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Secondary Data – Types, Methods and Examples

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Secondary Data

Secondary Data


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 This can include census data, historical documents, and archival records.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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Muhammad Hassan

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Secondary research: definition, methods, & examples.

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

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

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

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What is secondary research?

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

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

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

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

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

How to conduct secondary research

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

1.    Identify and define the research topic

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

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

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

2.    Find research and existing data sources

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

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

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

3.    Begin searching and collecting the existing data

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

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

4.    Combine the data and compare the results

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

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

5.    Analyze your data and explore further

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

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

Primary vs secondary research

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

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

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

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

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

Sources of Secondary Research

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

Internal data

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

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

External data

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

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

Three examples of secondary research methods in action

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

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

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

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

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

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

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

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

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

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

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

Advantages of secondary research

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

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

Disadvantages of secondary research

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

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

When do we conduct secondary research?

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

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

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

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

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

Questions to ask before conducting secondary research

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

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

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

  • What am I trying to achieve with this research?

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

  • How credible will my research be?

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

  • What is the date of the secondary research?

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

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

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

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

In it, you’ll learn more about:

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

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

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Home Market Research

Secondary Research: Definition, Methods and Examples.

secondary research

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

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

LEARN ABOUT: Research Process Steps

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

What is Secondary Research: Definition

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

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

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

LEARN ABOUT: Level of Analysis

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

LEARN ABOUT: Data Analytics Projects

Secondary Research Methods with Examples

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

research methodology secondary data example

The following are popularly used secondary research methods and examples:

1. Data Available on The Internet

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

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

2. Government and Non-Government Agencies

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

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

3. Public Libraries

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

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

4. Educational Institutions

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

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

5. Commercial Information Sources

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

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

Key Differences between Primary Research and Secondary Research

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

How to Conduct Secondary Research?

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

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

Here are the steps involved in conducting secondary research:

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

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

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

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

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

Advantages of Secondary Research

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

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

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

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

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

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

Disadvantages of Secondary Research

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

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

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

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

LEARN ABOUT: 12 Best Tools for Researchers

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

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

QuestionPro can be a useful tool for conducting secondary research in a variety of ways. You can create online surveys that target a specific population, collecting data that can be analyzed to gain insights into consumer behavior, attitudes, and preferences; analyze existing data sets that you have obtained through other means or benchmark your organization against others in your industry or against industry standards. The software provides a range of benchmarking tools that can help you compare your performance on key metrics, such as customer satisfaction, with that of your peers.

Using QuestionPro thoughtfully and strategically allows you to gain valuable insights to inform decision-making and drive business success. Start today for free! No credit card is required.



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What is Secondary Research? Types, Methods, Examples

Appinio Research · 20.09.2023 · 13min read

What Is Secondary Research Types Methods Examples

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

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

What is Secondary Research?

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

Benefits of Secondary Research

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

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

Primary Research vs. Secondary Research

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

Primary vs Secondary Research Comparison Appinio

Primary Research

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

Secondary Research

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

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

Types of Secondary Research

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

Here are the primary types of secondary research:

Internal Sources

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

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

External Sources

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

These sources offer a broader perspective on various subjects.

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

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

How to Conduct Secondary Research?

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

1. Define Your Research Objectives

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

2. Identify Relevant Sources

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

3. Evaluate Source Credibility

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

4. Extract and Analyze Information

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

5. Synthesize Findings

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

6. Address Limitations and Gaps

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

7. Contextualize Your Findings

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

8. Cite Your Sources

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

9. Integrate Secondary and Primary Research (If Applicable)

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

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

10. Communicate Your Findings

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

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

Secondary Research Examples

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

Market Analysis and Trend Forecasting

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

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

Academic Research and Literature Reviews

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

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

Competitive Landscape Assessment for Business Strategy

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

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

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

Secondary Research Limitations

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

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

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

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

How to Conduct Secondary Research in Minutes?

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

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

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  • What is Secondary Data? + [Examples, Sources, & Analysis]


  • Data Collection

Aside from consulting the primary origin or source, data can also be collected through a third party, a process common with secondary data. It takes advantage of the data collected from previous research and uses it to carry out new research.

Secondary data is one of the two main types of data, where the second type is the primary data. These 2 data types are very useful in research and statistics, but for the sake of this article, we will be restricting our scope to secondary data.

We will study secondary data, its examples, sources, and methods of analysis.

What is Secondary Data?  

Secondary data is the data that has already been collected through primary sources and made readily available for researchers to use for their own research. It is a type of data that has already been collected in the past.

A researcher may have collected the data for a particular project, then made it available to be used by another researcher. The data may also have been collected for general use with no specific research purpose like in the case of the national census.

Data classified as secondary for particular research may be said to be primary for another research. This is the case when data is being reused, making it primary data for the first research and secondary data for the second research it is being used for.

Sources of Secondary Data

Sources of secondary data include books, personal sources, journals, newspapers, websitess, government records etc. Secondary data are known to be readily available compared to that of primary data. It requires very little research and needs for manpower to use these sources.

With the advent of electronic media and the internet, secondary data sources have become more easily accessible. Some of these sources are highlighted below.

Books are one of the most traditional ways of collecting data. Today, there are books available for all topics you can think of.  When carrying out research, all you have to do is look for a book on the topic being researched, then select from the available repository of books in that area. Books, when carefully chosen are an authentic source of authentic data and can be useful in preparing a literature review.

  • Published Sources

There are a variety of published sources available for different research topics. The authenticity of the data generated from these sources depends majorly on the writer and publishing company. 

Published sources may be printed or electronic as the case may be. They may be paid or free depending on the writer and publishing company’s decision.

  • Unpublished Personal Sources

This may not be readily available and easily accessible compared to the published sources. They only become accessible if the researcher shares with another researcher who is not allowed to share it with a third party.

For example, the product management team of an organization may need data on customer feedback to assess what customers think about their product and improvement suggestions. They will need to collect the data from the customer service department, which primarily collected the data to improve customer service.

Journals are gradually becoming more important than books these days when data collection is concerned. This is because journals are updated regularly with new publications on a periodic basis, therefore giving to date information.

Also, journals are usually more specific when it comes to research. For example, we can have a journal on, “Secondary data collection for quantitative data ” while a book will simply be titled, “Secondary data collection”.

In most cases, the information passed through a newspaper is usually very reliable. Hence, making it one of the most authentic sources of collecting secondary data.

The kind of data commonly shared in newspapers is usually more political, economic, and educational than scientific. Therefore, newspapers may not be the best source for scientific data collection.

The information shared on websites is mostly not regulated and as such may not be trusted compared to other sources. However, there are some regulated websites that only share authentic data and can be trusted by researchers.

Most of these websites are usually government websites or private organizations that are paid, data collectors.

Blogs are one of the most common online sources for data and may even be less authentic than websites. These days, practically everyone owns a blog, and a lot of people use these blogs to drive traffic to their website or make money through paid ads.

Therefore, they cannot always be trusted. For example, a blogger may write good things about a product because he or she was paid to do so by the manufacturer even though these things are not true.

They are personal records and as such rarely used for data collection by researchers. Also, diaries are usually personal, except for these days when people now share public diaries containing specific events in their life.

A common example of this is Anne Frank’s diary which contained an accurate record of the Nazi wars.

  • Government Records

Government records are a very important and authentic source of secondary data. They contain information useful in marketing, management, humanities, and social science research.

Some of these records include; census data, health records, education institute records, etc. They are usually collected to aid proper planning, allocation of funds, and prioritizing of projects.

Podcasts are gradually becoming very common these days, and a lot of people listen to them as an alternative to radio. They are more or less like online radio stations and are generating increasing popularity.

Information is usually shared during podcasts, and listeners can use it as a source of data collection. 

Some other sources of data collection include:

  • Radio stations
  • Public sector records.

