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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 April 2, 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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How to Analyse Secondary Data for a Dissertation

Secondary data refers to data that has already been collected by another researcher. For researchers (and students!) with limited time and resources, secondary data, whether qualitative or quantitative can be a highly viable source of data.  In addition, with the advances in technology and access to peer reviewed journals and studies provided by the internet, it is increasingly popular as a form of data collection.  The question that frequently arises amongst students however, is: how is secondary data best analysed?

The process of data analysis in secondary research

Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective.  In simple terms there are three steps:

• Step One: Development of Research Questions
• Step Two: Identification of dataset
• Step Three: Evaluation of the dataset.

Let’s look at each of these in more detail:

Step One: Development of research questions

Using secondary data means you need to apply theoretical knowledge and conceptual skills to be able to use the dataset to answer research questions.  Clearly therefore, the first step is thus to clearly define and develop your research questions so that you know the areas of interest that you need to explore for location of the most appropriate secondary data.

Step Two: Identification of Dataset

This stage should start with identification, through investigation, of what is currently known in the subject area and where there are gaps, and thus what data is available to address these gaps.  Sources can be academic from prior studies that have used quantitative or qualitative data, and which can then be gathered together and collated to produce a new secondary dataset.  In addition, other more informal or “grey” literature can also be incorporated, including consumer report, commercial studies or similar.  One of the values of using secondary research is that original survey works often do not use all the data collected which means this unused information can be applied to different settings or perspectives.

Key point: Effective use of secondary data means identifying how the data can be used to deliver meaningful and relevant answers to the research questions.  In other words that the data used is a good fit for the study and research questions.

Step Three: Evaluation of the dataset for effectiveness/fit

A good tip is to use a reflective approach for data evaluation.  In other words, for each piece of secondary data to be utilised, it is sensible to identify the purpose of the work, the credentials of the authors (i.e., credibility, what data is provided in the original work and how long ago it was collected).  In addition, the methods used and the level of consistency that exists compared to other works. This is important because understanding the primary method of data collection will impact on the overall evaluation and analysis when it is used as secondary source. In essence, if there is no understanding of the coding used in qualitative data analysis to identify key themes then there will be a mismatch with interpretations when the data is used for secondary purposes.  Furthermore, having multiple sources which draw similar conclusions ensures a higher level of validity than relying on only one or two secondary sources.

A useful framework provides a flow chart of decision making, as shown in the figure below.

Following this process ensures that only those that are most appropriate for your research questions are included in the final dataset, but also demonstrates to your readers that you have been thorough in identifying the right works to use.

Writing up the Analysis

Once you have your dataset, writing up the analysis will depend on the process used.  If the data is qualitative in nature, then you should follow the following process.

Pre-Planning

• Read and re-read all sources, identifying initial observations, correlations, and relationships between themes and how they apply to your research questions.
• Once initial themes are identified, it is sensible to explore further and identify sub-themes which lead on from the core themes and correlations in the dataset, which encourages identification of new insights and contributes to the originality of your own work.

Structure of the Analysis Presentation

Introduction.

The introduction should commence with an overview of all your sources. It is good practice to present these in a table, listed chronologically so that your work has an orderly and consistent flow. The introduction should also incorporate a brief (2-3 sentences) overview of the key outcomes and results identified.

The body text for secondary data, irrespective of whether quantitative or qualitative data is used, should be broken up into sub-sections for each argument or theme presented. In the case of qualitative data, depending on whether content, narrative or discourse analysis is used, this means presenting the key papers in the area, their conclusions and how these answer, or not, your research questions. Each source should be clearly cited and referenced at the end of the work. In the case of qualitative data, any figures or tables should be reproduced with the correct citations to their original source. In both cases, it is good practice to give a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

Do not use direct quotes from secondary data unless they are:

• properly referenced, and
• are key to underlining a point or conclusion that you have drawn from the data.

All results sections, regardless of whether primary or secondary data has been used should refer back to the research questions and prior works. This is because, regardless of whether the results back up or contradict previous research, including previous works shows a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Summary of results

The summary of the results section of a secondary data dissertation should deliver a summing up of key findings, and if appropriate a conceptual framework that clearly illustrates the findings of the work. This shows that you have understood your secondary data, how it has answered your research questions, and furthermore that your interpretation has led to some firm outcomes.

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Home » Secondary Data – Types, Methods and Examples

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.

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What is Secondary Research?

