what is quantitative secondary research

General Nursing Research Guide

• Synonyms, Related Terms and Boolean Operators
• Boolean Operators and Modifiers
• Linfield Library Catalog
• Finding Quantitative, Qualitative, Primary and Secondary Sources
• Evaluating Sources

Quantitative and Qualitative

• What is Quantitative and Qualitative Research?

Quantitative research is a methodology that relies on the exploration of numerical patterns, and it can be precisely measured. Using scientific inquiry, quantitative research  relies on data that are observed or measured  to examine research questions.  Randomized Controlled Trials (RCT), Systematic Reviews, and Meta-Analysis  are all examples of Quantitative research.  

Qualitative research refers to any research based on something that is impossible to accurately and precisely measure. It focuses on the "why" rather than the "what" and relies on the direct experiences of human beings as meaning-making agents in their every day lives.  Case studies, Ethnography, Grounded Theory, Narrative Studies or Focus Groups , are all examples of Qualitative research. 

• Finding Quantitative and Qualitative Research in PubMed and CINAHL?

The best way to find quantitative and/or qualitative articles in either PubMed or CINAHL is to search by publication type.

For quantitative articles, select  Randomized Controlled Trials (RCT), Systematic Reviews, or Meta-Analysis  in either  CINAHL  or  PubMed. 

For qualitative, search for  Case Studies, Observational, Personal Narratives  in  PubMed , and  Anecdote, Case Study, Editorial, Interview, Meta Synthesis  in  CINAHL . 

You can also search both PubMed and CINAHL using 'quantitive' or 'qualitative' as part of your keyword search, but it's usually more effective to look for a specific publication type. 

Primary and Secondary Research

• What is Primary Research?

Primary research   in nursing is one that reports the original findings of a study or experiment. It is usually written by the person(s) conducting the research, and is often found in peer reviewed journals. 

Sources of primary research include:

Case studies

Clinical trials or randomized clinical trials (RCT)

Cohort studies

Dissertations or theses

Survey research

• What is Secondary Research?

S econdary Research  is one that summarizes, synthesizes or comments on original research. The Author(s) describe research done by others.

Sources of secondary research include:

Clinical practice guidelines

Meta-analysis

Patient education material

Reviews of literature

Systematic reviews

• Finding Primary Research in PubMed and CINAHL

To find primary research in PubMed and CINAHL, you'll need to filter by article type. Trying searching for Clinical Trials, Randomized Controlled Trials, or Case Study/Reports in either database to find primary research. 

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Secondary Analysis Research

In secondary data analysis (SDA) studies, investigators use data collected by other researchers to address different questions. Like primary data researchers, SDA investigators must be knowledgeable about their research area to identify datasets that are a good fit for an SDA. Several sources of datasets may be useful for SDA, and examples of some of these will be discussed. Advanced practice providers must be aware of possible advantages, such as economic savings, the ability to examine clinically significant research questions in large datasets that may have been collected over time (longitudinal data), generating new hypotheses or clarifying research questions, and avoiding overburdening sensitive populations or investigating sensitive areas. When reading an SDA report, the reader should be able to determine that the authors identified the limitation or disadvantages of their research. For example, a primary dataset cannot “fit” an SDA researcher’s study exactly, SDAs are inherently limited by the inability to definitively examine causality given their retrospective nature, and data may be too old to address current issues.

Secondary analysis of data collected by another researcher for a different purpose, or SDA, is increasing in the medical and social sciences. This is not surprising, given the immense body of health care–related research performed worldwide and the potential beneficial clinical implications of the timely expansion of primary research ( Johnston, 2014 ; Tripathy, 2013 ). Oncology advanced practitioners should understand why and how SDA studies are done, their potential advantages and disadvantages, as well as the importance of reading primary and secondary analysis research reports with the same discriminatory, evaluative eye for possible applicability to their practice setting.

To perform a primary research study, an investigator identifies a problem or question in a particular population that is amenable to the study, designs a research project to address that question, decides on a quantitative or qualitative methodology, determines an adequate sample size and recruits representative subjects, and systematically collects and analyzes data to address specific research questions. On the other hand, an SDA addresses new questions from that dataset previously gathered for a different primary study ( Castle, 2003 ). This might sound “easier,” but investigators who carry out SDA research must have a broad knowledge base and be up to date regarding the state of the science in their area of interest to identify important research questions, find appropriate datasets, and apply the same research principles as primary researchers.

Most SDAs use quantitative data, but some qualitative studies lend themselves to SDA. The researcher must have access to source data, as opposed to secondary source data (e.g., a medical record review). Original qualitative data sources could be videotaped or audiotaped interviews or transcripts, or other notes from a qualitative study ( Rew, Koniak-Griffin, Lewis, Miles, & O’Sullivan, 2000 ). Another possible source for qualitative analysis is open-ended survey questions that reflect greater meaning than forced-response items.

SECONDARY ANALYSIS PROCESS

An SDA researcher starts with a research question or hypothesis, then identifies an appropriate dataset or sets to address it; alternatively, they are familiar with a dataset and peruse it to identify other questions that might be answered by the available data ( Cheng & Phillips, 2014 ). In reality, SDA researchers probably move back and forth between these approaches. For example, an investigator who starts with a research question but does not find a dataset with all needed variables usually must modify the research question(s) based on the best available data.

Secondary data analysis researchers access primary data via formal (public or institutional archived primary research datasets) or informal data sharing sources (pooled datasets separately collected by two or more researchers, or other independent researchers in carrying out secondary analysis; Heaton, 2008 ). There are numerous sources of datasets for secondary analysis. For example, a graduate student might opt to perform a secondary analysis of an advisor’s research. University and government online sites may also be useful, such as the NYU Libraries Data Sources ( https://guides.nyu.edu/c.php?g=276966&p=1848686 ) or the National Cancer Institute, which has many subcategories of datasets ( https://www.cancer.gov/research/resources/search?from=0&toolTypes=datasets_databases ). The Google search engine is useful, and researchers can enter the search term “Archive sources of datasets (add key words related to oncology).”

In one secondary analysis method, researchers reuse their own data—either a single dataset or combined respective datasets to investigate new or additional questions for a new SDA.

Example of a Secondary Data Analysis

An example highlighting this method of reusing one’s own data is Winters-Stone and colleagues’ SDA of data from four previous primary studies they performed at one institution, published in the Journal of Clinical Oncology (JCO) in 2017. Their pooled sample was 512 breast cancer survivors (age 63 ± 6 years) who had been diagnosed and treated for nonmetastatic breast cancer 5.8 years (± 4.1 years) earlier. The investigators divided the cohort, which had no diagnosed neurologic conditions, into two groups: women who reported symptoms consistent with lower-extremity chemotherapy-induced peripheral neuropathy (CIPN; numbness, tingling, or discomfort in feet) vs. CIPN-negative women who did not have symptoms. The objectives of the study were to define patient-reported prevalence of CIPN symptoms in women who had received chemotherapy, compare objective and subjective measures of CIPN in these cancer survivors, and examine the relationship between CIPN symptom severity and outcomes. Objective and subjective measures were used to compare groups for manifestations influenced by CIPN (physical function, disability, and falls). Actual chemotherapy regimens administered had not been documented (a study limitation, but regimens likely included a taxane that is neurotoxic); therefore, investigators could only confirm that symptoms began during chemotherapy and how severely patients rated symptoms.

Up to 10 years after completing chemotherapy, 47% of women who had received chemotherapy were still having significant and potentially life-threatening sensory symptoms consistent with CIPN, did worse on physical function tests, reported poorer functioning, had greater disability, and had nearly twice the rate of falls compared with CIPN-negative women ( Winters-Stone et al., 2017 ). Furthermore, symptom severity was related to worse outcomes, while worsening cancer was not.

Stout (2017) recognized the importance of this secondary analysis in an accompanying editorial published in JCO, remarking that it was the first study that included both patient-reported subjective measures and objective measures of a clinically significant problem. Winter-Stone and others (2017) recognized that by analyzing what essentially became a large sample, they were able to achieve a more comprehensive understanding of the significance and impact of CIPN, and thus to challenge the notion that while CIPN may improve over time, it remains a major cancer survivorship issue. Thus, oncology advanced practitioners must systematically address CIPN at baseline and over time in vulnerable patients, and collaborate with others to implement potentially helpful interventions such as physical and occupational therapy ( Silver & Gilchrist, 2011 ). Other primary or secondary research projects might focus on the usefulness of such interventions.

ADVANTAGES OF SECONDARY DATA ANALYSIS

The advantages of doing SDA research that are cited most often are the economic savings—in time, money, and labor—and the convenience of using existing data rather than collecting primary data, which is usually the most time-consuming and expensive aspect of research ( Johnston, 2014 ; Rew et al., 2000 ; Tripathy, 2013 ). If there is a cost to access datasets, it is usually small (compared to performing the data collection oneself), and detailed information about data collection and statistician support may also be available ( Cheng & Phillips, 2014 ). Secondary data analysis may help a new investigator increase his/her clinical research expertise and avoid data collection challenges (e.g., recruiting study participants, obtaining large-enough sample sizes to yield convincing results, avoiding study dropout, and completing data collection within a reasonable time). Secondary data analyses may also allow for examining more variables than would be feasible in smaller studies, surveys of more diverse samples, and the ability to rethink data and use more advanced statistical techniques in analysis ( Rew et al., 2000 ).

Secondary Data Analysis to Answer Additional Research Questions

Another advantage is that an SDA of a large dataset, possibly combining data from more than one study or by using longitudinal data, can address high-impact, clinically important research questions that might be prohibitively expensive or time-consuming for primary study, and potentially generate new hypotheses ( Smith et al., 2011 ; Tripathy, 2013 ). Schadendorf and others (2015) did one such SDA: a pooled analysis of 12 phase II and phase III studies of ipilimumab (Yervoy) for patients with metastatic melanoma. The study goal was to more accurately estimate the long-term survival benefit of ipilimumab every 3 weeks for greater than or equal to 4 doses in 1,861 patients with advanced melanoma, two thirds of whom had been previously treated and one third who were treatment naive. Almost 89% of patients had received ipilimumab at 3 mg/kg (n = 965), 10 mg/kg (n = 706), or other doses, and about 54% had been followed for longer than 5 years. Across all studies, overall survival curves plateaued between 2 and 3 years, suggesting a durable survival benefit for some patients.

Irrespective of prior therapy, ipilimumab dose, or treatment regimen, median overall survival was 13.5 months in treatment naive patients and 10.7 months in previously treated patients ( Schadendorf et al., 2015 ). In addition, survival curves consistently plateaued at approximately year 3 and continued for up to 10 years (longest follow-up). This suggested that most of the 20% to 26% of patients who reached the plateau had a low risk of death from melanoma thereafter. The authors viewed these results as “encouraging,” given the historic median overall survival in patients with advanced melanoma of 8 to 10 months and 5-year survival of approximately 10%. They identified limitations of their SDA (discussed later in this article). Three-year survival was numerically (but not statistically significantly) greater for the patients who received ipilimumab at 10 mg/kg than at 3 mg/kg doses, which had been noted in one of the included studies.

The importance of this secondary analysis was clearly relevant to prescribers of anticancer therapies, and led to a subsequent phase III trial in the same population to answer the ipilimumab dose question. Ascierto and colleagues’ (2017) study confirmed ipilimumab at 10 mg/kg led to a significantly longer overall survival than at 3 mg/kg (15.7 months vs. 11.5 months) in a subgroup of patients not previously treated with a BRAF inhibitor or immune checkpoint inhibitor. However, this was attained at the cost of greater treatment-related adverse events and more frequent discontinuation secondary to severe ipilimumab-related adverse events. Both would be critical points for advanced practitioners to discuss with patients and to consider in relationship to the particular patient’s ability to tolerate a given regimen.

Secondary Data Analysis to Avoid Study Repetition and Over-Research

Secondary data analysis research also avoids study repetition and over-research of sensitive topics or populations ( Tripathy, 2013 ). For example, people treated for cancer in the United Kingdom are surveyed annually through the National Cancer Patient Experience Survey (NCPES), and questions regarding sexual orientation were first included in the 2013 NCPES. Hulbert-Williams and colleagues (2017) did a more rigorous SDA of this survey to gain an understanding of how lesbian, gay, or bisexual (LGB) patients’ experiences with cancer differed from heterosexual patients.

Sixty-four percent of those surveyed responded (n = 68,737) to the question regarding their “best description of sexual orientation.” 89.3% indicated “heterosexual/straight,” 425 (0.6%) indicated “lesbian or gay,” and 143 (0.2%) indicated “bisexual.” One insight gained from the study was that although the true population proportion of LGB was not known, the small number of self-identified LGB patients most likely did not reflect actual numbers and may have occurred because of ongoing unwillingness to disclose sexual orientation, along with the older mean age of the sample. Other cancer patients who selected “prefer not to answer” (3%), “other” (0.9%), or left the question blank (6%), were not included in the SDA to correctly avoid bias in assuming these responses were related to sexual orientation.

Bisexual respondents were significantly more likely to report that nurses or other health-care professionals informed them about their diagnosis, but that it was subsequently difficult to contact nurse specialists and get understandable answers from them; they were dissatisfied with their interaction with hospital nurses and the care and help provided by both health and social care services after leaving the hospital. Bisexual and lesbian/gay respondents wanted to be involved in treatment decision-making, but therapy choices were not discussed with them, and they were all less satisfied than heterosexuals with the information given to them at diagnosis and during treatment and aftercare—an important clinical implication for oncology advanced practitioners.

Hulbert-Williams and colleagues (2017) proposed that while health-care communication and information resources are not explicitly homophobic, we may perpetuate heterosexuality as “normal” by conversational cues and reliance on heterosexual imagery that implies a context exclusionary of LGB individuals. Sexual orientation equality is about matching care to individual needs for all patients regardless of sexual orientation rather than treating everyone the same way, which does not seem to have happened according to the surveyed respondents’ perceptions. In addition, although LGB respondents replied they did not have or chose to exclude significant others from their cancer experience, there was no survey question that clarified their primary relationship status. This is not a unique strategy for persons with cancer, as LGB individuals may do this to protect family and friends from the negative consequences of homophobia.

Hulbert-Williams and others (2017) identified that this dataset might be useful to identify care needs for patients who identify as LGBT or LGBTQ (queer or questioning; no universally used acronym) and be used to obtain more targeted information from subsequent surveys. There is a relatively small body of data for advanced practitioners and other providers that aid in the assessment and care (including supportive, palliative, and survivorship care) of LGBT individuals—a minority group with many subpopulations that may have unique needs. One such effort is the white paper action plan that came out of the first summit on cancer in the LGBT communities. In 2014, participants from the United States, the United Kingdom, and Canada met to identify LGBT communities’ concerns and needs for cancer research, clinical cancer care, health-care policy, and advocacy for cancer survivorship and LGBT health equity ( Burkhalter et al., 2016 ).

More specifically, Healthy People 2020 now includes two objectives regarding LGBT issues: (1) to increase the number of population-based data systems used to monitor Healthy People 2020 objectives, including a standardized set of questions that identify lesbian, gay, bisexual, and transgender populations; and (2) to increase the number of states and territories that include questions that identify sexual orientation and gender identity on state-level surveys or data systems ( Office of Disease Prevention and Health Promotion, 2019 ). We should help each patient to designate significant others’ (family or friends) degree of involvement in care, while recognizing that LGB patients may exclude their significant others if this process involves disclosing sexual orientation, as this may lead to continued social isolation of cancer patients. This SDA by Hulbert-Williams and colleagues (2017) produced findings in a relatively unexplored area of the overall care experiences of LGB patients.

DISADVANTAGES OF SECONDARY DATA ANALYSIS

Many drawbacks of SDA research center around the fact that a primary investigator collected data reflecting his/her unique perspectives and questions, which may not fit an SDA researcher’s questions ( Rew et al., 2000 ). Secondary data analysis researchers have no control over a desired study population, variables of interest, and study design, and probably did not have a role in collecting the primary data ( Castle, 2003 ; Johnston, 2014 ; Smith et al., 2011 ).

Furthermore, the primary data may not include particular demographic information (e.g., respondent zip codes, race, ethnicity, and specific ages) that were deleted to protect respondent confidentiality, or some other different variables that might be important in the SDA may not have been examined at all ( Cheng & Phillips, 2014 ; Johnston, 2014 ). Although primary data collection takes longer than SDA data collection, identifying and procuring suitable SDA data, analyzing the overall quality of the data, determining any limitations inherent in the original study, and determining whether there is an appropriate fit between the purpose of the original study and the purpose of the SDA can be very time consuming ( Castle, 2003 ; Cheng & Phillips, 2014 ; Rew et al., 2000 ).

Secondary data analysis research may be limited to descriptive, exploratory, and correlational designs and nonparametric statistical tests. By their nature, SDA studies are observational and retrospective, and the investigator cannot examine causal relationships (by a randomized, controlled design). An SDA investigator is challenged to decide whether archival data can be shaped to match new research questions; this means the researcher must have an in-depth understanding of the dataset and know how to alter research questions to match available data and recoded variables.

For example, in their pooled analysis of ipilimumab for advanced melanoma, Schadendorf and colleagues (2015) recognized study limitations that might also be disadvantages of other SDAs. These included the fact that they could not make definitive conclusions about the relationship of survival to ipilimumab dose because the study was not randomized, had no control group, and could not account for key baseline prognostic factors. Other limitations were differences in patient populations in several studies included in the SDA, studies that had been done over 10 years ago (although no other new therapies had improved overall survival during that time), and the fact that treatments received after ipilimumab could have affected overall survival.