What are the Secondary Data Collection Tools?

Popular tools used to collect secondary data include; bots, devices, libraries, etc. In order to ease the data collection process from the sources of secondary data highlighted above, researchers use these important tools which are explained below.

There are a lot of data online and it may be difficult for researchers to browse through all these data and find what they are actually looking for. In order to ease this process of data collection, programmers have created bots to do an automatic web scraping for relevant data.

These bots are “ software robots ” programmed to perform some task for the researcher. It is common for businesses to use bots to pull data from forums and social media for sentiment and competitive analysis.

  • Internet-Enabled Devices

This could be a mobile phone, PC, or tablet that has access to an internet connection. They are used to access journals, books, blogs, etc. to collect secondary data.

This is a traditional secondary data collection tool for researchers. The library contains relevant materials for virtually all the research areas you can think of, and it is accessible to everyone.

A researcher might decide to sit in the library for some time to collect secondary data or borrow the materials for some time and return when done collecting the required data.

Radio stations are one of the secondary sources of data collection, and one needs radio to access them. The advent of technology has even made it possible to listen to the radio on mobile phones, deeming it unnecessary to get a radio.

Secondary Data Analysis  

Secondary data analysis is the process of analyzing data collected from another researcher who primarily collected this data for another purpose. Researchers leverage secondary data to save time and resources that would have been spent on primary data collection.

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

How to Analyse Secondary Data

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

  • Statement of Purpose

Before collecting secondary data for analysis, you need to know your statement of purpose. That is, a clear understanding of why you are collecting the data—the ultimate aim of the research work and how this data will help achieve it.

This will help direct your path towards collecting the right data, and choosing the best data source and method of analysis.

  • Research Design

This is a written-down plan on how the research activities will be carried out. It describes the kind of data to be collected, the sources of data collection, method of data collection, tools, and even method of analysis.

A research design may also contain a timestamp of when each of these activities will be carried out. Therefore, serving as a guide for the secondary data analysis.

After identifying the purpose of the research, the researcher should design a research process that will guide the data analysis process.

  • Developing the Research Questions

It is not enough to just know the research purpose, you need to develop research questions that will help in better identifying Secondary data. This is because they are usually a pool of data to choose from, and asking the right questions will assist in collecting authentic data.

For example, a researcher trying to collect data about the best fish feeds to enable fast growth in fishes will have to ask questions like, What kind of fish is considered? Is the data meant to be quantitative or qualitative? What is the content of the fish feed? The growth rate in fishes after feeding on it, and so on.

  • Identifying Secondary Data

After developing the research questions, researchers use them as a guide to identifying relevant data from the data repository. For example, if the kind of data to be collected is qualitative, a researcher can filter out qualitative data.

The suitable secondary data will be the one that correctly answers the questions highlighted above. When looking for the solutions to a linear programming problem, for instance, the solutions will be numbers that satisfy both the objective and the constraints.

Any answer that doesn’t satisfy both, is not a solution.

  • Evaluating Secondary Data

This stage is what many classify as the real data analysis stage because it is the point where analysis is actually performed. However, the stages highlighted above are a part of the data analysis process, because they influence how the analysis is performed.

Once a dataset that appears viable in addressing the initial requirements discussed above is located, the next step in the process is the evaluation of the dataset to ensure the appropriateness for the research topic. The data is evaluated to ensure that it really addresses the statement of the problem and answers the research questions.

After which it will now be analyzed either using the quantitative method or the qualitative method depending on the type of data it is.

Advantages of Secondary Data

  • Ease of Access

Most of the sources of secondary data are easily accessible to researchers. Most of these sources can be accessed online through a mobile device.  People who do not have access to the internet can also access them through print.

They are usually available in libraries, book stores, and can even be borrowed from other people.

  • Inexpensive

Secondary data mostly require little to no cost for people to acquire them. Many books, journals, and magazines can be downloaded for free online.  Books can also be borrowed for free from public libraries by people who do not have access to the internet.

Researchers do not have to spend money on investigations, and very little is spent on acquiring books if any.

  • Time-Saving

The time spent on collecting secondary data is usually very little compared to that of primary data. The only investigation necessary for secondary data collection is the process of sourcing for necessary data sources.

Therefore, cutting the time that would normally be spent on the investigation. This will save a significant amount of time for the researcher 

  • Longitudinal and Comparative Studies

Secondary data makes it easy to carry out longitudinal studies without having to wait for a couple of years to draw conclusions. For example, you may want to compare the country’s population according to census 5 years ago, and now.

Rather than waiting for 5 years, the comparison can easily be made by collecting the census 5 years ago and now.

  • Generating new insights

When re-evaluating data, especially through another person’s lens or point of view, new things are uncovered. There might be a thing that wasn’t discovered in the past by the primary data collector, that secondary data collection may reveal.

For example, when customers complain about difficulty using an app to the customer service team, they may decide to create a user guide teaching customers how to use it. However, when a product developer has access to this data, it may be uncovered that the issue came from and UI/UX design that needs to be worked on.

Disadvantages of Secondary Data  

  • Data Quality:

The data collected through secondary sources may not be as authentic as when collected directly from the source. This is a very common disadvantage with online sources due to a lack of regulatory bodies to monitor the kind of content that is being shared.

Therefore, working with this kind of data may have negative effects on the research being carried out.

  • Irrelevant Data:

Researchers spend so much time surfing through a pool of irrelevant data before finally getting the one they need. This is because the data was not collected mainly for the researcher.

In some cases, a researcher may not even find the exact data he or she needs, but have to settle for the next best alternative. 

  • Exaggerated Data

Some data sources are known to exaggerate the information that is being shared. This bias may be some to maintain a good public image or due to a paid advert.

This is very common with many online blogs that even go a bead to share false information just to gain web traffic. For example, a FinTech startup may exaggerate the amount of money it has processed just to attract more customers.

A researcher gathering this data to investigate the total amount of money processed by FinTech startups in the US for the quarter may have to use this exaggerated data.

  • Outdated Information

Some of the data sources are outdated and there are no new available data to replace the old ones. For example, the national census is not usually updated yearly.

Therefore, there have been changes in the country’s population since the last census. However, someone working with the country’s population will have to settle for the previously recorded figure even though it is outdated.

Secondary data has various uses in research, business, and statistics. Researchers choose secondary data for different reasons, with some of it being due to price, availability, or even needs of the research.

Although old, secondary data may be the only source of data in some cases. This may be due to the huge cost of performing research or due to its delegation to a particular body (e.g. national census). 

In short, secondary data has its shortcomings, which may affect the outcome of the research negatively and also some advantages over primary data. It all depends on the situation, the researcher in question, and the kind of research being carried out.


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The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2: Statistical Analysis

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The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2: Statistical Analysis

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.

The goal of research in the social science is to gain a better understanding of the world and how well theoretical predictions match empirical realities. Secondary data analysis contributes to these objectives through the application of “creative analytical techniques to data that have been amassed by others” ( Kiecolt & Nathan, 1985 , p. 10). Primary researchers design new studies to answer research questions, whereas the secondary data analyst uses existing resources. There is a deliberate coupling of research design and data analysis in primary research; however, the secondary data analyst rarely has had input into the design of the original studies in terms of the sampling strategy and measures selected for the investigation. For better or worse, the secondary data analyst simply has access to the final products of the data collection process in the form of a codebook or set of codebooks and a cleaned data set.