Secondary research, also known as a literature review , preliminary research , historical research , background research , desk research , or library research , is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of knowledge on a topic, to identify trends or new practices, to test mathematical models or train machine learning systems, or to verify facts and figures. Secondary research is also used to justify the need for primary research as well as to justify and support other activities. For example, secondary research may be used to support a proposal to modernize a manufacturing plant, to justify the use of newly a developed treatment for cancer, to strengthen a business proposal, or to validate points made in a speech.

Why Is Secondary Research Important?

Because secondary research is used for so many purposes in so many settings, all professionals will be required to perform it at some point in their careers. For managers and entrepreneurs, regardless of the industry or profession, secondary research is a regular part of worklife, although parts of the research, such as finding the supporting documents, are often delegated to juniors in the organization. For all these reasons, it is essential to learn how to conduct secondary research, even if you are unlikely to ever conduct primary research.

Secondary research is also essential if your main goal is primary research. Research funding is obtained only by using secondary research to show the need for the primary research you want to conduct. In fact, primary research depends on secondary research to prove that it is indeed new and original research and not just a rehash or replication of somebody else’s work.

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15 Secondary Research Examples

Secondary research is the analysis, summary or synthesis of already existing published research. Instead of collecting original data, as in primary research , secondary research involves data or the results of data analyses already collected.

It is generally published in books, handbooks, textbooks, articles, encyclopedias, websites, magazines, literature reviews and meta-analyses. These are usually referred to as secondary sources .

Secondary research is a good place to start when wanting to acquire a broad view of a research area. It is usually easier to understand and may not require advanced training in research design and statistics.

Secondary Research Examples

1. literature review.

A literature review summarizes, reviews, and critiques the existing published literature on a topic.

Literature reviews are considered secondary research because it is a collection and analysis of the existing literature rather than generating new data for the study.

They hold value for academic studies because they enable us to take stock of the existing knowledge in a field, evaluate it, and identify flaws or gaps in the existing literature. As a result, they’re almost universally used by academics prior to conducting primary research.

Example 1: Workplace stress in nursing: a literature review

Citation: McVicar, A. (2003). Workplace stress in nursing: a literature review.  Journal of advanced nursing ,  44 (6), 633-642. Source: https://doi.org/10.1046/j.0309-2402.2003.02853.x

Summary: This study conducted a systematic analysis of literature on the causes of stress for nurses in the workplace. The study explored the literature published between 2000 and 2014. The authors found that the literature identifies several main causes of stress for nurses: professional relationships with doctors and staff, communication difficulties with patients and their families, the stress of emergency cases, overwork, lack of staff, and lack of support from the institutions. They conclude that understanding these stress factors can help improve the healthcare system and make it better for both nurses and patients.

Example 2: The impact of shiftwork on health: a literature review

Citation: Matheson, A., O’Brien, L., & Reid, J. A. (2014). The impact of shiftwork on health: a literature review.  Journal of Clinical Nursing ,  23 (23-24), 3309-3320. Source: https://doi.org/10.1111/jocn.12524

In this literature review, 118 studies were analyzed to examine the impact of shift work on nurses’ health. The findings were organized into three main themes: physical health, psychosocial health, and sleep. The majority of shift work research has primarily focused on these themes, but there is a lack of studies that explore the personal experiences of shift workers and how they navigate the effects of shift work on their daily lives. Consequently, it remains challenging to determine how individuals manage their shift work schedules. They found that, while shift work is an inevitable aspect of the nursing profession, there is limited research specifically targeting nurses and the implications for their self-care.

Example 3: Social media and entrepreneurship research: A literature review

Citation: Olanrewaju, A. S. T., Hossain, M. A., Whiteside, N., & Mercieca, P. (2020). Social media and entrepreneurship research: A literature review.  International Journal of Information Management ,  50 , 90-110. Source: https://doi.org/10.1016/j.ijinfomgt.2019.05.011

In this literature review, 118 studies were analyzed to examine the impact of shift work on nurses’ health. The findings were organized into three main themes: physical health, social health , and sleep. The majority of shift work research has primarily focused on these themes, but there is a lack of studies that explore the personal experiences of shift workers and how they navigate the effects of shift work on their daily lives. Consequently, it remains challenging to determine how individuals manage their shift work schedules. They found that, while shift work is an inevitable aspect of the nursing profession, there is limited research specifically targeting nurses and the implications for their self-care.

