secondary analysis of data collected by other researchers has become

A Guide To Secondary Data Analysis

What is secondary data analysis? How do you carry it out? Find out in this post.  

Historically, the only way data analysts could obtain data was to collect it themselves. This type of data is often referred to as primary data and is still a vital resource for data analysts.   

However, technological advances over the last few decades mean that much past data is now readily available online for data analysts and researchers to access and utilize. This type of data—known as secondary data—is driving a revolution in data analytics and data science.

Primary and secondary data share many characteristics. However, there are some fundamental differences in how you prepare and analyze secondary data. This post explores the unique aspects of secondary data analysis. We’ll briefly review what secondary data is before outlining how to source, collect and validate them. We’ll cover:

• What is secondary data analysis?
• How to carry out secondary data analysis (5 steps)
• Summary and further reading

Ready for a crash course in secondary data analysis? Let’s go!

1. What is secondary data analysis?

Secondary data analysis uses data collected by somebody else. This contrasts with primary data analysis, which involves a researcher collecting predefined data to answer a specific question. Secondary data analysis has numerous benefits, not least that it is a time and cost-effective way of obtaining data without doing the research yourself.

It’s worth noting here that secondary data may be primary data for the original researcher. It only becomes secondary data when it’s repurposed for a new task. As a result, a dataset can simultaneously be a primary data source for one researcher and a secondary data source for another. So don’t panic if you get confused! We explain exactly what secondary data is in this guide . 

In reality, the statistical techniques used to carry out secondary data analysis are no different from those used to analyze other kinds of data. The main differences lie in collection and preparation. Once the data have been reviewed and prepared, the analytics process continues more or less as it usually does. For a recap on what the data analysis process involves, read this post . 

In the following sections, we’ll focus specifically on the preparation of secondary data for analysis. Where appropriate, we’ll refer to primary data analysis for comparison. 

2. How to carry out secondary data analysis

Step 1: define a research topic.

The first step in any data analytics project is defining your goal. This is true regardless of the data you’re working with, or the type of analysis you want to carry out. In data analytics lingo, this typically involves defining:

• A statement of purpose
• Research design

Defining a statement of purpose and a research approach are both fundamental building blocks for any project. However, for secondary data analysis, the process of defining these differs slightly. Let’s find out how.

Step 2: Establish your statement of purpose

Before beginning any data analytics project, you should always have a clearly defined intent. This is called a ‘statement of purpose.’ A healthcare analyst’s statement of purpose, for example, might be: ‘Reduce admissions for mental health issues relating to Covid-19′. The more specific the statement of purpose, the easier it is to determine which data to collect, analyze, and draw insights from.

A statement of purpose is helpful for both primary and secondary data analysis. It’s especially relevant for secondary data analysis, though. This is because there are vast amounts of secondary data available. Having a clear direction will keep you focused on the task at hand, saving you from becoming overwhelmed. Being selective with your data sources is key.

Step 3: Design your research process

After defining your statement of purpose, the next step is to design the research process. For primary data, this involves determining the types of data you want to collect (e.g. quantitative, qualitative, or both ) and a methodology for gathering them.

For secondary data analysis, however, your research process will more likely be a step-by-step guide outlining the types of data you require and a list of potential sources for gathering them. It may also include (realistic) expectations of the output of the final analysis. This should be based on a preliminary review of the data sources and their quality.

Once you have both your statement of purpose and research design, you’re in a far better position to narrow down potential sources of secondary data. You can then start with the next step of the process: data collection.

Step 4: Locate and collect your secondary data

Collecting primary data involves devising and executing a complex strategy that can be very time-consuming to manage. The data you collect, though, will be highly relevant to your research problem.

Secondary data collection, meanwhile, avoids the complexity of defining a research methodology. However, it comes with additional challenges. One of these is identifying where to find the data. This is no small task because there are a great many repositories of secondary data available. Your job, then, is to narrow down potential sources. As already mentioned, it’s necessary to be selective, or else you risk becoming overloaded.  

Some popular sources of secondary data include:  

• Government statistics , e.g. demographic data, censuses, or surveys, collected by government agencies/departments (like the US Bureau of Labor Statistics).
• Technical reports summarizing completed or ongoing research from educational or public institutions (colleges or government).
• Scientific journals that outline research methodologies and data analysis by experts in fields like the sciences, medicine, etc.
• Literature reviews of research articles, books, and reports, for a given area of study (once again, carried out by experts in the field).
• Trade/industry publications , e.g. articles and data shared in trade publications, covering topics relating to specific industry sectors, such as tech or manufacturing.
• Online resources: Repositories, databases, and other reference libraries with public or paid access to secondary data sources.

Once you’ve identified appropriate sources, you can go about collecting the necessary data. This may involve contacting other researchers, paying a fee to an organization in exchange for a dataset, or simply downloading a dataset for free online .

Step 5: Evaluate your secondary data

Secondary data is usually well-structured, so you might assume that once you have your hands on a dataset, you’re ready to dive in with a detailed analysis. Unfortunately, that’s not the case! 

First, you must carry out a careful review of the data. Why? To ensure that they’re appropriate for your needs. This involves two main tasks:

Evaluating the secondary dataset’s relevance

• Assessing its broader credibility

Both these tasks require critical thinking skills. However, they aren’t heavily technical. This means anybody can learn to carry them out.

Let’s now take a look at each in a bit more detail.  

The main point of evaluating a secondary dataset is to see if it is suitable for your needs. This involves asking some probing questions about the data, including:

What was the data’s original purpose?

Understanding why the data were originally collected will tell you a lot about their suitability for your current project. For instance, was the project carried out by a government agency or a private company for marketing purposes? The answer may provide useful information about the population sample, the data demographics, and even the wording of specific survey questions. All this can help you determine if the data are right for you, or if they are biased in any way.

When and where were the data collected?

Over time, populations and demographics change. Identifying when the data were first collected can provide invaluable insights. For instance, a dataset that initially seems suited to your needs may be out of date.

On the flip side, you might want past data so you can draw a comparison with a present dataset. In this case, you’ll need to ensure the data were collected during the appropriate time frame. It’s worth mentioning that secondary data are the sole source of past data. You cannot collect historical data using primary data collection techniques.

Similarly, you should ask where the data were collected. Do they represent the geographical region you require? Does geography even have an impact on the problem you are trying to solve?

What data were collected and how?

A final report for past data analytics is great for summarizing key characteristics or findings. However, if you’re planning to use those data for a new project, you’ll need the original documentation. At the very least, this should include access to the raw data and an outline of the methodology used to gather them. This can be helpful for many reasons. For instance, you may find raw data that wasn’t relevant to the original analysis, but which might benefit your current task.

