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• What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

• The government
• Government agencies
• Non-governmental organizations
• Educational institutions
• Businesses or consultancies
• Libraries or archives
• Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

• Secondary data is very easy to source and readily available .
• It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
• As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
• Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

• Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
• Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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

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

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.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/secondary-research/

Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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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.

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4 Chapter 5 Secondary Research

Learning Objectives

By the end of this chapter, students must be able to:

• Explain the concept of secondary research
• Highlight the key benefits and limitations of secondary research
• Evaluate different sources of secondary data

What is Secondary Research?

In situations where the researcher has not been involved in the data gathering process (primary research), one may have to rely on existing information and data to arrive at specific research conclusions or outcomes. Secondary research, also known as desk research, is a research method that involves the use of information previously collected for another research purpose.

In this chapter, we are going to explain what secondary research is, how it works, and share some examples of it in practice.

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Sources of secondary data.

The two main sources of secondary data are:

• Internal sources
• External sources

Internal sources of secondary data exist within the organization. There could be reports, previous research findings, or old documents which may still be used to understand a particular phenomenon. This information may only be available to the organization’s members and could be a valuable asset.

External sources of secondary data lie outside the organization and refer to information held at the public library, government departments, council offices, various associations as well as in newspapers or journal articles.

Benefits of using Secondary Data

It is only logical for researchers to look for secondary information thoroughly before investing their time and resources in collecting primary data.  In academic research, scholars are not permitted to move to the next stage till they demonstrate they have undertaken a review of all previous studies. Suppose a researcher would like to examine the characteristics of a migrant population in the Western Sydney region. The following pieces of information are already available in various reports generated from the Australian Bureau of Statistics’ census data:

• Birthplace of residents
• Language spoken at home by residents
• Family size
• Income levels
• Level of education

By accessing such readily available secondary data, the researcher is able to save time, money, and effort. When the data comes from a reputable source, it further adds to the researchers’ credibility of identifying a trustworthy source of information.

Evaluation of Secondary Data

[1] Assessing secondary data is important. It may not always be available free of cost. The following factors must be considered as these relate to the reliability and validity of research results, such as whether:

• the source is trusted
• the sample characteristics, time of collection, and response rate (if relevant) of the data are appropriate
• the methods of data collection are appropriate and acceptable in your discipline
• the data were collected in a consistent way
• any data coding or modification is appropriate and sufficient
• the documentation of the original study in which the data were collected is detailed enough for you to assess its quality
• there is enough information in the metadata or data to properly cite the original source.

In addition to the above-mentioned points, some practical issues which need to be evaluated include the cost of accessing and the time frame involved in getting access to the data is relevant.

The infographic Secondary Sources created by Shonn M. Haren, 2015 is licensed under  a  Creative Commons Attribution 4.0 International Licence [2]

Table 2: differences between primary and secondary research.

• Griffith University n.d., Research data: get started, viewed 28 February 2022,<https://libraryguides.griffith.edu.au/finddata>. ↵
• Shonnmaren n.d., Secondary sources, viewed 28 February 2020, Wikimedia Commons, <https://commons.wikimedia.org/wiki/File:Secondary_Sources.png> ↵
• Qualtrics XM n.d., S econdary research: definition, methods and examples , viewed 28 February 2022,  <https://www.qualtrics.com/au/experience-management/research/secondary-research/#:~:text=Unlike%20primary%20research%2C%20secondary%20research,secondary%20research%20have%20their%20places>. ↵

About the author

name: Aila Khan

institution: Western Sydney University

Chapter 5 Secondary Research Copyright © by Aila Khan is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Secondary research: definition, methods, & examples.

19 min read This ultimate guide to secondary research helps you understand changes in market trends, customers buying patterns and your competition using existing data sources.

In situations where you’re not involved in the data gathering process ( primary research ), you have to rely on existing information and data to arrive at specific research conclusions or outcomes. This approach is known as secondary research.

In this article, we’re going to explain what secondary research is, how it works, and share some examples of it in practice.

Free eBook: The ultimate guide to conducting market research

What is secondary research?

Secondary research, also known as desk research, is a research method that involves compiling existing data sourced from a variety of channels . This includes internal sources (e.g.in-house research) or, more commonly, external sources (such as government statistics, organizational bodies, and the internet).