Example 4: Adoption of electric vehicle: A literature review and prospects for sustainability

Citation: Kumar, R. R., & Alok, K. (2020). Adoption of electric vehicle: A literature review and prospects for sustainability.  Journal of Cleaner Production ,  253 , 119911. Source: https://doi.org/10.1016/j.jclepro.2019.119911

This study is a literature review that aims to synthesize and integrate findings from existing research on electric vehicles. By reviewing 239 articles from top journals, the study identifies key factors that influence electric vehicle adoption. Themes identified included: availability of charging infrastructure and total cost of ownership. The authors propose that this analysis can provide valuable insights for future improvements in electric mobility.

Example 5: Towards an understanding of social media use in the classroom: a literature review

Citation: Van Den Beemt, A., Thurlings, M., & Willems, M. (2020). Towards an understanding of social media use in the classroom: a literature review.  Technology, Pedagogy and Education ,  29 (1), 35-55. Source: https://doi.org/10.1080/1475939X.2019.1695657

This study examines how social media can be used in education and the challenges teachers face in balancing its potential benefits with potential distractions. The review analyzes 271 research papers. They find that ambiguous results and poor study quality plague the literature. However, they identify several factors affecting the success of social media in the classroom, including: school culture, attitudes towards social media, and learning goals. The study’s value is that it organizes findings from a large corpus of existing research to help understand the topic more comprehensively.

2. Meta-Analyses

Meta-analyses are similar to literature reviews, but are at a larger scale and tend to involve the quantitative synthesis of data from multiple studies to identify trends and derive estimates of overall effect sizes.

For example, while a literature review might be a qualitative assessment of trends in the literature, a meta analysis would be a quantitative assessment, using statistical methods, of studies that meet specific inclusion criteria that can be directly compared and contrasted.

Often, meta-analysis aim to identify whether the existing data can provide an authoritative account for a hypothesis and whether it’s confirmed across the body of literature.

Example 6: Cholesterol and Alzheimer’s Disease Risk: A Meta-Meta-Analysis

Citation: Sáiz-Vazquez, O., Puente-Martínez, A., Ubillos-Landa, S., Pacheco-Bonrostro, J., & Santabárbara, J. (2020). Cholesterol and Alzheimer’s disease risk: a meta-meta-analysis.  Brain sciences ,  10 (6), 386. Source: https://doi.org/10.3390/brainsci10060386

This study examines the relationship between cholesterol and Alzheimer’s disease (AD). Researchers conducted a systematic search of meta-analyses and reviewed several databases, collecting 100 primary studies and five meta-analyses to analyze the connection between cholesterol and Alzheimer’s disease. They find that the literature compellingly demonstrates that low-density lipoprotein cholesterol (LDL-C) levels significantly influence the development of Alzheimer’s disease, but high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG) levels do not show significant effects. This is an example of secondary research because it compiles and analyzes data from multiple existing studies and meta-analyses rather than collecting new, original data.

Example 7: The power of feedback revisited: A meta-analysis of educational feedback research

Citation: Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research.  Frontiers in Psychology ,  10 , 3087. Source: https://doi.org/10.3389/fpsyg.2019.03087

This meta-analysis examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes. A key (albeit somewhat obvious) finding was that the manner in which the feedback is provided is a key factor in whether the feedback is effective.

Example 8: How Much Does Education Improve Intelligence? A Meta-Analysis

Citation: Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis.  Psychological science ,  29 (8), 1358-1369. Source: https://doi.org/10.1177/0956797618774253

This study investigates the relationship between years of education and intelligence test scores. The researchers analyzed three types of quasiexperimental studies involving over 600,000 participants to understand if longer education increases intelligence or if more intelligent students simply complete more education. They found that an additional year of education consistently increased cognitive abilities by 1 to 5 IQ points across all broad categories of cognitive ability. The effects persisted throughout the participants’ lives, suggesting that education is an effective way to raise intelligence. This study is an example of secondary research because it compiles and analyzes data from multiple existing studies rather than gathering new, original data.

Example 9: A meta-analysis of factors related to recycling

Citation: Geiger, J. L., Steg, L., Van Der Werff, E., & Ünal, A. B. (2019). A meta-analysis of factors related to recycling.  Journal of environmental psychology ,  64 , 78-97. Source: https://doi.org/10.1016/j.jenvp.2019.05.004

This study aims to identify key factors influencing recycling behavior across different studies. The researchers conducted a random-effects meta-analysis on 91 studies focusing on individual and household recycling. They found that both individual factors (such as recycling self-identity and personal norms) and contextual factors (like having a bin at home and owning a house) impacted recycling behavior. The analysis also revealed that individual and contextual factors better predicted the intention to recycle rather than the actual recycling behavior. The study offers theoretical and practical implications and suggests that future research should examine the effects of contextual factors and the interplay between individual and contextual factors.

