• CASP Subquestions
Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/
Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.
To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .
Elements for Data Extraction
Elements | Data Extraction |
---|---|
Research objectives | • Verbs used in objectives or aims |
• Focuses of study | |
Design justification | • If the article cited references for qualitative description |
• If the article offered rationale to choose qualitative description | |
• References cited | |
• Rationale reported | |
Theoretical or philosophical frameworks | • If the article has theoretical or philosophical frameworks for study |
• Theoretical or philosophical frameworks reported | |
• How the frameworks were used in data collection and analysis | |
Sampling and sample sizes | • Sampling strategies (e.g., purposeful sampling, maximum variation) |
• Sample size | |
Data collection and sources | • Data collection techniques (e.g., individual or focus-group interviews, interview guide, surveys, field notes) |
Data analysis | • Data analysis techniques (e.g., qualitative content analysis, thematic analysis, constant comparison) |
• If data saturation was achieved | |
Presentation of findings | • Statement of findings |
• Consistency with research objectives |
Data Extraction and Analysis Results
Authors Country | Research Objectives | Design justification | Theoretical/ philosophical frameworks | Sampling/ sample size | Data collection and data sources | Data analysis | Findings |
---|---|---|---|---|---|---|---|
• USA | • Explore • Responses to communication strategies | • (-) Reference • (-) Rationale | Not reported (NR) | • Purposive sampling/ maximum variation • 32 family members | • Interviews • Observations • Review of daily flow sheet • Demographics | • Inductive and deductive qualitative content analysis • (-) Data saturation | Five themes about family members’ perceptions of nursing communication approaches |
• Sweden | • Describe • Experiences of using guidelines in daily practice | • (-) Reference • (+) Rationale • Part of a research program | NR | • Unspecified • 8 care providers | • Semistructured, individual interviews • Interview guide | • Qualitative content analysis • (-) Data saturation | One theme and seven subthemes about care providers’ experiences of using guidelines in daily practice |
• USA | • Examine • Culturally specific views of processes and causes of midlife weight gain | • (-) Reference • (-) Rationale | Health belief model and Kleiman’s explanatory model | • Unspecified • 19 adults | • Semistructured, individual interview | • Conventional content analysis • (-) Data saturation | Three main categories (from the model) and eight subthemes about causes of weight gain in midlife |
• Iran | • Explore • Factors initiating responsibility among medical trainees | • (-) Reference • (+) Rationale | NR | • Convenience, snowball, and maximum variation sampling • 15 trainees and other professionals | • Semistructured, individual interview • Interview guide | • Conventional content analysis • Constant comparison • (+) Data saturation | Two themes and individual and non- individual-based factors per theme |
• Iran | • Explore • Factors related to job satisfaction and dissatisfaction | • (-) Reference • (-) Rationale | NR | • Convenience sampling • 85 nurses | • Semistructured focus group interviews • Interview guide | • Thematic analysis • (+) Data saturation | Three main themes and associated factors regarding job satisfaction and dissatisfaction |
• Norway | • Describe • Perceptions on simulation-based team training | • (-) Reference • (-) Rationale | NR | • Strategic sampling • 18 registered nurses | • Semistructured individual interviews | • Inductive content analysis • (-) Data saturation | One main category, three categories, and six sub- categories regarding nurses’ perceptions on simulation-based team training |
• USA | • Determine • Barriers and supports for attending college and nursing school | • (-) Reference • (-) Rationale | NR | • Unspecified • 45 students | • Focus-group interviews • Using Photovoice and SHOWeD | • Constant comparison • (-) Data saturation | Five themes about facilitators and barriers |
• USA | • Explore • Reasons for choosing home birth and birth experiences | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 20 women | • Semistructured focus-group interviews • Interview guide • Field notes | • Qualitative content analysis • (+) Data saturation | Five common themes and concepts about reasons for choosing home birth based on their birth experiences |
• New Zealand | • Explore • Normal fetal activity related to hunger and satiation | • (+) Reference • (+) Rationale • • Denzin & Lincoln (2011) | NR | • Purposive sampling • 19 pregnant women | • Semistructured individual interviews • Open-ended questions | • Inductive qualitative content analysis • Descriptive statistical analysis • (+) Data saturation | Four patterns regarding fetal activities in relation to meal anticipation, maternal hunger, maternal meal consummation, and maternal satiety |
• Italy | • Explore, describe, and compare • perceptions of nursing caring | • (+) Reference • (-) Rationale • | NR | • Purposive sampling • 20 nurses and 20 patients | • Semistructured individual interviews • Interview guide • Field notes during interviews | • Unspecified various analytic strategies including constant comparison • (-) Data saturation | Nursing caring from both patients’ and nurses’ perspectives – a summary of data in visible caring and invisible caring |
• Hong Kong | • Address • How to reduce coronary heart disease risks | • (+) Reference • (+) Rationale • Secondary analysis • • | NR | • Convenience and snowball sampling • 105 patients | • Focus-group interviews • Interview guide | • Content analysis • (+) Data saturation | Four categories about patients’ abilities to reduce coronary heart disease |
• Taiwan | • Explore • Reasons for young–old people not killing themselves | • (-) Reference • (-) Rationale | NR | • Convenience sampling • 31 older adults | • Semistructured individual interviews • Interview guide • Observation with memos/reflective journal | • Content analysis • (+) Data saturation | Six themes regarding reasons for not committing to suicide |
• USA | • Explore • Neonatal intensive care unit experiences | • (+) Reference • (+) Rationale • | NR | • Purposive sampling and convenience sample • 15 mothers | • Semistructured individual interviews • Interview guide | • Qualitative content analysis • (+) Data saturation | Four themes about participants’ experiences of neonatal intensive care unit |
• Colombia | • Investigate • Barriers/facilitators to implementing evidence-based nursing | • (+) Reference • (-) Rationale • | Ottawa model for research use: knowledge translation framework | • Convenience sampling • 13 nursing professionals | • Semistructured individual interviews • Interview guide | • Inductive qualitative content analysis • Constant comparison • (-) Data saturation | Four main barriers and potential facilitators to evidence-based nursing |
• Australia | • Explore • Perceptions and utilization of diaries | • (+) Reference • (-) Rationale • | NR | • Unspecified • 19 patients and families | • Responses to open-ended questions on survey | • Unspecified analysis strategy • (-) Data saturation | Five themes regarding perceptions on use of diaries and descriptive statistics using frequencies of utilization |
• USA | • Explore • Knowledge, attitudes, and beliefs about sexual consent | • (-) Reference • (-) Rationale • Part of a larger mixed-method study | Theory of planned behavior | • Purposive sampling • snowball sampling • 26 women | • Semistructured focus-group interviews • Interview guide | • Content analysis • (+) Data saturation | Three main categories and subthemes regarding sexual consent |