What questions were participants asked?

We’ve already touched on this, but the wording of survey questions—especially for qualitative datasets—is significant. Questions may deliberately be phrased to preclude certain answers. A question’s context may also impact the findings in a way that’s not immediately obvious. Understanding these issues will shape how you perceive the data.  

What is the form/shape/structure of the data?

Finally, to practical issues. Is the structure of the data suitable for your needs? Is it compatible with other sources or with your preferred analytics approach? This is purely a structural issue. For instance, if a dataset of people’s ages is saved as numerical rather than continuous variables, this could potentially impact your analysis. In general, reviewing a dataset’s structure helps better understand how they are categorized, allowing you to account for any discrepancies. You may also need to tidy the data to ensure they are consistent with any other sources you’re using.  

This is just a sample of the types of questions you need to consider when reviewing a secondary data source. The answers will have a clear impact on whether the dataset—no matter how well presented or structured it seems—is suitable for your needs.

Assessing secondary data’s credibility

After identifying a potentially suitable dataset, you must double-check the credibility of the data. Namely, are the data accurate and unbiased? To figure this out, here are some key questions you might want to include:

What are the credentials of those who carried out the original research?

Do you have access to the details of the original researchers? What are their credentials? Where did they study? Are they an expert in the field or a newcomer? Data collection by an undergraduate student, for example, may not be as rigorous as that of a seasoned professor.  

And did the original researcher work for a reputable organization? What other affiliations do they have? For instance, if a researcher who works for a tobacco company gathers data on the effects of vaping, this represents an obvious conflict of interest! Questions like this help determine how thorough or qualified the researchers are and if they have any potential biases.

Do you have access to the full methodology?

Does the dataset include a clear methodology, explaining in detail how the data were collected? This should be more than a simple overview; it must be a clear breakdown of the process, including justifications for the approach taken. This allows you to determine if the methodology was sound. If you find flaws (or no methodology at all) it throws the quality of the data into question.  

How consistent are the data with other sources?

Do the secondary data match with any similar findings? If not, that doesn’t necessarily mean the data are wrong, but it does warrant closer inspection. Perhaps the collection methodology differed between sources, or maybe the data were analyzed using different statistical techniques. Or perhaps unaccounted-for outliers are skewing the analysis. Identifying all these potential problems is essential. A flawed or biased dataset can still be useful but only if you know where its shortcomings lie.

Have the data been published in any credible research journals?

Finally, have the data been used in well-known studies or published in any journals? If so, how reputable are the journals? In general, you can judge a dataset’s quality based on where it has been published. If in doubt, check out the publication in question on the Directory of Open Access Journals . The directory has a rigorous vetting process, only permitting journals of the highest quality. Meanwhile, if you found the data via a blurry image on social media without cited sources, then you can justifiably question its quality!  

Again, these are just a few of the questions you might ask when determining the quality of a secondary dataset. Consider them as scaffolding for cultivating a critical thinking mindset; a necessary trait for any data analyst!

Presuming your secondary data holds up to scrutiny, you should be ready to carry out your detailed statistical analysis. As we explained at the beginning of this post, the analytical techniques used for secondary data analysis are no different than those for any other kind of data. Rather than go into detail here, check out the different types of data analysis in this post.

3. Secondary data analysis: Key takeaways

In this post, we’ve looked at the nuances of secondary data analysis, including how to source, collect and review secondary data. As discussed, much of the process is the same as it is for primary data analysis. The main difference lies in how secondary data are prepared.

Carrying out a meaningful secondary data analysis involves spending time and effort exploring, collecting, and reviewing the original data. This will help you determine whether the data are suitable for your needs and if they are of good quality.

Why not get to know more about what data analytics involves with this free, five-day introductory data analytics short course ? And, for more data insights, check out these posts:

• Discrete vs continuous data variables: What’s the difference?
• What are the four levels of measurement? Nominal, ordinal, interval, and ratio data explained
• What are the best tools for data mining?

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Methodology

• 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

Affiliation.

• 1 Rush University College of Nursing, Chicago, Illinois.
• PMID: 33343987
• PMCID: PMC7520737
• DOI: 10.6004/jadpro.2019.10.4.7

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.

© 2019 Harborside™.

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Conflict of interest statement

The author has no conflicts of interest to disclose.

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28 Secondary Data Analysis

Department of Psychology, Michigan State University

Richard E. Lucas, Department of Psychology, Michigan State University, East Lansing, MI

• Published: 01 October 2013
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Secondary data analysis refers to the analysis of existing data collected by others. Secondary analysis affords researchers the opportunity to investigate research questions using large-scale data sets that are often inclusive of under-represented groups, while saving time and resources. Despite the immense potential for secondary analysis as a tool for researchers in the social sciences, it is not widely used by psychologists and is sometimes met with sharp criticism among those who favor primary research. The goal of this chapter is to summarize the promises and pitfalls associated with secondary data analysis and to highlight the importance of archival resources for advancing psychological science. In addition to describing areas of convergence and divergence between primary and secondary data analysis, we outline basic steps for getting started and finding data sets. We also provide general guidance on issues related to measurement, handling missing data, and the use of survey weights.

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Chapter 2: Sociological Research

Research methods: secondary data analysis, learning outcomes.

• Describe secondary data analysis

Secondary Data

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through  secondary data analysis . Secondary data do not result from firsthand research collected from primary sources, but are the already completed work of other researchers. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines from any period in history.

Figure 1. This 1930 Chicago census record is an example of secondary data.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or Facebook.

Content Analysis of Poor in Magazines

Martin Gilens (1996) wanted to find out why survey research shows that the American public substantially exaggerates the percentage of African Americans among the poor. He examined whether media representations influence public perceptions and did a content analysis of photographs of poor people in American news magazines. He coded and then systematically recorded incidences of three variables: (1) Race: white, black, indeterminate; (2) Employed: working, not working; and (3) Age.

Gilens discovered that not only were African Americans markedly over-represented in news magazine photographs of poverty, but that the photos also tended to under-represent “sympathetic” subgroups of the poor—the elderly and working poor—while over-representing less sympathetic groups—unemployed, working age adults. Gilens concluded that by providing a distorted representation of poverty, U.S. news magazines “reinforce negative stereotypes of blacks as mired in poverty and contribute to the belief that poverty is primarily a ‘black problem’” (1996).

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization, publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of the 2008 recession; a racial demographic profile might be compared with data on education funding to examine the resources accessible to different groups.