The analysis of existing data sets is routine in disciplines such as economics, political science, and sociology, but it is less well established in psychology ( but see   Brooks-Gunn & Chase-Lansdale, 1991 ; Brooks-Gunn, Berlin, Leventhal, & Fuligini, 2000 ). Moreover, biases against secondary data analysis in favor of primary research may be present in psychology ( see   McCall & Appelbaum, 1991 ). One possible explanation for this bias is that psychology has a rich and vibrant experimental tradition, and the training of many psychologists has likely emphasized this approach as the “gold standard” for addressing research questions and establishing causality ( see , e.g., Cronbach, 1957 ). As a result, the nonexperimental methods that are typically used in secondary analyses may be viewed by some as inferior. Psychological scientists trained in the experimental tradition may not fully appreciate the unique strengths that nonexperimental techniques have to offer and may underestimate the time, effort, and skills required for conducting secondary data analyses in a competent and professional manner. Finally, biases against secondary data analysis might stem from lingering concerns over the validity of the self-report methods that are typically used in secondary data analysis. These can include concerns about the possibility that placement of items in a survey can influence responses (e.g., differences in the average levels of reported marital and life satisfaction when questions occur back to back as opposed to having the questions separated in the survey; see   Schwarz, 1999 ; Schwarz & Strack, 1999 ) and concerns with biased reporting of sensitive behaviors ( but see   Akers, Massey, & Clarke, 1983 ).

Despite the initial reluctance to widely embrace secondary data analysis as a tool for psychological research, there are promising signs that the skepticism toward secondary analyses will diminish as psychology seeks to position itself as a hub science that plays a key role in interdisciplinary inquiry ( see   Mroczek, Pitzer, Miller, Turiano, & Fingerman, 2011 ). Accordingly, there is a compelling argument for including secondary data analysis into the suite of methodological approaches used by psychologists ( see   Trzesniewski, Donnellan, & Lucas, 2011 ).

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. We limit our discussion to analyses based on large-scale and often longitudinal national data sets such as the National Longitudinal Study of Adolescent Health (Add Health), the British Household Panel Study (BHPS), the German Socioeconomic Panel Study (GSOEP), and the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SEC-CYD). However, much of our discussion applies to all secondary analyses. The perspective and specific recommendations found in this chapter draw on the edited volume by Trzesniewski et al. (2011 ). Following a general introduction to secondary data analysis, we will outline the necessary steps for getting started and finding data sets. Finally, we provide some general guidance on issues related to measurement, approaches to handling missing data, and survey weighting. Our treatment of these important topics is intended to draw attention to the relevant issues rather than to provide extensive coverage. Throughout, we take a practical approach to the issues and offer tips and guidance rooted in our experiences as data analysts and researchers with substantive interests in personality and life span developmental psychology.

Comparing Primary Research and Secondary Research

As noted in the opening section, it is possible that biases against secondary data analysis exist in the minds of some psychological scientists. To address these concerns, we have found it can be helpful to explicitly compare the processes of secondary analyses with primary research ( see also   McCall & Appelbaum, 1991 ). An idealized and simplified list of steps is provided in Table 28.1 . As is evident from this table, both techniques start with a research question that is ideally rooted in existing theory and previous empirical results. The areas of biggest divergence between primary and secondary approaches occur after researchers have identified their questions (i.e., Steps 2 through 5 in Table 28.1 ). At this point, the primary researcher develops a set of procedures and then engages in pilot testing to refine procedures and methods, whereas the secondary analyst searches for data sets and evaluates codebooks. The primary researcher attempts to refine her or his procedures, whereas the secondary analyst determines whether a particular resource is appropriate for addressing the question at hand. In the next stages, the primary researcher collects new data, whereas the secondary data analyst constructs a working data set from a much larger data archive. At these stages, both types of researchers must grapple with the practical considerations imposed by real world constraints. There is no such thing as a perfect single study ( see   Hunter & Schmidt, 2004 ), as all data sets are subject to limitations stemming from design and implementation. For example, the primary researcher may not have enough subjects to generate adequate levels of statistical power (because of a failure to take power calculations into account during the design phase, time or other resource constraints during the data collection phase, or because of problems with sample retention), whereas the secondary data analyst may have to cope with impoverished measurement of core constructs. Both sets of considerations will affect the ability of a given study to detect effects and provide unbiased estimates of effect sizes.

Table 28.1 also illustrates the fact that there are considerable areas of overlap between the two techniques. Researchers stemming from both traditions analyze data, interpret results, and write reports for dissemination to the wider scientific community. Both kinds of research require a significant investment of time and intellectual resources. Many skills required in conducting high-quality primary research are also required in conducting high-quality secondary data analysis including sound scientific judgment, attention to detail, and a firm grasp of statistical methodology.

Note: Steps modified and expanded from McCall and Appelbaum (1991 ).

We argue that both primary research and secondary data analysis have the potential to provide meaningful and scientifically valid research findings for psychology. Both approaches can generate new knowledge and are therefore reasonable ways of evaluating research questions. Blanket pronouncements that one approach is inherently superior to the other are usually difficult to justify. Many of the concerns about secondary data analysis are raised in the context of an unfair comparison—a contrast between the idealized conceptualization of primary research with the actual process of a secondary data analysis. Our point is that both approaches can be conducted in a thoughtful and rigorous manner, yet both approaches involve concessions to real-world constraints. Accordingly, we encourage all researchers and reviewers of papers to keep an open mind about the importance of both types of research.

Advantages and Disadvantages of Secondary Data Analysis

The foremost reason why psychologists should learn about secondary data analysis is that there are many existing data sets that can be used to answer interesting and important questions. Individuals who are unaware of these resources are likely to miss crucial opportunities to contribute new knowledge to the discipline and even risk reinventing the proverbial wheel by collecting new data. Regrettably, new data collection efforts may occur on a smaller scale than what is available in large national datasets. Researchers who are unaware of the potential treasure trove of variables in existing data sets risk unnecessarily duplicating considerable amounts of time and effort. At the very least, researchers may wish to familiarize themselves with publicly available data to truly address gaps in the literature when they undertake projects that involve new data collection.

The biggest advantage of secondary analyses is that the data have already been collected and are ready to be analyzed ( see   Hofferth, 2005 ), thus conserving time and resources. Existing data sources are often of much larger and higher quality than could be feasibly collected by a single investigator. This advantage is especially pronounced when considering the investments of time and money necessary to collect longitudinal data. Some data sets were collected with scientific sampling plans (such as the GSOEP), which make it possible to generalize the findings to a specific population. Further, many publicly available data sets are quite large, and therefore provide adequate statistical power for conducting many analyses, including hypotheses about statistical interactions. Investigations of interactions often require a surprisingly high number of participants to achieve respectable levels of statistical power in the face of measurement error ( see   Aiken & West, 1991 ). 1 Large-scale data sets are also well suited for subgroup analyses of populations that are often under-represented in smaller research studies.

Another advantage of secondary data analysis is that it forces researchers to adopt an open and transparent approach to their craft. Because data are publicly available, other investigators may attempt to replicate findings and specify alternative models for a given research question. This reality encourages transparency and detailed record keeping on the part of the researcher, including careful reporting of analysis and a reasoned justification for all analytic decisions. Freese (2007 ) has provided a useful discussion about policies for archiving material necessary for replicating results, and his treatment of the issues provides guidance to researchers interested in maintaining good records.

Despite the many advantages of secondary data analysis, it is not without its disadvantages. The most significant challenge is simply the flipside of the primary advantage—the data have already been collected by somebody else! Analysts must take advantage of what has been collected without input into design and measurement issues. In some cases, an existing data set may not be available to address the particular research questions of a given investigator without some limitations in terms of sampling, measurement, or other design feature. For example, data sets commonly used for secondary analysis often have a great deal of breadth in terms of the range of constructs assessed (e.g., finances, attitudes, personality, life satisfaction, physical health), but these constructs are often measured with a limited number of survey items. Issues of measurement reliability and validity are usually a major concern. Therefore, a strong grounding in basic and advanced psychometrics is extremely helpful for responding to criticisms and concerns about measurement issues that arise during the peer-review process.