• Sweden | • Describe • Experiences of knowledge development in wound management | • (+) Reference • (+) Rationale: weak • | NR | • Purposive sampling • 16 district nurses | • Individual interviews • Interview guide | • Qualitative content analysis • (-) Data saturation | Three categories and eleven sub-categories about knowledge development experiences in wound management |
• USA | • Describe • Parental-pain journey, beliefs about pain, and attitudes/behaviors related to children’s responses | • (+) Reference • (+) Rationale • • • Part of a larger mixed methods study | NR | • Purposive sampling • 9 parents | • Individual interviews • One open- ended question | • Qualitative content analysis • (+) Data saturation | Two main themes, categories, and subcategories about parents’ experiences of observing children’s pain |
• USA | • Describe • Challenges and barriers in providing culturally competent care | • (+) Reference • (+) Rationale • • Secondary analysis | NR | • Stratified sampling • 253 nurses | • Written responses to 2 open-ended questions on survey | • Thematic analysis • (-) Data saturation | Three themes regarding challenges/barriers |
• Denmark | • Describe • Experiences of childbirth | • (-) Reference • (-) Rationale • A substudy | NR | • Purposive sampling with maximum variation • Partners of 10 women | • Semistructured, individual interviews • Interview guide | • Thematic analysis • (+) Data saturation | Three themes and four subthemes about partners’ experiences of women’s childbirth |
• Australia | • Explore • Perceptions about medical nutrition and hydration at the end of life | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 10 nurses | • Focus-group interviews | • “analyzed thematically” • (-) Data saturation | One main theme and four subthemes regarding nurses’ perceptions on EOL- related medical nutrition and hydration |
• USA | • Describe • Reasons for leaving a home visiting program early | • (-) Reference • (-) Rationale | NR | • Convenience sample • 32 mothers, nurses, and nurse supervisors | • Semistructured, individual interviews • Focus-group interviews • Interview guide | • Inductive content analysis • Constant comparison approach • (+) Data saturation | Three sets of reasons for leaving a home visiting program |
• Sweden | • Explore and describe • Beliefs and attitudes around the decision for a caesarean section | • (+) Reference • (+) Rationale • • | NR | • Unspecified • 21 males | • Individual telephone interviews | • Thematic analysis • Constant comparison approach • (-) Data saturation | Two themes and subthemes in relation to the research objective |
• Taiwan | • Explore • Illness experiences of early onset of knee osteoarthritis | • (+) Reference • (+) Rationale • • • Part of a large research series | NR | • Purposive sampling • 17 adults | • Semistructured, Individual interviews • Interview guide • Memo/field notes (observations) | • Inductive content analysis • (+) Data saturation | Three major themes and nine subthemes regarding experiences of early onset-knee osteoarthritis |
• Australia | • Explore • Perceptions about bedside handover (new model) by nurses | • (+) Reference • (+) Rationale • • | NR | • Purposive sampling • 30 patients | • Semistructured, individual interviews • Interview guide | • Thematic content analysis • (-) Data analysis | Two dominant themes and related subthemes regarding patients’ thoughts about nurses’ bedside handover |
• Sweden | • Identify • Patterns in learning when living with diabetes | • (-) Reference • (-) Rationale | NR | • Purposive sampling with variations in age and sex • 13 participants | • Semistructured, individual interviews (3 times over 3 years) | • analysis process • Inductive qualitative content analysis • (-) Data saturation | Five main patterns of learning when living with diabetes for three years following diagnosis |
• Canada | • Evaluate • Book chat intervention based on a novel | • (-) Reference • (-) Rationale • Part of a larger research project | NR | • Unspecified • 11 long-term- care staff | • Questionnaire with two open- ended questions | • Thematic content analysis • (-) Data saturation | Five themes (positive comments) about the book chat with brief description |
• Taiwan | • Explore • Facilitators and barriers to implementing smoking- cessation counseling services | • (-) Reference • (-) Rationale | NR | • Unspecified • 16 nurse- counselors | • Semistructured individual interviews • Interview guide | • Inductive content analysis • Constant comparison • (-) Data saturation | Two themes and eight subthemes about facilitators and barriers described using 2-4 quotations per subtheme |
• USA | • Identify • Educational strategies to manage disruptive behavior | • (-) Reference • (-) Rationale • Part of a larger study | NR | • Unspecified • 9 nurses | • Semistructured, individual interviews • Interview guide | • Content analysis procedures • (-) Data saturation | Two main themes regarding education strategies for nurse educators |
• USA | • Explore • Experiences of difficulty resolving patient- related concerns | • (-) Reference • (-) Rationale • Secondary analysis | NR | • Unspecified • 1932 physician, nursing, and midwifery professionals | • E-mail survey with multiple- choice and free- text responses | • Inductive thematic analysis • Descriptive statistics • (-) Data saturation | One overarching theme and four subthemes about professionals’ experiences of difficulty resolving patient-related concerns |
• Singapore | • Explicate • Experience of quality of life for older adults | • (+) Reference • (+) Rationale • | Parse’s human becoming paradigm | • Unspecified • 10 elderly residents | • Individual interviews • Interview questions presented (Parse) | • Unspecified analysis techniques • (-) Data saturation | Three themes presented using both participants’ language and the researcher’s language |
• China | • Explore • Perspectives on learning about caring | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 20 nursing students | • Focus-group interviews • Interview guide | • Conventional content analysis • (-) Data saturation | Four categories and associated subcategories about facilitators and challenges to learning about caring |
• Poland | • Describe and assess • Components of the patient–nurse relationship and pediatric-ward amenities | • (+) Reference • (-) Rationale • | NR | • Purposeful, maximum variation sampling • 26 parents or caregivers and 22 children | • Individual interviews | • Qualitative content analysis • (-) Data saturation | Five main topics described from the perspectives of children and parents |
• Canada | • Evaluate • Acceptability and feasibility of hand-massage therapy | • (-) Reference • (-) Rationale • Secondary to a RCT | Focused on feasibility and acceptability | • Unspecified • 40 patients | • Semistructured, individual interviews • Field notes • Video recording | • Thematic analysis for acceptability • Quantitative ratings of video items for feasibility • (-) Data analysis | Summary of data focusing on predetermined indicators of acceptability and descriptive statistics to present feasibility |
• USA | • Understand • Challenges occurring during transitions of care | • (+) Reference • (+) Rationale • • Part of a larger study | NR | • Convenience sample • 22 nurses | • Focus groups • Interview guide | • Qualitative content analysis methods • (+) Data analysis | Three themes about challenges regarding transitions of care: |