One of the advantages of secondary data is that they are nonreactive research (or unobtrusive research), meaning that they do not include direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data doesn’t require entering a population, with all the investment and potential risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

But, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it is possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not include the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is a matter of public record. But the separate figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long the have been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, Robert S. Lynd and Helen Merrell Lynd gathered research for their book Middletown: A Study in Modern American Culture in the 1920s. Attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal the truth about small U.S. communities. Today, it is an illustration of attitudes and values of the 1920s.

Understanding Secondary Data and How to Use It in Research

• Key Concepts
• Major Sociologists
• News & Issues
• Research, Samples, and Statistics
• Recommended Reading
• Archaeology

Within sociology, many researchers collect new data for analytic purposes, but many others rely on secondary data in order to conduct a new study . When research uses secondary data, the kind of research they perform on it is called secondary analysis .

Key Takeaways: Secondary Data

• Secondary analysis is a research method that involves analyzing data collected by someone else.
• A great deal of secondary data resources and data sets are available for sociological research, many of which are public and easily accessible. 
• There are both pros and cons to using secondary data.
• Researchers can mitigate the cons of using secondary data by learning about the methods used to collect and clean the data in the first place, and by careful usage of it and honest reporting on it.

Secondary Analysis

Secondary analysis is the practice of using secondary data in research. As a research method, it saves both time and money and avoids unnecessary duplication of research effort. Secondary analysis is usually contrasted with primary analysis, which is the analysis of primary data independently collected by a researcher.

How Researchers Obtain Secondary Data

Unlike primary data, which is collected by a researcher herself in order to fulfill a particular research objective, secondary data is data that was collected by other researchers who likely had different research objectives. Sometimes researchers or research organizations share their data with other researchers in order to ensure that its usefulness is maximized. In addition, many government bodies within the U.S. and around the world collect data that they make available for secondary analysis. In many cases, this data is available to the general public, but in some cases, it is only available to approved users.

Secondary data can be both quantitative and qualitative in form. Secondary quantitative data is often available from official government sources and trusted research organizations . In the U.S., the U.S. Census , the General Social Survey , and the American Community Survey are some of the most commonly used secondary data sets within the social sciences. In addition, many researchers make use of data collected and distributed by agencies including the Bureau of Justice Statistics, the Environmental Protection Agency, the Department of Education, and the U.S. Bureau of Labor Statistics, among many others at federal, state, and local levels.

While this information was collected for a wide range of purposes including budget development, policy planning, and city planning, among others, it can also be used as a tool for sociological research. By reviewing and analyzing numerical data , sociologists can often uncover unnoticed patterns of human behavior and large-scale trends within society.

Secondary qualitative data is usually found in the form of social artifacts, like newspapers, blogs, diaries, letters, and emails, among other things. Such data is a rich source of information about individuals in society and can provide a great deal of context and detail to sociological analysis. This form of secondary analysis is also called content analysis .

Conduct Secondary Analysis

Secondary data represents a vast resource to sociologists. It is easy to come by and often free to use. It can include information about very large populations that would be expensive and difficult to obtain otherwise. Additionally, secondary data is available from time periods other than the present day. It is literally impossible to conduct primary research about events, attitudes, styles, or norms that are no longer present in today's world.

There are certain disadvantages to secondary data. In some cases, it may be outdated, biased, or improperly obtained. But a trained sociologist should be able to identify and work around or correct for such issues.

Validating Secondary Data Before Using It

To conduct meaningful secondary analysis, researchers must spend significant time reading and learning about the origins of the data sets. Through careful reading and vetting, researchers can determine:

• The purpose for which the material was collected or created
• The specific methods used to collect it
• The population studied and the validity of the sample captured
• The credentials and credibility of the collector or creator
• The limits of the data set (what information was not requested, collected, or presented)
• The historic and/or political circumstances surrounding the creation or collection of the material

In addition, before using secondary data, a researcher must consider how the data are coded or categorized and how this might influence the outcomes of secondary data analysis. She should also consider whether the data must be adapted or adjusted in some way prior to her conducting her own analysis.

Qualitative data is usually created under known circumstances by named individuals for a particular purpose. This makes it relatively easy to analyze the data with an understanding of biases, gaps, social context, and other issues.

Quantitative data, however, may require more critical analysis. It is not always clear how data was collected, why certain types of data were collected while others were not, or whether any bias was involved in the creation of tools used to collect the data. Polls, questionnaires, and interviews can all be designed to result in pre-determined outcomes.

When dealing with biased data, it is absolutely critical that the researcher is aware of the bias, its purpose, and its extent. However, biased data can still be extremely useful, as long as the researchers carefully consider the potential effects of the bias.

• Pros and Cons of Secondary Data Analysis
• Critical Thinking Definition, Skills, and Examples
• How to Conduct a Sociology Research Interview
• Secondary Sources in Research
• Data Sources For Sociological Research
• An Overview of Qualitative Research Methods
• What Is Ethnography?
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• What Is Naturalistic Observation? Definition and Examples
• What Is a Primary Source?
• Convenience Samples for Research
• Definition of Idiographic and Nomothetic
• What Is Panel Data?
• The Different Types of Sampling Designs in Sociology
• What Is Participant Observation Research?

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• Researcher’s Guide

Guidance on Secondary Analysis of Existing Data Sets

The University of Connecticut Institutional Review Board (IRB) recognizes that some research projects involving existing data sets and archives may not meet the definition of “human subjects” research requiring IRB review; some may meet definitions of research that is exempt from the federal regulations at 45 CFR part 46; and some may require IRB review. This document is intended to provide guidance on IRB policies and procedures and to reduce burdens associated with IRB review for investigators whose research involves only the analysis of existing data sets and archives. The IRB acknowledges the guidance document prepared by the University of Chicago Social and Behavioral Sciences IRB as the model for this Guidance.

Although projects that only involve secondary data analysis do not involve interactions or interventions with humans, they may still require IRB review, because the definition of “human subject” at 45 CFR 46.102(f) includes living individuals about whom an investigator obtains identifiable private information for research purposes .

1. When does secondary use of existing data not require IRB review?

In general, the secondary analysis of existing data does not require IRB review when it does not fall within the regulatory definition of research involving human subjects.

A. Public Use Data Sets

Public use data sets are prepared with the intent of making them available for the public. The data available to the public are not individually identifiable and therefore analysis would not involve human subjects. The IRB recognizes that the analysis of de-identified, publicly available data does not constitute human subjects research as defined at 45 CFR 46.102 and that it does not require IRB review. The IRB no longer requires the registration or review of studies involving the analysis of public use data sets unless a project merges multiple data sets and in so doing enables the identification of individuals whose data is analyzed. An IRB review may be required for a research study that relies exclusively on secondary use of anonymous information BUT records data linkage or disseminates results in such a way that it generates identifiable information.