A second consequence of the fact that the data have been collected by somebody else is that analysts may not have access to all of the information about data collection procedures and issues. The analyst simply receives a cleaned data set to use for subsequent analyses. Perhaps not obvious to the user is the amount of actual cleaning that occurred behind the scenes. Similarly, the complicated sampling procedures used in a given study may not be readily apparent to users, and this issue can prevent the appropriate use of survey weights ( Shrout & Napier, 2011 ).

Another significant disadvantage for secondary data analysis is the large amount of time and energy initially required to review data documentation. It can take hours and even weeks to become familiar with the codebooks and to discover which research questions have already been addressed by investigators using the existing data sets. It is very easy to underestimate how long it will take to move from an initial research idea to a competent final analysis. There is a risk that, unbeknownst to one another, researchers in different locations will pursue answers to the same research questions. On the other hand, once a researcher has become familiar with a data set and developed skills to work with the resource, they are able to pursue additional research questions resulting in multiple publications from the same data set. It is our experience that the process of learning about a data set can help generate new research ideas as it becomes clearer how the resource can be used to contribute to psychological science. Thus, the initial time and energy expended to learn about a resource can be viewed as initial investment that holds the potential to pay larger dividends over time.

Finally, a possible disadvantage concerns how secondary data analyses are viewed within particular subdisciplines of psychology and by referees during the peer-review process. Some journals and some academic departments may not value secondary data analyses as highly as primary research. Such preferences might break along Cronbach’s two disciplines or two streams of psychology—correlational versus experimental ( Cronbach, 1957 ; Tracy, Robins, & Sherman, 2009 ). The reality is that if original data collection is more highly valued in a given setting, then new investigators looking to build a strong case for getting hired or getting promoted might face obstacles if they base a career exclusively on secondary data analysis. Similarly, if experimental methods are highly valued and correlational methods are denigrated in a particular subfield, then results of secondary data analyses will face difficulties getting attention (and even getting published). The best advice is to be aware of local norms and to act accordingly.

Steps for Beginning a Secondary Data Analysis

Step 1: Find Existing Data Sets . After generating a substantive question, the first task is to find relevant data sets ( see   Pienta, O’Rouke, & Franks, 2011 ). In some cases researchers will be aware of existing data sets through familiarity with the literature given that many well-cited papers have used such resources. For example, the GSOEP has now been widely used to address questions about correlates and developmental course of subjective well-being (e.g., Baird, Lucas, & Donnellan, 2010 ; Gerstorf, Ram, Estabrook, Schupp, Wagner, & Lindenberger, 2008 ; Gerstorf, Ram, Goebel, Schupp, Lindenberger, & Wagner, 2010 ; Lucas, 2005 ; 2007 ), and thus, researchers in this area know to turn to this resource if a new question arises. In other cases, however, researchers will attempt to find data sets using established archives such as the University of Michigan’s Interuniversity Consortium for Political and Social Research (ICPSR; http://www.icpsr.umich.edu/icpsrweb/ICPSR/ ). In addition to ICPSR, there are a number of other major archives ( see   Pienta et al., 2011 ) that house potentially relevant data sets. Here are just a few starting points:

The Henry A. Murray Research Archive ( http://www.murray.harvard.edu/ )

The Howard W Odum Institute for Research in Social Science ( http://www.irss.unc.edu/odum/jsp/home2.jsp )

The National Opinion Research Center ( http://norc.org/homepage.htm )

The Roper Center of Public Opinion Research ( http://ropercenter.uconn.edu/ )

The United Kingdom Data Archive ( http://www.data-archive.ac.uk/ )

Individuals in charge of these archives and data depositories often catalog metadata, which is the technical term for information about the constituent data sets. Typical kinds of metadata include information about the original investigators, a description of the design and process of data collection, a list of the variables assessed, and notes about sampling weights and missing data. Searching through this information is an efficient way of gaining familiarity with data sets. In particular, the ICPSR has an impressive infrastructure for allowing researchers to search for data sets through a cataloguing of study metadata. The ICPSR is thus a useful starting point for finding the raw material for a secondary data analysis. The ICPSR also provides a new user tutorial for searching their holdings ( http://www.icpsr.umich.edu/icpsrweb/ICPSR/help/newuser.jsp ). We recommend that researchers search through their holdings to make a list of potential data sets. At that point, the next task is to obtain relevant codebooks to learn more about each resource.

Step 2: Read Codebooks . Researchers interesting in using an existing data set are strongly advised to thoroughly read the accompanying codebook ( Pienta et al., 2011 ). There are several reasons why a comprehensive understanding of the codebook is a critical first step when conducting a secondary data analysis. First, the codebook will detail the procedures and methods used to acquire the data and provide a list of all of the questions and assessments collected. A thorough reading of the codebook can provide insights into important covariates that can be included in subsequent models, and a careful reading will draw the analyst’s attention to key variables that will be missing because no such information was collected. Reading through a codebook can also help to generate new research questions.

Second, high-quality codebooks often report basic descriptive information for each variable such as raw frequency distributions and information about the extent of missing values. The descriptive information in the codebook can give investigators a baseline expectation for variables under consideration, including the expected distributions of the variables and the frequencies of under-represented groups (such as ethnic minority participants). Because it is important to verify that the descriptive statistics in the published codebook match those in the file analyzed by the secondary analyst, a familiarity with the codebook is essential. In addition to codebooks, many existing resources provide copies of the actual surveys completed by participants ( Pienta et al., 2011 ). However, the use of actual pencil-and-paper surveys is becoming less common with the advent of computer assisted interview techniques and Internet surveys. It is often the case that survey methods involve skip patterns (e.g., a participant is not asked about the consequences of her drinking if she responds that she doesn’t drink alcohol) that make it more difficult to assume the perspective of the “typical” respondent in a given study ( Pienta et al., 2011 ). Nonetheless, we recommend that analysts try to develop an understanding for the experiences of the participant in a given study. This perspective can help secondary analysts develop an intuitive understanding of certain patterns of missing data and anticipate concerns about question ordering effects ( see , e.g., Schwarz, 1999 ).

Step 3: Acquire Datasets and Construct a Working Datafile . Although there is a growing availability of Web-based resources for conducting basic analyses using selected data sets (e.g., the Survey Documentation Analysis software used by ICPSR), we are convinced that there is no substitute for the analysis of the raw data using the software packages of preference for a given investigator. This means that the analysts will need to acquire the data sets that they consider most relevant. This is typically a very straightforward process that involves acknowledging researcher responsibilities before downloading the entire data set from a website. In some cases, data are classified as restricted-use, and there are more extensive procedures for obtaining access that may involve submitting a detailed security plan and accompanying legal paperwork before becoming an authorized data user. When data involve children and other sensitive groups, Institutional Review Board approval is often required.

Each data set has different usage requirements, so it is difficult to provide blanket guidance. Researchers should be aware of the policies for using each data set and recognize their ethical responsibility for adhering to those regulations. A central issue is that the researcher must avoid deductive disclosure whereby otherwise anonymous participants are identified because of prior knowledge in conjunction with the personal characteristics coded in the dataset (e.g., gender, racial/ethnic group, geographic location, birth date). Such a practice violates the major ethical principles followed by responsible social scientists and has the potential to harm research participants.

Once the entire set of raw data is acquired, it is usually straightforward to import the files into the kinds of statistical packages used by researchers (e.g., R, SAS, SPSS, and STATA). At this point, it is likely that researchers will want to create smaller “working” file by pulling only relevant variables from the larger master files. It is often too cumbersome to work with a computer file that may have more than a thousand columns of information. The solution is to construct a working data file that has all of the needed variables tied to a particular research project. Researchers may also need to link multiple files by matching longitudinal data sets and linking to contextual variables such as information about schools or neighborhoods for data sets with a multilevel structure (e.g., individuals nested in schools or neighborhoods).