• Canada | • Understand • Factors that influence nurses’ retention in their current job | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 41 nurses | • Focus-group interviews • Interview guide | • Directed content analysis • (+) Data saturation | Nurses’ reasons to stay and leave their current job |
• Australia | • Extend • Understanding of caregivers’ views on advance care planning | • (+) Reference • (+) Rationale • • Grounded theory overtone | NR | • Theoretical sampling • 18 caregivers | • Semistructured focus group and individual interviews • Interview guide • Vignette technique | • Inductive, cyclic, and constant comparative analysis • (-) Data analysis | Three themes regarding caregivers’ perceptions on advance care planning |
• USA | • Describe • Outcomes older adults with epilepsy hope to achieve in management | • (-) Reference • (-) Rationale | NR | • Unspecified • 20 patients | • Individual interview | • Conventional content analysis • (-) Data saturation | Six main themes and associated subthemes regarding what older adults hoped to achieve in management of their epilepsy |
• The Netherlands | • Gain • Experience of personal dignity and factors influencing it | • (+) Reference • (-) Rationale • | Model of dignity in illness | • Maximum variation sampling • 30 nursing home residents | • Individual interviews • Interview guide | • Thematic analysis • Constant comparison • (+) Data saturation | The threatening effect of illness and three domains being threatened by illness in relation to participants’ experiences of personal dignity |
• USA | • Identify and describe • Needs in mental health services and “ideal” program | • (+) Reference • (+) Rationale • • There is a primary study | NR | • Unspecified • 52 family members | • Semistructured, individual and focus-group interviews | • “Standard content analytic procedures” with case-ordered meta-matrix • (-) Data saturation | Two main topics – (a) intervention modalities that would fit family members’ needs in mental health services and (b) topics that programs should address |
• USA | • “What are the perceptions of staff nurses regarding palliative care…?” | • (-) Reference • (-) Rationale | NR | • Purposive, convenience sampling • 18 nurses | • Semistructured and focus-group interviews • Interview guide | • Ritchie and Spencer’s framework for data analysis • (-) Data saturation | Five thematic categories and associated subcategories about nurses’ perceptions of palliative care |
• Canada | • Describe • Experience of caring for a relative with dementia | • (+) Reference • (+) Rationale • Sandelowski ( ; ) • Secondary analysis • Phenomenological overtone | NR | • Purposive sampling • 11 bereaved family members | • Individual interviews • 27 transcripts from the primary study | • Unspecified • (-) Data saturation | Five major themes regarding the journey with dementia from the time prior to diagnosis and into bereavement |
• Canada | • Describe Experience of fetal fibronectin testing | • (+) Reference • (+) Rationale • • | NR | • Unspecified • 17 women | • Semistructured individual interviews • Interview guide | • Conventional content analysis • (+) Data saturation | One overarching theme, three themes, and six subthemes about women’s experiences of fetal fibronectin testing |
• New Zealand | • Explore • Role of nurses in providing palliative and end-of-life care | • (+) Reference • (+) Rationale • • Part of a larger study | NR | • Purposeful sampling • 21 nurses | • Semistructured individual interviews | • Thematic analysis • (-) Data saturation | Three themes about practice nurses’ experiences in providing palliative and end-of-life care |
• Brazil | • Understand • Experience with postnatal depression | • (+) Reference • (-) Rationale • | NR | • Purposeful, criterion sampling • 15 women with postnatal depression | • Minimally structured, individual interviews | • Thematic analysis • (+) Data saturation | Two themes – women’s “bad thoughts” and their four types of responses to fear of harm (with frequencies) |
• Australia | • Understand • Experience of peripherally inserted central catheter insertion | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 10 patients | • Semistructured, individual interviews • Interview guide | • Thematic analysis • (+) Data saturation | Four themes regarding patients’ experiences of peripherally inserted central catheter insertion |
• USA | • Discover • Context, values, and background meaning of cultural competency | • (+) Reference • (+) Rationale • | Focused on cultural competence | • Purposive, maximum variation, and network • 20 experts | • Semistructured, individual interviews | • Within-case and across-case analysis • (-) Data saturation | Three themes regarding cultural competency |
• USA | • Explore and describe • Cancer experience | • (+) Reference • (+) Rationale • | NR | • Unspecified • 15 patients | • Longitudinal individual interviews (4 time points) • 40 interviews | • Inductive content analysis • (-) Data saturation | Processes and themes about adolescent identify work and cancer identify work across the illness trajectory |
• Sweden | • Explore • Experiences of giving support to patients during the transition | • (-) Reference • (-) Rationale | Focused on support and transition | • Unspecified (but likely purposeful sampling) • 8 nurses | • Semistructured Individual interviews • Interview guide | • Content analysis • (-) Data saturation | One theme, three main categories, and eight associated categories |
• Taiwan | • Describe • Process of women’s recovery from stillbirth | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 21 women | • Individual interview techniques | • Inductive analytic approaches ( ) • (+) Data saturation | Three stages (themes) regarding the recovery process of Taiwanese women with stillbirth |
• Iran | • Describe • Perspectives of causes of medication errors | • (+) Reference • (+) Rationale • | NR | • Purposeful sampling • 24 nursing students | • Focus-group interviews • Observations with notes | • Content analysis • (-) Data saturation | Two main themes about nursing students’ perceptions on causes of medication errors |
• Iran | • Explore • Image of nursing | • (-) Reference • (-) Rationale | NR | • Purposeful sampling • 18 male nurses | • Semistructured individual, interviews • Field notes | • Content analysis • (-) Data saturation | Two main views (themes) on nursing presented with subthemes per view |
• Spain | • Ascertain • Barriers to sexual expression | • (-) Reference • (-) Rationale | NR | • Maximum variation • 100 staff and residents | • Semistructured, individual interview | • Content analysis • (-) Data saturation | 40% of participants without identification of barriers and 60% with seven most cited barriers to sexual expression in the long-term care setting |
• Canada | • Explore • Perceptions of empowerment in academic nursing environments | • (+) Reference • (+) Rationale • Sandelowski ( , ) | Theories of structural power in organizations and psychological empowerment | • Unspecified • 8 clinical instructors | • Semistructured, individual • interview guide | • Unspecified (but used pre-determined concepts) • (+) Data saturation | Structural empowerment and psychological empowerment described using predetermined concepts |
• China | • Investigate • Meaning of life and health experience with chronic illness | • (+) Reference • (+) Rationale • Sandelowski ( , ) | Positive health philosophy | • Purposive, convenience sampling • 11 patients | • Individual interviews • Observations of daily behavior with field notes | • Thematic analysis • (-) Data saturation | Four themes regarding the meaning of life and health when living with chronic illnesses |
Note . NR = not reported
Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).