READING SECONDARY ANALYSIS RESEARCH

Primary and secondary data investigators apply the same research principles, which should be evident in research reports ( Cheng & Phillips, 2014 ; Hulbert-Williams et al., 2017 ; Johnston, 2014 ; Rew et al., 2000 ; Smith et al., 2011 ; Tripathy, 2013 ).

• ● Did the investigator(s) make a logical and convincing case for the importance of their study?
• ● Is there a clear research question and/or study goals or objectives?
• ● Are there operational definitions for the variables of interest?
• ● Did the authors acknowledge the source of the original data and acquire ethical approval (as necessary)?
• ● Did the authors discuss the strengths and weaknesses of the dataset? For example, how old are the data? Is the dataset sufficiently large to have confidence in the results (adequately powered)?
• ● How well do the data seem to “fit” the SDA research question and design?
• ● Does the methods section allow you, the reader, to “see” how the study was done (e.g., how the sample was selected, the tools/instruments that were used, as well their validity and reliability to measure what was intended, the data collection process, and how the data was analyzed)?
• ● Do the findings, discussion, and conclusions—positive or negative—allow you to answer the “So what?” question, and does your evaluation match the investigator’s conclusion?

Answering these questions allows the advanced practice provider reader to assess the possible value of a secondary analysis (similarly to a primary research) report and its applicability to practice, and to identify further issues or areas for scientific inquiry.

The author has no conflicts of interest to disclose.

• Ascierto P. A., Del Vecchio M., Robert C., Mackiewicz A., Chiarion-Sileni V., Arance A.,…Maio M. (2017). Ipilimumab 10 mg/kg versus ipilimumab 3 mg/kg in patients with unresectable or metastatic melanoma: A randomised, double-blind, multicentre, phase 3 trial . Lancet Oncology , 18 ( 5 ), 611–622. 10.1016/S1470-2045(17)30231-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burkhalter J. E., Margolies L., Sigurdsson H. O., Walland J., Radix A., Rice D.,…Maingi S. (2016). The National LGBT Cancer Action Plan: A white paper of the 2014 National Summit on Cancer in the LGBT Communities . LGBT Health , 3 ( 1 ), 19–31. 10.1089/lgbt.2015.0118 [ CrossRef ] [ Google Scholar ]
• Castle J. E. (2003). Maximizing research opportunities: Secondary data analysis . Journal of Neuroscience Nursing , 35 ( 5 ), 287–290. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/14593941 [ PubMed ] [ Google Scholar ]
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• Hulbert-Williams N. J., Plumpton C. O., Flowers P., McHugh R., Neal R. D., Semlyen J., & Storey L. (2017). The cancer care experiences of gay, lesbian and bisexual patients: A secondary analysis of data from the UK Cancer Patient Experience Survey . European Journal of Cancer Care , 26 ( 4 ). 10.1111/ecc.12670 [ PubMed ] [ CrossRef ] [ Google Scholar ]
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• Office of Disease Prevention and Health Promotion. (2019). Lesbian, gay, bisexual, and transgender health . Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/lesbian-gay-bisexual-and-transgender-health
• Rew L., Koniak-Griffin D., Lewis M. A., Miles M., & O’Sullivan A. (2000). Secondary data analysis: New perspective for adolescent research . Nursing Outlook , 48 ( 5 ), 223–239. 10.1067/mno.2000.104901 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schadendorf D., Hodi F. S., Robert C., Weber J. S., Margolin K., Hamid O.,…Wolchok J. D. (2015). Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma . Journal of Clinical Oncology , 33 ( 17 ), 1889–1894. 10.1200/JCO.2014.56.2736 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
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Home Market Research

Secondary Research: Definition, Methods and Examples.

In the world of research, there are two main types of data sources: primary and secondary. While primary research involves collecting new data directly from individuals or sources, secondary research involves analyzing existing data already collected by someone else. Today we’ll discuss secondary research.

One common source of this research is published research reports and other documents. These materials can often be found in public libraries, on websites, or even as data extracted from previously conducted surveys. In addition, many government and non-government agencies maintain extensive data repositories that can be accessed for research purposes.

LEARN ABOUT: Research Process Steps

While secondary research may not offer the same level of control as primary research, it can be a highly valuable tool for gaining insights and identifying trends. Researchers can save time and resources by leveraging existing data sources while still uncovering important information.

What is Secondary Research: Definition

Secondary research is a research method that involves using already existing data. Existing data is summarized and collated to increase the overall effectiveness of the research.

One of the key advantages of secondary research is that it allows us to gain insights and draw conclusions without having to collect new data ourselves. This can save time and resources and also allow us to build upon existing knowledge and expertise.

When conducting secondary research, it’s important to be thorough and thoughtful in our approach. This means carefully selecting the sources and ensuring that the data we’re analyzing is reliable and relevant to the research question . It also means being critical and analytical in the analysis and recognizing any potential biases or limitations in the data.

LEARN ABOUT: Level of Analysis

Secondary research is much more cost-effective than primary research , as it uses already existing data, unlike primary research, where data is collected firsthand by organizations or businesses or they can employ a third party to collect data on their behalf.

LEARN ABOUT: Data Analytics Projects

Secondary Research Methods with Examples

Secondary research is cost-effective, one of the reasons it is a popular choice among many businesses and organizations. Not every organization is able to pay a huge sum of money to conduct research and gather data. So, rightly secondary research is also termed “ desk research ”, as data can be retrieved from sitting behind a desk.

The following are popularly used secondary research methods and examples:

1. Data Available on The Internet

One of the most popular ways to collect secondary data is the internet. Data is readily available on the internet and can be downloaded at the click of a button.

This data is practically free of cost, or one may have to pay a negligible amount to download the already existing data. Websites have a lot of information that businesses or organizations can use to suit their research needs. However, organizations need to consider only authentic and trusted website to collect information.

2. Government and Non-Government Agencies

Data for secondary research can also be collected from some government and non-government agencies. For example, US Government Printing Office, US Census Bureau, and Small Business Development Centers have valuable and relevant data that businesses or organizations can use.

There is a certain cost applicable to download or use data available with these agencies. Data obtained from these agencies are authentic and trustworthy.

3. Public Libraries

Public libraries are another good source to search for data for this research. Public libraries have copies of important research that were conducted earlier. They are a storehouse of important information and documents from which information can be extracted.

The services provided in these public libraries vary from one library to another. More often, libraries have a huge collection of government publications with market statistics, large collection of business directories and newsletters.

4. Educational Institutions

Importance of collecting data from educational institutions for secondary research is often overlooked. However, more research is conducted in colleges and universities than any other business sector.

The data that is collected by universities is mainly for primary research. However, businesses or organizations can approach educational institutions and request for data from them.

5. Commercial Information Sources

Local newspapers, journals, magazines, radio and TV stations are a great source to obtain data for secondary research. These commercial information sources have first-hand information on economic developments, political agenda, market research, demographic segmentation and similar subjects.

Businesses or organizations can request to obtain data that is most relevant to their study. Businesses not only have the opportunity to identify their prospective clients but can also know about the avenues to promote their products or services through these sources as they have a wider reach.

Learn More: Data Collection Methods: Types & Examples

Key Differences between Primary Research and Secondary Research

Understanding the distinction between primary research and secondary research is essential in determining which research method is best for your project. These are the two main types of research methods, each with advantages and disadvantages. In this section, we will explore the critical differences between the two and when it is appropriate to use them.

How to Conduct Secondary Research?

We have already learned about the differences between primary and secondary research. Now, let’s take a closer look at how to conduct it.

Secondary research is an important tool for gathering information already collected and analyzed by others. It can help us save time and money and allow us to gain insights into the subject we are researching. So, in this section, we will discuss some common methods and tips for conducting it effectively.

Here are the steps involved in conducting secondary research:

1. Identify the topic of research: Before beginning secondary research, identify the topic that needs research. Once that’s done, list down the research attributes and its purpose.

2. Identify research sources: Next, narrow down on the information sources that will provide most relevant data and information applicable to your research.

3. Collect existing data: Once the data collection sources are narrowed down, check for any previous data that is available which is closely related to the topic. Data related to research can be obtained from various sources like newspapers, public libraries, government and non-government agencies etc.

4. Combine and compare: Once data is collected, combine and compare the data for any duplication and assemble data into a usable format. Make sure to collect data from authentic sources. Incorrect data can hamper research severely.

4. Analyze data: Analyze collected data and identify if all questions are answered. If not, repeat the process if there is a need to dwell further into actionable insights.

Advantages of Secondary Research

Secondary research offers a number of advantages to researchers, including efficiency, the ability to build upon existing knowledge, and the ability to conduct research in situations where primary research may not be possible or ethical. By carefully selecting their sources and being thoughtful in their approach, researchers can leverage secondary research to drive impact and advance the field. Some key advantages are the following:

1. Most information in this research is readily available. There are many sources from which relevant data can be collected and used, unlike primary research, where data needs to collect from scratch.

2. This is a less expensive and less time-consuming process as data required is easily available and doesn’t cost much if extracted from authentic sources. A minimum expenditure is associated to obtain data.

3. The data that is collected through secondary research gives organizations or businesses an idea about the effectiveness of primary research. Hence, organizations or businesses can form a hypothesis and evaluate cost of conducting primary research.

4. Secondary research is quicker to conduct because of the availability of data. It can be completed within a few weeks depending on the objective of businesses or scale of data needed.

As we can see, this research is the process of analyzing data already collected by someone else, and it can offer a number of benefits to researchers.

Disadvantages of Secondary Research

On the other hand, we have some disadvantages that come with doing secondary research. Some of the most notorious are the following:

1. Although data is readily available, credibility evaluation must be performed to understand the authenticity of the information available.

2. Not all secondary data resources offer the latest reports and statistics. Even when the data is accurate, it may not be updated enough to accommodate recent timelines.

3. Secondary research derives its conclusion from collective primary research data. The success of your research will depend, to a greater extent, on the quality of research already conducted by primary research.

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In conclusion, secondary research is an important tool for researchers exploring various topics. By leveraging existing data sources, researchers can save time and resources, build upon existing knowledge, and conduct research in situations where primary research may not be feasible.

There are a variety of methods and examples of secondary research, from analyzing public data sets to reviewing previously published research papers. As students and aspiring researchers, it’s important to understand the benefits and limitations of this research and to approach it thoughtfully and critically. By doing so, we can continue to advance our understanding of the world around us and contribute to meaningful research that positively impacts society.

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

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

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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What Is Secondary Data? A Complete Guide

What is secondary data, and why is it important? Find out in this post.

Within data analytics, there are many ways of categorizing data. A common distinction, for instance, is that between qualitative and quantitative data . In addition, you might also distinguish your data based on factors like sensitivity. For example, is it publicly available or is it highly confidential?  

Probably the most fundamental distinction between different types of data is their source. Namely, are they primary, secondary, or third-party data? Each of these vital data sources supports the data analytics process in its own way. In this post, we’ll focus specifically on secondary data. We’ll look at its main characteristics, provide some examples, and highlight the main pros and cons of using secondary data in your analysis.  

We’ll cover the following topics:  

What is secondary data?

• What’s the difference between primary, secondary, and third-party data?
• What are some examples of secondary data?
• How to analyse secondary data
• Advantages of secondary data
• Disadvantages of secondary data
• Wrap-up and further reading

Ready to learn all about secondary data? Then let’s go.

1. What is secondary data?

Secondary data (also known as second-party data) refers to any dataset collected by any person other than the one using it.  

Secondary data sources are extremely useful. They allow researchers and data analysts to build large, high-quality databases that help solve business problems. By expanding their datasets with secondary data, analysts can enhance the quality and accuracy of their insights. Most secondary data comes from external organizations. However, secondary data also refers to that collected within an organization and then repurposed.

Secondary data has various benefits and drawbacks, which we’ll explore in detail in section four. First, though, it’s essential to contextualize secondary data by understanding its relationship to two other sources of data: primary and third-party data. We’ll look at these next.

2. What’s the difference between primary, secondary, and third-party data?

To best understand secondary data, we need to know how it relates to the other main data sources: primary and third-party data.

What is primary data?

‘Primary data’ (also known as first-party data) are those directly collected or obtained by the organization or individual that intends to use them. Primary data are always collected for a specific purpose. This could be to inform a defined goal or objective or to address a particular business problem. 

For example, a real estate organization might want to analyze current housing market trends. This might involve conducting interviews, collecting facts and figures through surveys and focus groups, or capturing data via electronic forms. Focusing only on the data required to complete the task at hand ensures that primary data remain highly relevant. They’re also well-structured and of high quality.

As explained, ‘secondary data’ describes those collected for a purpose other than the task at hand. Secondary data can come from within an organization but more commonly originate from an external source. If it helps to make the distinction, secondary data is essentially just another organization’s primary data. 

Secondary data sources are so numerous that they’ve started playing an increasingly vital role in research and analytics. They are easier to source than primary data and can be repurposed to solve many different problems. While secondary data may be less relevant for a given task than primary data, they are generally still well-structured and highly reliable.

What is third-party data?

‘Third-party data’ (sometimes referred to as tertiary data) refers to data collected and aggregated from numerous discrete sources by third-party organizations. Because third-party data combine data from numerous sources and aren’t collected with a specific goal in mind, the quality can be lower. 

Third-party data also tend to be largely unstructured. This means that they’re often beset by errors, duplicates, and so on, and require more processing to get them into a usable format. Nevertheless, used appropriately, third-party data are still a useful data analytics resource. You can learn more about structured vs unstructured data here . 

OK, now that we’ve placed secondary data in context, let’s explore some common sources and types of secondary data.

3. What are some examples of secondary data?

External secondary data.

Before we get to examples of secondary data, we first need to understand the types of organizations that generally provide them. Frequent sources of secondary data include:  

• Government departments
• Public sector organizations
• Industry associations
• Trade and industry bodies
• Educational institutions
• Private companies
• Market research providers

While all these organizations provide secondary data, government sources are perhaps the most freely accessible. They are legally obliged to keep records when registering people, providing services, and so on. This type of secondary data is known as administrative data. It’s especially useful for creating detailed segment profiles, where analysts hone in on a particular region, trend, market, or other demographic.

Types of secondary data vary. Popular examples of secondary data include:

• Tax records and social security data
• Census data (the U.S. Census Bureau is oft-referenced, as well as our favorite, the U.S. Bureau of Labor Statistics )
• Electoral statistics
• Health records
• Books, journals, or other print media
• Social media monitoring, internet searches, and other online data
• Sales figures or other reports from third-party companies
• Libraries and electronic filing systems
• App data, e.g. location data, GPS data, timestamp data, etc.

Internal secondary data 

As mentioned, secondary data is not limited to that from a different organization. It can also come from within an organization itself.  

Sources of internal secondary data might include:

• Sales reports
• Annual accounts
• Quarterly sales figures
• Customer relationship management systems
• Emails and metadata
• Website cookies

In the right context, we can define practically any type of data as secondary data. The key takeaway is that the term ‘secondary data’ doesn’t refer to any inherent quality of the data themselves, but to how they are used. Any data source (external or internal) used for a task other than that for which it was originally collected can be described as secondary data.

4. How to analyse secondary data

The process of analysing secondary data can be performed either quantitatively or qualitatively, depending on the kind of data the researcher is dealing with. The quantitative method of secondary data analysis is used on numerical data and is analyzed mathematically. The qualitative method uses words to provide in-depth information about data.

There are different stages of secondary data analysis, which involve events before, during, and after data collection. These stages include:

• Statement of purpose: Before collecting secondary data, you need to know your statement of purpose. This means you should have a clear awareness of the goal of the research work and how this data will help achieve it. This will guide you to collect the right data, then choosing the best data source and method of analysis.
• Research design: This is a plan on how the research activities will be carried out. It describes the kind of data to be collected, the sources of data collection, the method of data collection, tools used, and method of analysis. Once the purpose of the research has been identified, the researcher should design a research process that will guide the data analysis process.
• Developing the research questions: Once you’ve identified the research purpose, an analyst should also prepare research questions to help identify secondary data. For example, if a researcher is looking to learn more about why working adults are increasingly more interested in the “gig economy” as opposed to full-time work, they may ask, “What are the main factors that influence adults decisions to engage in freelance work?” or, “Does education level have an effect on how people engage in freelance work?
• Identifying secondary data: Using the research questions as a guide, researchers will then begin to identify relevant data from the sources provided. If the kind of data to be collected is qualitative, a researcher can filter out qualitative data—for example.
• Evaluating secondary data: Once relevant data has been identified and collates, it will be evaluated to ensure it fulfils the criteria of the research topic. Then, it is analyzed either using the quantitative or qualitative method, depending on the type of data it is.

You can learn more about secondary data analysis in this post .  

5. Advantages of secondary data

Secondary data is suitable for any number of analytics activities. The only limitation is a dataset’s format, structure, and whether or not it relates to the topic or problem at hand. 

When analyzing secondary data, the process has some minor differences, mainly in the preparation phase. Otherwise, it follows much the same path as any traditional data analytics project. 

More broadly, though, what are the advantages and disadvantages of using secondary data? Let’s take a look.

Advantages of using secondary data

It’s an economic use of time and resources: Because secondary data have already been collected, cleaned, and stored, this saves analysts much of the hard work that comes from collecting these data firsthand. For instance, for qualitative data, the complex tasks of deciding on appropriate research questions or how best to record the answers have already been completed. Secondary data saves data analysts and data scientists from having to start from scratch.  

It provides a unique, detailed picture of a population: Certain types of secondary data, especially government administrative data, can provide access to levels of detail that it would otherwise be extremely difficult (or impossible) for organizations to collect on their own. Data from public sources, for instance, can provide organizations and individuals with a far greater level of population detail than they could ever hope to gather in-house. You can also obtain data over larger intervals if you need it., e.g. stock market data which provides decades’-worth of information.  