Explicit guidance about managing a working data file can be found in Willms (2011 ). Here, we simply highlight some particularly useful advice: (1) keep exquisite notes about what variables were selected and why; (2) keep detailed notes regarding changes to each variable and reasons why; and (3) keep track of sample sizes throughout this entire process. The guiding philosophy is to create documentation that is clear enough for an outside user to follow the logic and procedures used by the researcher. It is far too easy to overestimate the power of memory only to be disappointed when it comes time to revisit a particular analysis. Careful documentation can save time and prevent frustration. Willms (2011 ) noted that “keeping good notes is the sine qua non of the trade” (p. 33).

Step 4: Conduct Analyses . After assembling the working data file, the researcher will likely construct major study variables by creating scale composites (e.g., the mean of the responses to the items assessing the same construct) and conduct initial analyses. As previously noted, a comparison of the distributions and sample sizes with those in the study codebook is essential at this stage. Any deviations for the variables in the working data file and the codebook should be understood and documented. It is particularly useful to keep track of missing values to make sure that they have been properly coded. It should go without saying that an observed value of-9999 will typically require recoding to a missing value in the working file. Similarly, errors in reverse scoring items can be particularly common (and troubling) so researchers are well advised to conduct through item-level and scale analyses and check to make sure that reverse scoring was done correctly (e.g., examine the inter-item correlation matrix when calculating internal consistency estimates to screen for negative correlations). Willms (2011 ) provides some very savvy advice for the initial stages of actual data analysis: “Be wary of surprise findings” (p. 35). He noted that “too many times I have been excited by results only to find that I have made some mistake” (p. 35). Caution, skepticism, and a good sense of the underlying data set are essential for detecting mistakes.

An important comment about the nature of secondary data analysis is again worth emphasizing: These data sets are available to others in the scholarly community. This means that others should be able to replicate your results! It is also very useful to adopt a self-critical perspective because others will be able to subject findings to their own empirical scrutiny. Contemplate alternative explanations and attempt to conduct analyses to evaluate the plausibility of these explanations. Accordingly, we recommend that researchers strive to think of theoretically relevant control variables and include them in the analytic models when appropriate. Such an approach is useful both from the perspective of scientific progress (i.e., attempting to curb confirmation biases) and in terms of surviving the peer-review process.

Special Issue: Measurement Concerns in Existing Datasets

One issue with secondary data analyses that is likely to perplex psychologists are concerns regarding the measurement of core constructs. The reality is that many of the measures available in large-scale data sets consist of a subset of items derived from instruments commonly used by psychologists ( see   Russell & Matthews, 2011 ). For example, the 10-item Rosenberg Self-Esteem scale ( Rosenberg, 1965 ) is the most commonly used measure of global self-esteem in the literature ( Donnellan, Trzesniewski, & Robins, 2011 ). Measures of self-esteem are available in many data sets like Monitoring the Future ( see   Trzesniewski & Donnellan, 2010 ) but these measures are typically shorter than the original Rosenberg scale. Similarly, the GSOEP has a single-item rating of subjective well-being in the form of happiness, whereas psychologists might be more accustomed to measuring this construct with at least five items (e.g., Diener, Emmons, Larsen, & Griffin, 1985 ). Researchers using existing data sets will have to grapple with the consequences of having relatively short assessments in terms of the impact on reliability and validity.

For purposes of this chapter, we will make use of a conventional distinction between reliability and validity. Reliability will refer to the degree of measurement error present in a given set of scores (or alternatively the degree of consistency or precision in scores), whereas validity will refer to the degree to which measures capture the construct of interest and predict other variables in ways that are consistent with theory. More detailed but accessible discussions of reliability and validity can be found in Briggs and Cheek (1986 ), Clark and Watson (1995 ), John and Soto (2007 ), Messick (1995 ), Simms (2008 ), and Simms and Watson (2007 ). Widaman, Little, Preacher, and Sawalani (2011 ) have provided a discussion of these issues in the context of the shortened assessments available in existing data sets.

Short Measures and Reliability . Classical Test Theory (e.g., Lord & Novick, 1968 ) is the measurement perspective most commonly used among psychologists. According to this measurement philosophy, any observed score is a function of the underlying attribute (the so-called “true score”) and measurement error. Reliability is conceptualized as any deviation or inconsistency in observed scores for the same attribute across multiple assessments of that attribute. A thought experiment may help crystallize insights about reliability (e.g., Lord & Novick, 1968 ): Imagine a thousand identical clones each completing the same self-esteem instrument simultaneously. The underlying self-esteem attribute (i.e., the true scores) should be the same for each clone (by definition), whereas the observed scores may fluctuate across clones because of random measurement errors (e.g., a single clone misreading an item vs. another clone being frustrated by an extremely hot testing room). The extent of the observed fluctuations in reported scores across clones offers insight into how much measurement error is present in this instrument. If scores are tightly clustered around a single value, then measurement error is minimal; however, if scores are dramatically different across clones, then there is a clear indication of problems with reliability. The measure is imprecise because it yields inconsistent values across the same true scores.

These ideas about reliability can be applied to observed samples of scores such that the total observed variance is attributable to true score variance (i.e., true individual differences in underlying attributes) and variance stemming from random measurement errors. The assumption that measurement error is random means that it has an expected value of zero across observations. Using this framework, reliability can then be defined as the ratio of true score variance to the total observed variance. An assessment that is perfectly reliable (i.e., has no measurement error) will have a ratio of 1.0, whereas an assessment that is completely unreliable will yield a ratio of 0.0 ( see   John & Soto, 2007 , for an expanded discussion). This perspective provides a formal definition of a reliability coefficient.

Psychologists have developed several tools to estimate the reliability of their measures, but the approach that is most commonly used is coefficient a ( Cronbach, 1951 ; see   Schmitt, 1996 , for an accessible review). This approach considers reliability from the perspective of internal consistency. The basic idea is that fluctuations across items assessing the same construct reflect the presence of measurement error. The formula for the standardized α is a fairly simple function of the average inter-item correlation (a measure of inter-item homogeneity) and the total number of items in a scale. The α coefficient is typically judged acceptable if it is above 0.70, but the justification for this particular cutoff is somewhat arbitrary ( see   Lance, Butts, & Michels, 2006 ). Researchers are therefore advised to take a more critical perspective on this statistic. A relevant concern is that α is negatively impacted when the measure is short.

Given concerns with scale length and α, many methodologically oriented researchers recommend evaluating and reporting the average inter-item correlation because it can be interpreted independently of length and thus represents a “more straightforward indicator of internal consistency” ( Clark & Watson, 1995 , p. 316). Consider that it is common to observe an average inter-item correlation for the 10-item Rosenberg Self-Esteem ( Rosenberg, 1965 ) scale around 0.40 (this is based on typically reported a coefficients; see   Donnellan et al., 2011 ). This same level of internal homogeneity (i.e., an inter-item correlation of 0.40) yields an α of around 0.67 with a 3-item scale but an α of around 0.87 with 10 items. A measure of a broader construct like Extraversion may generate an average inter-item correlation of 0.20 ( Clark & Watson, 1995 , p. 316), which would translate to an α of 0.43 for a 3-item scale and 0.71 for a 10-item scale. The point is that α coefficients will fluctuate with scale length and the breadth of the construct. Because most scales in existing resources are short, the α coefficients might fall below the 0.70 convention despite having a respectable level of inter-item correlation.