In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).
A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).
In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).
Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:
The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).
Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.
In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.
In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.
Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.
In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).
The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.
Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.
In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.
In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.
In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:
“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).
This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).
However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.
Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).
Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.
In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.
Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.
A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.
This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.
Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.
Conflict of Interest Statement
The Authors declare that there is no conflict of interest.
Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.
Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.
Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.
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Chapter 3: Developing a Research Question
As you can see, there is much to think about and many decisions to be made as you begin to define your research question and your research project. Something else you will need to consider in the early stages is whether your research will be exploratory, descriptive, or explanatory. Each of these types of research has a different aim or purpose, consequently, how you design your research project will be determined in part by this decision. In the following paragraphs we will look at these three types of research.
Researchers conducting exploratory research are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study; he or she wants to figure out the lay of the land with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research participants, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy his or her own curiosity about a topic could also conduct exploratory research. Conducting exploratory research on a topic is often a necessary first step, both to satisfy researcher curiosity about the subject and to better understand the phenomenon and the research participants in order to design a larger, subsequent study. See Table 2.1 for examples.
Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research would be an appropriate strategy. A descriptive may, for example, aim to describe a pattern. For example, researchers often collect information to describe something for the benefit of the general public. Market researchers rely on descriptive research to tell them what consumers think of their products. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing. See Table 3.1 for examples.
The third type of research, explanatory research, seeks to answer “why” questions. In this case, the researcher is trying to identify the causes and effects of whatever phenomenon is being studied. An explanatory study of college students’ addictions to their electronic gadgets, for example, might aim to understand why students become addicted. Does it have anything to do with their family histories? Does it have anything to do with their other extracurricular hobbies and activities? Does it have anything to do with the people with whom they spend their time? An explanatory study could answer these kinds of questions. See Table 3.1 for examples.
Table 3.1 Exploratory, descriptive and explanatory research differences (Adapted from Adjei, n.d.).
Degree of Problem Definition | Key variables not define | Key variables not define | Key variables not define |
“The quality of service is declining and we don’t know why.” | “What have been the trends in organizational downsizing over the past ten years?” | “Which of two training programs is more effective for reducing labour turnover? | |
“Would people be interested in our new product idea? | “Did last year’s product recall have an impact on our company’s share price?” | “Can I predict the value of energy stocks if I know the current dividends and growth rates of dividends?” | |
“How important is business process reengineering as a strategy? | “Has the average merger rate for financial institutions increased in the past decade?” | “Do buyers prefer our product in a new package?” |
Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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Chapter 3. Psychological Science
Learning objectives.
Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.
Research design | Goal | Advantages | Disadvantages |
---|---|---|---|
Descriptive | To create a snapshot of the current state of affairs | Provides a relatively complete picture of what is occurring at a given time. Allows the development of questions for further study. | Does not assess relationships among variables. May be unethical if participants do not know they are being observed. |
Correlational | To assess the relationships between and among two or more variables | Allows testing of expected relationships between and among variables and the making of predictions. Can assess these relationships in everyday life events. | Cannot be used to draw inferences about the causal relationships between and among the variables. |
Experimental | To assess the causal impact of one or more experimental manipulations on a dependent variable | Allows drawing of conclusions about the causal relationships among variables. | Cannot experimentally manipulate many important variables. May be expensive and time consuming. |
Source: Stangor, 2011. |
Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).
Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.
Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).
Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.
In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.
The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.
A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.
Coder name: | ||||
This table represents a sample coding sheet from an episode of the “strange situation,” in which an infant (usually about one year old) is observed playing in a room with two adults — the child’s mother and a stranger. Each of the four coding categories is scored by the coder from 1 (the baby makes no effort to engage in the behaviour) to 7 (the baby makes a significant effort to engage in the behaviour). More information about the meaning of the coding can be found in Ainsworth, Blehar, Waters, and Wall (1978). | ||||
Coding categories explained | ||||
Proximity | The baby moves toward, grasps, or climbs on the adult. | |||
Maintaining contact | The baby resists being put down by the adult by crying or trying to climb back up. | |||
Resistance | The baby pushes, hits, or squirms to be put down from the adult’s arms. | |||
Avoidance | The baby turns away or moves away from the adult. | |||
Episode | Coding categories | |||
---|---|---|---|---|
Proximity | Contact | Resistance | Avoidance | |
Mother and baby play alone | 1 | 1 | 1 | 1 |
Mother puts baby down | 4 | 1 | 1 | 1 |
Stranger enters room | 1 | 2 | 3 | 1 |
Mother leaves room; stranger plays with baby | 1 | 3 | 1 | 1 |
Mother re-enters, greets and may comfort baby, then leaves again | 4 | 2 | 1 | 2 |
Stranger tries to play with baby | 1 | 3 | 1 | 1 |
Mother re-enters and picks up baby | 6 | 6 | 1 | 2 |
Source: Stang0r, 2011. |
The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .
A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .
In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.
The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).
A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).
In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.
Or they may be more spread out away from it, as seen in Figure 3.8.
One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.
An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.
Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.
In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.
One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .
When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.
Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .
The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.
It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.
An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.
Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):
Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).
Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)
In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.
Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.
In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.
The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):
Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17
Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).
Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.
The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.
Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.
Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.
Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.
Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0
Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.
Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.
Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.
Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.
Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).
Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.
Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.
Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.
Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]
Figure 3.10 long description: Types of scatter plots.
[Return to Figure 3.10]
Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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Updated: June 19, 2024
Published: June 15, 2024
When embarking on a research project, selecting the right methodology can be the difference between success and failure. With various methods available, each suited to different types of research, it’s essential you make an informed choice. This blog post will provide tips on how to choose a research methodology that best fits your research goals .
We’ll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. It involves defining questions, collecting data, analyzing results, and drawing conclusions.
Meanwhile, a research methodology is a structured plan that outlines how your research is to be conducted. A complete methodology should detail the strategies, processes, and techniques you plan to use for your data collection and analysis.
The first step of a research methodology is to identify a focused research topic, which is the question you seek to answer. By setting clear boundaries on the scope of your research, you can concentrate on specific aspects of a problem without being overwhelmed by information. This will produce more accurate findings.
