may make the resulting theory more nuanced and reflective of the complexity of participants’ lived experiences. In many cases, the information from the negative case analysis may send the researcher back to the field or back to a previous step in data analysis. The researcher must find a way to account for the negative case, which may require at an extreme completely rejecting the prior analysis, and at a minimum, more carefully describing the categories, themes, and emergent theory.        

Step five (evaluating results):  As noted in the previous step, the process of assessing results has already begun. However, even after the researcher has conducted a negative case analysis and axial coding, the results are subjected to at least one more assessment. At this point the researcher may feel h/his interpretative abilities are exhausted and it is time to get a new perspective form other outside researchers, members of the community of study, or insider perspectives from some of the participants.  Member checking  (Lincoln & Guba, 1985) comes into play here. The researcher may choose to share the results with members of the study to see if the categories, themes, and resulting theory make sense to them. Do they represent their lived experiences? Member checking can be done very informally as in conversations with individuals, or the researcher may choose to circulate a summary of the findings with survey responses, or conduct a focus group discussion with members. It is not necessary, nor usually possible, to solicit feedback from everyone in the study.  The goal is to determine if the researcher’s conclusions ring true to the participants’ actual experiences and perceptions. It is an ethical tool to add value and credibility to the final report of the study.

(For a lecture on this topic, see sociologist Graham Gibbs, University of Huddersfield, UK, “Grounded Theory: Core Elements,”  http://www.youtube.com/watch?v=4SZDTp3_New )

Discourse Analysis and Conversation Analysis

There are two other interdisciplinary methods for studying spoken and written texts or discourse, including the multiple paralinguistic ways in which the speaker delivers the words. Discourse Analysis focuses on speech (or texts) as communicative acts and examines how people use language to construct meaning. The focus of analysis is on the content of what is said and the metamessages the content conveys, perhaps about identities, relationships, positions on a social issue, etc. Conversation Analysis focuses on how the speakers communicate – the patterns of using conversational turn-taking tools such as questions, pauses, volume, and more, to negotiate identities, relationships, etc. Both methods examine the text or discourse in a line-by-line fashion with detailed transcriptions. Conversation Analysis requires the added transcription devices to indicate the amount of time between turns at talk, the paralinguistic qualities of the voice, etc. (see Gail Jefferson’s transcription system, in Sacks, Schegloff & Jefferson, 1974). As you might guess, the meanings derived from both methods are deeply embedded in the unique cultural understanding. Discourse Analysis is often used in rhetorical studies, which is reviewed in another section of this textbook. It is also used heavily in the communication sub-field of performance studies (see for example, Carlin & Park-Fuller, 2012; Palczewski, 2001). In both of these areas the researcher is attempting to make meaning of words and often nonverbal messages to provide new insights and/or greater understanding. Conversation analysis is used in some ethnography if the focus is on studying the structure of interaction in language use/conversation (see for example, Hall, 2009).

Data Write-Up

When one reaches this phase of the research process it is easy to think the creative, critical thinking work is done, but particularly in qualitative research, it is not. Writing the results is part of the meaning making process. Writing itself, is often viewed as an analysis method (Richardson, 2003). It is an opportunity for the researcher to synthesize what was learned and once again review the meanings rendered, but this time in the larger context of previous research summarized in the literature review. Often returning to this larger body of knowledge will push the researcher to make deeper and/or more complex connections among their own themes and meanings rendered.   

Not all qualitative research reports follow the traditional social science organization, but if you are not writing an autoethnography or narrative study, we recommend the social science organization for clarity. It contains four basic parts. The first is the introduction and literature review, second is a description of the research methods selected for collection and analysis, third is a description of the results or meanings formed from the data, and fourth is a discussion of the study.

A metaphor that might be helpful for visualizing how your paper will look is an old-fashioned hourglass. The introduction and literature review begins broadly and then the paper narrows to a focus on the specific study’s design. The paper ends more broadly again where the author places h/his specific results back into the context of previous research and the larger field of study to consider how the study contributed to the larger field.