Secondary data can build useful relationships: Acquiring secondary data usually involves making connections with organizations and analysts in fields that share some common ground with your own. This opens the door to a cross-pollination of disciplinary knowledge. You never know what nuggets of information or additional data resources you might find by building these relationships.

Secondary data tend to be high-quality: Unlike some data sources, e.g. third-party data, secondary data tends to be in excellent shape. In general, secondary datasets have already been validated and therefore require minimal checking. Often, such as in the case of government data, datasets are also gathered and quality-assured by organizations with much more time and resources available. This further benefits the data quality , while benefiting smaller organizations that don’t have endless resources available.

It’s excellent for both data enrichment and informing primary data collection: Another benefit of secondary data is that they can be used to enhance and expand existing datasets. Secondary data can also inform primary data collection strategies. They can provide analysts or researchers with initial insights into the type of data they might want to collect themselves further down the line.

6. Disadvantages of secondary data

They aren’t always free: Sometimes, it’s unavoidable—you may have to pay for access to secondary data. However, while this can be a financial burden, in reality, the cost of purchasing a secondary dataset usually far outweighs the cost of having to plan for and collect the data firsthand.  

The data isn’t always suited to the problem at hand: While secondary data may tick many boxes concerning its relevance to a business problem, this is not always true. For instance, secondary data collection might have been in a geographical location or time period ill-suited to your analysis. Because analysts were not present when the data were initially collected, this may also limit the insights they can extract.

The data may not be in the preferred format: Even when a dataset provides the necessary information, that doesn’t mean it’s appropriately stored. A basic example: numbers might be stored as categorical data rather than numerical data. Another issue is that there may be gaps in the data. Categories that are too vague may limit the information you can glean. For instance, a dataset of people’s hair color that is limited to ‘brown, blonde and other’ will tell you very little about people with auburn, black, white, or gray hair.  

You can’t be sure how the data were collected: A structured, well-ordered secondary dataset may appear to be in good shape. However, it’s not always possible to know what issues might have occurred during data collection that will impact their quality. For instance, poor response rates will provide a limited view. While issues relating to data collection are sometimes made available alongside the datasets (e.g. for government data) this isn’t always the case. You should therefore treat secondary data with a reasonable degree of caution.

Being aware of these disadvantages is the first step towards mitigating them. While you should be aware of the risks associated with using secondary datasets, in general, the benefits far outweigh the drawbacks.

7. Wrap-up and further reading

In this post we’ve explored secondary data in detail. As we’ve seen, it’s not so different from other forms of data. What defines data as secondary data is how it is used rather than an inherent characteristic of the data themselves. 

To learn more about data analytics, check out this free, five-day introductory data analytics short course . You can also check out these articles to learn more about the data analytics process:

• What is data cleaning and why is it important?
• What is data visualization? A complete introductory guide
• 10 Great places to find free datasets for your next project

What is Secondary Research? Types, Methods, Examples

Appinio Research · 20.09.2023 · 13min read

Have you ever wondered how researchers gather valuable insights without conducting new experiments or surveys? That's where secondary research steps in—a powerful approach that allows us to explore existing data and information others collect.

Whether you're a student, a professional, or someone seeking to make informed decisions, understanding the art of secondary research opens doors to a wealth of knowledge.

What is Secondary Research?

Secondary Research refers to the process of gathering and analyzing existing data, information, and knowledge that has been previously collected and compiled by others. This approach allows researchers to leverage available sources, such as articles, reports, and databases, to gain insights, validate hypotheses, and make informed decisions without collecting new data.

Benefits of Secondary Research

Secondary research offers a range of advantages that can significantly enhance your research process and the quality of your findings.

• Time and Cost Efficiency: Secondary research saves time and resources by utilizing existing data sources, eliminating the need for data collection from scratch.
• Wide Range of Data: Secondary research provides access to vast information from various sources, allowing for comprehensive analysis.
• Historical Perspective: Examining past research helps identify trends, changes, and long-term patterns that might not be immediately apparent.
• Reduced Bias: As data is collected by others, there's often less inherent bias than in conducting primary research, where biases might affect data collection.
• Support for Primary Research: Secondary research can lay the foundation for primary research by providing context and insights into gaps in existing knowledge.
• Comparative Analysis : By integrating data from multiple sources, you can conduct robust comparative analyses for more accurate conclusions.
• Benchmarking and Validation: Secondary research aids in benchmarking performance against industry standards and validating hypotheses.

Primary Research vs. Secondary Research

When it comes to research methodologies, primary and secondary research each have their distinct characteristics and advantages. Here's a brief comparison to help you understand the differences.

Primary Research

• Data Source: Involves collecting new data directly from original sources.
• Data Collection: Researchers design and conduct surveys, interviews, experiments, or observations.
• Time and Resources: Typically requires more time, effort, and resources due to data collection.
• Fresh Insights: Provides firsthand, up-to-date information tailored to specific research questions.
• Control: Researchers control the data collection process and can shape methodologies.

Secondary Research

• Data Source: Involves utilizing existing data and information collected by others.
• Data Collection: Researchers search, select, and analyze data from published sources, reports, and databases.
• Time and Resources: Generally more time-efficient and cost-effective as data is already available.
• Existing Knowledge: Utilizes data that has been previously compiled, often providing broader context.
• Less Control: Researchers have limited control over how data was collected originally, if any.

Choosing between primary and secondary research depends on your research objectives, available resources, and the depth of insights you require.

Types of Secondary Research

Secondary research encompasses various types of existing data sources that can provide valuable insights for your research endeavors. Understanding these types can help you choose the most relevant sources for your objectives.

Here are the primary types of secondary research:

Internal Sources

Internal sources consist of data generated within your organization or entity. These sources provide valuable insights into your own operations and performance.

• Company Records and Data: Internal reports, documents, and databases that house information about sales, operations, and customer interactions.
• Sales Reports and Customer Data: Analysis of past sales trends, customer demographics, and purchasing behavior.
• Financial Statements and Annual Reports: Financial data, such as balance sheets and income statements, offer insights into the organization's financial health.

External Sources

External sources encompass data collected and published by entities outside your organization.

These sources offer a broader perspective on various subjects.

• Published Literature and Journals: Scholarly articles, research papers, and academic studies available in journals or online databases.
• Market Research Reports: Reports from market research firms that provide insights into industry trends, consumer behavior, and market forecasts.
• Government and NGO Databases: Data collected and maintained by government agencies and non-governmental organizations, offering demographic, economic, and social information.
• Online Media and News Articles: News outlets and online publications that cover current events, trends, and societal developments.

Each type of secondary research source holds its value and relevance, depending on the nature of your research objectives. Combining these sources lets you understand the subject matter and make informed decisions.

How to Conduct Secondary Research?

Effective secondary research involves a thoughtful and systematic approach that enables you to extract valuable insights from existing data sources. Here's a step-by-step guide on how to navigate the process:

1. Define Your Research Objectives

Before delving into secondary research, clearly define what you aim to achieve. Identify the specific questions you want to answer, the insights you're seeking, and the scope of your research.

2. Identify Relevant Sources

Begin by identifying the most appropriate sources for your research. Consider the nature of your research objectives and the data type you require. Seek out sources such as academic journals, market research reports, official government databases, and reputable news outlets.

3. Evaluate Source Credibility

Ensuring the credibility of your sources is crucial. Evaluate the reliability of each source by assessing factors such as the author's expertise, the publication's reputation, and the objectivity of the information provided. Choose sources that align with your research goals and are free from bias.

4. Extract and Analyze Information

Once you've gathered your sources, carefully extract the relevant information. Take thorough notes, capturing key data points, insights, and any supporting evidence. As you accumulate information, start identifying patterns, trends, and connections across different sources.

5. Synthesize Findings

As you analyze the data, synthesize your findings to draw meaningful conclusions. Compare and contrast information from various sources to identify common themes and discrepancies. This synthesis process allows you to construct a coherent narrative that addresses your research objectives.

6. Address Limitations and Gaps

Acknowledge the limitations and potential gaps in your secondary research. Recognize that secondary data might have inherent biases or be outdated. Where necessary, address these limitations by cross-referencing information or finding additional sources to fill in gaps.

7. Contextualize Your Findings

Contextualization is crucial in deriving actionable insights from your secondary research. Consider the broader context within which the data was collected. How does the information relate to current trends, societal changes, or industry shifts? This contextual understanding enhances the relevance and applicability of your findings.

8. Cite Your Sources

Maintain academic integrity by properly citing the sources you've used for your secondary research. Accurate citations not only give credit to the original authors but also provide a clear trail for readers to access the information themselves.

9. Integrate Secondary and Primary Research (If Applicable)

In some cases, combining secondary and primary research can yield more robust insights. If you've also conducted primary research, consider integrating your secondary findings with your primary data to provide a well-rounded perspective on your research topic.

You can use a market research platform like Appinio to conduct primary research with real-time insights in minutes!

10. Communicate Your Findings

Finally, communicate your findings effectively. Whether it's in an academic paper, a business report, or any other format, present your insights clearly and concisely. Provide context for your conclusions and use visual aids like charts and graphs to enhance understanding.

Remember that conducting secondary research is not just about gathering information—it's about critically analyzing, interpreting, and deriving valuable insights from existing data. By following these steps, you'll navigate the process successfully and contribute to the body of knowledge in your field.

Secondary Research Examples

To better understand how secondary research is applied in various contexts, let's explore a few real-world examples that showcase its versatility and value.

Market Analysis and Trend Forecasting

Imagine you're a marketing strategist tasked with launching a new product in the smartphone industry. By conducting secondary research, you can:

• Access Market Reports: Utilize market research reports to understand consumer preferences, competitive landscape, and growth projections.
• Analyze Trends: Examine past sales data and industry reports to identify trends in smartphone features, design, and user preferences.
• Benchmark Competitors: Compare market share, customer satisfaction , and pricing strategies of key competitors to develop a strategic advantage.
• Forecast Demand: Use historical sales data and market growth predictions to estimate demand for your new product.

Academic Research and Literature Reviews

Suppose you're a student researching climate change's effects on marine ecosystems. Secondary research aids your academic endeavors by:

• Reviewing Existing Studies: Analyze peer-reviewed articles and scientific papers to understand the current state of knowledge on the topic.
• Identifying Knowledge Gaps: Identify areas where further research is needed based on what existing studies still need to cover.
• Comparing Methodologies: Compare research methodologies used by different studies to assess the strengths and limitations of their approaches.
• Synthesizing Insights: Synthesize findings from various studies to form a comprehensive overview of the topic's implications on marine life.

Competitive Landscape Assessment for Business Strategy

Consider you're a business owner looking to expand your restaurant chain to a new location. Secondary research aids your strategic decision-making by:

• Analyzing Demographics: Utilize demographic data from government databases to understand the local population's age, income, and preferences.
• Studying Local Trends: Examine restaurant industry reports to identify the types of cuisines and dining experiences currently popular in the area.
• Understanding Consumer Behavior: Analyze online reviews and social media discussions to gauge customer sentiment towards existing restaurants in the vicinity.
• Assessing Economic Conditions: Access economic reports to evaluate the local economy's stability and potential purchasing power.

These examples illustrate the practical applications of secondary research across various fields to provide a foundation for informed decision-making, deeper understanding, and innovation.

Secondary Research Limitations

While secondary research offers many benefits, it's essential to be aware of its limitations to ensure the validity and reliability of your findings.

• Data Quality and Validity: The accuracy and reliability of secondary data can vary, affecting the credibility of your research.
• Limited Contextual Information: Secondary sources might lack detailed contextual information, making it important to interpret findings within the appropriate context.
• Data Suitability: Existing data might not align perfectly with your research objectives, leading to compromises or incomplete insights.
• Outdated Information: Some sources might provide obsolete information that doesn't accurately reflect current trends or situations.
• Potential Bias: While secondary data is often less biased, biases might still exist in the original data sources, influencing your findings.
• Incompatibility of Data: Combining data from different sources might pose challenges due to variations in definitions, methodologies, or units of measurement.
• Lack of Control: Unlike primary research, you have no control over how data was collected or its quality, potentially affecting your analysis. Understanding these limitations will help you navigate secondary research effectively and make informed decisions based on a well-rounded understanding of its strengths and weaknesses.

Secondary research is a valuable tool that businesses can use to their advantage. By tapping into existing data and insights, companies can save time, resources, and effort that would otherwise be spent on primary research. This approach equips decision-makers with a broader understanding of market trends, consumer behaviors, and competitive landscapes. Additionally, benchmarking against industry standards and validating hypotheses empowers businesses to make informed choices that lead to growth and success.

As you navigate the world of secondary research, remember that it's not just about data retrieval—it's about strategic utilization. With a clear grasp of how to access, analyze, and interpret existing information, businesses can stay ahead of the curve, adapt to changing landscapes, and make decisions that are grounded in reliable knowledge.

How to Conduct Secondary Research in Minutes?

In the world of decision-making, having access to real-time consumer insights is no longer a luxury—it's a necessity. That's where Appinio comes in, revolutionizing how businesses gather valuable data for better decision-making. As a real-time market research platform, Appinio empowers companies to tap into the pulse of consumer opinions swiftly and seamlessly.

• Fast Insights: Say goodbye to lengthy research processes. With Appinio, you can transform questions into actionable insights in minutes.
• Data-Driven Decisions: Harness the power of real-time consumer insights to drive your business strategies, allowing you to make informed choices on the fly.
• Seamless Integration: Appinio handles the research and technical complexities, freeing you to focus on what truly matters: making rapid data-driven decisions that propel your business forward.

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Primary vs secondary research – what’s the difference.

14 min read Find out how primary and secondary research are different from each other, and how you can use them both in your own research program.

Primary vs secondary research: in a nutshell

The essential difference between primary and secondary research lies in who collects the data.

• Primary research definition

When you conduct primary research, you’re collecting data by doing your own surveys or observations.

• Secondary research definition:

In secondary research, you’re looking at existing data from other researchers, such as academic journals, government agencies or national statistics.

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When to use primary vs secondary research

Primary research and secondary research both offer value in helping you gather information.

Each research method can be used alone to good effect. But when you combine the two research methods, you have the ingredients for a highly effective market research strategy. Most research combines some element of both primary methods and secondary source consultation.

So assuming you’re planning to do both primary and secondary research – which comes first? Counterintuitive as it sounds, it’s more usual to start your research process with secondary research, then move on to primary research.

Secondary research can prepare you for collecting your own data in a primary research project. It can give you a broad overview of your research area, identify influences and trends, and may give you ideas and avenues to explore that you hadn’t previously considered.

Given that secondary research can be done quickly and inexpensively, it makes sense to start your primary research process with some kind of secondary research. Even if you’re expecting to find out what you need to know from a survey of your target market, taking a small amount of time to gather information from secondary sources is worth doing.

Primary research

Primary market research is original research carried out when a company needs timely, specific data about something that affects its success or potential longevity.

Primary research data collection might be carried out in-house by a business analyst or market research team within the company, or it may be outsourced to a specialist provider, such as an agency or consultancy. While outsourcing primary research involves a greater upfront expense, it’s less time consuming and can bring added benefits such as researcher expertise and a ‘fresh eyes’ perspective that avoids the risk of bias and partiality affecting the research data.

Primary research gives you recent data from known primary sources about the particular topic you care about, but it does take a little time to collect that data from scratch, rather than finding secondary data via an internet search or library visit.

Primary research involves two forms of data collection:

• Exploratory research This type of primary research is carried out to determine the nature of a problem that hasn’t yet been clearly defined. For example, a supermarket wants to improve its poor customer service and needs to understand the key drivers behind the customer experience issues. It might do this by interviewing employees and customers, or by running a survey program or focus groups.
• Conclusive research This form of primary research is carried out to solve a problem that the exploratory research – or other forms of primary data – has identified. For example, say the supermarket’s exploratory research found that employees weren’t happy. Conclusive research went deeper, revealing that the manager was rude, unreasonable, and difficult, making the employees unhappy and resulting in a poor employee experience which in turn led to less than excellent customer service. Thanks to the company’s choice to conduct primary research, a new manager was brought in, employees were happier and customer service improved.

Examples of primary research

All of the following are forms of primary research data.

• Customer satisfaction survey results
• Employee experience pulse survey results
• NPS rating scores from your customers
• A field researcher’s notes
• Data from weather stations in a local area
• Recordings made during focus groups

Primary research methods

There are a number of primary research methods to choose from, and they are already familiar to most people. The ones you choose will depend on your budget, your time constraints, your research goals and whether you’re looking for quantitative or qualitative data.

A survey can be carried out online, offline, face to face or via other media such as phone or SMS. It’s relatively cheap to do, since participants can self-administer the questionnaire in most cases. You can automate much of the process if you invest in good quality survey software.

Primary research interviews can be carried out face to face, over the phone or via video calling. They’re more time-consuming than surveys, and they require the time and expense of a skilled interviewer and a dedicated room, phone line or video calling setup. However, a personal interview can provide a very rich primary source of data based not only on the participant’s answers but also on the observations of the interviewer.

Focus groups

A focus group is an interview with multiple participants at the same time. It often takes the form of a discussion moderated by the researcher. As well as taking less time and resources than a series of one-to-one interviews, a focus group can benefit from the interactions between participants which bring out more ideas and opinions. However this can also lead to conversations going off on a tangent, which the moderator must be able to skilfully avoid by guiding the group back to the relevant topic.

Secondary research

Secondary research is research that has already been done by someone else prior to your own research study.