Given these considerations, we recommend that researchers consider the average inter-item correlation more explicitly when working with secondary data sets. It is also important to consider the breadth of the underlying construct to generate expectations for reasonable levels of item homogeneity as indexed by the average inter-item correlation. Clark and Watson (1995 ; see also   Briggs & Cheek, 1986 ) recommend values of around 0.40 to 0.50 for measures of fairly narrow constructs (e.g., self-esteem) and values of around 0.15 to 0.20 for measures of broader constructs (e.g., neuroticism). It is our experience that considerations about internal consistency often need to be made explicit in manuscripts so that reviewers will not take an unnecessarily harsh perspective on α’s that fall below their expectations. Finally, we want to emphasize that internal consistency is but one kind of reliability. In some cases, it might be that test—retest reliability is more informative and diagnostic of the quality of a measure ( McCrae, Kurtz, Yamagata, & Terracciano, 2011 ). Fortunately, many secondary data sets are longitudinal so it possible to get an estimate of longer term test-retest reliability from the existing data.

Beyond simply reporting estimates of reliability, it is worth considering why measurement reliability is such an important issue in the first place. One consequence of reliability for substantive research is that measurement imprecision tends to depress observed correlations with other variables. This notion of attenuation resulting from measurement error and a solution were discussed by Spearman as far back as 1904 ( see , e.g., pp. 88–94). Unreliable measures can affect the conclusions drawn from substantive research by imposing a downward bias on effect size estimation. This is perhaps why Widaman et al. (2011 ) advocate using latent variable structural modeling methods to combat this important consequence of measurement error. Their recommendation is well worth considering for those with experience with this technique ( see   Kline, 2011 , for an introduction). Regardless of whether researchers use observed variables or latent variables for their analyses, it is important to recognize and appreciate the consequences of reliability.

Short Measures and Validity . Validity, for our purposes, reflects how well a measure captures the underlying conceptual attribute of interest. All discussions of validity are based, in part, on agreement in a field as to how to understand the construct in question. Validity, like reliability, is assessed as a matter of degree rather than a categorical distinction between valid or invalid measures. Cronbach and Meehl (1955 ) have provided a classic discussion of construct validity, perhaps the most overarching and fundamental form of validity considered in psychological research ( see also   Smith, 2005 ). However, we restrict our discussion to content validity and criterion-related validity because these two types of validity are particularly relevant for secondary data analysis and they are more immediately addressable.

Content validity describes how well a measure captures the entire domain of the construct in question. Judgments regarding content validity are ideally made by panels of experts familiar with the focal construct. A measure is considered construct deficient if it fails to assess important elements of the construct. For example, if thoughts of suicide are an integral aspect of the concept depression and a given self-report measure is missing items that tap this content, then the measure would be deemed construct-deficient. A measure can also suffer from construct contamination if it includes extraneous items that are irrelevant to the focal construct. For example, if somatic symptoms like a rapid heartbeat are considered to reflect the construct of anxiety and not part of depression, then a depression inventory that has such an item would suffer from construct contamination. Given the reduced length of many assessments, concerns over construct deficiency are likely to be especially pressing. A short assessment may not include enough items to capture the full breadth of a broad construct. This limitation is not readily addressed and should be acknowledged ( see   Widaman et al., 2011 ). In particular, researchers may need to clearly specify that their findings are based on a narrower content domain than is normally associated with the focal construct of interest.

A subtle but important point can arise when considering the content of measures with particularly narrow content. Internal consistency will increase when there is redundancy among items in the scale; however, the presence of similar items may decrease predictive power. This is known as the attenuation paradox in psycho metrics ( see   Clark & Watson, 1995 ). When items are nearly identical, they contribute redundant information about a very specific aspect of the construct. However, the very specific attribute may not have predictive power. In essence, reliability can be maximized at the expense of creating a measure that is not very useful from the point of view of prediction (and likely explanation). Indeed, Clark and Watson (1995 ) have argued that the “goal of scale construction is to maximize validity rather than reliability” (p. 316). In short, an evaluation of content validity is also important when considering the predictive power of a given measure.

Whereas content validity is focused on the internal attributes of a measure, criterion-related validity is based on the empirical relations between measures and other variables. Using previous research and theory surrounding the focal construct, the researcher should develop an expectation regarding the magnitude and direction of observed associations (i.e., correlations) with other variables. A good supporting theory of a construct should stipulate a pattern of association, or nomological network, concerning those other variables that should be related and unrelated to the focal construct. This latter requirement is often more difficult to specify from existing theories, which tend to provide a more elaborate discussion of convergent associations rather than discriminant validity ( Widaman et al., 2011 ). For example, consider a very truncated nomological network for Agreeableness (dispositional kindness and empathy). Measures of this construct should be positively associated with romantic relationship quality, negatively related to crime (especially violent crime), and distinct from measures of cognitive ability such as tests of general intelligence.

Evaluations of criterion-related validity can be conducted within a data set as researchers document that a measure has an expected pattern of associations with existing criterion-related variables. Investigators using secondary data sets may want to conduct additional research to document the criterion-related validity of short measures with additional convenience samples (e.g., the ubiquitous college student samples used by many psychologists; Sears, 1986 ). For example, there are six items in the Add Health data set that appear to measure self-esteem (e.g., “I have a lot of good qualities” and “I like myself just the way I am”) ( see   Russell, Crockett, Shen, &Lee, 2008 ). Although many of the items bear a strong resemblance to the items on the Rosenberg Self-Esteem scale ( Rosenberg, 1965 ), they are not exactly the same items. To obtain some additional data on the usefulness of this measure, we administered the Add Health items to a sample of 387 college students at our university along with the Rosenberg Self-Esteem scale and an omnibus measure of personality based on the Five-Factor model ( Goldberg, 1999 ). The six Add Health items were strongly correlated with the Rosenberg ( r = 0.79), and both self-esteem measures had a similar pattern of convergent and divergent associations with the facets of the Five-Factor model (the two profiles were very strongly associated: r > 0.95). This additional information can help bolster the case for the validity of the short Add Health self-esteem measure.

Special Issue: Missing Data in Existing Data Sets

Missing data is a fact of life in research— individuals may drop out of longitudinal studies or refuse to answer particular questions. These behaviors can affect the generalizability of findings because results may only apply to those individuals who choose to complete a study or a measure. Missing data can also diminish statistical power when common techniques like listwise deletion are used (e.g., only using cases with complete information, thereby reducing the sample size) and even lead to biased effect size estimates (e.g., McKnight & McKnight, 2011 ; McKnight, McKnight, Sidani, & Figuredo, 2007 ; Widaman, 2006 ). Thus, concerns about missing data are important for all aspects of research, including secondary data analysis. The development of specific techniques for appropriately handling missing data is an active area of research in quantitative methods ( Schafer & Graham, 2002 ).

Unfortunately, the literature surrounding missing data techniques is often technical and steeped in jargon, as noted by McKnight et al. (2007 ). The reality is that researchers attempting to understand issues of missing data need to pay careful attention to terminology. For example, a novice researcher may not immediately grasp the classification of missing data used in the literature ( see   Schafer & Graham, 2002 , for a clear description). Consider the confusion that may stem from learning that data are missing at random (MAR) versus data are missing completely at random (MCAR). The term MAR does not mean that missing values only occurred because of chance factors. This is the case when data are missing completely at random (MCAR). Data that are MCAR are absent because of truly random factors. Data that are MAR refers to the situation in which the probability that the observations are missing depends only on other available information in the data set. Data that are MAR can be essentially “ignored” when the other factors are included in a statistical model. The last type of missing data, data missing not at random (MNAR), is likely to characterize the variables in many real-life data sets. As it stands, methods for handing data that are MAR and MCAR are better developed and more easily implemented than methods for handling data MNAR. Thus, many applied researchers will assume data are MAR for purposes of statistical modeling (and the ability to sleep comfortably at night). Fortunately, such an assumption might not create major problems for many analyses and may in fact represent the “practical state of the art” ( Schafer & Graham, 2002 , p. 173).