There are some basic criteria for the whole report to keep in mind. They are the same criteria proposed in the data analysis process. And, by the way, these are good criteria to look for as you assess other’s reports, as well.

• Transparency in writing about the process is key. Transparency refers to the need to be open, clear and explicit in describing the research procedures used to design the study, conduct data collection and data analysis (Seale, Gobo, Gurbrium & Silverman, 2004).The reader should be able to trace your steps and follow how you came to the conclusions you did. If the reader cannot do so, the author is hiding what may be useful information to help others understand how s/he came to the conclusions formed. 
• Representativeness requires the researcher to be sure the results are grounded in the words and experiences of the participants and not overly controlled by the researcher’s voice. Participant quotes are your data, the narrative in the report is your voice weaving these quotes together to make meaning. Thus much of the write up will include description – the participants' actual words and behaviors to support your claims (themes or conclusions). The reader should not feel the patterns, themes, or other conclusions were drawn out of thin air. The reader should be able to see 2-3 exemplars for each claim made about the results of the study.
• Reflexivity is related to transparency. It is an effort to be open with the reader and acknowledge that you as the researcher are a part of what is being researched. You are not an objective observer uninfluenced by the study or detached from its results. The reflexive author is careful to separate h/his own feelings and comments from what the participants said, and careful to note when such a clear separation is not possible. Reflexive writing is being critical of one’s own work. This does not mean always being negative, pointing out limitations, just that one recognizes h/his potential role in the process.

Introduction and Literature Review

The introduction and literature review are combined in this step. It is useful to think of them as a coherent rationale for the study you conducted or propose to conduct. The Rationale is drawn from life experiences, review of news reports, popular press, secondary sources (as in websites, textbooks or published reviews of literature and most importantly, previous original scholarship on the topic. As in quantitative research, research questions that guide original qualitative research are generally expected to be based on previous research. Some ethnographers and autoethnographers prefer not to review the literature prior to collecting original research so that their views are not tainted. In this case, the literature view portion of the final report would go in the discussion of the results of the study, rather than as a part of the rationale of the study.

The rationale is built by answering the following questions in the paper, usually in this order:    

• A statement of the problem that needs to be studied (this can be a social problem, an academic theory that needs to be developed, a lack of experience needed, etc.) The definition of problem is meant to be broadly defined here. This is the attention-getter of the paper. It should compel the reader to want to know more. (Usual length is from a paragraph to two pages, double-spaced.)
• Embedded in the statement of the problem and why it needs to be addressed, the researcher introduces for the first time any key terms that will be used repeatedly in the study. These should be briefly defined for the reader with a citation of the source. For example, if you were doing a study on how people build an intercultural relationship with someone from a different race or ethnicity, What key terms would you be using? Mostly likely you would need to define culture, race/ethnicity, prejudice, intercultural relationships and relational development. While there may be other concepts, only the most central ones need to be introduced here.
• A preview of the type of research warranted from the review of the problem.
• There are multiple ways to organize a review of the literature. You will want to select what helped you organize the information and understand it best. Topical organization is most popular, but it might be useful to organize the review in a chronological fashion. The organization of sub-headings should help lead the reader to understand why you have decided on the design of the study you selected.
• The question that is commonly asked is, “how much do I need to include from each study?” There is no specific answer – thank goodness. There is room for your thoughts. Basically include what is most relevant to the present study you will or did do and not more. Thus, if you adopted methods from a study then describe the study’s methods, but if you are only citing the study as evidence that your topic is important, then just cite the study, as in parentheses at the end of a statement you make as in: (Ellis, 2004). 
• A review of literature is not just a cut and pasting of a series of research abstracts. Your voice synthesizing the previous research should be central in the review.
• The review of previous research literature usually concludes with the proposal of one to three overall research questions the author will attempt to answer with the specific study proposed. (Length varies a great deal depending on the instructor’s assignment and/or publishers’ page limits. A rule of thumb is that it be almost as long as the reporting of results from your study, from five to sixteen pages).

Research Methods

As in quantitative research, there are two sub-headings to this: data collection and data analysis.