Secondary research is generally the best place to start any research project as it will reveal whether someone has already researched the same topic you’re interested in, or a similar topic that helps lay some of the groundwork for your research project.

Even if your preliminary secondary research doesn’t turn up a study similar to your own research goals, it will still give you a stronger knowledge base that you can use to strengthen and refine your research hypothesis. You may even find some gaps in the market you didn’t know about before.

The scope of secondary research resources is extremely broad. Here are just a few of the places you might look for relevant information.

Books and magazines

A public library can turn up a wealth of data in the form of books and magazines – and it doesn’t cost a penny to consult them.

Market research reports

Secondary research from professional research agencies can be highly valuable, as you can be confident the data collection methods and data analysis will be sound

Scholarly journals, often available in reference libraries

Peer-reviewed journals have been examined by experts from the relevant educational institutions, meaning there has been an extra layer of oversight and careful consideration of the data points before publication.

Government reports and studies

Public domain data, such as census data, can provide relevant information for your research project, not least in choosing the appropriate research population for a primary research method. If the information you need isn’t readily available, try contacting the relevant government agencies.

White papers

Businesses often produce white papers as a means of showcasing their expertise and value in their field. White papers can be helpful in secondary research methods, although they may not be as carefully vetted as academic papers or public records.

Trade or industry associations

Associations may have secondary data that goes back a long way and offers a general overview of a particular industry. This data collected over time can be very helpful in laying the foundations of your particular research project.

Private company data

Some businesses may offer their company data to those conducting research in return for fees or with explicit permissions. However, if a business has data that’s closely relevant to yours, it’s likely they are a competitor and may flat out refuse your request.

Learn more about secondary research

Examples of secondary research data

These are all forms of secondary research data in action:

• A newspaper report quoting statistics sourced by a journalist
• Facts from primary research articles quoted during a debate club meeting
• A blog post discussing new national figures on the economy
• A company consulting previous research published by a competitor

Secondary research methods

Literature reviews.

A core part of the secondary research process, involving data collection and constructing an argument around multiple sources. A literature review involves gathering information from a wide range of secondary sources on one topic and summarizing them in a report or in the introduction to primary research data.

Content analysis

This systematic approach is widely used in social science disciplines. It uses codes for themes, tropes or key phrases which are tallied up according to how often they occur in the secondary data. The results help researchers to draw conclusions from qualitative data.

Data analysis using digital tools

You can analyze large volumes of data using software that can recognize and categorize natural language. More advanced tools will even be able to identify relationships and semantic connections within the secondary research materials.

Comparing primary vs secondary research

We’ve established that both primary research and secondary research have benefits for your business, and that there are major differences in terms of the research process, the cost, the research skills involved and the types of data gathered. But is one of them better than the other?

The answer largely depends on your situation. Whether primary or secondary research wins out in your specific case depends on the particular topic you’re interested in and the resources you have available. The positive aspects of one method might be enough to sway you, or the drawbacks – such as a lack of credible evidence already published, as might be the case in very fast-moving industries – might make one method totally unsuitable.

Here’s an at-a-glance look at the features and characteristics of primary vs secondary research, illustrating some of the key differences between them.

What are the pros and cons of primary research?

Primary research provides original data and allows you to pinpoint the issues you’re interested in and collect data from your target market – with all the effort that entails.

Benefits of primary research:

• Tells you what you need to know, nothing irrelevant
• Yours exclusively – once acquired, you may be able to sell primary data or use it for marketing
• Teaches you more about your business
• Can help foster new working relationships and connections between silos
• Primary research methods can provide upskilling opportunities – employees gain new research skills

Limitations of primary research:

• Lacks context from other research on related subjects
• Can be expensive
• Results aren’t ready to use until the project is complete
• Any mistakes you make in in research design or implementation could compromise your data quality
• May not have lasting relevance – although it could fulfill a benchmarking function if things change

What are the pros and cons of secondary research?

Secondary research relies on secondary sources, which can be both an advantage and a drawback. After all, other people are doing the work, but they’re also setting the research parameters.

Benefits of secondary research:

• It’s often low cost or even free to access in the public domain
• Supplies a knowledge base for researchers to learn from
• Data is complete, has been analyzed and checked, saving you time and costs
• It’s ready to use as soon as you acquire it

Limitations of secondary research

• May not provide enough specific information
• Conducting a literature review in a well-researched subject area can become overwhelming
• No added value from publishing or re-selling your research data
• Results are inconclusive – you’ll only ever be interpreting data from another organization’s experience, not your own
• Details of the research methodology are unknown
• May be out of date – always check carefully the original research was conducted

Related resources

Business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, request demo.

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• What is Secondary Research? + [Methods & Examples]

In some situations, the researcher may not be directly involved in the data gathering process and instead, would rely on already existing data in order to arrive at research outcomes. This approach to systematic investigation is known as secondary research. 

There are many reasons a researcher may want to make use of already existing data instead of collecting data samples, first-hand. In this article, we will share some of these reasons with you and show you how to conduct secondary research with Formplus. 

What is Secondary  Research?

Secondary research is a common approach to a systematic investigation in which the researcher depends solely on existing data in the course of the research process. This research design involves organizing, collating and analyzing these data samples for valid research conclusions. 

Secondary research is also known as desk research since it involves synthesizing existing data that can be sourced from the internet, peer-reviewed journals , textbooks, government archives, and libraries. What the secondary researcher does is to study already established patterns in previous researches and apply this information to the specific research context. 

Interestingly, secondary research often relies on data provided by primary research and this is why some researches combine both methods of investigation. In this sense, the researcher begins by evaluating and identifying gaps in existing knowledge before adopting primary research to gather new information that will serve his or her research. 

What are Secondary Research Methods?

As already highlighted, secondary research involves data assimilation from different sources, that is, using available research materials instead of creating a new pool of data using primary research methods. Common secondary research methods include data collection through the internet, libraries, archives, schools and organizational reports. 

• Online Data

Online data is data that is gathered via the internet. In recent times, this method has become popular because the internet provides a large pool of both free and paid research resources that can be easily accessed with the click of a button. 

While this method simplifies the data gathering process , the researcher must take care to depend solely on authentic sites when collecting information. In some way, the internet is a virtual aggregation for all other sources of secondary research data. 

• Data from Government and Non-government Archives

You can also gather useful research materials from government and non-government archives and these archives usually contain verifiable information that provides useful insights on varying research contexts. In many cases, you would need to pay a sum to gain access to these data. 

The challenge, however, is that such data is not always readily available due to a number of factors. For instance, some of these materials are described as classified information as such, it would be difficult for researchers to have access to them. 

• Data from Libraries

Research materials can also be accessed through public and private libraries. Think of a library as an information storehouse that contains an aggregation of important information that can serve as valid data in different research contexts. 

Typically, researchers donate several copies of dissertations to public and private libraries; especially in cases of academic research. Also, business directories, newsletters, annual reports and other similar documents that can serve as research data, are gathered and stored in libraries, in both soft and hard copies. 

• Data from Institutions of Learning

Educational facilities like schools, faculties, and colleges are also a great source of secondary data; especially in academic research. This is because a lot of research is carried out in educational institutions more than in other sectors. 

It is relatively easier to obtain research data from educational institutions because these institutions are committed to solving problems and expanding the body of knowledge. You can easily request research materials from educational facilities for the purpose of a literature review. 

Secondary research methods can also be categorized into qualitative and quantitative data collection methods . Quantitative data gathering methods include online questionnaires and surveys, reports about trends plus statistics about different areas of a business or industry.  

Qualitative research methods include relying on previous interviews and data gathered through focus groups which helps an organization to understand the needs of its customers and plan to fulfill these needs. It also helps businesses to measure the level of employee satisfaction with organizational policies. 

When Do We Conduct Secondary Research?

Typically, secondary research is the first step in any systematic investigation. This is because it helps the researcher to understand what research efforts have been made so far and to utilize this knowledge in mapping out a novel direction for his or her investigation. 

For instance, you may want to carry out research into the nature of a respiratory condition with the aim of developing a vaccine. The best place to start is to gather existing research material about the condition which would help to point your research in the right direction. 

When sifting through these pieces of information, you would gain insights into methods and findings from previous researches which would help you define your own research process. Secondary research also helps you to identify knowledge gaps that can serve as the name of your own research. 

Questions to ask before conducting Secondary Research

Since secondary research relies on already existing data, the researcher must take extra care to ensure that he or she utilizes authentic data samples for the research. Falsified data can have a negative impact on the research outcomes; hence, it is important to always carry out resource evaluation by asking a number of questions as highlighted below:

• What is the purpose of the research? Again, it is important for every researcher to clearly define the purpose of the research before proceeding with it. Usually, the research purpose determines the approach that would be adopted. 
• What is my research methodology? After identifying the purpose of the research, the next thing to do is outline the research methodology. This is the point where the researcher chooses to gather data using secondary research methods. 
• What are my expected research outcomes? 
• Who collected the data to be analyzed? Before going on to use secondary data for your research, it is necessary to ascertain the authenticity of the information. This usually affects the data reliability and determines if the researcher can trust the materials.  For instance, data gathered from personal blogs and websites may not be as credible as information obtained from an organization’s website. 
• When was the data collected? Data recency is another factor that must be considered since the recency of data can affect research outcomes. For instance, if you are carrying out research into the number of women who smoke in London, it would not be appropriate for you to make use of information that was gathered 5 years ago unless you plan to do some sort of data comparison. 
• Is the data consistent with other data available from other sources? Always compare and contrast your data with other available research materials as this would help you to identify inconsistencies if any.
• What type of data was collected? Take care to determine if the secondary data aligns with your research goals and objectives. 
• How was the data collected? 

Advantages of Secondary Research

• Easily Accessible With secondary research, data can easily be accessed in no time; especially with the use of the internet. Apart from the internet, there are different data sources available in secondary research like public libraries and archives which are relatively easy to access too. 
• Secondary research is cost-effective and it is not time-consuming. The researcher can cut down on costs because he or she is not directly involved in the data collection process which is also time-consuming. 
• Secondary research helps researchers to identify knowledge gaps which can serve as the basis of further systematic investigation. 
• It is useful for mapping out the scope of research thereby setting the stage for field investigations. When carrying out secondary research, the researchers may find that the exact information they were looking for is already available, thus eliminating the need and expense incurred in carrying out primary research in these areas. 

Disadvantages of Secondary Research  

• Questionable Data: With secondary research, it is hard to determine the authenticity of the data because the researcher is not directly involved in the research process. Invalid data can affect research outcomes negatively hence, it is important for the researcher to take extra care by evaluating the data before making use of it. 
• Generalization: Secondary data is unspecific in nature and may not directly cater to the needs of the researcher. There may not be correlations between the existing data and the research process. 
• Common Data: Research materials in secondary research are not exclusive to an individual or group. This means that everyone has access to the data and there is little or no “information advantage” gained by those who obtain the research.
• It has the risk of outdated research materials. Outdated information may offer little value especially for organizations competing in fast-changing markets.

How to Conduct Online Surveys with Formplus 

Follow these 5 steps to create and administer online surveys for secondary research: 

• Sign into Formplus

In the Formplus builder, you can easily create an online survey for secondary research by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on “Create Form ” to begin. 

• Edit Form Title

Click on the field provided to input your form title, for example, “Secondary Research Survey”.

• Click on the edit button to edit the form.

• Add Fields: Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for questionnaires in the Formplus builder. 
• Edit fields
• Click on “Save”
• Preview form. 
• Customize your Form

With the form customization options in the form builder, you can easily change the outlook of your form and make it more unique and personalized. Formplus allows you to change your form theme, add background images and even change the font according to your needs. 

• Multiple Sharing Options

Formplus offers multiple form sharing options which enables you to easily share your questionnaire with respondents. You can use the direct social media sharing buttons to share your form link to your organization’s social media pages. 

You can send out your survey form as email invitations to your research subjects too. If you wish, you can share your form’s QR code or embed it on your organization’s website for easy access. 

Why Use Formplus as a Secondary Research Tool?

• Simple Form Builder Solution

The Formplus form builder is easy to use and does not require you to have any knowledge in computer programming, unlike other form builders. For instance, you can easily add form fields to your form by dragging and dropping them from the inputs section in the builder. 

In the form builder, you can also modify your fields to be hidden or read-only and you can create smart forms with save and resume options, form lookup, and conditional logic. Formplus also allows you to customize your form by adding preferred background images and your organization’s logo. 

• Over 25 Form Fields

With over 25 versatile form fields available in the form builder, you can easily collect data the way you like. You can receive payments directly in your form by adding payment fields and you can also add file upload fields to allow you receive files in your form too. 

• Offline Form feature

With Formplus, you can collect data from respondents even without internet connectivity . Formplus automatically detects when there is no or poor internet access and allows forms to be filled out and submitted in offline mode. 

Offline form responses are automatically synced with the servers when the internet connection is restored. This feature is extremely useful for field research that may involve sourcing for data in remote and rural areas plus it allows you to scale up on your audience reach. 

• Team and Collaboration

 You can add important collaborators and team members to your shared account so that you all can work on forms and responses together. With the multiple users options, you can assign different roles to team members and you can also grant and limit access to forms and folders. 

This feature works with an audit trail that enables you to track changes and suggestions made to your form as the administrator of the shared account. You can set up permissions to limit access to the account while organizing and monitoring your form(s) effectively. 

• Embeddable Form

Formplus allows you to easily add your form with respondents with the click of a button. For instance, you can directly embed your form in your organization’s web pages by adding Its unique shortcode to your site’s HTML. 

You can also share your form to your social media pages using the social media direct sharing buttons available in the form builder. You can choose to embed the form as an iframe or web pop-up that is easy to fill. 

With Formplus, you can share your form with numerous form respondents in no time. You can invite respondents to fill out your form via email invitation which allows you to also track responses and prevent multiple submissions in your form. 

In addition, you can also share your form link as a QR code so that respondents only need to scan the code to access your form. Our forms have a unique QR code that you can add to your website or print in banners, business cards and the like. 

While secondary research can be cost-effective and time-efficient, it requires the researcher to take extra care in ensuring that the data is authentic and valid. As highlighted earlier, data in secondary research can be sourced through the internet, archives, and libraries, amongst other methods. 

Secondary research is usually the starting point of systematic investigation because it provides the researcher with a background of existing research efforts while identifying knowledge gaps to be filled. This type of research is typically used in science and education. 

It is, however, important to note that secondary research relies on the outcomes of collective primary research data in carrying out its systematic investigation. Hence, the success of your research will depend, to a greater extent, on the quality of data provided by primary research in relation to the research context.

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Secondary Research Advantages, Limitations, and Sources

Summary: secondary research should be a prerequisite to the collection of primary data, but it rarely provides all the answers you need. a thorough evaluation of the secondary data is needed to assess its relevance and accuracy..

5 minutes to read. By author Michaela Mora on January 25, 2022 Topics: Relevant Methods & Tips , Business Strategy , Market Research

Secondary research is based on data already collected for purposes other than the specific problem you have. Secondary research is usually part of exploratory market research designs.

The connection between the specific purpose that originates the research is what differentiates secondary research from primary research. Primary research is designed to address specific problems. However, analysis of available secondary data should be a prerequisite to the collection of primary data.

Advantages of Secondary Research

Secondary data can be faster and cheaper to obtain, depending on the sources you use.

Secondary research can help to:

• Answer certain research questions and test some hypotheses.
• Formulate an appropriate research design (e.g., identify key variables).
• Interpret data from primary research as it can provide some insights into general trends in an industry or product category.
• Understand the competitive landscape.

Limitations of Secondary Research

The usefulness of secondary research tends to be limited often for two main reasons:

Lack of relevance

Secondary research rarely provides all the answers you need. The objectives and methodology used to collect the secondary data may not be appropriate for the problem at hand.

Given that it was designed to find answers to a different problem than yours, you will likely find gaps in answers to your problem. Furthermore, the data collection methods used may not provide the data type needed to support the business decisions you have to make (e.g., qualitative research methods are not appropriate for go/no-go decisions).

Lack of Accuracy

Secondary data may be incomplete and lack accuracy depending on;

• The research design (exploratory, descriptive, causal, primary vs. repackaged secondary data, the analytical plan, etc.)
• Sampling design and sources (target audiences, recruitment methods)
• Data collection method (qualitative and quantitative techniques)
• Analysis point of view (focus and omissions)
• Reporting stages (preliminary, final, peer-reviewed)
• Rate of change in the studied topic (slowly vs. rapidly evolving phenomenon, e.g., adoption of specific technologies).
• Lack of agreement between data sources.

Criteria for Evaluating Secondary Research Data

Before taking the information at face value, you should conduct a thorough evaluation of the secondary data you find using the following criteria:

• Purpose : Understanding why the data was collected and what questions it was trying to answer will tell us how relevant and useful it is since it may or may not be appropriate for your objectives.
• Methodology used to collect the data : Important to understand sources of bias.
• Accuracy of data: Sources of errors may include research design, sampling, data collection, analysis, and reporting.
• When the data was collected : Secondary data may not be current or updated frequently enough for the purpose that you need.
• Content of the data : Understanding the key variables, units of measurement, categories used and analyzed relationships may reveal how useful and relevant it is for your purposes.
• Source reputation : In the era of purposeful misinformation on the Internet, it is important to check the expertise, credibility, reputation, and trustworthiness of the data source.

Secondary Research Data Sources

Compared to primary research, the collection of secondary data can be faster and cheaper to obtain, depending on the sources you use.

Secondary data can come from internal or external sources.

Internal sources of secondary data include ready-to-use data or data that requires further processing available in internal management support systems your company may be using (e.g., invoices, sales transactions, Google Analytics for your website, etc.).