Along with clarifying your research topic, your methodology should also address your research methods. Let’s look at the four main types of research: descriptive, correlational, experimental, and diagnostic.
Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering “what” questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies , and case studies to gather qualitative or quantitative data.
A real-world example of descriptive research is a survey investigating consumer behavior toward a competitor’s product. By analyzing the survey results, the company can gather detailed insights into how consumers perceive a competitor’s product, which can inform their marketing strategies and product development.
Correlational research examines the statistical relationship between two or more variables to determine whether a relationship exists. Correlational research is particularly useful when ethical or practical constraints prevent experimental manipulation. It is often employed in fields such as psychology, education, and health sciences to provide insights into complex real-world interactions, helping to develop theories and inform further experimental research.
An example of correlational research is the study of the relationship between smoking and lung cancer. Researchers observe and collect data on individuals’ smoking habits and the incidence of lung cancer to determine if there is a correlation between the two variables. This type of research helps identify patterns and relationships, indicating whether increased smoking is associated with higher rates of lung cancer.
Experimental research is a scientific approach where researchers manipulate one or more independent variables to observe their effect on a dependent variable. This method is designed to establish cause-and-effect relationships. Fields like psychology , medicine, and social sciences frequently employ experimental research to test hypotheses and theories under controlled conditions.
A real-world example of experimental research is Pavlov’s Dog experiment. In this experiment, Ivan Pavlov demonstrated classical conditioning by ringing a bell each time he fed his dogs. After repeating this process multiple times, the dogs began to salivate just by hearing the bell, even when no food was presented. This experiment helped to illustrate how certain stimuli can elicit specific responses through associative learning.
Diagnostic research tries to accurately diagnose a problem by identifying its underlying causes. This type of research is crucial for understanding complex situations where a precise diagnosis is necessary for formulating effective solutions. It involves methods such as case studies and data analysis and often integrates both qualitative and quantitative data to provide a comprehensive view of the issue at hand.
An example of diagnostic research is studying the causes of a specific illness outbreak. During an outbreak of a respiratory virus, researchers might conduct diagnostic research to determine the factors contributing to the spread of the virus. This could involve analyzing patient data, testing environmental samples, and evaluating potential sources of infection. The goal is to identify the root causes and contributing factors to develop effective containment and prevention strategies.
Using an established research method is imperative, no matter if you are researching for marketing , technology , healthcare , engineering, or social science. A methodology lends legitimacy to your research by ensuring your data is both consistent and credible. A well-defined methodology also enhances the reliability and validity of the research findings, which is crucial for drawing accurate and meaningful conclusions.
Additionally, methodologies help researchers stay focused and on track, limiting the scope of the study to relevant questions and objectives. This not only improves the quality of the research but also ensures that the study can be replicated and verified by other researchers, further solidifying its scientific value.
Choosing the best research methodology for your project involves several key steps to ensure that your approach aligns with your research goals and questions. Here’s a simplified guide to help you make the best choice.
Clearly define the objectives of your research. What do you aim to discover, prove, or understand? Understanding your goals helps in selecting a methodology that aligns with your research purpose.
Determine whether your research will involve numerical data, textual data, or both. Quantitative methods are best for numerical data, while qualitative methods are suitable for textual or thematic data.
Becoming familiar with the four types of research – descriptive, correlational, experimental, and diagnostic – will enable you to select the most appropriate method for your research. Many times, you will want to use a combination of methods to gather meaningful data.
Consider the resources available to you, including time, budget, and access to data. Some methodologies may require more resources or longer timeframes to implement effectively.
Look at previous research in your field to see which methodologies were successful. This can provide insights and help you choose a proven approach.
By following these steps, you can select a research methodology that best fits your project’s requirements and ensures robust, credible results.
Upon completing your research, the next critical step is to analyze and interpret the data you’ve collected. This involves summarizing the key findings, identifying patterns, and determining how these results address your initial research questions. By thoroughly examining the data, you can draw meaningful conclusions that contribute to the body of knowledge in your field.
It’s essential that you present these findings clearly and concisely, using charts, graphs, and tables to enhance comprehension. Furthermore, discuss the implications of your results, any limitations encountered during the study, and how your findings align with or challenge existing theories.
Your research project should conclude with a strong statement that encapsulates the essence of your research and its broader impact. This final section should leave readers with a clear understanding of the value of your work and inspire continued exploration and discussion in the field.
Now that you know how to perform quality research , it’s time to get started! Applying the right research methodologies can make a significant difference in the accuracy and reliability of your findings. Remember, the key to successful research is not just in collecting data, but in analyzing it thoughtfully and systematically to draw meaningful conclusions. So, dive in, explore, and contribute to the ever-growing body of knowledge with confidence. Happy researching!
At UoPeople, our blog writers are thinkers, researchers, and experts dedicated to curating articles relevant to our mission: making higher education accessible to everyone.
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Descriptive Statistics
Descriptive statistics in research: a critical component of data analysis.
15 min read With any data, the object is to describe the population at large, but what does that mean and what processes, methods and measures are used to uncover insights from that data? In this short guide, we explore descriptive statistics and how it’s applied to research.
With any kind of data, the main objective is to describe a population at large — and using descriptive statistics, researchers can quantify and describe the basic characteristics of a given data set.
For example, researchers can condense large data sets, which may contain thousands of individual data points or observations, into a series of statistics that provide useful information on the population of interest. We call this process “describing data”.
In the process of producing summaries of the sample, we use measures like mean, median, variance, graphs, charts, frequencies, histograms, box and whisker plots, and percentages. For datasets with just one variable, we use univariate descriptive statistics. For datasets with multiple variables, we use bivariate correlation and multivariate descriptive statistics.
Want to find out the definitions? Univariate descriptive statistics: this is when you want to describe data with only one characteristic or attribute
Bivariate correlation: this is when you simultaneously analyse (compare) two variables to see if there is a relationship between them
Multivariate descriptive statistics: this is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable
Then, after describing and summarising the data, as well as using simple graphical analyses, we can start to draw meaningful insights from it to help guide specific strategies. It’s also important to note that descriptive statistics can employ and use both quantitative and qualitative research .
Describing data is undoubtedly the most critical first step in research as it enables the subsequent organisation, simplification and summarisation of information — and every survey question and population has summary statistics. Let’s take a look at a few examples.
Consider for a moment a number used to summarise how well a striker is performing in football — goals scored per game. This number is simply the number of shots taken against how many of those shots hit the back of the net (reported to three significant digits). If a striker is scoring 0.333, that’s one goal for every three shots. If they’re scoring one in four, that’s 0.250.