Data Collection.  Here the researcher describes the general research design proposed to answer the research questions stated at the end of the literature review. The criterion of transparency is key to follow here. This section may range from two to approximately six pages. This section includes the following, usually in this order:

• Participants – method used for soliciting participants, ethical guidelines followed in soliciting them and in terms of promises of confidentiality, or other concerns to not ask more of them than is necessary. Forms of consent are referenced and included in the appendices of the paper. Lastly, in qualitative research, if there are not too many participants, the researcher might include their first name or pseudonym and a brief description of each person’s demographics collected as relevant to the study.
• Data Collection – a detailed description of the methods chosen (e.g. interviews, ethnographic observation, …), how they were conducted, and why these choices are relevant for the general research questions asked. Actual interview questions are usually put in the appendices.

Data Analysis.  Here the researcher explains which analysis tools (e.g. content analysis, theme analysis) were selected and why they are a good fit for the general research questions asked and the specific data collection methods employed. This section includes:

• Description of how data immersion was accomplished (e.g. length of time taken, number of readings, etc.)
• Definition of the unit of analysis used with a few examples
• How the data was coded and what emerged as the coding scheme
• How the researcher managed the data and reduced it to a focus that was attainable and relevant

This is where the author describes what was constructed in step four and five of the general data analysis process: the conceptual development and evaluation of results or meta-analysis. The author describes what meanings were taken from the data based on the analysis process. A suggested outline:

• A list of the categories or themes and exemplar texts for each to demonstrate them
• If negative case analysis was conducted, how it changed the resulting interpretations.
• Results from the meta-analysis of themes or categories. What did the researcher find about how these might be related? What is the deeper and broader level of meaning attained from the analysis process?

This is where a criterion used in autoethnography might be useful (Ellis, 2004). The author is expected to use  evocative writing  -- writing that calls up emotional responses from readers. It compels readers to engage with the material beyond a cognitive level. By calling for writing that is compelling, we are not suggesting the author try to manipulate the readers’ emotions. It is just that in autoethnography the quality of a study is measured so to speak by how much it compels the readers, draws them into the reality being described, and helps them see the experience from an inside view. We think this is a good criterion for most qualitative research on human experience.

If you imagine the introduction and conclusion of your research report as covers on a book that are mirror opposites, the conclusion should respond to and connect with the introduction. The goal is that when the reader finishes s/he will feel like the book just snapped shut. The author connects the findings from the present study to the larger social problem being addressed and the larger body of research in the literature review. When the covers snap shut, the author has done a good job of answering the basic question of, “so what?”  “Why these research questions, why these participants, why this research design, why these interpretations of meaning, why do the results matter?” The following outline is offered to help assure you answer the question of “so what?”

• Preview of the sub-headings in the conclusion
• Summary of the key findings (themes, theory development and sub-categories supporting them  (less quotes are needed here).
• Contributions of the study – both practically (e.g. for practitioners working with students, patients, for people to apply in their daily life, etc.) and academically (e.g. contributions to theory, methods, topic of study – the larger field of literature on the subject). Note this is especially where the author returns to the literature review and selects specific sources that the results of the study might speak to.
• Limitations of the study (e.g. in participant selection, data collection and analysis methods applied.
• Suggestions for future research (often based on the limitations of the present study).

Other Aspects of the Paper

For a more detailed description of the writing process and format, also see Chapter 7 of this book: “ Conclusion: Presenting Your Results ".

There are other parts of the academic paper you should include in your final write-up. We have provided useful resources for you to consider when including these aspects as part of your paper.

For an example paper that uses the required APA format for a research paper write-up, see the following source:  http://owl.english.purdue.edu/media/pdf/20090212013008_560.pdf .  However, this example uses quantitative data, so be aware the results section will not look like your qualitative study results. Qualitative reports tend to be longer.