Prior primary qualitative and quantitative research conducted by the company are also common sources of secondary data. They often generate more questions and help formulate new primary research needed.

However, if there are no internal data collection systems yet or prior research, you probably won’t have much usable secondary data at your disposal.

External sources of secondary data include:

• Published materials
• External databases
• Syndicated services.

Published Materials

Published materials can be classified as:

• General business sources: Guides, directories, indexes, and statistical data.
• Government sources: Census data and other government publications.

External Databases

In many industries across a variety of topics, there are private and public databases that can bed accessed online or by downloading data for free, a fixed fee, or a subscription.

These databases can include bibliographic, numeric, full-text, directory, and special-purpose databases. Some public institutions make data collected through various methods, including surveys, available for others to analyze.

Syndicated Services

These services are offered by companies that collect and sell pools of data that have a commercial value and meet shared needs by a number of clients, even if the data is not collected for specific purposes those clients may have.

Syndicated services can be classified based on specific units of measurements (e.g., consumers, households, organizations, etc.).

The data collection methods for these data may include:

• Surveys (Psychographic and Lifestyle, advertising evaluations, general topics)
• Household panels (Purchase and media use)
• Electronic scanner services (volume tracking data, scanner panels, scanner panels with Cable TV)
• Audits (retailers, wholesalers)
• Direct inquiries to institutions
• Clipping services tracking PR for institutions
• Corporate reports

You can spend hours doing research on Google in search of external sources, but this is likely to yield limited insights. Books, articles journals, reports, blogs posts, and videos you may find online are usually analyses and summaries of data from a particular perspective. They may be useful and give you an indication of the type of data used, but they are not the actual data. Whenever possible, you should look at the actual raw data used to draw your own conclusion on its value for your research objectives. You should check professionally gathered secondary research.

Here are some external secondary data sources often used in market research that you may find useful as starting points in your research. Some are free, while others require payment.

• Pew Research Center : Reports about the issues, attitudes, and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis, and other empirical social science research.
• Data.Census.gov : Data dissemination platform to access demographic and economic data from the U.S. Census Bureau.
• Data.gov : The US. government’s open data source with almost 200,00 datasets ranges in topics from health, agriculture, climate, ecosystems, public safety, finance, energy, manufacturing, education, and business.
• Google Scholar : A web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.
• Google Public Data Explorer : Makes large, public-interest datasets easy to explore, visualize and communicate.
• Google News Archive : Allows users to search historical newspapers and retrieve scanned images of their pages.
• Mckinsey & Company : Articles based on analyses of various industries.
• Statista : Business data platform with data across 170+ industries and 150+ countries.
• Claritas : Syndicated reports on various market segments.
• Mintel : Consumer reports combining exclusive consumer research with other market data and expert analysis.
• MarketResearch.com : Data aggregator with over 350 publishers covering every sector of the economy as well as emerging industries.
• Packaged Facts : Reports based on market research on consumer goods and services industries.
• Dun & Bradstreet : Company directory with business information.

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Understanding primary & secondary, quantitative & qualitative research methods in UX

How ux research methods get categorized.

Eva Schicker

Learning as much as possible about our users and their needs is the foundation of UX. To gather this user data, we have different types of research available.

For research engagement, we need to understand the categories and common methods UX designers use throughout product development, from ideation to launch, from post-launch to subsequent iterations.

Who and what

There are two main ways to conduct research, each leading to an array of research tools that can be utilized.

Firstly, it’s about who conducts the research, and secondly, what type of data is being gathered.

Defining primary and secondary research: Who

Primary and secondary are categories to define who is conducting the research. We distinguish primary research as research we conduct, and secondary research as research that is conducted by someone else .

Written by Eva Schicker

Hello. I write about UX, UI, AI, animation, tech, fiction & art through the eyes of a designer & painter. I live in NYC. Book author, UX Grad GA NYC.

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• Published: 12 August 2024

Evaluation of didactic units on historical thinking and active methods

• Pedro Miralles-Sánchez   ORCID: orcid.org/0000-0002-2436-3012 1 ,
• Jairo Rodríguez-Medina   ORCID: orcid.org/0000-0002-6466-5525 2 &
• Raquel Sánchez-Ibáñez 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1032 ( 2024 ) Cite this article

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The purpose of this study is to evaluate the effects of an implementation of eight didactic units on historical thinking and active methods as part of a teacher training programme. All this with four specific objectives that try to find out changes in the methodology, motivation, satisfaction and learning of the students. To this end, the research is carried out by means of a mixed method using quantitative data, obtained from a pretest/posttest, and qualitative data, obtained from a focus group and interviews. The target groups of the teaching units are secondary and high school students aged between 13 and 18 years. A total of 114 students of these students participated in the data collection with a pretest/posttest, six master students in the focus group, and three teachers and three secondary and high school students were interviewed. The results obtained indicated that significant differences of medium effect were found in the pre and post phase factor in learning and satisfaction, and of large effect in methodology and motivation. As for the gender factor, significant differences of small effect were found in motivation and satisfaction, with higher values for women. The positive statements of both master’s students and high school students and teachers were quite striking, although the limitations and difficulties must be highlighted. It is concluded that the design of this type of didactic units has meant a significant improvement, achieving that the students have developed a notorious improvement in their perception of the objectives studied.

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Concepts for historical and geographical thinking in Sweden’s and Spain’s Primary Education curricula

The impact of content knowledge on the adoption of a critical curriculum model by history teachers-in-training.

Primary and secondary school teachers’ perceptions of their social science training needs

Introduction.

Research in history didactics has distinguished two types of historical content. On the one hand, substantive or first-order content. These are those which refer both to concepts or principles and to specific historical dates and events. On the other hand, strategic, second-order content or historical meta-concepts as methodological concepts. These are related to the historian’s skills, the search for, selection and treatment of historical sources, empathy or historical perspective, related to the definition of historical thinking (Sáiz and Gómez, 2016 ). This didactic approach aims for students to learn to think historically by deploying different strategies and competences to analyse and respond to different historical questions and to understand the past in a more complex way. These competences and strategies are related to the search for, selection and treatment of historical sources, empathy, multi-causal explanation, or historical perspective; in short, the functions of a historian (Peck and Seixas, 2008 ; Seixas and Morton, 2013 ). These concepts are variable and do not form a closed and invariable list, but each author gives greater importance to certain aspects (Gómez Carrasco et al., ( 2017 )).

Since the late 1980s, an effort has been made in the British field to analyse second-order concepts in students’ argumentation. Here the Concepts of History and Teaching Approaches project (Lee et al. 1996 ) stands out, which investigated the historical concepts that students should acquire. At the same time, in the USA, through Wineburg ( 2001 ), work began with cognitive psychology techniques (experts and novices) to investigate the skills that students should acquire, with the well-known historical thinking and its competences finally being developed by mainly Canadian and American authors (Ercikan and Seixas, 2015 ; Seixas and Morton, 2013 ; VanSledright, 2014 ; Wineburg et al., 2013 ). For their part, the work of Chapman ( 2011 ) and the Constructing History 11–19 project (Cooper and Chapman, 2009 ) delve deeper into this line of reasoning in the use of sources, a thematic field also addressed in other countries such as the Netherlands (Van Drie and Van Boxtel, 2008 ) and Chile (Henríquez and Ruíz, 2014 ).

The importance of teaching historical thinking in the classroom lies in the fact that historical thinking does not develop naturally, but needs explicit teaching (Wineburg, 2001 ). To develop these competences, the introduction of the historian’s method and techniques and historical awareness are key elements, with appropriate techniques and instruments to assess them (Domínguez, 2015 ). To develop them, a methodological change in the classroom is necessary, as is already being proposed and discussed in countries such as Portugal (Gago, 2018 ), Spain (Navarro and De Alba, 2015 ) or the United Kingdom (Smith, 2019 ). This change implies moving from the current dominance of expository teaching strategies to a greater presence of enquiry strategies that help to promote the development of independence, critical thinking, and autonomous learning in students.

Working with historical sources, which can begin even earlier, is valued positively by students in upper secondary education, as it promotes a research experience in which students construct their knowledge about the past (Prieto, Gómez and Miralles, 2013 ), however, this type of experience is not usually abundant in classrooms at this stage in Spain. The abuse of the lecture and the passive role reserved for students ends up making them, for the most part, limit themselves to studying what is offered in class by not seeking information from other sources and memorising the information they receive (Sáiz and López-Facal, 2015 ). Consequently, it is very difficult to create critical citizenship in students, as they may believe everything the teacher tells them, as they are not familiar with enquiry (Guirao, 2013 ).

When it comes to identifying teaching models, it is worth highlighting the line of research developed by Trigwell and Prosser ( 2004 ) based on interviews with teachers and a questionnaire called Approaches to Teaching Inventory (ATI) (Trigwell et al., 2005 ). They identified four different conceptions of teaching and three methodologies, establishing five approaches which can be grouped into three broad models or ways of teaching. In the first model, the role of the teacher is greater, since the importance lies in the transmission of content, students assume a passive role, limiting themselves to receiving and memorising the knowledge transmitted by teachers, thus establishing a unidirectional relationship, without considering their experience, previous knowledge, characteristics or context. The most used methodological strategy is the master class and the main resources used are the textbook and class notes. In addition, a final examination of the learning contents is usually established (Hernández et al., 2012 ; Guerrero-Romera et al., 2022 ).

On the other hand, there is learner-centred teaching which differs from the previous one in that the teacher’s intention is to provoke conceptual change and intellectual growth in the learner. Thus, the teacher acts as a guide, guiding students in the process of constructing their own knowledge, encouraging their conceptions, and providing them with opportunities to interact, debate, investigate and reflect. The aim of this model is for students to learn content by questioning and reflecting on it. The strategies employed are active and inquiry based. In contrast to the previous model, which encourages competitiveness and individualism, this approach favours interaction and cooperation between the individuals involved in the teaching and learning process and prioritises continuous assessment (Vermunt and Verloop, 1999 ; Kember and Kwan, 2000 ; Trigwell et al., 2005 ; Henze and van Driel, 2011 ). Finally, there is a third, intermediate model based on teacher-student interaction, although it should be noted that there is a hierarchical relationship between the different approaches, with each including elements of the previous one (Guerrero-Romera et al., 2022 ).

Evaluative studies of formative processes such as this one are seeing an increase in the field of history education especially in terms of changing the conceptual model of history teaching (Carretero et al., 2017 ; Metzger and Harris, 2018 ). Some work, such as that being carried out in the Netherlands, focuses on evaluative research that is more focused on teaching practice (De Groot-Reuvekamp et al., 2018 ; Van Straaten et al., 2018 ). Regarding the evaluation of historical thinking effects, we can recently highlight Tirado-Olivares et al. ( 2024 ) relating it to academic performance, or Bartelds et al. ( 2020 ) highlighting the importance of historical empathy. It is also worth highlighting the research carried out by the University of Murcia (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), which implemented training units focused on historical thinking skills and changes in the way of teaching. This research therefore seeks to be a significant improvement compared to traditional methods used in the teaching of social sciences, as it seeks to develop essential skills for critical thinking and citizenship training, and to evaluate its effectiveness through rigorous methods and a scientific approach. All this to encourage a critical spirit and autonomous learning and therefore the formation of critical and independent citizens who know how to judge for themselves the vicissitudes that civic life in democracy demands of them.

The main objective of this article is to detect if there are significant changes in students after the design and implementation of eight didactic units (DU from now on) to promote the learning of historical thinking skills through active teaching methods. To achieve the objective, it has been divided into the following specific objectives:

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods Table 1 .

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 2 .

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 3 .

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods.

Research design

This is an evaluative type of DU research of historical thinking and active methods with a mixed explanatory approach and a quasi-experimental A-B design. The research method is therefore mixed, qualitative, and quantitative data have been collected and analysed in a rigorous way in response to the research objective, organising them into specific research objectives and integrating the two forms of data and their results into conclusions framed in the theory and scientific production studied (Creswell & Plano Clark, 2017 ). The selection of the eight DU was made at random, as we have worked with the students who have been tutored by us during the internship period. On one hand, a quantitative analysis of the data obtained by means of a Likert-type questionnaire (1–5) was carried out. Questionnaire designs are extremely common in the field of education, as they can be applied to a multitude of problems and allow data to be collected on many variables and outcomes to be measured (Sapsford & Jupp, 2006 ). On the other hand, the decision was to apply a qualitative exploratory method through a focus group with master’s students who applied the DU and interviews with practising teachers and students who witnessed these units (supplementary material, Figs. 1 – 3 ). Interviews are useful when you want subjects to describe complex phenomena and facts that are the object of study (Pérez-Juste et al., 2012 ), as well as focus groups. The focus group was recorded via an online Zoom meeting (Archibald et al., 2019 ) and then transcribed using artificial intelligence (Notta AI), while the interviews were answered on the spot individually in writing.

The quantitative analysis (R Core Team, 2023 ), a repeated measures mixed factorial design with one within-subjects factor (the time of assessment) and one between-subjects factor (gender) was used. The within-subject factor has two levels (pretest and posttest) and the between-subject factor has three levels (female and male). The dependent variables were the scores obtained in each of the subscales of the questionnaires Secondary school students’ assessment of History teaching and Secondary school students’ opinion of the implementation of the History training unit (supplementary material Figs. 4 and 5 ). For the qualitative analysis, a descriptive analysis was carried out using the qualitative research software Atlas.Ti 23, which is widely used in research in the field of Social Science Didactics (Rüssen, 1997 ; Sánchez-Ibáñez, Martínez-Nieto ( 2015 )). As a complement to this software, the ChatGPT tool has also been used to improve the accuracy of the codes and data analysis, as an aid both in designing the codes of the transcripts, organising the main conclusions obtained from the coding of the participants’ responses (Lopezosa & Codina, 2023 ), and finding out the percentage of occurrence of words. All codes are open and non-exclusive, so that the same response can be associated with more than one code.

Participants

This is a non-probabilistic convenience sample composed in the quantitative analysis of 114 young people aged between 12 and 20 years (M = 15.63, SD = 1.54). Fifty-one males (44%) and 65 females (56%) participated in the pre-test. In the post-test 50 males (44%) and 64 females (56%) participated. Of these, 14 men and 10 women were from the first year of high school, 5 men and 18 women were from the second year of high school, 11 men and 8 women were from the second year of ESO, 14 men and 21 women from the third year of ESO and 7 men and 10 women from the fourth year of ESO (Fig. 1 ). As for the focus group, 6 students of the master’s degree in teaching, 2 men and 4 women aged between 22–45 years, participated. The interviews were conducted with 3 secondary school teachers, 2 men and 1 woman aged 40–60 and 3 pupils aged 13–17 respectively.

Distribution by Gender and Grade.

Instruments

For the collection of quantitative data, two closed-response questionnaires based on a Likert-type scale (1–5) were used. The questionnaires given to pupils were entitled Assessment of Secondary School pupils on the teaching of History (pretest) and Opinion of Secondary School pupils on the implementation of the History unit (posttest). The questionnaires have 37 items divided into four categories corresponding to each of the specific research objectives: Assessment of the implementation of the DU in the teaching/learning process; Assessment of student motivation in an innovative DU; Analysis of student satisfaction with an innovative DU; Analysis of student learning and its results to check whether the DU has been effective (supplementary material Figs. 4 and 5 ). For its part, the qualitative analysis was used to complement the quantitative research by relating its questions to the objectives and thus elucidating the impact of the OD. It consists of both a focus group with trainee teachers consisting of nine questions and interviews with classroom tutors and students with a total of sixteen questions (supplementary material Figs. 1 – 3 ).

Validation of these instruments has been essential to ensure that the data collected are accurate and reliable, through peer review and pilot testing on a small group of participants to assess the effectiveness and relevance of the questions and observation procedures (Gómez et al., 2021 a; Rodríguez et al., 2020 ; Miralles-Sánchez et al., 2023 ).

This research is based on a research project consisting of four phases: prior observation of the classroom (December 2022-February 2023), design of training units (March-April 2023), implementation of training units (May-July 2023) and evaluation of results (September 2023-July 2024). The design of the DU and the data collection were thanks to a training programme implemented during the academic year 2022/23 in a Spanish university for students of the Master’s degree in teacher training in the speciality of Geography, History and History of Art. Held from 10 January to 17 March 2023, the duration of the activity involved a total of 18 face-to-face hours where students attended a series of lectures given by expert lecturers in Didactics of Social Sciences with the aim of helping students to carry out a Master’s Final Project (MFP) based on the implementation and evaluation of a didactic DU on historical thinking and active methods during the internship period of the Master’s. The activity consisted of 6 sessions: presentation and approach of the MFP, concepts of historical thinking, teaching methods and active evaluation processes, quantitative and qualitative analysis of data in educational research, and guidelines for the presentation and bibliography of the MFP.

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of methodology scores. No significant differences were observed between sexes (MH = 35.93, SD = 5.60; MM = 36.43, SD = 5.83) in the initial (pre) assessment (F (1,112 = 5.83). 83) at baseline (pre) assessment (F (1,112) = 0.21, p = 0.64) and no gender differences between groups (MH = 43.32, SD = 6.91; MM = 44.53, SD = 7.58) were observed at posttest (F (1,112) = 0.77, p = 0.38).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.08, p = 0.77). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 91.88, p < 0.01) with a large effect size (partial η2 = 0.26). Figure 2 shows the result graphically.

Differences in Methodology Scores by Gender and Phase.