A classic example is a student’s grade point average (GPA). This single number describes the general performance of a student across a range of course experiences and classes. It doesn’t tell us anything about the difficulty of the courses the student is taking, or what those courses are, but it does provide a summary that enables a degree of comparison with people or other units of data.
Ultimately, descriptive statistics make it incredibly easy for people to understand complex (or data intensive) quantitative or qualitative insights across large data sets.
Take your research and subsequent analysis to the next level
To quantitatively summarise the characteristics of raw, ungrouped data, we use the following types of descriptive statistics:
Following the application of any of these approaches, the raw data then becomes ‘grouped’ data that’s logically organised and easy to understand. To visually represent the data, we then use graphs, charts, tables etc.
Let’s look at the different types of measurement and the statistical methods that belong to each:
Measures of Central Tendency are used to describe data by determining a single representative of central value. For example, the mean, median or mode.
Measures of Dispersion are used to determine how spread out a data distribution is with respect to the central value, e.g. the mean, median or mode. For example, while central tendency gives the person the average or central value, it doesn’t describe how the data is distributed within the set.
Measures of Frequency Distribution are used to describe the occurrence of data within the data set (count).
The methods of each measure are summarised in the table below:
Measures of Central Tendency | Measures of Dispersion | Measures of Frequency Distribution |
---|---|---|
Mean | Range | Count |
Median | Standard deviation | |
Mode | Quartile deviation | |
Variance | ||
Absolute deviation |
Mean: The most popular and well-known measure of central tendency. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set.
Median: The median is the middle score for a set of data that has been arranged in order of magnitude. If you have an even number of data, e.g. 10 data points, take the two middle scores and average the result.
Mode: The mode is the most frequently occurring observation in the data set.
Range: The difference between the highest and lowest value.
Standard deviation: Standard deviation measures the dispersion of a data set relative to its mean and is calculated as the square root of the variance.
Quartile deviation : Quartile deviation measures the deviation in the middle of the data.
Variance: Variance measures the variability from the average of mean.
Absolute deviation: The absolute deviation of a dataset is the average distance between each data point and the mean.
Count: How often each value occurs.
Descriptive statistics (or analysis) is considered more vast than other quantitative and qualitative methods as it provides a much broader picture of an event, phenomenon or population.
But that’s not all: it can use any number of variables, and as it collects data and describes it as it is, it’s also far more representative of the world as it exists.
However, it’s also important to consider that descriptive analyses lay the foundation for further methods of study. By summarising and condensing the data into easily understandable segments, researchers can further analyse the data to uncover new variables or hypotheses.
Mostly, this practice is all about the ease of data visualisation. With data presented in a meaningful way, researchers have a simplified interpretation of the data set in question. That said, while descriptive statistics helps to summarise information, it only provides a general view of the variables in question.
It is, therefore, up to the researchers to probe further and use other methods of analysis to discover deeper insights.
Things you can do with descriptive statistics:
They could then ‘describe’ the data to build a clear picture and understanding of who their buyers are, including things like preferences, business challenges, income and so on.
Let’s say you wanted to assess propensity to buy over several months or years for a specific target market and product. With descriptive statistics, you could quickly summarise the data and extract the precise data points you need to understand the trends in product purchase behaviour.
How do different demographics respond to certain variables? For example, you might want to run a customer study to see how buyers in different job functions respond to new product features or price changes. Are all groups as enthusiastic about the new features and likely to buy? Or do they have reservations? This kind of data will help inform your overall product strategy and potentially how you tier solutions.
When you have a belief or hypothesis but need to prove it, you can use descriptive techniques to ascertain underlying patterns or assumptions.
With the data presented and surmised in a way that everyone can understand (and infer connections from), you can delve deeper into specific data points to uncover deeper and more meaningful insights — or run more comprehensive research.
To use your surveys as an effective tool for customer engagement and understanding, every survey goal and item should answer one simple, yet highly important question:
“What am I really asking?”
It might seem trivial, but by having this question frame survey research, it becomes significantly easier for researchers to develop the right questions that uncover useful, meaningful and actionable insights.
Planning becomes easier, questions clearer and perspective far wider and yet nuanced.
Hypothesise — what’s the problem that you’re trying to solve? Far too often, organisations collect data without understanding what they’re asking, and why they’re asking it.
Finally, focus on the end result. What kind of data do you need to answer your question? Also, are you asking a quantitative or qualitative question? Here are a few things to consider:
Furthermore…
As well as understanding what you’re really asking, there are several other considerations for your data:
How you select your sample is what makes your research replicable and meaningful. Having a truly random sample helps prevent bias, increasingly the quality of evidence you find.
Before starting your research project, have a clear plan for avoiding sample error. Use larger sample sizes, and apply random sampling to minimise the potential for bias.
Remember, you can sample 500 respondents selected randomly from a population and they will closely reflect the actual population 95% of the time.
Match your survey methods to the sample you select. For example, how do your current customers prefer communicating? Do they have any shared characteristics or preferences? A mixed-method approach is critical if you want to drive action across different customer segments.
Surveys created using a survey research software can support researchers in a number of ways:
These considerations have been included in Qualtrics’ survey software , which summarises and creates visualisations of data, making it easy to access insights, measure trends, and examine results without complexity or jumping between systems.
What makes Qualtrics so different from other survey providers is that it is built in consultation with trained research professionals and includes high-tech statistical software like Qualtrics Stats iQ .
With just a click, the software can run specific analyses or automate statistical testing and data visualisation. Testing parameters are automatically chosen based on how your data is structured (e.g. categorical data will run a statistical test like Chi-squared), and the results are translated into plain language that anyone can understand and put into action.
Stats iQ includes a variety of statistical analyses, including: describe, relate, regression, cluster, factor, TURF, and pivot tables — all in one place!
Built-in artificial intelligence and advanced algorithms automatically choose and apply the right statistical analyses and return the insights in plain english so everyone can take action.
For more experienced stats users, built-in R code templates allow you to run even more sophisticated analyses by adding R code snippets directly in your survey analysis.
Regression analysis – Measures the degree of influence of independent variables on a dependent variable (the relationship between two or multiple variables).
Analysis of Variance (ANOVA) test – Commonly used with a regression study to find out what effect independent variables have on the dependent variable. It can compare multiple groups simultaneously to see if there is a relationship between them.
Conjoint analysis – Asks people to make trade-offs when making decisions, then analyses the results to give the most popular outcome. Helps you understand why people make the complex choices they do.