Secondary research comes in several formats, such as published datasets, reports, and survey responses , and can also be sourced from websites, libraries, and museums.

The information is usually free — or available at a limited access cost — and gathered using surveys , telephone interviews, observation, face-to-face interviews, and more.

When using secondary research, researchers collect, verify, analyze and incorporate it to help them confirm research goals for the research period.

As well as the above, it can be used to review previous research into an area of interest. Researchers can look for patterns across data spanning several years and identify trends — or use it to verify early hypothesis statements and establish whether it’s worth continuing research into a prospective area.

How to conduct secondary research

There are five key steps to conducting secondary research effectively and efficiently:

1.    Identify and define the research topic

First, understand what you will be researching and define the topic by thinking about the research questions you want to be answered.

Ask yourself: What is the point of conducting this research? Then, ask: What do we want to achieve?

This may indicate an exploratory reason (why something happened) or confirm a hypothesis. The answers may indicate ideas that need primary or secondary research (or a combination) to investigate them.

2.    Find research and existing data sources

If secondary research is needed, think about where you might find the information. This helps you narrow down your secondary sources to those that help you answer your questions. What keywords do you need to use?

Which organizations are closely working on this topic already? Are there any competitors that you need to be aware of?

Create a list of the data sources, information, and people that could help you with your work.

3.    Begin searching and collecting the existing data

Now that you have the list of data sources, start accessing the data and collect the information into an organized system. This may mean you start setting up research journal accounts or making telephone calls to book meetings with third-party research teams to verify the details around data results.

As you search and access information, remember to check the data’s date, the credibility of the source, the relevance of the material to your research topic, and the methodology used by the third-party researchers. Start small and as you gain results, investigate further in the areas that help your research’s aims.

4.    Combine the data and compare the results

When you have your data in one place, you need to understand, filter, order, and combine it intelligently. Data may come in different formats where some data could be unusable, while other information may need to be deleted.

After this, you can start to look at different data sets to see what they tell you. You may find that you need to compare the same datasets over different periods for changes over time or compare different datasets to notice overlaps or trends. Ask yourself: What does this data mean to my research? Does it help or hinder my research?

5.    Analyze your data and explore further

In this last stage of the process, look at the information you have and ask yourself if this answers your original questions for your research. Are there any gaps? Do you understand the information you’ve found? If you feel there is more to cover, repeat the steps and delve deeper into the topic so that you can get all the information you need.

If secondary research can’t provide these answers, consider supplementing your results with data gained from primary research. As you explore further, add to your knowledge and update your findings. This will help you present clear, credible information.

Primary vs secondary research

Unlike secondary research, primary research involves creating data first-hand by directly working with interviewees, target users, or a target market. Primary research focuses on the method for carrying out research, asking questions, and collecting data using approaches such as:

• Interviews (panel, face-to-face or over the phone)
• Questionnaires or surveys
• Focus groups

Using these methods, researchers can get in-depth, targeted responses to questions, making results more accurate and specific to their research goals. However, it does take time to do and administer.

Unlike primary research, secondary research uses existing data, which also includes published results from primary research. Researchers summarize the existing research and use the results to support their research goals.

Both primary and secondary research have their places. Primary research can support the findings found through secondary research (and fill knowledge gaps), while secondary research can be a starting point for further primary research. Because of this, these research methods are often combined for optimal research results that are accurate at both the micro and macro level.

Sources of Secondary Research

There are two types of secondary research sources: internal and external. Internal data refers to in-house data that can be gathered from the researcher’s organization. External data refers to data published outside of and not owned by the researcher’s organization.

Internal data

Internal data is a good first port of call for insights and knowledge, as you may already have relevant information stored in your systems. Because you own this information — and it won’t be available to other researchers — it can give you a competitive edge . Examples of internal data include:

• Database information on sales history and business goal conversions
• Information from website applications and mobile site data
• Customer-generated data on product and service efficiency and use
• Previous research results or supplemental research areas
• Previous campaign results

External data

External data is useful when you: 1) need information on a new topic, 2) want to fill in gaps in your knowledge, or 3) want data that breaks down a population or market for trend and pattern analysis. Examples of external data include:

• Government, non-government agencies, and trade body statistics
• Company reports and research
• Competitor research
• Public library collections
• Textbooks and research journals
• Media stories in newspapers
• Online journals and research sites

Three examples of secondary research methods in action

How and why might you conduct secondary research? Let’s look at a few examples:

1.    Collecting factual information from the internet on a specific topic or market

There are plenty of sites that hold data for people to view and use in their research. For example, Google Scholar, ResearchGate, or Wiley Online Library all provide previous research on a particular topic. Researchers can create free accounts and use the search facilities to look into a topic by keyword, before following the instructions to download or export results for further analysis.