In addition to being identifiable, existing data must include “private information” in order to constitute research involving human subjects. Private information is defined as information which has been provided for specific purposes by an individual and which the individual can reasonably expect will not be made public (e.g., a medical or school record). For example, a study involving only analysis of the published salaries and benefits of university presidents would not need IRB review since this information is not private.

B. De-identified Data

If a dataset has been stripped of all identifying information and there is no way it could be linked back to the subjects from whom it was originally collected (through a key to a coding system or by other means), its subsequent use by the Principal Investigator or by another researcher would not constitute human subjects research, since the data is no longer identifiable. “Identifiable” means the identity of the subject is known or may be readily ascertained by the investigator or associated with the information. In general, information is considered to be identifiable when it can be linked to specific individuals by the researcher either directly or indirectly through coding systems, or when characteristics of the information obtained are such that a reasonably knowledgeable person could ascertain the identities of individuals. Even though a dataset has been stripped of direct identifiers (e.g., names, addresses, student ID numbers, etc.), it may still be possible to identify an individual through a combination of other characteristics (e.g., age, gender, ethnicity, place of employment).

Example: Many student research projects involve secondary analysis of data that belongs to, or was initially collected by, their faculty advisor or another investigator. If the student is provided with a de-identified, non-coded data set, the use of the data does not constitute research with human subjects because there is no interaction with any individual and no identifiable private information will be used.

Coded data: Secondary analysis of coded private information is not considered to be research involving human subjects and would not require IRB review IF the investigator(s) cannot readily ascertain the identity of the individuals to whom the coded private information pertains as a result of one of the following circumstances:

• The investigators and the holder of the key have entered into an agreement prohibiting the release of the key to the investigators under any circumstances, until the individuals are deceased (HHS regulations for humans subjects research do not require the IRB to review and approve this agreement);
• There are IRB-approved written policies and operating procedures for a repository or data management center that prohibit the release of the key to the investigator under any circumstances, until the individuals are deceased; or
• There are other legal requirements prohibiting the release of the key to the investigators, until the individuals are deceased.

For more information on when analysis of coded data is or is not human subjects research, see the HHS Office for Human Research Protections Guidance on Research Involving Coded Private Information or Biological Specimens at http://www.hhs.gov/ohrp/policy/cdebiol.html .

Note: If a student is analyzing coded data from a faculty advisor/sponsor who retains a key, this would be human subjects research, because the faculty advisor is considered an investigator on the student’s protocol, and can readily ascertain the identity of the subjects since he/she holds the key to the coded data. If the student’s work fits within the scope of the initial protocol from which the dataset originates, the faculty advisor (or investigator who holds the dataset) may wish to consider adding the student and his/her work to the original protocol by means of an amendment application rather than having the student submit a new application for review.

Example: Researcher B plans to examine the relationships between attention deficit hyperactivity disorder (ADHD), oppositional defiance disorder, and teen drug abuse using data collected by Agencies I, II, and III that work with “at risk” youth. The data will be coded and the agencies have entered into an agreement prohibiting release of the key to the researcher that could connect the data with identifiers. The use of the data would not constitute research with human subjects.

If the IRB determines that the project does not constitute human subjects research, the IRB will notify the investigator. If the IRB determines that the project does involve human subjects research, the investigator will be asked to submit a protocol for consideration by the IRB.

2. When is the secondary use of existing data exempt?

There are six categories of research activities involving human subjects that may be exempt from the requirements of the federal regulations on human subjects research protections (45 CFR 46.101(2)(b)). However, only one exemption category (Category 4) applies specifically to existing data. If research is found to be exempt, it need not receive full or expedited review. In order to qualify for an exempt determination, an IRB-5 application must be submitted in InfoEd for IRB review.

Research involving collection or study of existing data, documents, and records can be exempted under Category 4 of the federal regulations if: (i) the sources of such data are publicly available; or (ii) the information is recorded by the investigator in such a manner that subjects cannot be identified, directly or through identifiers linked to the subjects.

The latter condition of this category applies in cases where the investigators initially have access to identifiable private information but abstract the data needed for the research in such a way that the information can no longer be connected to the identity of the subjects. This means that the abstracted data set does not include direct identifiers (names, social security numbers, addresses, phone numbers, etc.) or indirect identifiers (codes or pseudonyms that are linked to the subject’s identity). Furthermore, it must not be possible to identify subjects by combining a number of characteristics (e.g., date of birth, gender, position, and place of employment). This is especially relevant in smaller datasets, where the population is confined to a limited subject pool. The following do not qualify for exemption: Research involving prisoners, and FDA-regulated research.

Example: Student A will be given access to data from her faculty advisor’s health survey research project. The data consists of coded survey responses, and the advisor will retain a key that would link the data to identifiers. The student will extract the information she needs for her project without including any identifying information and without retaining the code. The use of the data does constitute research with human subjects because the initial data set is identifiable (albeit through a coding system); however, it would qualify for exempt status.

3. When does the secondary use of existing data require expedited or full board review?

If secondary analysis of existing data does involve research with human subjects and does not qualify for exempt status as explained above, the project must be reviewed either through expedited procedures or by the full (convened) IRB, and an IRB-1 protocol application must be submitted in InfoEd for IRB review.

Consent: Researchers using data previously collected under another study should consider whether the currently proposed research is a “compatible use” with what subjects agreed to in the original consent form. For non-exempt projects, a consent process description or justification for a waiver must be included in the research protocol.

The IRB may require that informed consent for secondary analysis be obtained from subjects whose data will be accessed.

Alternatively, the IRB can consider a request for a waiver of one or more elements of informed consent under 45 CFR 46.116(d). In order to approve such waiver, the IRB must first be satisfied that the research:

• presents minimal risk (no risks of harm, considering probability and magnitude, greater than those ordinarily encountered in daily life or during the performance of routine examinations or tests); and
• the waiver or alteration will not adversely affect the rights and welfare of the subjects; and
• the research could not practicably be carried out without the waiver or alteration; and
• whenever appropriate, the subjects will be provided with additional pertinent information after participation.

“Restricted Use Data”: Certain agencies and research organizations release files to researchers with specific restrictions regarding their use and storage. These restrictions are typically described in a data use or restricted use data agreement the organization requires be signed in order to receive the data. The records frequently contain identifiers or extensive variables that combined might enable identification, even though this is not the intent of the researcher. Research using these data sets requires expedited or full board level review. Note that the data use or restricted use data agreement must be reviewed by Sponsored Programs Services (SPS) prior to institutional approval. The IRB will not approve the study until the agreement receives approval by SPS. The protocol may be submitted to the IRB at the same time the agreement is submitted to SPS.