Example 10: Stress management interventions for police officers and recruits

Citation: Patterson, G. T., Chung, I. W., & Swan, P. W. (2014). Stress management interventions for police officers and recruits: A meta-analysis.  Journal of experimental criminology ,  10 , 487-513. Source: https://doi.org/10.1007/s11292-014-9214-7

The meta-analysis systematically reviews randomized controlled trials and quasi-experimental studies that explore the effects of stress management interventions on outcomes among police officers. It looked at 12 primary studies published between 1984 and 2008. Across the studies, there were a total of 906 participants. Interestingly, it found that the interventions were not effective. Here, we can see how secondary research is valuable sometimes for showing there is no clear trend or consensus in existing literature. The conclusions suggest a need for further research to develop and implement more effective interventions addressing specific stressors and using randomized controlled trials.

3. Textbooks

Academic textbooks tend not to present new research. Rather, they present key academic information in ways that are accessible to university students and academics.

As a result, we can consider textbooks to be secondary rather than primary research. They’re collections of information and research produced by other people, then re-packaged for a specific audience.

Textbooks tend to be written by experts in a topic. However, unlike literature reviews and meta-analyses, they are not necessarily systematic in nature and are not designed to progress current knowledge through identifying gaps, weaknesses, and strengths in the existing literature.

Example 11: Psychology for the Third Millennium: Integrating Cultural and Neuroscience Perspectives

This textbook aims to bridge the gap between two distinct domains in psychology: Qualitative and Cultural Psychology , which focuses on managing meaning and norms, and Neuropsychology and Neuroscience, which studies brain processes. The authors believe that by combining these areas, a more comprehensive general psychology can be achieved, which unites the biological and cultural aspects of human life. This textbook is considered a secondary source because it synthesizes and integrates information from various primary research studies, theories, and perspectives in the field of psychology.

Example 12: Cultural Sociology: An Introduction

Citation: Bennett, A., Back, L., Edles, L. D., Gibson, M., Inglis, D., Jacobs, R., & Woodward, I. (2012).  Cultural sociology: an introduction . New York: John Wiley & Sons.

This student textbook introduces cultural sociology and proposes that it is a valid model for sociological thinking and research. It gathers together existing knowledge within the field to prevent an overview of major sociological themes and empirical approaches utilized within cultural sociological research. It does not present new research, but rather packages existing knowledge in sociology and makes it understandable for undergraduate students.

Example 13: A Textbook of Community Nursing

Citation: Chilton, S., & Bain, H. (Eds.). (2017).  A textbook of community nursing . New York: Routledge.

This textbook presents an evidence-based introduction to professional topics in nursing. In other words, it gathers evidence from other research and presents it to students. It covers areas such as care approaches, public health, eHealth, therapeutic relationships, and mental health. Like many textbooks, it brings together its own secondary research with user-friendly elements like exercises, activities, and hypothetical case studies in each chapter.

4. White Papers

White papers are typically produced within businesses and government departments rather than academic research environments.

Generally, a white paper will focus on a specific topic of concern to the institution in order to present a state of the current situation as well as opportunities that could be pursued for change, improvement, or profit generation in the future.

Unlike a literature review, a white paper generally doesn’t follow standards of academic rigor and may be presented with a bias toward, or focus on, a company or institution’s mission and values.

Example 14: Future of Mobility White Paper

Citation: Shaheen, S., Totte, H., & Stocker, A. (2018). Future of Mobility White Paper.  UC Berkeley: Institute of Transportation Studies at UC Berkeley Source: https://doi.org/10.7922/G2WH2N5D

This white paper explores the how transportation is changing due to concerns over climate change, equity of access to transit, and rapid technological advances (such as shared mobility and automation). The authors aggregate current information and research on key trends, emerging technologies/services, impacts on California’s transportation ecosystem, and future growth projections by reviewing state agency publications, peer-reviewed articles, and forecast reports from various sources. This white paper is an example of secondary research because it synthesizes and integrates information from multiple primary research sources, expert interviews, and input from an advisory committee of local and state transportation agencies.