Abstract & Titles.

http://writing.wisc.edu/Handbook/presentations_abstracts.html   http://owl.english.purdue.edu/owl/resource/560/1/

Tables, References, & Other Materials.

http://owl.english.purdue.edu/owl/resource/560/19/   http://owl.english.purdue.edu/owl/resource/560/05/

Data Presentation

Instructors will often ask you to present an oral version of your study in addition to the written report. This is a great way to help you gain more full comprehension of what you did in your study. If you can explain it well to others, you likely understand more deeply what you accomplished. The oral presentation is also a great way to prepare you for academic or other professional conference presentations that will help add to your resume. Prior to submitting original research to be considered for publication in journals or edited books, researchers share their studies orally to gain feedback and revise the written version of a study for submission to publication.

Two of the most common venues are oral presentations as a part of a panel of speakers and poster presentations. You might also be called upon to write an executive summary of the results of your study that makes it more accessible for people to review quickly. There are good resources for doing all of these online, so we have provided these here.

Oral Presentations

http://writing.wisc.edu/Handbook/presentations_oral.html   http://writing.wisc.edu/Handbook/presentations_delivery.html

Poster Presentations

http://writing.wisc.edu/Handbook/presentations_poster.html

Executive Summary

http://www.csun.edu/~vcecn006/summary.html http://www.stanford.edu/group/gender/ResearchPrograms/DualCareer/DualCareerFinalExecSum.pdf  (an example of an executive summary for university policy makers, from a research study on dual career academic couples) http://www.kff.org/entmedia/upload/7618ES.pdf  (another example of an executive summary from a study of food advertising to children on television)

Alhojailan, M.I. (2012). Thematic analysis: A critical review of its process and evaluation.  West East Journal of Social Sciences, 1 (1), 39-47. [access at  http://www.westeastinstitute.com/journals/wp-content/uploads/2013/02/4-Mohammed-Ibrahim-Alhojailan-Full-Paper-Thematic-Analysis-A-Critical-Review-Of-Its-Process-And-Evaluation.pdf ]

Blumer, H. (1986).  Symbolic interactionism: Perspective and method. Berkeley: University of California Press.

Bochner, A., & Ellis, C. (1992). Personal narrative as a social approach to interpersonal communication.  Communication Theory, 2,  65-72.

Carlin, P. S., & Park-Fuller, L. M. (2012). Disaster narrative emergent/cies: Performing loss, identity and resistance.  Text and Performance Quarterly, 32 (1), 20-37.

Cohen, M., & Avanzino, S. (2010). We are people first: Framing organizational assimilation experiences of the physically disabled using co-cultural theory.  Communication Studies ,  61 (3), 272-303.

DeFrancisco, V., and Chatham-Carpenter, A.  (2000). Self in community: African American women’s views of self-esteem.  Howard Journal of Communication, 11  (2), 73-92.

Denscombe, M. (2010).  The good research guide for small-scale social research projects  (4th ed.). Maidenhead, England: Open University Press.

Denzin, N. K. (2006).  Sociological Methods: A Sourcebook.  Piscataway, NJ: Transaction.

Dilthey, W. (2010).  Selected works, volume II: Understanding the human world . R. A. Makkreel & Frithjof, R. (Eds.). Princeton, NJ: Princeton University Press.

Ellis, C. (2004).  The ethnographic I: A methodological novel about autoethnography.  Walnut Creek, CA: AltaMira Press

Ellis, C., & Bochner, A. (2000). Autoethnography, personal narrative, reflexivity: Research as subject. In N. Denzin & Y. Lincoln (Eds.),  Handbook of qualitative research  (2nd ed., pp. 733-768). Thousand Oaks, CA: Sage.

Gadamer, H. G. (1976).  Philosophical hermeneutics  (D. E. Linge, Trans.). Berkeley: University of California Press.

Geertz, C. (1973).  The interpretation of cultures . New York: Basic Books.

Glaser, B., & Strauss, A. (1967).  The discovery of grounded theory: Strategies for qualitative  research.  New York: Aldine.

Grbich, C. (2007).  Qualitative data analysis: An introduction.  Thousand Oaks, CA: Sage.

Hiles, D. R. (2008). Transparency. In L. M. Givens (Ed.),  The Sage encyclopedia of qualitative  research methods.  Thousand Oaks, CA:Sage.