1) Student C will be given access to coded mental health assessments from his faculty advisor’s research project. The student plans to analyze the data with a code attached to each record, and the advisor will retain a key to the code that would link the data to identifiers. The use of the data does constitute research with human subjects and does not qualify for exempt status since subjects can be identified. This student project would require an IRB-1 protocol application to be submitted in InfoEd for expedited or full board review by the IRB.

Note: As previously noted, if the student’s work fits within the scope of the initial protocol from which the dataset originates, the faculty advisor (or investigator who holds the dataset) may wish to consider adding the student and his/her work to the original protocol by means of an amendment application rather than having the student submit a new application for expedited or full board review.

2) Student D is applying to the National Center for Health Statistics for use of data from the National Health and Nutrition Examination Survey that includes geographic identifiers and date of examination. The analysis of this restricted use data would require IRB-1 protocol application to be submitted in InfoEd for expedited or full board review by the IRB.

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Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

Secondary data refers to information that has been collected, processed, and published by someone else, rather than the researcher gathering the data firsthand. This can include data from sources such as government publications, academic journals, market research reports, and other existing datasets.

Secondary Data Types

Types of secondary data are as follows:

• Published data: Published data refers to data that has been published in books, magazines, newspapers, and other print media. Examples include statistical reports, market research reports, and scholarly articles.
• Government data: Government data refers to data collected by government agencies and departments. This can include data on demographics, economic trends, crime rates, and health statistics.
• Commercial data: Commercial data is data collected by businesses for their own purposes. This can include sales data, customer feedback, and market research data.
• Academic data: Academic data refers to data collected by researchers for academic purposes. This can include data from experiments, surveys, and observational studies.
• Online data: Online data refers to data that is available on the internet. This can include social media posts, website analytics, and online customer reviews.
• Organizational data: Organizational data is data collected by businesses or organizations for their own purposes. This can include data on employee performance, financial records, and customer satisfaction.
• Historical data : Historical data refers to data that was collected in the past and is still available for research purposes. This can include census data, historical documents, and archival records.
• International data: International data refers to data collected from other countries for research purposes. This can include data on international trade, health statistics, and demographic trends.
• Public data : Public data refers to data that is available to the general public. This can include data from government agencies, non-profit organizations, and other sources.
• Private data: Private data refers to data that is not available to the general public. This can include confidential business data, personal medical records, and financial data.
• Big data: Big data refers to large, complex datasets that are difficult to manage and analyze using traditional data processing methods. This can include social media data, sensor data, and other types of data generated by digital devices.

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

• Published sources: Researchers can gather secondary data from published sources such as books, journals, reports, and newspapers. These sources often provide comprehensive information on a variety of topics.
• Online sources: With the growth of the internet, researchers can now access a vast amount of secondary data online. This includes websites, databases, and online archives.
• Government sources : Government agencies often collect and publish a wide range of secondary data on topics such as demographics, crime rates, and health statistics. Researchers can obtain this data through government websites, publications, or data portals.
• Commercial sources: Businesses often collect and analyze data for marketing research or customer profiling. Researchers can obtain this data through commercial data providers or by purchasing market research reports.
• Academic sources: Researchers can also obtain secondary data from academic sources such as published research studies, academic journals, and dissertations.
• Personal contacts: Researchers can also obtain secondary data from personal contacts, such as experts in a particular field or individuals with specialized knowledge.

Secondary Data Formats

Secondary data can come in various formats depending on the source from which it is obtained. Here are some common formats of secondary data:

• Numeric Data: Numeric data is often in the form of statistics and numerical figures that have been compiled and reported by organizations such as government agencies, research institutions, and commercial enterprises. This can include data such as population figures, GDP, sales figures, and market share.
• Textual Data: Textual data is often in the form of written documents, such as reports, articles, and books. This can include qualitative data such as descriptions, opinions, and narratives.
• Audiovisual Data : Audiovisual data is often in the form of recordings, videos, and photographs. This can include data such as interviews, focus group discussions, and other types of qualitative data.
• Geospatial Data: Geospatial data is often in the form of maps, satellite images, and geographic information systems (GIS) data. This can include data such as demographic information, land use patterns, and transportation networks.
• Transactional Data : Transactional data is often in the form of digital records of financial and business transactions. This can include data such as purchase histories, customer behavior, and financial transactions.
• Social Media Data: Social media data is often in the form of user-generated content from social media platforms such as Facebook, Twitter, and Instagram. This can include data such as user demographics, content trends, and sentiment analysis.

Secondary Data Analysis Methods

Secondary data analysis involves the use of pre-existing data for research purposes. Here are some common methods of secondary data analysis:

• Descriptive Analysis: This method involves describing the characteristics of a dataset, such as the mean, standard deviation, and range of the data. Descriptive analysis can be used to summarize data and provide an overview of trends.
• Inferential Analysis: This method involves making inferences and drawing conclusions about a population based on a sample of data. Inferential analysis can be used to test hypotheses and determine the statistical significance of relationships between variables.
• Content Analysis: This method involves analyzing textual or visual data to identify patterns and themes. Content analysis can be used to study the content of documents, media coverage, and social media posts.
• Time-Series Analysis : This method involves analyzing data over time to identify trends and patterns. Time-series analysis can be used to study economic trends, climate change, and other phenomena that change over time.
• Spatial Analysis : This method involves analyzing data in relation to geographic location. Spatial analysis can be used to study patterns of disease spread, land use patterns, and the effects of environmental factors on health outcomes.
• Meta-Analysis: This method involves combining data from multiple studies to draw conclusions about a particular phenomenon. Meta-analysis can be used to synthesize the results of previous research and provide a more comprehensive understanding of a particular topic.

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

• Define your research question: Start by defining your research question and identifying the specific information you need to answer it. This will help you identify the type of secondary data you need and where to find it.
• Identify relevant sources: Identify potential sources of secondary data, including published sources, online databases, government sources, and commercial data providers. Consider the reliability and validity of each source.
• Evaluate the quality of the data: Evaluate the quality and reliability of the data you plan to use. Consider the data collection methods, sample size, and potential biases. Make sure the data is relevant to your research question and is suitable for the type of analysis you plan to conduct.
• Collect the data: Collect the relevant data from the identified sources. Use a consistent method to record and organize the data to make analysis easier.
• Validate the data: Validate the data to ensure that it is accurate and reliable. Check for inconsistencies, missing data, and errors. Address any issues before analyzing the data.
• Analyze the data: Analyze the data using appropriate statistical and analytical methods. Use descriptive and inferential statistics to summarize and draw conclusions from the data.
• Interpret the results: Interpret the results of your analysis and draw conclusions based on the data. Make sure your conclusions are supported by the data and are relevant to your research question.
• Communicate the findings : Communicate your findings clearly and concisely. Use appropriate visual aids such as graphs and charts to help explain your results.