The master’s students emphasise that none of them were previously familiar with the theory of historical thinking, having recently learned it in class, although some had experience of teaching with active methods. They emphasise the importance of interactive and participatory methods, as well as the crucial role of the teacher in the educational experience, recognising positive changes in current teaching, although with divergent opinions on the influence of students on methodology. The positive experience with students and the inclusion of relevant points in teaching are highlighted, but the persistence of traditional methods that are not very active and the resistance of some students to participatory methods are criticised, representing a challenge in contemporary teaching Fig. 3 .

Changes and improvements in DU according to master’s students.

Significant statements

“So I think that the figure of the teacher will always be…. All that helps, all the technique, everything we learn and all that, but I think that the figure of the teacher is fundamental, it is important.” - He emphasises the importance of the role of the teacher and the relationship that the teacher establishes with the students.

“I think it’s changing a lot because before you went to class and the teacher would give you a lecture or whatever and the students were very dispersed, but I think that is changing now, and as we bring in new generations, I think it’s going to change a bit more.” - He sees a positive change in the way history teaching is approached.

“No, I think so, in a certain sense it has changed, because it is true that at secondary school, when you are a teenager you see two types of teachers, a teacher who practically limits himself to lecturing you and that’s it, and others who question you more.” - He expresses that teaching has not changed completely, suggesting that there are still teachers who adopt fewer interactive approaches.

“I’ve had bad history teachers all my life, you know, the kind that came in and talked to me unfunnily about things that had happened and that was it.” - Reflects a past negative experience with less committed history teachers.

“So, it’s true that when I was a student, I felt that sometimes history classes were very theoretical and so on, but it’s true that when I came to class as a non-student, I saw that sometimes teachers have to adopt this methodology because otherwise it’s impossible.” - She acknowledges that sometimes teachers are forced to adopt fewer interactive methods due to student resistance.

“My internship tutor said that students are not used to any of this and that in reality many are comfortable in this role of going to the institute like someone who goes to the cinema, to see the teacher or tell the story and then I’ll study and do the exam and that’s it.” - He points to the resistance of some students to more participatory methods as a challenge in today’s teaching.

On the other hand, they stress the crucial role of an active and engaging methodology to enhance the learning experience, with the consideration that there is no single methodology effective for all groups. However, they also mention the importance of dosing or reducing content to avoid information overload, as well as the need for continuous observation and analysis to determine the most effective methods, with a willingness to adapt according to the results. While some participants emphasise the relevance of methodology over content, others argue that both are crucial and should be tailored to each group. In general, there is convergence on the difficulty in achieving active student participation, attributing this to a lack of empathy or resistance towards interactive activities, recognising the importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness. The need to simplify teaching and focus on relevant aspects of the curriculum is mentioned, as well as the need to face technological challenges with alternative plans. Their commitment to quality teaching, willingness to learn and adapt is also highlighted, although areas for improvement such as more detailed planning, time and classroom management are mentioned.

Literal and derived mentions of relevant words in the code “Changes and improvements in interventions”: Methodology: 34 times (5.53%), Activities: 21 times (3.43%), Technology: 21 times (3.43%), Content: 18 times (2.94%), Plan: 10 times (1.63%), Topic: 6 times (0.98%), Participate: 6 times (0.98%), Exam: 5 times (0.82%), Adapt: 5 times (0.82%).

As far as secondary school students are concerned, in general, there is a diversity of opinions among students regarding the methodology of teaching history. Some prefer more dynamic and visual approaches, while others are happy with the traditional way of teaching. The perception of motivation also highlights the importance of active participation and discussion in the learning process. This variability may be attributable to personal experiences, levels of interest in the subject or perceptions about the purpose of history education. To gain a deeper understanding, it would be useful to further explore the reasons behind students’ responses. Students’ ratings of the current teacher’s experience suggest that teaching experience and ability are considered important factors in teaching effectiveness.

While Teacher 1 and Teacher 3 recognise aspects of the competence-based approach to historical thinking in teaching practice, Teacher 2 is not familiar with the specific term. Regarding the development of historical competences in pupils, Teacher 1 highlights the importance of adapting materials to children’s understanding from an early age, while Teacher 2 suggests interdepartmental collaboration and family involvement to improve outcomes. Teacher 3 recognises the need for continuous improvement and stresses the importance of learning from mistakes. In relation to teaching perspectives and approaches, Teacher 3 emphasises the connection between historical events and social, economic and political contexts over time, highlighting the importance of ‘historical empathy’. Finally, teachers agree on the challenges and complexities of teaching historical competences, highlighting the need to make them understandable for students and to avoid reducing them to mere memorisation.

Regarding active learning methodologies such as project or problem-based learning, there are differences in its implementation between Teacher 1, who uses it more in lower grades due to exam preparation, and Teacher 2, who offers a short answer. Teacher 3 shows experience in educational innovation projects, indicating a predisposition towards more innovative approaches. The commitment and dedication required is highlighted, as well as the lack of detail on implementation by Teacher 1, which may limit its wider application due to the associated stress and workload. Several challenges and limitations in the implementation of active teaching methodologies are highlighted. These challenges include existing workload, loneliness among colleagues, lack of digital resources both at school and at home for students, limited time in the classroom, language barrier in understanding concepts, lack of teacher training, distrust of new methodologies, and the complexity of catering for diversity in the classroom. In addition, it is stressed that the impact of the methodology on student learning requires adequate assessment and collaborative work to generate significant changes.

Finally, it should be noted that the three teachers agree that active methodologies and historical thinking are not widespread in secondary classrooms. The reasons mainly point to lack of training, time constraints, lack of resources and mistrust on the part of teachers. Inertia in the education system, resistance to changing traditional pedagogical practices and a preference for safe and rote approaches are also mentioned. We can see that resistance to change seems to be a significant barrier. Lack of training and institutional support is highlighted as a key problem. The importance of satisfying studious learners through traditional methods is mentioned as a potential barrier to adopting more creative and reflective approaches.

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of the motivation scores. No significant differences were observed between sexes (MH = 22.45, SD = 4.86; MM = 23.33 SD = 5.40) in the initial (pre) assessment (F (1,112) = 0.82, p  = 0.36). However, significant differences were observed at the posttest as a function of gender (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) with a small effect size (partial η2 = . Significant differences were observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

Repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 1.08, p  = 0.30). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 48.83, p < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Figure 4 shows the result graphically. Therefore, motivation increased in both groups after the intervention, but especially in the female group.

Differences in Motivation Scores by Gender and Phase.

Master students highlight a higher motivation (8 positive occurrences in the code “Improvements and difficulties in the DU” 1.23%) and satisfaction (4 positive occurrences in this code 0.61%) among students despite facing difficulties. Some participants noted an improvement in their teaching skills after applying the DU, highlighting the importance of practical experience and the application of theoretical concepts in lesson planning and execution. The implementation of gamification and flipped classroom was mentioned to make teaching more attractive, showing the ability to adapt to challenging situations and look for alternative solutions. The importance of the teacher in the learning experience was highlighted and difficulties related to the implementation of technology in the classroom and the resistance of some students to participate in interactive activities were pointed out.

“Overall it did increase a lot of satisfaction and their motivation regarding the subject.”

“In general what I planned worked and it worked more than anything else in the time I had planned.”

“Well, I think that yes, it worked for them, that it was something they had never given before and it was totally different and they liked it.”

“I mean, yes there are digital whiteboards, yes there are projectors, but it’s complicated, especially to apply, in this case, a didactic unit.”

“So, the cooperative work part is fine, the inverted classroom, fatal.”

“But I also think that it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when they were in the classroom, it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when it was two hours, it was noticeable because just before break time I was already tired”.

On the other hand, in general, the perception of the secondary school students interviewed on the effectiveness of the trainee teachers’ teaching method is ambiguous and could benefit from more specific details on the perceived changes. As an analysis we can indicate that the introduction of these DU seems to have had a positive impact on students’ attention and motivation, the use of audio-visual methods and interactivity are prominent aspects of the new methodology that students appreciate. The relationship between the way of teaching and the retention of information for exams is highlighted as an important point for student satisfaction, and resources such as slides, and short videos are specific elements that students find useful. Therefore, the new way of working of the trainee teacher seems to have generated a positive experience for the students, improving participation, motivation, and information retention.

Teachers in this regard highlight positive results, such as improved motivation and reduced student boredom, as well as increased class participation. However, they recognise that the effectiveness of techniques may vary and that training in new active learning methodologies is needed to address student diversity and to keep up to date. In addition, they highlight a shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. The importance of adaptability of methodologies is emphasised, as their effectiveness depends on factors such as the subject matter, the group of learners and the resources available. It is pointed out that student motivation can influence their adaptation to the methodologies, and the use of visual and playful techniques to engage less motivated students is suggested. In addition, it is emphasised that the aim of teaching history is to enable students to interpret the world today, thus encouraging critical thinking. The effectiveness of diversity intervention programmes is acknowledged, highlighting the importance of making the content relevant to each learner.

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of satisfaction scores is observed. No significant differences were observed between sexes (MH = 21.98, SD = 3.72; MM = 22.13 SD = 3.43) in the initial (pre) assessment (F (1,112) = 0.05, p  = 0.83). However, significant differences were observed at the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 3.04, p  = 0.08). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). That is, the intervention had a significant effect on students’ satisfaction with the subject. Figure 5 shows the result graphically.

Differences in Satisfaction Scores by Gender and Stage.

As a general observation we can indicate that all three secondary school pupils interviewed have positive perceptions of the usefulness of history. The definitions of history are varied, but they share the central idea of past events, and the pupils’ responses show a basic understanding of the importance of history in understanding the present and developing critical skills. Their interest in learning about the past is highlighted and it is noted that the content of lessons and the amount of work for exams are important considerations for some students. Students’ comments suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve student motivation. It would be beneficial to delve deeper into the responses to better understand the underlying reasons behind their perceptions and to gain a more complete picture of their experience with the subject.

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of perceived learning scores is observed. Table 4 presents the results for perceived learning on a scale of 13 to 65. No significant gender differences were observed (MH = 40.27, SD = 5.40; MM = 40.67, SD = 5.14) at the initial (pre) assessment (F (1,112) = 0.16, p  = 0.69). There were also no significant sex differences at posttest (MH = 43.94, SD = 6.32; MM = 45.39, SD = 6.38) (F (1,112) = 1.46, p  = 0.23).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.82, p  = 0.37). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Fig. 6 shows the result graphically.

Differences in Perceived Learning Scores by Gender and Stage.

Master’s students recognise the usefulness of the theory of historical thinking in the planning and execution of classes, as well as the importance of the ethical dimension of history and the need to connect history with citizenship education. The use of primary sources and active methodology to involve students in historical analysis is highlighted. Furthermore, the importance of contextualising history teaching in the immediate environment and addressing social, cultural, and political issues to develop critical thinking in students is emphasised. However, there are divergences among the participants in terms of the perceived novelty of the theory of historical thinking, the depth of ethical exploration in the historical context and the inclusion of themes. Finally, the importance of connecting history with current affairs is mentioned, although this may present challenges in the handling of sensitivities and emotions during the teaching of certain historical topics.

For their part, teachers seem to agree that history teaching should not be limited to the transmission of historical facts, but should also encourage critical thinking, reflection and active participation in social problems. Citizenship education is seen as a process that goes beyond the acquisition of knowledge, including the development of analytical skills and the ability to question and criticise social and political reality.

Discussion and conclusions

If we look at the first objective, we can see that a significant effect of the phase factor (Pre vs Post) was observed in the methodology (F (1,108) = 91.88, p  < 0.01) with a large effect size (partial η2 = 0.26). In turn, we can see corroboration of this change as master’s students highlight in their statements the importance of interactive and participatory methods, as well as the role of the teacher in the educational experience. They recognise positive changes in current teaching, highlighting the positive experience with children and the inclusion of relevant points, but they criticise the persistence of traditional methods that are not very active and the resistance of some students to participatory methods. This represents a challenge in contemporary teaching, with difficulties in achieving active student participation attributed to a lack of empathy or resistance to interactive activities. The importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness is therefore highlighted, although some also point out the need to dose the content and adapt according to the results.

For their part, high school students emphasise the importance of visual resources, discussions and the connection between past and present in history teaching, as well as teaching experience and skill, reflecting diversity in preferences and learning styles. The effectiveness of the trainee teachers’ teaching methods is ambiguously perceived and may need more specific details on perceived changes. On the other hand, high school teachers recognise the need for training in new methodologies to address student diversity and to keep up to date, highlighting a shift towards a more active and participatory approach to learning. This coincides with the results of Sánchez et al. ( 2020 ) where they note an advance in teachers’ perception of a methodology oriented towards fostering historical and critical thinking in students. However, these teachers face various difficulties and limitations in the implementation of these methodologies, such as workload, lack of digital resources and the language barrier. The impact of the methodologies on learning requires adequate assessment and collaborative work to generate significant changes, being one of the main challenges for education in the future. Consequently, we believe it is crucial that educational administrations encourage the motivation and training of both new and old teachers in order to achieve the necessary methodological improvement in the teaching of history. Teachers suggested that the use of visual and playful techniques engage less motivated students, and the aim of fostering critical thinking through history teaching is highlighted, so the effectiveness of the intervention programmes for diversity is recognised, emphasising the relevance of the content for each student.

This may lead us to see that the generalised perception of students in the pre-test denotes the persistence of the traditional teaching model with the absence of active methods, digital resources, and historical thinking skills. Monteagudo-Fernández et al. ( 2020 ) obtain similar results in a study with secondary education and baccalaureate students, confirming the existence of a traditional model in the teaching of history that excludes cooperative and inquiry-based methodologies. This reality must point towards a didactic model that prioritises competence learning and student activism in their learning process, highlighting advocates such as Carretero et al., ( 2017 ) or Metzger & Harris, ( 2018 ), who are committed to a methodological change that moves away from the predominant conceptual model for teaching history.

In terms of motivation, we can see that a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 48.83, p  < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Thus, motivation increased in both groups after the intervention, but especially in the female group. The master’s students corroborate this by highlighting a higher motivation and satisfaction among students despite facing difficulties, while for high school students, in general, the new way of working of the trainee teacher seems to have generated a positive experience, improving participation, motivation and retention of information. The importance of active participation and discussion in the learning process is particularly emphasised by the high school students. Teachers highlight positive results, such as improved motivation and reduced student boredom, as well as increased participation in class. However, there is no significant statement regarding a difference in motivation with respect to gender, which may suggest that this is a change that is little perceived by teachers and students, but which is present and should be considered when applying these active and historical thinking methods.

These results are similar to those presented by several authors (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), who also highlight as the most important factor that motivation is due to the use of resources other than the school textbook, which is very good news for continuing to take steps towards methodological complementarity, so that the students themselves are aware that by using all kinds of resources to learn, they can and should be more motivated. In these studies (Gómez et al., 2021a ; Gómez et al., 2021b ), they also found that the item with the lowest score in their pretest is the one that states that students are motivated because they can contribute their points of view and knowledge, something that clearly does not occur in traditional classes where the students’ role as receivers predominates. For his part, Singer ( 1996 ) considers gender to be one of the most significant predictors in relation to teaching approaches. In this sense, Maquilón, Sánchez and Cuesta ( 2016 ), in their study of active Primary School teachers, point out that men tend to opt for an approach based on the transmission and reproduction of information, while women are inclined towards a more student-centred approach.

In satisfaction, significant differences were also observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06), as for motivation (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) (partial η2 = 0.04). However, repeated measures analysis of variance did not produce a significant result of interaction effect between sex and phase (F (1,108) = 3.04, p  = 0.08). A significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). In other words, the intervention had a significant effect on students’ satisfaction with the subject, in agreement with what was stated by the master’s students and teaching staff on the improvement of student motivation and satisfaction. They highlight the relationship between the way of teaching and the retention of information for the exams as an important point for their satisfaction. High school students highlight that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and comprehension of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve pupils’ motivation.

Finally, on learning, a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Master’s students highlight the importance of the teacher in the learning experience and difficulties related to the implementation of technology in the classroom and the reluctance of some students to participate in interactive activities were noted, although the crucial role of this methodology in enhancing the learning experience is highlighted, with the consideration that there is no single methodology effective for all groups. Students suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns. Teachers for their part highlight the shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. However, this requires adequate assessments and collaborative work to generate significant changes, as well as continuous training in active learning methodologies and strategies, considered essential nowadays.

There is still an overuse of textbooks and the expository strategy by teachers who teach History (Carretero and Van Alphen, 2014 ; Colomer et al., 2018 ). However, more and more teachers in Spain are in favour of a teaching model in which the student acquires a greater role through the implementation of innovative resources (heritage, written and oral sources, new technologies) and educational strategies that encourage the active participation of students in the teaching and learning process (project-based learning, gamification, flipped classroom) (Gómez et al., 2018 ; Gómez et al., 2021a ; Sánchez et al., 2020 ). It is therefore important to be aware of developments in the incorporation of competence-based social sciences teaching and a learner-centred model at all levels of education.