The literature on missing data techniques is growing, so we simply recommend that researchers keep current on developments in this area. McKnight et al. (2007 ) and Widaman (2006 ) both provide an accessible primer on missing data techniques. In keeping with the largely practical bent to the chapter, we suggest that researchers keep careful track of the amount of missing data present in their analyses and report such information clearly in research papers ( see   McKnight & McKnight, 2011 ). Similarly, we recommend that researchers thoroughly screen their data sets for evidence that missing values depend on other measured variables (e.g., scores at Time 1 might be associated with Time 2 dropout). In general, we suggest that researchers avoid listwise and pairwise deletion methods because there is very little evidence that these are good practices ( see   Jeličić, Phelps, & Lerner, 2009 ; Widaman, 2006 ). Rather, it might be easiest to use direct fitting methods such as the estimation procedures used in conventional structural equation modeling packages (e.g., Full Information Maximum Likelihood; see   Allison, 2003 ). At the very least, it is usually instructive to compare results using listwise deletion with results obtained with direct model fitting in terms of the effect size estimates and basic conclusions regarding the statistical significance of focal coefficients.

Special Issue: Sample Weighting in Existing Data Sets

One of the advantages of many existing data sets is that they were collected using probabilistic sampling methods so that researchers can obtain unbiased population estimates. Such estimates, however, are only obtained when complex survey weights are formally incorporated into the statistical modeling procedures. Such weighting schemes can affect the correlations between variables, and therefore all users of secondary data sets should become familiar with sampling design when they begin working with a new data set. A considerable amount of time and effort is dedicated toward generating complex weighting schemes that account for the precise sampling strategies used in the given study, and users of secondary data sets should give careful consideration to using these weights appropriately.

In some cases, the addition of sampling weights will have little substantive implication on findings, so extensive concern over weighting might be overstated. On the other hand, any potential difference is ultimately an empirical question, so researchers are well advised to consider the importance of sampling weights ( Shrout & Napier, 2011 ). The problem is that many psychologists are not well versed in the use of sampling weights ( Shrout & Napier, 2011 ). Thus, psychologists may not be in a strong position to evaluate whether sample weighting concerns are relevant. In addition, it is sometimes necessary to use specialized software packages or add-ons to adjust analytic models appropriately for sampling weights. Programs such as STATA and SAS have such capabilities in the base package, whereas packages like SPSS sometimes require a complex survey model add-on that integrates with its existing capabilities. Whereas the graduate training of the modal sociologist or demographer is likely to emphasize survey research and thus presumably cover sampling, this is not the case with the methodological training of many psychologists ( Aiken, West, & Millsap, 2008 ). Psychologists who are unfamiliar with sample weighting procedures are well advised to seek the counsel of a survey methodologist before undertaking data analysis.

In terms of practical recommendations, it is important for the user of the secondary data set to develop a clear understanding of how the data were collected by reading documentation about the design and sampling procedure ( Shrout & Napier, 2011 ). This insight will provide a conceptual framework for understanding weighting schemes and for deciding how to appropriately weight the data. Once researchers have a clear idea of the sampling scheme and potential weights, actually incorporating available weights into analyses is not terribly difficult, provided researchers have the appropriate software ( Shrout & Napier, 2011 ). Weighting tutorials are often available for specific data sets. For example, the Add Health project has a document describing weighting ( http://www.cpc.unc.edu/projects/addhealth/faqs/aboutdata/weight1.pdf ) as does the Centers for Disease Control and Prevention for use with their Youth Risk Behavior Surveys ( http://www.cdc.gov/HealthyYouth/yrbs/pdf/YRBS_analysis_software.pdf ). These free documents may also provide useful and accessible background even for those who may not use the data from these projects.

Secondary data analysis refers to the analysis of existing data that may not have been explicitly collected to address a particular research question. Many of the quantitative techniques described in this volume can be applied using existing resources. To be sure, strong data analytic skills are important for fully realizing the potential benefits of secondary data sets, and such skills can help researchers recognize the limits of a data set for any given analysis.

In particular, measurement issues are likely to create the biggest hurdles for psychologists conducting secondary analyses in terms of the challenges associated with offering a reasonable interpretation of the results and in surviving the peer-review process. Accordingly, a familiarity with basic issues in psychometrics is very helpful. Beyond such skills, the effective use of these existing resources requires patience and strong attention to detail. Effective secondary data analysis also requires a fair bit of curiosity to seek out those resources that might be used to make important contribution to psychological science.

Ultimately, we hope that the field of psychology becomes more and more accepting of secondary data analysis. As psychologists use this approach with increasing frequency, it is likely that the organizers of major ongoing data collection efforts will be increasingly open to including measures of prime interest to psychologists. The individuals in charge of projects like the BHPS, the GSOEP, and the National Center for Education Statistics ( http://nces.ed.gov/ ) want their data to be used by the widest possible audiences and will respond to researcher demands. We believe that it is time that psychologists join their colleagues in economics, sociology, and political science in taking advantage of these existing resources. It is also time to move beyond divisive discussions surrounding the presumed superiority of primary data collection over secondary analysis. There is no reason to choose one over the other when the field of psychology can profit from both. We believe that the relevant topics of debate are not about the method of initial data collection but, rather, about the importance and intrinsic interest of the underlying research questions. If the question is important and the research design and measures are suitable, then there is little doubt in our minds that secondary data analysis can make a contribution to psychological science.

Author Note

M. Brent Donnellan, Department of Psychology, Michigan State University, East Lansing, MI 48824.

Richard E. Lucas, Department of Psychology, Michigan State University, East Lansing, MI 48824.

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An illustration of a magnifying glass over a stack of reports representing secondary research.

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 (aka desk research or complementary 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.

colleagues working on a secondary research

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.

laying out the keypoints of a secondary research on a board

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 secondary research companies and 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.

analyzing data resulting from a secondary research

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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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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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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Dissertations 4: methodology: methods.

  • Introduction & Philosophy
  • Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  


There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   


Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

research methodology secondary data example

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  




procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  


Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 


Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 


Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

  • Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

5 Minute Method logo

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 


Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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Primary Research vs Secondary Research: A Comparative Analysis

Understand the differences between primary research vs secondary research. Learn how they can be used to generate valuable insights.

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Primary research and secondary research are two fundamental approaches used in research studies to gather information and explore topics of interest. Both primary and secondary research offer unique advantages and have their own set of considerations, making them valuable tools for researchers in different contexts.

Understanding the distinctions between primary and secondary research is crucial for researchers to make informed decisions about the most suitable approach for their study objectives and available resources.

What is Primary Research?

Primary research refers to the collection and analysis of data directly from original sources. It involves gathering information directly to address specific research objectives and generate new insights. This research method conducts surveys, interviews, observations, experiments, or focus groups to obtain data that is relevant to the research question at hand. By engaging directly with subjects or sources, primary research provides firsthand and up-to-date information, allowing researchers to have control over the data collection process and adjust it to their specific needs.

Types of Primary Research

There are several types of primary research methods commonly used in various fields:

Surveys are the systematic collection of data through questionnaires or interviews, aiming to gather information from a large number of participants. Surveys can be conducted in person, over the phone, through mail, or online.

Interviews entail direct one-on-one or group interactions with individuals or key informants to obtain detailed information about their experiences, opinions, or expertise. Interviews can be structured (using predetermined questions) or unstructured (allowing for open-ended discussions).


Observational research carefully observes and documents behaviors, interactions, or phenomena in real-life settings. It can be done in a participant or non-participant manner, depending on the level of involvement of the researcher.

Data analysis

Examining and interpreting collected data, data analysis uncovers patterns, trends, and insights, providing a deeper understanding of the research topic. It enables drawing meaningful conclusions for decision-making and guides further research.

Focus groups

Focus groups facilitated group discussions with a small number of participants who shared their opinions, attitudes, and experiences on a specific topic. This method allows for interactive and in-depth exploration of a subject.

Benefits of Primary Research

Original and specific data: Primary research provides first hand data directly relevant to the research objectives, ensuring its freshness and specificity to the research context.