Examples of Secondary Data

Here are some examples of secondary data from different fields:

• Healthcare : Hospital records, medical journals, clinical trial data, and disease registries are examples of secondary data sources in healthcare. These sources can provide researchers with information on patient demographics, disease prevalence, and treatment outcomes.
• Marketing : Market research reports, customer surveys, and sales data are examples of secondary data sources in marketing. These sources can provide marketers with information on consumer preferences, market trends, and competitor activity.
• Education : Student test scores, graduation rates, and enrollment statistics are examples of secondary data sources in education. These sources can provide researchers with information on student achievement, teacher effectiveness, and educational disparities.
• Finance : Stock market data, financial statements, and credit reports are examples of secondary data sources in finance. These sources can provide investors with information on market trends, company performance, and creditworthiness.
• Social Science : Government statistics, census data, and survey data are examples of secondary data sources in social science. These sources can provide researchers with information on population demographics, social trends, and political attitudes.
• Environmental Science : Climate data, remote sensing data, and ecological monitoring data are examples of secondary data sources in environmental science. These sources can provide researchers with information on weather patterns, land use, and biodiversity.

Purpose of Secondary Data

The purpose of secondary data is to provide researchers with information that has already been collected by others for other purposes. Secondary data can be used to support research questions, test hypotheses, and answer research objectives. Some of the key purposes of secondary data are:

• To gain a better understanding of the research topic : Secondary data can be used to provide context and background information on a research topic. This can help researchers understand the historical and social context of their research and gain insights into relevant variables and relationships.
• To save time and resources: Collecting new primary data can be time-consuming and expensive. Using existing secondary data sources can save researchers time and resources by providing access to pre-existing data that has already been collected and organized.
• To provide comparative data : Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• To support triangulation: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• To supplement primary data : Secondary data can be used to supplement primary data by providing additional information or insights that were not captured by the primary research. This can help researchers gain a more complete understanding of the research topic and draw more robust conclusions.

When to use Secondary Data

Secondary data can be useful in a variety of research contexts, and there are several situations in which it may be appropriate to use secondary data. Some common situations in which secondary data may be used include:

• When primary data collection is not feasible : Collecting primary data can be time-consuming and expensive, and in some cases, it may not be feasible to collect primary data. In these situations, secondary data can provide valuable insights and information.
• When exploring a new research area : Secondary data can be a useful starting point for researchers who are exploring a new research area. Secondary data can provide context and background information on a research topic, and can help researchers identify key variables and relationships to explore further.
• When comparing and contrasting research findings: Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• When triangulating research findings: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• When validating research findings : Secondary data can be used to validate primary research findings by providing additional sources of data that support or refute the primary findings.

Characteristics of Secondary Data

Secondary data have several characteristics that distinguish them from primary data. Here are some of the key characteristics of secondary data:

• Non-reactive: Secondary data are non-reactive, meaning that they are not collected for the specific purpose of the research study. This means that the researcher has no control over the data collection process, and cannot influence how the data were collected.
• Time-saving: Secondary data are pre-existing, meaning that they have already been collected and organized by someone else. This can save the researcher time and resources, as they do not need to collect the data themselves.
• Wide-ranging : Secondary data sources can provide a wide range of information on a variety of topics. This can be useful for researchers who are exploring a new research area or seeking to compare and contrast research findings.
• Less expensive: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Potential for bias : Secondary data may be subject to biases that were present in the original data collection process. For example, data may have been collected using a biased sampling method or the data may be incomplete or inaccurate.
• Lack of control: The researcher has no control over the data collection process and cannot ensure that the data were collected using appropriate methods or measures.
• Requires careful evaluation : Secondary data sources must be evaluated carefully to ensure that they are appropriate for the research question and analysis. This includes assessing the quality, reliability, and validity of the data sources.

Advantages of Secondary Data

There are several advantages to using secondary data in research, including:

• Time-saving : Collecting primary data can be time-consuming and expensive. Secondary data can be accessed quickly and easily, which can save researchers time and resources.
• Cost-effective: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Large sample size : Secondary data sources often have larger sample sizes than primary data sources, which can increase the statistical power of the research.
• Access to historical data : Secondary data sources can provide access to historical data, which can be useful for researchers who are studying trends over time.
• No ethical concerns: Secondary data are already in existence, so there are no ethical concerns related to collecting data from human subjects.
• May be more objective : Secondary data may be more objective than primary data, as the data were not collected for the specific purpose of the research study.

Limitations of Secondary Data

While there are many advantages to using secondary data in research, there are also some limitations that should be considered. Some of the main limitations of secondary data include:

• Lack of control over data quality : Researchers do not have control over the data collection process, which means they cannot ensure the accuracy or completeness of the data.
• Limited availability: Secondary data may not be available for the specific research question or study design.
• Lack of information on sampling and data collection methods: Researchers may not have access to information on the sampling and data collection methods used to gather the secondary data. This can make it difficult to evaluate the quality of the data.
• Data may not be up-to-date: Secondary data may not be up-to-date or relevant to the current research question.
• Data may be incomplete or inaccurate : Secondary data may be incomplete or inaccurate due to missing or incorrect data points, data entry errors, or other factors.
• Biases in data collection: The data may have been collected using biased sampling or data collection methods, which can limit the validity of the data.
• Lack of control over variables: Researchers have limited control over the variables that were measured in the original data collection process, which can limit the ability to draw conclusions about causality.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Secondary Analysis: A Case Exemplar

Judith ann tate.

The Ohio State University, College of Nursing

Mary Beth Happ

Qualitative secondary analysis (QSA) is the use of qualitative data collected by someone else or to answer a different research question. Secondary analysis of qualitative data provides an opportunity to maximize data utility particularly with difficult to reach patient populations. However, QSA methods require careful consideration and explicit description to best understand, contextualize, and evaluate the research results. In this paper, we describe methodologic considerations using a case exemplar to illustrate challenges specific to QSA and strategies to overcome them.

Health care research requires significant time and resources. Secondary analysis of existing data provides an efficient alternative to collecting data from new groups or the same subjects. Secondary analysis, defined as the reuse of existing data to investigate a different research question ( Heaton, 2004 ), has a similar purpose whether the data are quantitative or qualitative. Common goals include to (1) perform additional analyses on the original dataset, (2) analyze a subset of the original data, (3) apply a new perspective or focus to the original data, or (4) validate or expand findings from the original analysis ( Hinds, Vogel, & Clarke-Steffen, 1997 ). Synthesis of knowledge from meta-analysis or aggregation may be viewed as an additional purpose of secondary analysis ( Heaton, 2004 ).