Hundley, H. L., & Shyles, L. (2010). US teenagers’ perceptions and awareness of digital technology: A focus group approach.  New Media & Society ,  12 (3), 417-433.

Husserl, E. (1990).  On the phenomenology of the consciousness of internal time (1893–1917) , (J. B. Brough, Trans.). Dordrecht: Kluwer.

Keyton, J. (2011).  Communication research: Asking questions, finding answers  (3rd ed.). New York: McGraw Hill.

Lincoln, Y, S., & Guba, E. G. (1985).  Naturalistic inquiry.  Beverly Hills, CA: Sage.

Lindlof, T. R. (1995).  Qualitative communication research methods.  Thousand Oaks, CA: Sage.

Lindlof, T. R., & Taylor, B. C. (2002).  Qualitative research methods  (2nd ed.). Thousand Oaks, CA: Sage.

Lofland, J. & Lofland, L. H. (1995).  Analyzing social settings: A guide to qualitative observation and analysis.  Belmont, CA: Wadsworth.

Meares, M. M., Oetzel, J.G., Torres, A., Derkacs, D., & Ginossar, T. (2004). Employee mistreatment and muted voices in the culturally diverse workplace.  Journal of Applied Communication, 32  (1), 4-27.

Owen, W. F. (1984). Interpretive themes in relational communication.  Quarterly Journal of Speech, 70 , 274-287.

Palczewski, C. H. (2001, Summer). Contesting pornography: Terministic catharsis and definitional argument.  Argument and Advocacy, 38,  1-17.

Richardson, L. (2003). Writing: A method of inquiry.In N. K. Denzin & Y. S. Lincoln (Eds.),  Collecting and interpreting qualitative materials  (pp. 499-541). Thousand Oaks, CA: Sage.

Ricoeur P. (1981).  Hermeneutics and human science: Essays on language, action and interpretation  (J. Thompson, Trans.). London: Cambridge University Press.

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking in conversation,  Language, 50,  696-735.

Saldana, J. (2009). The coding manual for qualitative researchers. Thousand Oaks, CA: Sage.

Schutz, A. (1967). Phenomenology of the social world. (G. Walsh & F. Lehnert, Trans.). Evanston: Northwestern University Press.

Schwandt, T. (2001).  The SAGE dictionary of qualitative inquiry . Thousand Oaks, CA: Sage.

Seale, C., Gobo, G., Gubrium, J. F., & Silverman, D. (Eds.). (2004).  Qualitative research practice.  London: Sage.

Spiggle, S. (1994). Analysis and interpretation of qualitative data in consumer research.  Journal of Consumer Research, 21,  491-503.

Stewart, D. & Mickunas, A. (1990). Exploring phenomenology: A guide to the field and its literature. Athens, OH: Ohio University Press.

Weber, M. (1962). Basic concepts in sociology. (H.P. Secher, Trans.) New York, Citadel Press.

Weinstein, S. (2007). Pregnancy, pimps, and “clichèd love things”: Writing through gender and sexuality. Written Communication, 24(1), 28-48.

Wieder, D. L (1999). Ethnomethodology, conversation analysis, microanalysis, and the ethnography of speaking (EM-CA-MA-ES): Resonances and basic issues. Research on Language and Social Interaction, 32( 1-2 ), 163-171.

Wong, S. & Goodwin, R. (2009). Experiencing marital satisfaction across three cultures: A qualitative study. Journal of Social and Personal Relationships, 30, 734-749.

Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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How to Write Research Methodology

Last Updated: May 27, 2024 Approved

This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Jennifer Mueller, JD . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 528,444 times.

The research methodology section of any academic research paper gives you the opportunity to convince your readers that your research is useful and will contribute to your field of study. An effective research methodology is grounded in your overall approach – whether qualitative or quantitative – and adequately describes the methods you used. Justify why you chose those methods over others, then explain how those methods will provide answers to your research questions. [1] X Research source

Describing Your Methods

• In your restatement, include any underlying assumptions that you're making or conditions that you're taking for granted. These assumptions will also inform the research methods you've chosen.
• Generally, state the variables you'll test and the other conditions you're controlling or assuming are equal.