We can conclude from the above that the programme was quite effective in the objectives studied. In the quantitative data we observed an improvement in the students’ perception of all the variables studied after the intervention, especially the change in methodology and the improvement in motivation had a large effect size. Moreover, it can be noted that the DOMs applied most of the methods, techniques, and resources we proposed in the training programme (supplementary material Fig. 6 ). On the other hand, we found quite positive statements about the programme from both master’s students and high school students and teachers as we have seen in the different points. However, it is important to point out the limitations and difficulties reported by teachers and students when implementing this type of unit, as well as the fact that there were some weaknesses in this study, such as the small quantitative and qualitative sample group. As a possible future improvement when carrying out the interviews and organising the focus group, it is possible to point out that it could be organised with more time and written commitment from the participants, as the initial intention was for 8 teachers, secondary school students and Master’s students to participate, respectively, one for each unit applied. The limitations of their availability played a negative role in the collection of more qualitative data, as participation was voluntary and, in the case of high school students, parental approval was required.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Archibald MM, Ambagtsheer RC, Casey MG, Lawless M (2019) Using Zoom Videoconferencing for Qualitative Data Collection: Perceptions and Experiences of Researchers and Participants. International Journal of Qualitative Methods 18. https://doi.org/10.1177/1609406919874596

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RSI and JR-M: conceived and designed the project and doctoral thesis of which this study is part. PMS and JR-M.: have made methodology, data collection and formal analysis. PM-S and JR-M have co-written the manuscript and RSI contributed to revisions, having read and approved the submitted manuscript. All authors have read and agreed to the published version of the manuscript.

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This study was performed in line with the principles of the Declaration of Helsinki. It is part of grant PRE2021-097619, funded by MCIN/AEI/10.13039/501100011033 and ESF + . It is part of the research project “La enseñanza y el aprendizaje de competencias históricas en bachillerato: un reto para lograr una ciudadanía crítica y democrática” (PID2020-113453RB-I00), funded by the Agencia Estatal de Investigación (AEI/10.13039/501100011033). This project was granted favourable by Ethics Research Committee of the University of Murcia 8/03/2021.

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Miralles-Sánchez, P., Rodríguez-Medina, J. & Sánchez-Ibáñez, R. Evaluation of didactic units on historical thinking and active methods. Humanit Soc Sci Commun 11 , 1032 (2024). https://doi.org/10.1057/s41599-024-03546-9

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The relationship between overtime and burnout among alabama secondary music ensemble directors.

Ashley Deleen Gresko , Liberty University Follow

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Donald Palmire

Stress, Burnout, Working Hours, Workload, Overtime, Digital Overtime, Teacher Attrition, Music Education, Secondary Music Directors

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Gresko, Ashley Deleen, "The Relationship Between Overtime and Burnout Among Alabama Secondary Music Ensemble Directors" (2024). Doctoral Dissertations and Projects . 5847. https://digitalcommons.liberty.edu/doctoral/5847

Although there is an existing body of literature discussing stress and burnout among music educators, there has yet to be a significant study regarding the relationship between working hours and the burnout of secondary music ensemble directors. This study covers the perspectives of secondary school instrumental and choral music ensemble directors in Alabama. This quantitative research study identifies influences of overtime that require additional exploration regarding the burnout of Alabama secondary school music ensemble directors. This study surveyed secondary music ensemble directors in Alabama to illustrate the correlation between overtime and burnout. This study is vital to music ensemble directors by identifying what sources contribute to or mitigates stress and burnout. This project adds new perspectives to the body of research concerning educator stress and burnout. Moreover, this study could encourage further research into stress, burnout, working hours, overtime, and digital overtime.

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• Volume 11, Issue 1
• Mycophenolate and azathioprine efficacy in interstitial lung disease: a systematic review and meta-analysis
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• http://orcid.org/0000-0003-2254-5119 Francesco Lombardi 1 ,
• http://orcid.org/0000-0002-1340-2688 Iain Stewart 2 ,
• http://orcid.org/0000-0002-8250-6464 Laura Fabbri 2 ,
• Wendy Adams 3 ,
• http://orcid.org/0000-0003-0784-1331 Leticia Kawano-Dourado 4 , 5 ,
• Christopher J Ryerson 6 and
• http://orcid.org/0000-0002-7929-2119 Gisli Jenkins 7
• REMAP-ILD Consortium
• 1 Pulmonary Medicine , Policlinico Universitario Agostino Gemelli , Roma , Italy
• 2 National Heart & Lung Institute , Imperial College London , London , UK
• 3 Action for Pulmonary Fibrosis , London , UK
• 4 HCOR Research Institute , Hospital do Coracao , Sao Paulo , Brazil
• 5 Pulmonary Division , University of Sao Paulo , Sao Paulo , Brazil
• 6 Medicine , The University of British Columbia , Vancouver , British Columbia , Canada
• 7 Imperial College London , London , UK
• Correspondence to Dr Francesco Lombardi; lombardi.f89{at}gmail.com

Objectives Mycophenolate mofetil (MMF) and azathioprine (AZA) are immunomodulatory treatments in interstitial lung disease (ILD). This systematic review aimed to evaluate the efficacy of MMF or AZA on pulmonary function in ILD.

Design Population included any ILD diagnosis, intervention included MMF or AZA treatment, outcome was delta change from baseline in per cent predicted forced vital capacity (%FVC) and gas transfer (diffusion lung capacity of carbon monoxide, %DLco). The primary endpoint compared outcomes relative to placebo comparator, the secondary endpoint assessed outcomes in treated groups only.

Eligibility criteria Randomised controlled trials (RCTs) and prospective observational studies were included. No language restrictions were applied. Retrospective studies and studies with high-dose concomitant steroids were excluded.

Data synthesis The systematic search was performed on 9 May. Meta-analyses according to drug and outcome were specified with random effects, I 2 evaluated heterogeneity and Grading of Recommendations, Assessment, Development and Evaluation evaluated certainty of evidence. Primary endpoint analysis was restricted to RCT design, secondary endpoint included subgroup analysis according to prospective observational or RCT design.

Results A total of 2831 publications were screened, 12 were suitable for quantitative synthesis. Three MMF RCTs were included with no significant effect on the primary endpoints (%FVC 2.94, 95% CI −4.00 to 9.88, I 2 =79.3%; %DLco −2.03, 95% CI −4.38 to 0.32, I 2 =0.0%). An overall 2.03% change from baseline in %FVC (95% CI 0.65 to 3.42, I 2 =0.0%) was observed in MMF, and RCT subgroup summary estimated a 4.42% change from baseline in %DL CO (95% CI 2.05 to 6.79, I 2 =0.0%). AZA studies were limited. All estimates were considered very low certainty evidence.

Conclusions There were limited RCTs of MMF or AZA and their benefit in ILD was of very low certainty. MMF may support preservation of pulmonary function, yet confidence in the effect was weak. To support high certainty evidence, RCTs should be designed to directly assess MMF efficacy in ILD.

PROSPERO registration number CRD42023423223.

• Interstitial Fibrosis
• Respiratory Function Test

Data availability statement

Data are available in a public, open access repository. We cited published study.

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https://doi.org/10.1136/bmjresp-2023-002163

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Mycophenolate mofetil (MMF) and azathioprine (AZA) are two immunomodulatory drugs used in the treatment of connective tissue disease with both drugs having mechanisms that target lymphocytes. While increasingly used in treatment of interstitial lung disease (ILD), there is limited evidence for the efficacy of MMF or AZA in improving outcomes.

WHAT THIS STUDY ADDS

We undertook a systematic review and meta-analysis to assess whether administration MMF or AZA in ILD was associated with changes in pulmonary function and gas transfer. There was an unclear benefit of MMF on ILD. There was no significant difference in outcome when compared with placebo or standard of care. A minor increase in per cent predicted forced vital capacity and diffusion lung capacity of carbon monoxide from baseline was observed in MMF. Studies on AZA were limited.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Findings may provide indication of an attenuation on lung function decline, however, all estimates should be considered weak evidence with a high likelihood that additional trials may change effect estimates in a manner sufficient to influence decision-making. The limited number of controlled studies in MMF and AZA highlight an important need for additional well-designed randomised controlled trials to directly test their efficacy in ILD.

Introduction

Interstitial lung disease (ILD) is a diverse group of conditions that affect the interstitial structure of the lungs. These diseases can be characterised by progressive lung damage, resulting in symptoms such as dyspnoea, decreased exercise tolerance and a diminished quality of life. 1 Forced vital capacity (FVC) and the diffusion lung capacity of carbon monoxide (DL CO ) are widely used to assess the severity of disease and predict prognosis of people with ILD. 2

Mycophenolate mofetil (MMF) and azathioprine (AZA) are two immunomodulatory drugs commonly used in the treatment of connective tissue disease (CTD) and associated ILD (CTD-ILD). MMF works by blocking the de novo synthesis of DNA, thereby inhibiting the proliferation of lymphocytes. AZA is a purine analogue that hinders purine synthesis and becomes incorporated into DNA during the anabolic process. Similar to MMF, this mechanism of action makes both drugs more specific for targeting lymphocytes, as lymphocytes do not have a salvage pathway in DNA synthesis. 3

There is limited evidence for the safety or efficacy of MMF or AZA in improving outcomes for people with ILD. 4 This systematic review and meta-analysis aims to assess whether the administration of MMF or AZA in ILD is associated with changes in pulmonary function and gas transfer, and to synthesise evidence of safety profiles.

Search strategy

The prespecified protocol was submitted to PROSPERO on 3 May 2023 and registered on 16 May 2023 (CRD42023423223). The search strategy was last performed on 9 May 2023.

The population was defined as people with ILD (Idiopathic pulmonary fibrosis (IPF), chronic hypersensitivity pneumonia and all CTD-ILD, including systemic scleroderma) the intervention was MMF or AZA; the comparator was placebo or standard of care; the primary outcomes were per cent predicted FVC (%FVC) and DL CO (%DL CO ). Adverse events, respiratory symptoms, quality of life and mortality were investigated as secondary outcomes. Relevant studies were searched in Medline and Embase using comprehensive search terms ( online supplemental documents 1 and 2 ). Relevant ongoing trials were searched on clinicaltrials.gov ( online supplemental document 3 ).

Supplemental material

Inclusion criteria.

Eligible studies included interventional randomised controlled trials (RCTs) and observational prospective studies of adults (>18 years old) diagnosed with any ILD, where at least one arm was treated with MMF or AZA. Low doses of steroids concomitant with or prior to MMF or AZA treatment were allowed, while we excluded studies with concomitant high-dose therapies (≥20 mg/day of prednisone or equivalent). Finally, we excluded studies that did not report %FVC or %DL CO . No language restrictions were applied.

Study selection and data extraction

Two authors (FL and LF) independently assessed the titles and abstracts of the identified studies according to the eligibility criteria. Subsequently, two authors (FL and LF) evaluated the full text of the selected articles to determine their inclusion. Any disagreements were resolved through discussion and consensus with a third author (IS) resolving any remaining disagreements.

Data were independently extracted using a proforma and confirmed by two authors (FL and LF). Extracted data included study design, authors, year of publication; patient data namely age, reported sex or gender, duration of disease at the time of evaluation, aetiology of the disease and intervention characteristics, including MMF or AZA treatment, dose and duration of treatments. Primary outcomes of interest, %FVC and %DL CO , were extracted, along with any secondary outcomes reported, at baseline and follow-up time point closest to 12 months.

Continuous primary outcomes were collected as mean and SD at baseline and follow-up time points. When studies reported other summary values, these were converted to mean and SD. 5 Secondary outcomes reported as dichotomous and categorical variables were extracted as ratio and/or per cent.

Risk of bias

Two authors (FL and LF) independently used the Cochrane ‘Risk of Bias’ assessment tool 2.0 to evaluate the included RCTs prior to quantitative synthesis. 6 Risk of bias in the observational prospective studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. 7 To assess the risk of bias in single-arm observational cohorts, specifically for evaluation of ‘selection bias’ and ‘comparative bias’ on the Newcastle-Ottawa Quality Assessment Scale, baseline time points were considered as the ‘not exposed cohort’ and the follow-up time point as the ‘exposed cohort’. Studies that were determined to have a high risk of bias were excluded from quantitative synthesis.

Statistical analysis

When two or more studies were available for a specific treatment, a random effects meta-analysis with inverse-variance was performed to evaluate the effect of the treatment on %FVC and %DL CO values. Estimates were expressed as weighted mean difference (WMD) with 95% CI.

Where there were sufficient RCT data, the primary endpoint analysis assessed the delta difference in %FVC and %DL CO at follow-up from baseline in respiratory function for MMF or AZA relative to the comparator. In a secondary endpoint analysis, the difference in %FVC and %DL CO between follow-up and baseline in people receiving of MMF or AZA was compared. Analyses were performed according to drug, prespecified subgroup analyses were performed according to study design (RCT or prospective observation study) and follow-up time (6 months or 12 months and over).

Heterogeneity was evaluated using I 2 statistic to interpret the proportion of the total variability that was due to between-study heterogeneity, as well as inspection of forest plots. All analyses were performed by using Stata SE V.17.0.

Assessment of certainty of evidence

The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of evidence in effect estimates from RCT data exclusively. The level of certainty was evaluated as high, moderate, low or very low, considering factors of risk of bias, inconsistency, indirectness, imprecision and publication bias. 8 Publication bias was inspected with asymmetry in funnel plots and Egger’s test.

Patient and public involvement

Representatives from the Action for Pulmonary Fibrosis charity were involved in the design and dissemination of this systematic review. Members of the REMAP-ILD Consortium include charity representatives.

Search of relevant studies

A total of 2831 publications from Embase and Medline were identified. After removal of duplicates and evaluating the titles and abstracts, 23 studies were assessed for eligibility. Among these, 11 studies were excluded due to retrospective design (n=2), incompleteness (n=2), lack of the outcome of interest (n=2) or the presence of concomitant treatment with high doses of steroids (n=5) ( figure 1 , online supplemental table 1 ). A total of 13 studies were eligible for qualitative synthesis ( table 1 ). 9–21 Separately, four ongoing MMF studies were identified, including one phase II RCT, two open-label trials and one prospective cohort study; two studies address pulmonary involvement of systemic sclerosis, one study recruits participants with fibrotic hypersensitivity pneumonitis and one study focuses on idiopathic inflammatory myopathy ILD ( online supplemental document 3 ).

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Preferred reporting items for systematic review and meta-analysis (PRISMA) flow of study search and inclusion. AZA, azathioprine; MMF, mycophenolate mofetil.

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Reported study characteristics of included cohorts

A moderate risk of bias was observed for the blinding of outcome assessment in all the included RCTs, 12 14 15 19–21 as there were no mentioned strategies to blind the pulmonary function test evaluations ( figure 2A ). Roig et al 21 and Zhang et al 20 were considered at high risk of bias in terms of blinding of participants and personnel, as they compared intravenous and oral (per os) treatments without implementing a double dummy strategy. Due to the high risks of bias across a number of domains and insufficient data reporting, the study by Roig et al 21 was excluded from quantitative synthesis. In the assessment of prospective observational studies, six studies 10 11 13 16–18 had selection bias in the ascertainment of exposure, but all studies were considered adequate ( figure 2B , online supplemental table 2 ).

Qualitative synthesis: risk of bias. (A) Risk of bias in RCTs assessed using Cochrane ROB2.0 tool. (B) Risk of bias assessed using Newcastle-Ottawa Quality assessment scale for cohort studies. Green has been assessed as: three or four stars in selection bias; two stars in comparability, three stars in outcome. Yellow has been assessed as: two stars in selection bias; one star in comparability, two stars in outcome. RCTs, randomised controlled trial; ROB2.0, Risk of Bias 2.0.

MMF and AZA efficacy in primary endpoint relative to comparator

MMF or AZA were tested in a total of four trials, with three trials using MMF 15 19 20 and one trial using AZA. 14 Only MMF trials were included in primary analysis with a total of 249 participants, of which 119 were in the intervention arm and 130 were in the comparator arm ( figure 3A ). In primary analysis, the overall delta change in %FVC values from baseline to follow-up was not significantly different between the intervention and comparator arms (WMD 2.94, 95% CI −4.00 to 9.88, I 2 =79.3%). Significant heterogeneity was observed and the estimate was interpreted to have very low certainty ( table 2 , online supplemental figure 1A ).

Primary endpoint analysis of efficacy on pulmonary function relative to comparator. (A) Forest plot of difference in %FVC in treatment of MMF versus comparators at follow-up. (B) Forest plot of difference in %DLco in treatment of MMF versus comparators at follow-up. Positive values indicate improvement relative to comparator, negative values indicate decline relative to comparator. Presented with cohort size (N) for intervention and comparator, weighted mean difference (WMD) and 95% CI. Follow-up time reported in months. %DLco, per cent predicted diffusion lung capacity of carbon monoxide; %FVC, per cent predicted forced vital capacity; MMF, mycophenolate mofetil.

GRADE approach to rate certainty of effect estimates

The overall delta change in %DL CO from baseline to follow-up was not significantly different in the interventional arm compared with the comparator arm (WMD %DLco −2.03, 95% CI −4.38 to 0.32, I 2 =0.0% ( figure 3B ). Heterogeneity was not observed and the estimate was interpreted to have very low certainty ( table 2 , online supplemental figure 2B ).

MMF or AZA efficacy in secondary endpoints

A total of 6 prospective observational studies 9–11 16–18 and 5 RCTs 12 14 15 19 20 were included in secondary analysis of the difference between follow-up and baseline in %FVC, including a combined sample of 267 evaluated at baseline and 244 at follow-up, representing 7.5% loss to follow up. In prespecified subgroup analysis by drug ( online supplemental figure 3A ), treatment with AZA suggested a decline in %FVC with treatment, although this was not statistically significant (two studies; WMD −6.14, 95% CI −12.88 to 0.61, I 2 =48.3%). Treatment with MMF was observed to have a small and significant increase in %FVC value at follow-up (nine studies; WMD 2.03, 95% CI 0.65 to 3.42, I 2 =0.0%). Additional subgroup analyses performed on MMF treatment observed similar effect sizes according to study design and very low certainty of evidence ( figure 4A , table 2 ), while a greater effect of MMF was observed at follow-up of 12 months or over with no significant heterogeneity between time points ( online supplemental figure 4A ).