T-Test – Helps you compare whether two data groups have different mean values and allows the user to interpret whether differences are meaningful or merely coincidental.
Crosstab analysis – Used in quantitative market research to analyse categorical data – that is, variables that are different and mutually exclusive, and allows you to compare the relationship between two variables in contingency tables.
Now that you have a better understanding of descriptive statistics in research and how you can leverage statistical analysis methods correctly, now’s the time to utilise a tool that can take your research and subsequent analysis to the next level.
Try out a Qualtrics survey software demo so you can see how it can take you through descriptive research and further research projects from start to finish.
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9 examples of meaningful life goals, how to motivate yourself to achieve life goals: 4 tips, turn your goals into reality.
Everyone’s journey in life is different. You probably know someone who meanders through life with frequent stops to smell the roses — and someone else who rushes around at breakneck speed, knocking things off their task priority list and hardly pausing to take a breath.
Whether you’re the wanderer, the whirlwind, or somewhere in between, you can benefit from thinking about your life goals. If you go with the flow, clarifying your goals can help you dig in and fight for what’s important. And if you rush from one short-term goal to the next, setting life goals can help you stand back and use your energy more strategically.
It’s well worth setting aside some time to develop a list of life goals that deeply align with your values , are built around your life’s purpose , and are part of your overall life plan .
Most children have clear life goals. Take Toby, David, and Alina, three kindergarteners who were asked what they wanted to be when they grew up .
Toby said he wanted to be “a veterinarian so I can help pets get better.” David said, “a fireman since I like explosions and fire.” And Alina said, “I want to be a customer in a store. I will buy broccoli, tomatoes, and carrots. When I get home, I will make soup.”
Whether you’re a go-getter like David or more in Alina’s speed, your life goals might feel less concrete as you age. It’s easy to let financial imperatives and seemingly urgent tasks distract you from more important objectives.
But communicating your life goals (even just to yourself) has surprising health benefits. One study found that journaling about life goals for 20 minutes on four consecutive days reduced physical illness five months later. Another found that students who either wrote or talked about their life goals were less likely to visit the health center due to illness.
Setting your overarching priorities also offers you a sense of purpose in everything you do, so you don’t wake up one day wondering what you have to show for the time that’s passed.
Here are nine life goal examples you can adapt to suit your interests and personal values .
Getting out of your comfort zone is a great way to develop new skills, conquer your fear of failure , and stay humble. It also helps you cultivate a growth mindset — the understanding that you can improve your skills immeasurably through constant learning, determination , and hard work.
You could challenge yourself to grow personally by doing something that scares you ( public speaking , skydiving, or networking ). But this goal isn’t about going bungee jumping every day. Instead, it’s about getting comfortable being uncomfortable .
For you, that might look like steadily working toward a fitness or health goal, taking steps to achieve a professional goal, or taking social risks while pursuing a friendship goal .
Practicing mindfulness, or slowing down and paying attention to the present moment, has impressive benefits. Mindfulness reduces stress, improves memory and focus, makes you a better problem-solver, and improves your relationships , to name only a few.
Setting a mindfulness-related personal goal might look like developing a regular yoga or meditation practice , cultivating a healthier relationship with food through mindful eating , or committing to manage stress and improve your well-being through mindful breathing .
Perhaps you secretly think you’d do a great job as CEO of your company. Or maybe you’ve always wanted to start your own business or work in a different industry .
Whatever it is, saying it out loud and turning it into a concrete goal sets you on the path toward achieving it. Defining success means you can start planning the small steps you must take to get there.
This might involve improving your leadership skills , preparing for a promotion , making a career change in your 40s , or changing careers in your 50s .
Deciding to work toward financial security is a powerful way to focus your attention on what you need to do to get there. Potential financial life goals include:
Choose the financial goal that motivates you most and then break it into milestones you can work toward and celebrate along the way.
Balancing the needs of self and others is one of life’s most challenging and gratifying tasks. If you tend to care for everyone else and put yourself last, set a life goal to fill your own cup first through self-care practices , asking for help , and carving out time for yourself .
And if you want to focus on others and strengthen your connections, you could set a relationship goal to become a better friend , parent , or partner.
Learning something new puts you on a fast track to personal growth by cultivating humility , critical thinking skills , and mental clarity . If you’ve wanted to dive into a new skill but haven’t found the time, turning it into a life goal might motivate you to pursue it more seriously.
It doesn’t matter what your new skill is — you just need to feel excited about it. Here are some suggestions:
Whenever you learn a new skill, you’re also learning how to learn , which sets you up to learn new skills in the future.
For most people, adding a new family member is both exciting and intimidating. While you can never fully prepare for a birth, adoption, foster child, or even pet adoption, setting family goals can help you consider any financial, emotional, and professional conditions you’d like to satisfy before welcoming the new arrival.
Setting a goal to expand your family may affect other big life decisions. If you plan to start a family in the next few years, you might want to structure your job searches to prioritize paid parental leave and benefits like flexible paid time off.
If you have a book, poetry collection, or album of original songs locked inside you, maybe now’s the time to pursue this creative dream. It’s far too easy to put creative projects on the back burner when you’re just trying to make it through your workday. But for many, it’s these projects that make them feel most alive.
Stories abound of creative people who were working normal jobs before they got their big break. Harper Lee started off as an airline clerk, Anne Rice was an insurance claims examiner, and Art Garfunkel was a math teacher. Maybe you’ll be next. If you don’t set this meaningful creative goal, you’ll never know.
Giving back to your community or the world in general makes you happier, healthier, and more connected . Research even shows that life goals that focus on improving life for others make you happier than goals where you’re the only one who benefits .
Here are some ways to give back:
The sheer magnitude of most life goals can make them feel overwhelming . It’s important to break them down into smaller, more manageable pieces that support your achievement of larger long-term goals. Here are a few ways to stay motivated as you transform important life goals into action.
A vision board is a visual representation of a goal. To create a vision board, find photos, quotes, and other objects (get creative!) that inspire you and put them together. Then put the board above your desk or in a place where you’ll pass by it frequently.
The SMART goal framework adds helpful structure to goals that are too vague or abstract. According to this framework, goals should be:
If your goal is to learn to cook, a SMART version might be: “Learn to cook five different healthy dinners that the whole family enjoys by the end of this year.”
Some life goals better suit the SMART goal framework than others, so experiment to find out what works.
Breaking big goals into more manageable steps keeps you on track and prevents you from becoming overwhelmed. If your goal is to learn Arabic, you could break that into the following milestones:
Milestones encourage you to measure — and, more importantly, celebrate — your progress regularly.