Example 15: White Paper Concerning Philosophy of Education and Environment

Citation: Humphreys, C., Blenkinsop, S. White Paper Concerning Philosophy of Education and Environment.  Stud Philos Educ   36 (1): 243–264. Source: https://doi.org/10.1007/s11217-017-9567-2

This white paper acknowledges the increasing significance of climate change, environmental degradation, and our relationship with nature, and the need for philosophers of education and global citizens to respond. The paper examines five key journals in the philosophy of education to identify the scope and content of current environmental discussions. By organizing and summarizing the located articles, it assesses the possibilities and limitations of these discussions within the philosophy of education community. This white paper is an example of secondary research because it synthesizes and integrates information from multiple primary research sources, specifically articles from the key journals in the field, to analyze the current state of environmental discussions.

5. Academic Essays

Students’ academic essays tend to present secondary rather than primary research. The student is expected to study current literature on a topic and use it to present a thesis statement.

Academic essays tend to require rigorous standards of analysis, critique, and evaluation, but do not require systematic investigation of a topic like you would expect in a literature review.

In an essay, a student may identify the most relevant or important data from a field of research in order to demonstrate their knowledge of a field of study. They may also, after demonstrating sufficient knowledge and understanding, present a thesis statement about the issue.

Secondary research involves data that has already been collected. The published research might be reviewed, included in a meta-analysis, or subjected to a re-analysis.

These findings might be published in a peer-reviewed journal or handbook, become the foundation of a book for public consumption, or presented in a more narrative form for a popular website or magazine.

Sources for secondary research can range from scientific journals to government databases and archived data accumulated by research institutes.

University students might engage in secondary research to become familiar with an area of research. That might help spark an intriguing hypothesis for a research project of master’s thesis.

Secondary research can yield new insights into human behavior , or confirm existing conceptualizations of psychological constructs.

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Dissociation Examples (Psychology)
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Zone of Proximal Development Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

• Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
• Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
• Chris Drew (PhD) #molongui-disabled-link 15 Zone of Proximal Development Examples
• Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition

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Secondary Research Guide: Definition, Methods, Examples

Apr 3, 2024

8 min. read

The internet has vastly expanded our access to information, allowing us to learn almost anything about everything. But not all market research is created equal , and this secondary research guide explains why.

There are two key ways to do research. One is to test your own ideas, make your own observations, and collect your own data to derive conclusions. The other is to use secondary research — where someone else has done most of the heavy lifting for you. 

Here’s an overview of secondary research and the value it brings to data-driven businesses.

Secondary Research Definition: What Is Secondary Research?

Primary vs Secondary Market Research

What Are Secondary Research Methods?

Advantages of secondary research, disadvantages of secondary research, best practices for secondary research, how to conduct secondary research with meltwater.

Secondary research definition: The process of collecting information from existing sources and data that have already been analyzed by others.

Secondary research provides a foundation to help you understand a topic, with the goal of building on existing knowledge. They often cover the same information as primary sources, but they add a layer of analysis and explanation to them.

Users can choose from several secondary research types and sources, including:

• Journal articles
• Research papers

With secondary sources, users can draw insights, detect trends , and validate findings to jumpstart their research efforts.

Primary vs. Secondary Market Research

We’ve touched a little on primary research , but it’s essential to understand exactly how primary and secondary research are unique.

Think of primary research as the “thing” itself, and secondary research as the analysis of the “thing,” like these primary and secondary research examples:

• An expert gives an interview (primary research) and a marketer uses that interview to write an article (secondary research).
• A company conducts a consumer satisfaction survey (primary research) and a business analyst uses the survey data to write a market trend report (secondary research).
• A marketing team launches a new advertising campaign across various platforms (primary research) and a marketing research firm, like Meltwater for market research , compiles the campaign performance data to benchmark against industry standards (secondary research).

In other words, primary sources make original contributions to a topic or issue, while secondary sources analyze, synthesize, or interpret primary sources.

Both are necessary when optimizing a business, gaining a competitive edge , improving marketing, or understanding consumer trends that may impact your business.

Secondary research methods focus on analyzing existing data rather than collecting primary data . Common examples of secondary research methods include:

• Literature review . Researchers analyze and synthesize existing literature (e.g., white papers, research papers, articles) to find knowledge gaps and build on current findings.
• Content analysis . Researchers review media sources and published content to find meaningful patterns and trends.
• AI-powered secondary research . Platforms like Meltwater for market research analyze vast amounts of complex data and use AI technologies like natural language processing and machine learning to turn data into contextual insights.