This can be useful for exploring a new market that your organization wants to consider entering. For instance, by viewing the U.S Census Bureau demographic data for that area, you can see what the demographics of your target audience are , and create compelling marketing campaigns accordingly.

2.    Finding out the views of your target audience on a particular topic

If you’re interested in seeing the historical views on a particular topic, for example, attitudes to women’s rights in the US, you can turn to secondary sources.

Textbooks, news articles, reviews, and journal entries can all provide qualitative reports and interviews covering how people discussed women’s rights. There may be multimedia elements like video or documented posters of propaganda showing biased language usage.

By gathering this information, synthesizing it, and evaluating the language, who created it and when it was shared, you can create a timeline of how a topic was discussed over time.

3.    When you want to know the latest thinking on a topic

Educational institutions, such as schools and colleges, create a lot of research-based reports on younger audiences or their academic specialisms. Dissertations from students also can be submitted to research journals, making these places useful places to see the latest insights from a new generation of academics.

Information can be requested — and sometimes academic institutions may want to collaborate and conduct research on your behalf. This can provide key primary data in areas that you want to research, as well as secondary data sources for your research.

Advantages of secondary research

There are several benefits of using secondary research, which we’ve outlined below:

• Easily and readily available data – There is an abundance of readily accessible data sources that have been pre-collected for use, in person at local libraries and online using the internet. This data is usually sorted by filters or can be exported into spreadsheet format, meaning that little technical expertise is needed to access and use the data.
• Faster research speeds – Since the data is already published and in the public arena, you don’t need to collect this information through primary research. This can make the research easier to do and faster, as you can get started with the data quickly.
• Low financial and time costs – Most secondary data sources can be accessed for free or at a small cost to the researcher, so the overall research costs are kept low. In addition, by saving on preliminary research, the time costs for the researcher are kept down as well.
• Secondary data can drive additional research actions – The insights gained can support future research activities (like conducting a follow-up survey or specifying future detailed research topics) or help add value to these activities.
• Secondary data can be useful pre-research insights – Secondary source data can provide pre-research insights and information on effects that can help resolve whether research should be conducted. It can also help highlight knowledge gaps, so subsequent research can consider this.
• Ability to scale up results – Secondary sources can include large datasets (like Census data results across several states) so research results can be scaled up quickly using large secondary data sources.

Disadvantages of secondary research

The disadvantages of secondary research are worth considering in advance of conducting research :

• Secondary research data can be out of date – Secondary sources can be updated regularly, but if you’re exploring the data between two updates, the data can be out of date. Researchers will need to consider whether the data available provides the right research coverage dates, so that insights are accurate and timely, or if the data needs to be updated. Also, fast-moving markets may find secondary data expires very quickly.
• Secondary research needs to be verified and interpreted – Where there’s a lot of data from one source, a researcher needs to review and analyze it. The data may need to be verified against other data sets or your hypotheses for accuracy and to ensure you’re using the right data for your research.
• The researcher has had no control over the secondary research – As the researcher has not been involved in the secondary research, invalid data can affect the results. It’s therefore vital that the methodology and controls are closely reviewed so that the data is collected in a systematic and error-free way.
• Secondary research data is not exclusive – As data sets are commonly available, there is no exclusivity and many researchers can use the same data. This can be problematic where researchers want to have exclusive rights over the research results and risk duplication of research in the future.

When do we conduct secondary research?

Now that you know the basics of secondary research, when do researchers normally conduct secondary research?

It’s often used at the beginning of research, when the researcher is trying to understand the current landscape . In addition, if the research area is new to the researcher, it can form crucial background context to help them understand what information exists already. This can plug knowledge gaps, supplement the researcher’s own learning or add to the research.