An action plan is a map of the steps you’ll take to realize your goal. A good action plan describes the tasks and subtasks involved in achieving your goal and sets a target date for each.
Creating an action plan is an excellent way to avoid becoming stymied by what programmers call “ yak shaving ”: the seemingly endless series of preliminary tasks you have to do before you can start the real task.
If you want to learn self-defense, you might realize you need to research a local self-defense school. And before you do that, you need to learn about different self-defense methods to find the right one.
Figuring out these sub-tasks and writing them down as action steps with deadlines will help you make steady progress and stop procrastination in its tracks .
Setting life goals is the first step toward achieving them. After that, you’ll need to call on motivation, inspiration , and sheer grit to reach them.
It’s important to fight for goals you really care about. But if your priorities change, there’s no shame in dropping one life goal and picking up another. You’re not the same person you were five years ago, and you won’t be the same person five years from now.
The best goals are those you revisit periodically and adapt to changing circumstances .
You might find, for example, that buying your dream car no longer seems like the best path to a fulfilling life. Instead, like Alina, you just want to make a great vegetable soup.
Discover how personalized coaching can guide you toward fulfilling your dreams and ambitions.
Elizabeth Perry is a Coach Community Manager at BetterUp. She uses strategic engagement strategies to cultivate a learning community across a global network of Coaches through in-person and virtual experiences, technology-enabled platforms, and strategic coaching industry partnerships. With over 3 years of coaching experience and a certification in transformative leadership and life coaching from Sofia University, Elizabeth leverages transpersonal psychology expertise to help coaches and clients gain awareness of their behavioral and thought patterns, discover their purpose and passions, and elevate their potential. She is a lifelong student of psychology, personal growth, and human potential as well as an ICF-certified ACC transpersonal life and leadership Coach.
Being the boss: 10 tips to find work-life balance for managers, how being intentional can improve your life, emotional goals: 20 examples and how to reach them, how to write a 10 year plan (with examples) and reach your goals, a goal for each part of your life: 13 types of goals that you need to set, how to make an action plan to achieve your goals and follow it, setting smart health goals: be clever about your well-being, how to get your life together in 10 simple steps, similar articles, 20 family goals to practice with your loved ones, how to excel at life planning (a life planning template), long-term versus short-term goals: use both to succeed, get closer to your dreams: 20 examples of monthly goals that work, 5 long-term goals examples (+ tips to achieve them), grow model for coaching: achieve goals and boost performance, setting goals for 2024 to ring in the new year right, stay connected with betterup, get our newsletter, event invites, plus product insights and research..
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The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data. ... Descriptive research allows for a wide range of data collection methods, including surveys, observational studies, ...
Descriptive research methods. Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable.. Surveys. Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages ...
Applications of descriptive research with examples. A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey, though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research?
Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia. ... and where. Obtaining enough knowledge about the research topic is an important component of research. The main goal is to observe and catalog all the variables and conditions that affect the ...
As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis. I nterpret results: Interpret your findings in light of your research question and objectives.
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account ...
Descriptive research design. Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis. As a survey method, descriptive research designs will help ...
Definition of descriptive research. Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon. The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.
Descriptive Research vs. Exploratory Research. Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause.
Descriptive research methods. Descriptive research is usually defined as a type of quantitative research, though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable.. Surveys. Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages ...
goal is to identify and describe trends and variation in populations, create new measures of key phenomena, or describe samples in studies aimed at ... Example of Descriptive Research that Uses Network and Cluster Analysis as Descriptive Tools 25 Box 12. Visualization as Data Simplification 32 Box 13. Summary of Data Visualization Tips 37
Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research." Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples. ...
The end goal is to measure the result of different temperature of water ... A descriptive research design is a type of research design that aims to obtain information to systematically describe a ...
Video 2.4.1. Descriptive Research Design provides explanation and examples for quantitative descriptive research.A closed-captioned version of this video is available here.. Descriptive research is distinct from correlational research, in which researchers formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and ...
For example, suppose you are a website beta testing an app feature. In that case, descriptive research invites users to try the feature, tracking their behavior and then asking their opinions. Can be applied to many research methods and areas. Examples include healthcare, SaaS, psychology, political studies, education, and pop culture.
The research has employed a descriptive design, facilitating the description, explanation, and validation of the research findings (Siedlecki, 2020). When prioritizing cause-effect relationships ...
Descriptive research can be statistical research. The main objective of this type of research is to describe the data and characteristics of what is being studied. The idea behind this type of research is to study frequencies, averages, and other statistical calculations. Although this research is highly accurate, it does not gather the causes ...
Types of descriptive research. Observational method. Case studies. Surveys. Recap. Descriptive research methods are used to define the who, what, and where of human behavior and other ...
There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions (contextual research questions); 2) describe a phenomenon (descriptive research questions); 3) assess the effectiveness of existing methods, protocols, ...
Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena (Polit & Beck, 2009, 2014).QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or ...
Descriptive research. Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research would be an appropriate strategy. A descriptive may, for example, aim to describe a pattern. For example, researchers often collect information to describe something for the benefit of the general public.
Explain the goals of descriptive research and the statistical techniques used to interpret it. Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality. Review the procedures of experimental research and explain how it can be used to draw causal inferences.
This blog post will provide tips on how to choose a research methodology that best fits your research goals. We'll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. ... Let's look at the four main types of research: descriptive ...
Scientific research can be categorized into: a) descriptive research, with the main goal to summarize characteristics of a group (or person); b) predictive research, with the main goal to forecast future outcomes that can be used for screening, selection, or monitoring; and c) explanatory research, with the main goal to understand the underlying causal mechanism, which can then be used to ...
Descriptive research is considered more vast than other quantitative and qualitative methods as it provides a broader picture of an event or population. ... If a striker is scoring 0.333, that's one goal for every three shots. If they're scoring one in four, that's 0.250. A classic example is a student's grade point average (GPA). This ...
Keep yourself motivated by setting reasonable goals. Relevant: Your goals should be relevant to you—that is, they should align with your long-term aspirations and values. Think of this as the "why" of your goal. Time-bound: Set a deadline for your goals so you can stay on track and motivated. Getting started on professional development goals
Some life goals better suit the SMART goal framework than others, so experiment to find out what works. 3. Mark milestones. Breaking big goals into more manageable steps keeps you on track and prevents you from becoming overwhelmed. If your goal is to learn Arabic, you could break that into the following milestones: First month: Learn the alphabet