Researchers today have access to more market research tools and technology than ever before, allowing them to streamline their efforts and improve their findings.

Want to see how Meltwater can complement your secondary market research efforts? Simply fill out the form at the bottom of this post, and we'll be in touch.

Conducting secondary research offers benefits in every job function and use case, from marketing to the C-suite. Here are a few advantages you can expect.

Cost and time efficiency

Using existing research saves you time and money compared to conducting primary research. Secondary data is readily available and easily accessible via libraries, free publications, or the Internet. This is particularly advantageous when you face time constraints or when a project requires a large amount of data and research.

Access to large datasets

Secondary data gives you access to larger data sets and sample sizes compared to what primary methods may produce. Larger sample sizes can improve the statistical power of the study and add more credibility to your findings.

Ability to analyze trends and patterns

Using larger sample sizes, researchers have more opportunities to find and analyze trends and patterns. The more data that supports a trend or pattern, the more trustworthy the trend becomes and the more useful for making decisions. 

Historical context

Using a combination of older and recent data allows researchers to gain historical context about patterns and trends. Learning what’s happened before can help decision-makers gain a better current understanding and improve how they approach a problem or project.

Basis for further research

Ideally, you’ll use secondary research to further other efforts . Secondary sources help to identify knowledge gaps, highlight areas for improvement, or conduct deeper investigations.

Tip: Learn how to use Meltwater as a research tool and how Meltwater uses AI.

Secondary research comes with a few drawbacks, though these aren’t necessarily deal breakers when deciding to use secondary sources.

Reliability concerns

Researchers don’t always know where the data comes from or how it’s collected, which can lead to reliability concerns. They don’t control the initial process, nor do they always know the original purpose for collecting the data, both of which can lead to skewed results.

Potential bias

The original data collectors may have a specific agenda when doing their primary research, which may lead to biased findings. Evaluating the credibility and integrity of secondary data sources can prove difficult.

Outdated information

Secondary sources may contain outdated information, especially when dealing with rapidly evolving trends or fields. Using outdated information can lead to inaccurate conclusions and widen knowledge gaps.

Limitations in customization

Relying on secondary data means being at the mercy of what’s already published. It doesn’t consider your specific use cases, which limits you as to how you can customize and use the data.

A lack of relevance

Secondary research rarely holds all the answers you need, at least from a single source. You typically need multiple secondary sources to piece together a narrative, and even then you might not find the specific information you need.

To make secondary market research your new best friend, you’ll need to think critically about its strengths and find ways to overcome its weaknesses. Let’s review some best practices to use secondary research to its fullest potential.

Identify credible sources for secondary research

To overcome the challenges of bias, accuracy, and reliability, choose secondary sources that have a demonstrated history of excellence . For example, an article published in a medical journal naturally has more credibility than a blog post on a little-known website.

Assess credibility based on peer reviews, author expertise, sampling techniques, publication reputation, and data collection methodologies. Cross-reference the data with other sources to gain a general consensus of truth.

The more credibility “factors” a source has, the more confidently you can rely on it. 

Evaluate the quality and relevance of secondary data

You can gauge the quality of the data by asking simple questions:

• How complete is the data? 
• How old is the data? 
• Is this data relevant to my needs?
• Does the data come from a known, trustworthy source?

It’s best to focus on data that aligns with your research objectives. Knowing the questions you want to answer and the outcomes you want to achieve ahead of time helps you focus only on data that offers meaningful insights.

Document your sources 

If you’re sharing secondary data with others, it’s essential to document your sources to gain others’ trust. They don’t have the benefit of being “in the trenches” with you during your research, and sharing your sources can add credibility to your findings and gain instant buy-in.

Secondary market research offers an efficient, cost-effective way to learn more about a topic or trend, providing a comprehensive understanding of the customer journey . Compared to primary research, users can gain broader insights, analyze trends and patterns, and gain a solid foundation for further exploration by using secondary sources.

Meltwater for market research speeds up the time to value in using secondary research with AI-powered insights, enhancing your understanding of the customer journey. Using natural language processing, machine learning, and trusted data science processes, Meltwater helps you find relevant data and automatically surfaces insights to help you understand its significance. Our solution identifies hidden connections between data points you might not know to look for and spells out what the data means, allowing you to make better decisions based on accurate conclusions. Learn more about Meltwater's power as a secondary research solution when you request a demo by filling out the form below:

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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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