Secondary research can also be used in conjunction with primary research. Secondary research can become the formative research that helps pinpoint where further primary research is needed to find out specific information. It can also support or verify the findings from primary research.

You can use secondary research where high levels of control aren’t needed by the researcher, but a lot of knowledge on a topic is required from different angles.

Secondary research should not be used in place of primary research as both are very different and are used for various circumstances.

Questions to ask before conducting secondary research

Before you start your secondary research, ask yourself these questions:

• Is there similar internal data that we have created for a similar area in the past?

If your organization has past research, it’s best to review this work before starting a new project. The older work may provide you with the answers, and give you a starting dataset and context of how your organization approached the research before. However, be mindful that the work is probably out of date and view it with that note in mind. Read through and look for where this helps your research goals or where more work is needed.

• What am I trying to achieve with this research?

When you have clear goals, and understand what you need to achieve, you can look for the perfect type of secondary or primary research to support the aims. Different secondary research data will provide you with different information – for example, looking at news stories to tell you a breakdown of your market’s buying patterns won’t be as useful as internal or external data e-commerce and sales data sources.

• How credible will my research be?

If you are looking for credibility, you want to consider how accurate the research results will need to be, and if you can sacrifice credibility for speed by using secondary sources to get you started. Bear in mind which sources you choose — low-credibility data sites, like political party websites that are highly biased to favor their own party, would skew your results.

• What is the date of the secondary research?

When you’re looking to conduct research, you want the results to be as useful as possible , so using data that is 10 years old won’t be as accurate as using data that was created a year ago. Since a lot can change in a few years, note the date of your research and look for earlier data sets that can tell you a more recent picture of results. One caveat to this is using data collected over a long-term period for comparisons with earlier periods, which can tell you about the rate and direction of change.

• Can the data sources be verified? Does the information you have check out?

If you can’t verify the data by looking at the research methodology, speaking to the original team or cross-checking the facts with other research, it could be hard to be sure that the data is accurate. Think about whether you can use another source, or if it’s worth doing some supplementary primary research to replicate and verify results to help with this issue.

We created a front-to-back guide on conducting market research, The ultimate guide to conducting market research , so you can understand the research journey with confidence.

In it, you’ll learn more about:

• What effective market research looks like
• The use cases for market research
• The most important steps to conducting market research
• And how to take action on your research findings

Download the free guide for a clearer view on secondary research and other key research types for your business.

Related resources

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, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Lactuca super-pangenome reduces bias towards reference genes in lettuce research

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• ORCID record for Dirk-Jan M. van Workum
• ORCID record for Sarah L. Mehrem
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Breeding of lettuce ( Lactuca sativa L.), the most important leafy vegetable worldwide, for enhanced disease resistance and resilience relies on multiple wild relatives to provide the necessary genetic diversity. In this study, we constructed a super-pangenome based on four Lactuca species (representing the primary, secondary and tertiary gene pools) and comprising 474 accessions. We include 68 newly sequenced accessions to improve cultivar coverage and add important foundational breeding lines. With the super-pangenome we find substantial presence/absence variation (PAV) and copy-number variation (CNV). Functional enrichment analyses of core and variable genes show that transcriptional regulators are conserved whereas disease resistance genes are variable. PAV-genome-wide association studies (GWAS) and CNV-GWAS are largely congruent with single-nucleotide polymorphism (SNP)-GWAS. Importantly, they also identify several major novel quantitative trait loci (QTL) for resistance against Bremia lactucae in variable regions not present in the reference lettuce genome. The usability of the super-pangenome is demonstrated by identifying the likely origin of non-reference resistance loci from the wild relatives Lactuca serriola, Lactuca saligna and Lactuca virosa . The provided methodology and data provide a strong basis for research into PAVs, CNVs and other variation underlying important biological traits of lettuce and other crops.

Competing Interest Statement

The authors have declared no competing interest.

https://www.doi.org/10.4121/c7935d6a-d6ae-42e7-af7e-0ae8cddf70d7.v1

https://github.com/LettuceKnow/linear_pangenome_building

https://www.ebi.ac.uk/ena/browser/view/PRJEB63589

https://www.bioinformatics.nl/lettuce/

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