Qualitative studies utilize several different data sources, such as interviews, observations, field notes, archival meeting minutes or clinical record notes, to produce rich descriptions of human experiences within a social context. The work typically requires significant resources (e.g., personnel effort/time) for data collection and analysis. When feasible, qualitative secondary analysis (QSA) can be a useful and cost-effective alternative to designing and conducting redundant primary studies. With advances in computerized data storage and analysis programs, sharing qualitative datasets has become easier. However, little guidance is available for conducting, structuring procedures, or evaluating QSA ( Szabo & Strang, 1997 ).

QSA has been described as “an almost invisible enterprise in social research” ( Fielding, 2004 ). Primary data is often re-used; however, descriptions of this practice are embedded within the methods section of qualitative research reports rather than explicitly identified as QSA. Moreover, searching or classifying reports as QSA is difficult because many researchers refrain from identifying their work as secondary analyses ( Hinds et al., 1997 ; Thorne, 1998a ). In this paper, we provide an overview of QSA, the purposes, and modes of data sharing and approaches. A unique, expanded QSA approach is presented as a methodological exemplar to illustrate considerations.

QSA Typology

Heaton (2004) classified QSA studies based on the relationship between the secondary and primary questions and the scope of data analyzed. Types of QSA included studies that (1) investigated questions different from the primary study, (2) applied a unique theoretical perspective, or (3) extended the primary work. Heaton’s literature review (2004) showed that studies varied in the choice of data used, from selected portions to entire or combined datasets.

Modes of Data Sharing

Heaton (2004) identified three modes of data sharing: formal, informal and auto-data. Formal data sharing involves accessing and analyzing deposited or archived qualitative data by an independent group of researchers. Historical research often uses formal data sharing. Informal data sharing refers to requests for direct access to an investigator’s data for use alone or to pool with other data, usually as a result of informal networking. In some instances, the primary researchers may be invited to collaborate. The most common mode of data sharing is auto-data, defined as further exploration of a qualitative data set by the primary research team. Due to the iterative nature of qualitative research, when using auto-data, it may be difficult to determine where the original study questions end and discrete, distinct analysis begins ( Heaton, 1998 ).

An Exemplar QSA

Below we describe a QSA exemplar conducted by the primary author of this paper (JT), a member of the original research team, who used a supplementary approach to examine concepts revealed but not fully investigated in the primary study. First, we describe an overview of the original study on which the QSA was based. Then, the exemplar QSA is presented to illustrate: (1) the use of auto-data when the new research questions are closely related to or extend the original study aims ( Table 1 ), (2) the collection of additional clinical record data to supplement the original dataset and (3) the performance of separate member checking in the form of expert review and opinion. Considerations and recommendations for use of QSA are reviewed with illustrations taken from the exemplar study ( Table 2 ). Finally, discussion of conclusions and implications is included to assist with planning and implementation of QSA studies.

Research question comparison

Application of the Exemplar Qualitative Secondary Analysis (QSA)

Aitken, L. M., Marshall, A. P., Elliott, R., & McKinley, S. (2009). Critical care nurses' decision making: sedation assessment and management in intensive care. Journal of Clinical Nursing, 18 (1), 36–45.

Morse, J., & Field, P. (1995). Qualitative research methods for health professionals. (2nd ed.). Thousand Oaks, CA: Sage Publishing.

Patel, R. P., Gambrell, M., Speroff, T.,…Strength, C. (2009). Delirium and sedation in the intensive care unit: Survey of behaviors and attitudes of 1384 healthcare professionals. Critical Care Medicine, 37 (3), 825–832.

Shehabi, Y., Botha, J. A., Boyle, M. S., Ernest, D., Freebairn, R. C., Jenkins, I. R., … Seppelt, I. M. (2008). Sedation and delirium in the intensive care unit: an Australian and New Zealand perspective. Anaesthesia & Intensive Care, 36 (4), 570–578.

Tanios, M. A., de Wit, M., Epstein, S. K., & Devlin, J. W. (2009). Perceived barriers to the use of sedation protocols and daily sedation interruption: a multidisciplinary survey. Journal of Critical Care, 24 (1), 66–73.

Weinert, C. R., & Calvin, A. D. (2007). Epidemiology of sedation and sedation adequacy for mechanically ventilated patients in a medical and surgical intensive care unit. Critical Care Medicine , 35(2), 393–401.

The Primary Study

Briefly, the original study was a micro-level ethnography designed to describe the processes of care and communication with patients weaning from prolonged mechanical ventilation (PMV) in a 28-bed Medical Intensive Care Unit ( Broyles, Colbert, Tate, & Happ, 2008 ; Happ, Swigart, Tate, Arnold, Sereika, & Hoffman, 2007 ; Happ et al, 2007 , 2010 ). Both the primary study and the QSA were approved by the Institutional Review Board at the University of Pittsburgh. Data were collected by two experienced investigators and a PhD student-research project coordinator. Data sources consisted of sustained field observations, interviews with patients, family members and clinicians, and clinical record review, including all narrative clinical documentation recorded by direct caregivers.

During iterative data collection and analysis in the original study, it became apparent that anxiety and agitation had an effect on the duration of ventilator weaning episodes, an observation that helped to formulate the questions for the QSA ( Tate, Dabbs, Hoffman, Milbrandt & Happ, 2012 ). Thus, the secondary topic was closely aligned as an important facet of the primary phenomenon. The close, natural relationship between the primary and QSA research questions is demonstrated in the side-by-side comparison in Table 1 . This QSA focused on new questions which extended the original study to recognition and management of anxiety or agitation, behaviors that often accompany mechanical ventilation and weaning but occur throughout the trajectory of critical illness and recovery.

Considerations when Undertaking QSA ( Table 2 )

Practical advantages.

A key practical advantage of QSA is maximizing use of existing data. Data collection efforts represent a significant percentage of the research budget in terms of cost and labor ( Coyer & Gallo, 2005 ). This is particularly important in view of the competition for research funding. Planning and implementing a qualitative study involves considerable time and expertise not only for data collecting (e.g., interviews, participant observation or focus group), but in establishing access, credibility and relationships ( Thorne, 1994 ) and in conducting the analysis. The cost of QSA is often seen as negligible since the outlay of resources for data collection is assumed by the original study. However, QSA incurs costs related to storage, researcher’s effort for review of existing data, analysis, and any further data collection that may be necessary.