Secondary endpoint analysis of efficacy on pulmonary function compared with baseline. Subgroup analysis of MMF overall and summary estimates presented by study design of trial or prospective observational study. 4 (A) Forest plot of change in %FVC at follow-up versus baseline. (B) Forest plot of change in %DLco versus baseline. Positive values indicate improvement relative to baseline, negative values indicate decline relative to baseline. Presented with cohort size (N) for intervention and comparator, weighted mean difference (WMD) and 95% CIs. Follow-up time reported in months. %DLco, per cent predicted diffusion lung capacity of carbon monoxide; %FVC, per cent predicted forced vital capacity; MMF, mycophenolate mofetil.

Data from a total of 7 observational studies 9–11 13 16–18 and 5 RCTs 12 14 15 19 20 were available for analysis of %DL CO , including 262 and 234 patients, respectively, at baseline and follow-up representing a 10.7% loss to follow up. In subgroup analysis by drug ( online supplemental figure 3B ), treatment with AZA suggested a decline (two studies; −5.72, 95% CI −13.79 to 2.34, I 2 =49.8%), while treatment with MMF suggested an increase (10 studies; 1.62, 95% CI −1.70 to 4.94, I 2 =60.5%), although effect estimates did not reach significance and substantial heterogeneity was observed. Additional subgroup analyses performed on MMF treatment observed a significant decline in %DL CO in prospective observation studies (WMD −1.36, 95% CI −2.37 to −0.36, I 2 =0.0%) and a significant improvement in RCTs (WMD 4.42, 95% CI 2.05 to 6.79; I 2 =0.0%), with substantial heterogeneity between subgroups and very low certainty in evidence ( figure 4B , table 2 ). Subgroup analysis on follow-up time did not observe a significant effect in %DL CO with no significant heterogeneity observed between groups ( figure 4B ).

Qualitative synthesis of adverse events

All the studies reported adverse events. The most frequent adverse events in the treated arms were diarrhoea and pneumonia, followed by lympho/leucopenia, anaemia and skin infection ( online supplemental table 3 ).

Four studies reported on respiratory symptoms. 11 12 15 18 In the study by Mankikian et al , no significant difference was observed in the change from baseline in dyspnoea and cough between the treated patients and the placebo group. Naidu et al reported an improvement in respiratory symptoms in both arms of the study, with no significant difference between the treatment and control groups. Liossis et al reported an improvement in respiratory symptoms compared with baseline after administration of MMF. Vaiarello et al evaluated symptoms during a cardiopulmonary exercise test before and after MMF treatment, observing no significant difference in dyspnoea measured by the Borg scale.

Two studies reported change in quality of life. 12 15 Mankikian et al and Naidu et al evaluated the change of quality of life between the interventional and the control arm using respectively the SF-36 V.1.3 questionnaire and the Medical Outcome Survey SF-36 V.2. Both these studies reported no difference in the QoL in MMF arm compared with control. None of the included studies reported on mortality.

This systematic review and meta-analysis suggested an unclear benefit of MMF or AZA on FVC or DL CO in people with ILD. Secondary endpoint analysis of change over time stratified by treatment suggested a minor increase in %FVC or %DL CO  compared with baseline in MMF treated groups. The review highlighted a limited number of trials and prospective observational studies that directly tested the effect of MMF or AZA on lung function in the current literature, particularly precluding interpretations on the efficacy of AZA.

All estimates based on MMF RCT data were of very low GRADE certainty of evidence. Risk of bias was deemed moderate as one trial included unblinded participants, one study was post hoc analysis of trial data, and all trials had potential issues in blinding of outcome assessment. Heterogeneity and differences in the direction of effect across RCTs contributed to inconsistency. Imprecision was considered high due to limited RCTs, small samples and small effect sizes with wide CIs. Indirectness was deemed moderate as studies included different diagnoses. There was no strong evidence of publication bias. While these findings provide some indication of the effect, all estimates should be considered weak evidence with a high likelihood that additional studies may change effect estimates in a manner sufficient to influence decision-making.

Primary endpoint analysis in MMF observed no significant effect of treatment vs comparator groups for %FVC or %DL CO , although a non-significant effect in %DL CO favoured comparator. In contrast, secondary endpoint analysis suggested that MMF treatments could improve on baseline pulmonary function, although this may be insufficient relative to placebo. In further subanalyses restricted to MMF, greater improvement in %FVC was observed at longer follow-up, with no difference according to study design. Conversely, greater improvement in %DL CO was observed in trial designs, with no difference according to follow-up timing. While heterogeneity was minimised in subgroup analyses, effect sizes were small.

In the narrative review of adverse events, we found that both treatments were well tolerated, however, studies on real-world data suggest difficulties in tolerability. 4 The most frequent adverse events observed with MMF and AZA treatment included respiratory infections and haematological disorders. It is noteworthy that these adverse events were often mild and did not typically require specific treatment nor differ to events encountered in standard treatments. MMF or AZA interruption due to adverse events led to treatment discontinuation only in a few cases. Symptoms appeared to slightly improve after treatment commenced, but stricter interventional vs placebo studies are needed to assess the real effect on patient-reported outcomes.

The first meta-analysis examining the safety and efficacy of MMF in ILD associated with systemic sclerosis, conducted by Tzouvelekis et al included both retrospective and one prospective study. The outcomes of their study align with our findings, indicating an acceptable safety profile for MMF without clear evidence regarding its effectiveness on pulmonary function. 22 Similarly, network meta-analysis in systemic sclerosis associated ILD did not identify significant treatment efficacy of MMF, nor AZA in combination with cyclosporin-A. 23 Further studies are necessary across ILD diagnoses to ascertain potential efficacy in disease subtypes.

This study employed a comprehensive search strategy and strict inclusion criteria, which focused on prospective designs and trials. To support quality, estimates were specifically provided for trial designs along with GRADE assessment. We did not include restrictions on study language or cohort size. MMF and AZA were evaluated in prespecified subgroup analysis based on drug. Where study designs included other treatments, data were collected to support interpretation of MMF or AZA with omission of the drug in comparator arms. Effects regarding AZA should be interpreted with great caution due to limited studies and insufficient studies for primary analysis. Those involving AZA included an active intervention of Cyclosporin-A in the comparator, with addition of AZA in the treatment group, precluded specific interpretation of AZA alone. The limited representation of AZA in the recent literature may be partially attributed to the results of the PANTHER trial, where AZA in combination with n-acetylcysteine and prednisone led to worse outcomes in patients with IPF. 24 Mankikian et al designed an RCT randomising rituximab+MMF versus MMF, we extracted data only from the MMF arm for secondary endpoints. 12 Furthermore, studies were not consistent in ILD diagnosis inclusion, with the majority of prospective observational studies including systemic sclerosis-associated ILD; trials included IPF, non-specific interstitial pneumonia and CTD-ILD, which may contribute to heterogeneity in effect estimates. While ongoing studies were identified, MMF studies did not included blinded phase III RCTs and no AZA studies were identified.

In conclusion, the beneficial impact of MMF and AZA on pulmonary function in patients with ILD is uncertain with some weak evidence that suggests a need to further investigate the effect of MMF in preserving function. While MMF and AZA were generally well tolerated in patients with ILD, it is important to note that the certainty of effects on pulmonary function was very low. Further well-designed RCTs across diagnoses of fibrotic and inflammatory ILD are necessary to support high certainty evidence.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

No ethical approval was sought as the study uses summary information from published literature.

Acknowledgments

We express our gratitude to librarian Jacqueline Kemp, Imperial College London, for her valuable assistance in the development of the search strategy. Additionally, we would like to extend our thanks to Dr Liu Bin, Imperial College London, for providing the translation of Chinese manuscripts.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Twitter @istamina, @IPFdoc

FL and IS contributed equally.

Collaborators REMAP-ILD Consortium: Alexandre Biasi Cavalcanti (Hospital of Coracao), Ali Mojibian (Black Tusk Research Group), Amanda Bravery (Imperial College Clinical Trials Unit), Amanda Goodwin (University of Nottingham), Ana Etges (Federal University of Rio Grande do Sul), Ana Sousa Marcelino Boshoff (Imperial College Clinical Trials Unit), Andreas Guenther (Justus-Liebig-University of Giessen), Andrew Briggs (London School of Hygiene and Tropical Medicine), Andrew Palmer (University of Tasmania), Andrew Wilson (University of East Anglia), Anjali Crawshaw (University Hospitals Birmingham), Anna-MariaHoffmann-Vold (Oslo University Hospital), Anne Bergeron (University Hospitals Geneva), Anne Holland (Monash University), Anthony Gordon (Imperial College London), Antje Prasse (Hannover Medical School), Argyrios Tzouvelekis (Yale University), Athina Trachalaki (Imperial College London), Athol Wells (Royal Brompton Hospital), Avinash Anil Nair (Christian Medical College Vellore), Barbara Wendelberger (Berry Consultants), Ben Hope-Gill (Cardiff and Vale University Hospital), Bhavika Kaul (U.S. Department of Veterans Affairs Center for Innovation in Quality, Effectiveness, and Safety; Baylor College of Medicine and University of California San Francisco), Bibek Gooptu (University of Leicester), Bruno Baldi (Pulmonary Division, Heart Institute (InCor), University of Sao Paulo Medical School, Sao Paulo, Brazil), Bruno Crestani (Public Assistance Hospital of Paris), Carisi Anne Polanczyk (Federal University of Rio Grande do Sul), Carlo Vancheri (University of Catania), Carlos Robalo (European Respiratory Society), Charlotte Summers (University of Cambridge), Chris Grainge (University of Newcastle), Chris Ryerson (Department of Medicine and Centre of Heart Lung Innovations, University of British Columbia), Christophe von Garnier (Centre Hospitalier Universitaire Vaudois), Christopher Huntley (University Hospitals Birmingham), Claudia Ravaglia (University of Bologna), Claudia Valenzuela (Hospital Universitario de La Princesa), Conal Hayton (Manchester University Hospital), Cormac McCarthy (University College Dublin), Daniel Chambers (Queensland Health), Dapeng Wang (National Heart and Lung Institute, Imperial College London), Daphne Bablis (Imperial College Clinical Trials Unit), David Thicket (University of Birmingham), David Turner (University of East Anglia), Deepak Talwar (Metro Respiratory Centre Pulmonology & Sleep Medicine), Deji Adegunsoye (University of Chicago), Devaraj Anand (Royal Brompton Hospital), Devesh Dhasmana (University of St. Andrews), Dhruv Parek (Brimingham University), Diane Griffiths (University Hospitals Birmingham), Duncan Richards (Oxford University), Eliana Santucci (Hospital of Coracao), Elisabeth Bendstrup (Aarhus University), Elisabetta Balestro (University of Padua), Eliza Tsitoura (University of Crete), Emanuela Falaschetti (Imperial College London), Emma Karlsen (Black Tusk Research Group), Ena Gupta (University of Vermont Health Network), Erica Farrand (University of California, San Fransisco), Fasihul Khan (University of Nottingham), Felix Chua (Royal Brompton Hospital), Fernando J Martinez (Weill Cornell Medicine), Francesco Bonella (Essen University Hospital), Francesco Lombardi (Division of Pulmonary Medicine, Fondazione Policlinico Universitario Agostino Gemelli IRCCS), Gary M Hunninghake (Brigham and Women's Hospital), Gauri Saini (Nottingham University Hospital), George Chalmers (Glasgow Royal Infirmary), Gisli Jenkins (Imperial College London), Gunnar Gudmundsson (University of Iceland), Harold Collard (University of California, San Francisco), Helen Parfrey (Royal Papworth Hospital NHS Foundation Trust), Helmut Prosch (Medical University of Vienna), Hernan Fainberg (Imperial College London), Huzaifa Adamali (North Bristol NHS Trust), Iain Stewart (National Heart and Lung Institute, Imperial College London), Ian Forrest (Newcastle Hospitals NHS Foundation Trust), Ian Glaspole (Alfred Hospital), Iazsmin Bauer-Ventura (The University of Chicago), Imre Noth (University of Virginia), Ingrid Cox (University of Tasmania), Irina Strambu (University of Medicine and Pharmacy), Jacobo Sellares (Hospital Clínic de Barcelona), James Eaden (Sheffield University Hospitals), Janet Johnston (Manchester Royal Infirmary NHS Foundation Trust), Jeff Swigris (National Jewish Health), John Blaikley (Manchester University), John S Kim (University of Virginia), Jonathan Chung (The University of Chicago), Joseph A Lasky (Tulane & Pulmonary Fibrosis Foundation), Joseph Jacob (University College London), Joyce Lee (University of Colorado), Juergen Behr (Ludwig Maximilian University of Munich), Karin Storrer (Federal University of Sao Paulo), Karina Negrelli (Hospital of Curacao), Katarzyna Lewandowska (Institute of Tuberculosis and Lung Diseases), Kate Johnson (The University of British Colombia), Katerina Antoniou (University of Crete), Katrin Hostettler (University Hospital Basel), Kerri Johannson (University of Calgary), Killian Hurley (Royal College of Surgeons, Ireland), Kirsty Hett (Cardiff and Vale University Health Board), Larissa Schwarzkopf (The Institute for Therapy Research), Laura Fabbri (National Heart and Lung Institute, Imperial College London), Laura Price (Royal Brompton Hospital), Laurence Pearmain (Manchester University), Leticia Kawano-Dourado (Hcor Research Institute, Hospital do Coracao, Sao Paulo, Brazil. 2. Pulmonary Division, University of Sao Paulo, Sao Paulo, Brazil. 3. MAGIC Evidence Ecosystem Foundation, Oslo, Norway), Liam Galvin (European Pulmonary Fibrosis Federation), Lisa G. Spencer (Liverpool University Hospitals NHS Foundation Trust), Lisa Watson (Sheffield University Hospitals), Louise Crowley (Queen Elizabeth Hospital, University Hospitals Birmingham), Luca Richeldi (Agostino Gemelli IRCCS University Hospital Foundation), Lucilla Piccari (Department of Pulmonary Medicine, Hospital del Mar, Barcelona (Spain)), Manuela Funke Chambour (University of Bern), Maria Molina-Molina (IDIBELL Bellvitge Biomedical Research Institute), Mark Jones (Southampton University), Mark Spears (University of Dundee Scotland), Mark Toshner (University of Cambridge), Marlies Wijsenbeek-Lourens (Erasmus University Medical Hospital), Martin Brutsche (Kantonsspital St.Gallen), Martina Vasakova (Faculty Thomayer Hospital), Melanie Quintana (Berry Consultants), Michael Gibbons (University of Exeter), Michael Henry (Cork University Hospital), Michael Keane (University College Dublin), Michael Kreuter (Heidelberg University Hospital), Milena Man Iuliu Hatieganu (University of Medicine and Pharmacy), Mohsen Sadatsafavi (The University of British Colombia), Naftali Kaminski (Yale University), Nazia Chaudhuri (Ulster University), Nick Weatherley (Sheffield University Hospitals), Nik Hirani (The University of Edinburgh), Ovidiu Fira Mladinescu Victor Babes (University of Medicine and Pharmacy), Paolo Spagnolo (University of Padua), Paul Beirne (Leeds Teaching Hospitals NHS Foundation Trust), Peter Bryce (Pulmonary Fibrosis Trust), Peter George (Royal Brompton Hospital), Philip L Molyneaux (Imperial College London), Pilar Rivera Ortega (Interstitial Lung Disease Unit, Department of Respiratory Medicine, Wythenshawe Hospital. Manchester University NHS Foundation Trust. United Kingdom.), Radu Crisan-Dabija (University of Medicine and Pharmacy "Grigore T. Popa" Iasi), Rahul Maida (University of Birmingham), Raphael Borie (Public Assistance Hospital of Paris), Roger Lewis (Berry Consultants), Rui Rolo (Braga Hospital), Sabina Guler (University Hospital of Bern), Sabrina Paganoni (Massachusetts General Hospital), Sally Singh (University of Leicester.), Sara Freitas (University Hospital Coimbra), Sara Piciucchi (Department of Radiology, GB Morgagni Hospital; Azienda USL Romagna), Shama Malik (Action for Pulmonary Fibrosis), Shaney Barratt (North Bristol NHS Trust), Simon Hart (University of Hull), Simone Dal Corso (Monash University), Sophie Fletcher (Southampton University), Stefan Stanel (Manchester University NHS Foundation Trust), Stephen Bianchi (Thornbury Hospital), Steve Jones (Action for Pulmonary Fibrosis), Wendy Adams (Action for Pulmonary Fibrosis).

Contributors FL: protocol development, formal analysis, data curation, writing–original draft. IS: protocol development, formal analysis, methodology, supervision, writing–original draft, guarantor. LF: protocol development, data curation, writing–review and editing. WA: protocol development, writing–review and editing. LK-D: protocol development, writing–review and editing. CJR: protocol development, writing–review and editing. GJ: conceptualisation, protocol development, supervision, writing–review and editing.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests GJ is supported by a National Institute for Health Research (NIHR) Research Professorship (NIHR reference RP-2017-08-ST2-014). GJ is a trustee of Action for Pulmonary Fibrosis and reports personal fees from Astra Zeneca, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Chiesi, Daewoong, Galapagos, Galecto, GlaxoSmithKline, Heptares, NuMedii, PatientMPower, Pliant, Promedior, Redx, Resolution Therapeutics, Roche, Veracyte and Vicore. CJR reports grants from Boehringer Ingelheim, and honoraria or consulting fees from Boehringer Ingelheim, Pliant Therapeutics, Astra Zeneca, Trevi Therapeutics, Veracyte, Hoffmann-La Roche, Cipla. FL, IS, LF, WA and LK-D report no competing interests.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

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