• If you want to research and document measurable social trends, or evaluate the impact of a particular policy on various variables, use a quantitative approach focused on data collection and statistical analysis.
• If you want to evaluate people's views or understanding of a particular issue, choose a more qualitative approach.
• You can also combine the two. For example, you might look primarily at a measurable social trend, but also interview people and get their opinions on how that trend is affecting their lives.

• For example, if you conducted a survey, you would describe the questions included in the survey, where and how the survey was conducted (such as in person, online, over the phone), how many surveys were distributed, and how long your respondents had to complete the survey.
• Include enough detail that your study can be replicated by others in your field, even if they may not get the same results you did. [4] X Research source

• Qualitative research methods typically require more detailed explanation than quantitative methods.
• Basic investigative procedures don't need to be explained in detail. Generally, you can assume that your readers have a general understanding of common research methods that social scientists use, such as surveys or focus groups.

• For example, suppose you conducted a survey and used a couple of other research papers to help construct the questions on your survey. You would mention those as contributing sources.

Justifying Your Choice of Methods

• Describe study participants specifically, and list any inclusion or exclusion criteria you used when forming your group of participants.
• Justify the size of your sample, if applicable, and describe how this affects whether your study can be generalized to larger populations. For example, if you conducted a survey of 30 percent of the student population of a university, you could potentially apply those results to the student body as a whole, but maybe not to students at other universities.

• Reading other research papers is a good way to identify potential problems that commonly arise with various methods. State whether you actually encountered any of these common problems during your research.

• If you encountered any problems as you collected data, explain clearly the steps you took to minimize the effect that problem would have on your results.

• In some cases, this may be as simple as stating that while there were numerous studies using one method, there weren't any using your method, which caused a gap in understanding of the issue.
• For example, there may be multiple papers providing quantitative analysis of a particular social trend. However, none of these papers looked closely at how this trend was affecting the lives of people.

Connecting Your Methods to Your Research Goals

• Depending on your research questions, you may be mixing quantitative and qualitative analysis – just as you could potentially use both approaches. For example, you might do a statistical analysis, and then interpret those statistics through a particular theoretical lens.

• For example, suppose you're researching the effect of college education on family farms in rural America. While you could do interviews of college-educated people who grew up on a family farm, that would not give you a picture of the overall effect. A quantitative approach and statistical analysis would give you a bigger picture.

• If in answering your research questions, your findings have raised other questions that may require further research, state these briefly.
• You can also include here any limitations to your methods, or questions that weren't answered through your research.

• Generalization is more typically used in quantitative research. If you have a well-designed sample, you can statistically apply your results to the larger population your sample belongs to.

Template to Write Research Methodology

Community Q&A

• Organize your methodology section chronologically, starting with how you prepared to conduct your research methods, how you gathered data, and how you analyzed that data. [13] X Research source Thanks Helpful 0 Not Helpful 0
• Write your research methodology section in past tense, unless you're submitting the methodology section before the research described has been carried out. [14] X Research source Thanks Helpful 0 Not Helpful 0
• Discuss your plans in detail with your advisor or supervisor before committing to a particular methodology. They can help identify possible flaws in your study. [15] X Research source Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ http://expertjournals.com/how-to-write-a-research-methodology-for-your-academic-article/
• ↑ http://libguides.usc.edu/writingguide/methodology
• ↑ https://www.skillsyouneed.com/learn/dissertation-methodology.html
• ↑ https://uir.unisa.ac.za/bitstream/handle/10500/4245/05Chap%204_Research%20methodology%20and%20design.pdf
• ↑ https://elc.polyu.edu.hk/FYP/html/method.htm

About This Article

To write a research methodology, start with a section that outlines the problems or questions you'll be studying, including your hypotheses or whatever it is you're setting out to prove. Then, briefly explain why you chose to use either a qualitative or quantitative approach for your study. Next, go over when and where you conducted your research and what parameters you used to ensure you were objective. Finally, cite any sources you used to decide on the methodology for your research. To learn how to justify your choice of methods in your research methodology, scroll down! Did this summary help you? Yes No

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