Another advantage of QSA is access to data from an assembled cohort. In conducting original primary research, practical concerns arise when participants are difficult to locate or reluctant to divulge sensitive details to a researcher. In the case of vulnerable critically ill patients, participation in research may seem an unnecessary burden to family members who may be unwilling to provide proxy consent ( Fielding, 2004 ). QSA permits new questions to be asked of data collected previously from these vulnerable groups ( Rew, Koniak-Griffin, Lewis, Miles, & O'Sullivan, 2000 ), or from groups or events that occur with scarcity ( Thorne, 1994 ). Participants’ time and effort in the primary study therefore becomes more worthwhile. In fact, it is recommended that data already collected from existing studies of vulnerable populations or about sensitive topics be analyzed prior to engaging new participants. In this way, QSA becomes a cumulative rather than a repetitive process ( Fielding, 2004 ).

Data Adequacy and Congruency

Secondary researchers must determine that the primary data set meets the needs of the QSA. Data may be insufficient to answer a new question or the focus of the QSA may be so different as to render the pursuit of a QSA impossible ( Heaton, 1998 ). The underlying assumptions, sampling plan, research questions, and conceptual framework selected to answer the original study question may not fit the question posed during QSA ( Coyer & Gallo, 2005 ). The researchers of the primary study may have selectively sampled participants and analyzed the resulting data in a manner that produced a narrow or uneven scope of data ( Hinds et al., 1997 ). Thus, the data needed to fully answer questions posed by the QSA may be inadequately addressed in the primary study. A critical review of the existing dataset is an important first step in determining whether the primary data fits the secondary questions ( Hinds et al., 1997 ).

Passage of Time

The timing of the QSA is another important consideration. If the primary study and secondary study are performed sequentially, findings of the original study may influence the secondary study. On the other hand, studies performed concurrently offer the benefit of access to both the primary research team and participants member checking ( Hinds et al., 1997 ).

The passage of time since the primary study was conducted can also have a distinct effect on the usefulness of the primary dataset. Data may be outdated or contain a historical bias ( Coyer & Gallo, 2005 ). Since context changes over time, characteristics of the phenomena of interest may have changed. Analysis of older datasets may not illuminate the phenomena as they exist today.( Hinds et al., 1997 ) Even if participants could be re-contacted, their perspectives, memories and experiences change. The passage of time also has an affect on the relationship of the primary researchers to the data – so auto-data may be interpreted differently by the same researcher with the passage of time. Data are bound by time and history, therefore, may be a threat to internal validity unless a new investigator is able to account for these effects when interpreting data ( Rew et al., 2000 ).

Researcher stance/Context involvement

Issues related to context are a major source of criticism of QSA ( Gladstone, Volpe, & Boydell, 2007 ). One of the hallmarks of qualitative research is the relationship of the researcher to the participants. It can be argued that removing active contact with participants violates this premise. Tacit understandings developed in the field may be difficult or impossible to reconstruct ( Thorne, 1994 ). Qualitative fieldworkers often react and redirect the data collection based on a growing knowledge of the setting. The setting may change as a result of external or internal factors. Interpretation of researchers as participants in a unique time and social context may be impossible to re-construct even if the secondary researchers were members of the primary team ( Mauthner, Parry, & Milburn, 1998 ). Because the context in which the data were originally produced cannot be recovered, the ability of the researcher to react to the lived experience may be curtailed in QSA ( Gladstone et al., 2007 ). Researchers utilize a number of tactics to filter and prioritize what to include as data that may not be apparent in either the written or spoken records of those events ( Thorne, 1994 ). Reflexivity between the researcher, participants and setting is impossible to recreate when examining pre-existing data.

Relationship of QSA Researcher to Primary Study

The relationship of the QSA researcher to the primary study is an important consideration. When the QSA researcher is not part of the original study team, contractual arrangements detailing access to data, its format, access to the original team, and authorship are required ( Hinds et al., 1997 ). The QSA researcher should assess the condition of the data, documents including transcripts, memos and notes, and clarity and flow of interactions ( Hinds et al., 1997 ). An outline of the original study and data collection procedures should be critically reviewed ( Heaton, 1998 ). If the secondary researcher was not a member of the original study team, access to the original investigative team for the purpose of ongoing clarification is essential ( Hinds et al., 1997 ).

Membership on the original study team may, however, offer the secondary researcher little advantage depending on their role in the primary study. Some research team members may have had responsibility for only one type of data collection or data source. There may be differences in involvement with analysis of the primary data.

Informed Consent of Participants

Thorne (1998) questioned whether data collected for one study purpose can ethically be re-examined to answer another question without participants’ consent. Many institutional review boards permit consent forms to include language about the possibility of future use of existing data. While this mechanism is becoming routine and welcomed by researchers, concerns have been raised that a generic consent cannot possibly address all future secondary questions and may violate the principle of full informed consent ( Gladstone et al., 2007 ). Local variations in study approval practices by institutional review boards may influence the ability of researchers to conduct a QSA.

Rigor of QSA

The primary standards for evaluating rigor of qualitative studies are trustworthiness (logical relationship between the data and the analytic claims), fit (the context within which the findings are applicable), transferability (the overall generalizability of the claims) and auditabilty (the transparency of the procedural steps and the analytic moves processes) ( Lincoln & Guba, 1991 ). Thorne suggests that standard procedures for assuring rigor can be modified for QSA ( Thorne, 1994 ). For instance, the original researchers may be viewed as sources of confirmation while new informants, other related datasets and validation by clinical experts are sources of triangulation that may overcome the lack of access to primary subjects ( Heaton, 2004 ; Thorne, 1994 ).

Our observations, derived from the experience of posing a new question of existing qualitative data serves as a template for researchers considering QSA. Considerations regarding quality, availability and appropriateness of existing data are of primary importance. A realistic plan for collecting additional data to answer questions posed in QSA should consider burden and resources for data collection, analysis, storage and maintenance. Researchers should consider context as a potential limitation to new analyses. Finally, the cost of QSA should be fully evaluated prior to making a decision to pursue QSA.

Acknowledgments

This work was funded by the National Institute of Nursing Research (RO1-NR07973, M Happ PI) and a Clinical Practice Grant from the American Association of Critical Care Nurses (JA Tate, PI).

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Disclosure statement: Drs. Tate and Happ have no potential conflicts of interest to disclose that relate to the content of this manuscript and do not anticipate conflicts in the foreseeable future.

Contributor Information

Judith Ann Tate, The Ohio State University, College of Nursing.

Mary Beth Happ, The Ohio State University, College of Nursing.

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