Control over data collection: Researchers have control over the design, implementation, and data collection process, allowing them to adapt the research methods and instruments to suit their needs.

Depth of understanding: Primary research methods, such as interviews and focus groups, enable researchers to gain a deep understanding of participants’ perspectives, experiences, and motivations.

Validity and reliability: By directly collecting data from original sources, primary research enhances the validity and reliability of the findings, reducing potential biases associated with using secondary or existing data.

Challenges of Primary Research

Time and Resource-intensive: Primary research requires careful planning, data collection, analysis, and interpretation. It may require recruiting participants, conducting interviews or surveys, and analyzing data, all of which require time and resources.

Sampling limitations: Primary research often relies on sampling techniques to select participants. Ensuring a representative sample that accurately reflects the target population can be challenging, and sampling biases may affect the generalizability of the findings.

Subjectivity: The involvement of researchers in primary research methods, such as interviews or observations, introduces the potential for subjective interpretations or biases that can influence the data collection and analysis process.

Limited generalizability: Findings from primary research may have limited generalizability due to the specific characteristics of the sample or context. It is essential to acknowledge the scope and limitations of the findings and avoid making broad generalizations beyond the studied sample or context.

What is Secondary Research?

It is a method of research that relies on data that is readily available, rather than gathering new data through primary research methods. Secondary research relies on reviewing and analyzing sources such as published studies, reports, articles, books, government databases, and online resources to extract relevant information for a specific research objective.

Sources of Secondary Research

Published studies and academic journals.

Researchers can review published studies and academic journals to gather information, data, and findings related to their research topic. These sources often provide comprehensive and in-depth analyses of specific subjects.

Reports and white papers

Reports and white papers produced by research organizations, government agencies, and industry associations provide valuable data and insights on specific topics or sectors. These documents often contain statistical data, market research, trends, and expert opinions.

Books and reference materials

Books and reference materials written by experts in a particular field can offer comprehensive overviews, theories, and historical perspectives that contribute to secondary research.

Online databases

Online databases, such as academic libraries, research repositories, and specialized platforms, provide access to a vast array of published research articles, theses, dissertations, and conference proceedings.

Benefits of Secondary Research

Time and Cost-effectiveness: Secondary research saves time and resources since the data and information already exist and are readily accessible. Researchers can utilize existing resources instead of conducting time-consuming primary research.

Wide range of data: Secondary research provides access to a wide range of data sources, including large-scale surveys, census data, and comprehensive reports. This allows researchers to explore diverse perspectives and make comparisons across different studies.

Comparative analyses: Researchers can compare findings from different studies or datasets, allowing for cross-referencing and verification of results. This enhances the robustness and validity of research outcomes.

Ethical considerations: Secondary research does not involve direct interaction with participants, which reduces ethical concerns related to privacy, informed consent, and confidentiality.

Challenges of Secondary Research

Data availability and quality: The availability and quality of secondary data can vary. Researchers must critically evaluate the credibility, reliability, and relevance of the sources to ensure the accuracy of the information used in their research.

Limited control over data: Researchers have limited control over the design, collection methods, and variables included in the secondary data. The data may not perfectly align with the research objectives, requiring careful selection and analysis.

Potential bias and outdated information: Secondary data may contain inherent biases or limitations introduced by the original researchers. Additionally, the data may become outdated, and newer information or developments may not be captured.

Lack of customization: Since secondary data is collected for various purposes, it may not perfectly align with the specific research needs. Researchers may encounter limitations in terms of variables, definitions, or granularity of data.

Comparing Primary and Secondary Research

Primary research vs secondary research, examples of primary and secondary research, examples of primary research.

  • Conducting a survey to collect data on customer satisfaction and preferences for a new product directly from the target audience.
  • Designing and conducting an experiment to test the effectiveness of a new teaching method by comparing the learning outcomes of students in different groups.
  • Observing and documenting the behavior of a specific animal species in its natural habitat to gather data for ecological research.
  • Organizing a focus group with potential consumers to gather insights and feedback on a new advertising campaign.
  • Conducting interviews with healthcare professionals to understand their experiences and perspectives on a specific medical treatment.

Examples of Secondary Research

  • Accessing a market research report to gather information on consumer trends, market size, and competitor analysis in the smartphone industry.
  • Using existing government data on unemployment rates to analyze the impact of economic policies on employment patterns.
  • Examining historical records and letters to understand the political climate and social conditions during a particular historical event.
  • Conducting a meta-analysis of published studies on the effectiveness of a specific medication to assess its overall efficacy and safety.

How to Use Primary and Secondary Research Together

Having explored the distinction between primary research vs secondary research, the integration of these two approaches becomes a crucial consideration. By incorporating primary and secondary research, a comprehensive and well-informed research methodology can be achieved. The utilization of secondary research provides researchers with a broader understanding of the subject, allowing them to identify gaps in knowledge and refine their research questions properly.

Primary research methods, such as surveys or interviews, can then be employed to collect new data that directly address these research questions. The findings from primary research can be compared and validated against the existing knowledge obtained through secondary research. By combining the insights from both types of research, researchers can fill knowledge gaps, strengthen the reliability of their findings through triangulation, and draw meaningful conclusions that contribute to the overall understanding of the subject matter.

Ethical Considerations for Primary and Secondary Research

In primary research, researchers must obtain informed consent from participants, ensuring they are fully aware of the study’s purpose, procedures, and any potential risks or benefits involved. Confidentiality and anonymity should be maintained to safeguard participants’ privacy. Researchers should also ensure that the data collection methods and research design are conducted in an ethical manner, adhering to ethical guidelines and standards set by relevant institutional review boards or ethics committees.

In secondary research, ethical considerations primarily revolve around the proper and responsible use of existing data sources. Researchers should respect copyright laws and intellectual property rights when accessing and using secondary data. They should also critically evaluate the credibility and reliability of the sources to ensure the validity of the data used in their research. Proper citation and acknowledgment of the original sources are essential to maintain academic integrity and avoid plagiarism.

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Types of market research: Methods and examples


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Here at GWI we publish a steady stream of blogs, reports, and other resources that dig deep into specific market research topics.

But what about the folks who’d appreciate a more general overview of market research that explains the big picture? Don’t they deserve some love too?

Of course they do. That’s why we’ve created this overview guide focusing on types of market research and examples. With so many market research companies to choose from, having a solid general understanding of how this sector works is essential for any brand or business that wants to pick the right market research partner.

So with that in mind, let’s start at the very beginning and get clear on…

Market research definition

At the risk of stating the slightly obvious, market research is the gathering and analyzing of data on consumers, competitors, distributors, and markets. As such it’s not quite the same as consumer research , but there’s significant overlap.

Market research matters because it can help you take the guesswork out of getting through to audiences. By studying consumers and gathering information on their likes, dislikes, and so on, brands can make evidence-based decisions instead of relying on instinct or experience. 

research methodology secondary data example

What is market research?

Market research is the organized gathering of information about target markets and consumers’ needs and preferences. It’s an important component of business strategy and a major factor in maintaining competitiveness.

If a business wants to know – really know – what sort of products or services consumers want to buy, along with where, when, and how those products and services should be marketed, it just makes sense to ask the prospective audience. 

Without the certainty that market research brings, a business is basically hoping for the best. And while we salute their optimism, that’s not exactly a reliable strategy for success.

What are the types of market research?

Primary research .

Primary research is a type of market research you either conduct yourself or hire someone to do on your behalf.

A classic example of primary research involves going directly to a source – typically customers or prospective customers in your target market – to ask questions and gather information about a product or service. Interviewing methods include in-person, online surveys, phone calls, and focus groups.

The big advantage of primary research is that it’s directly focused on your objectives, so the outcome will be conclusive, detailed insights – particularly into customer views – making it the gold standard.

The disadvantages are it can be time-consuming and potentially costly, plus there’s a risk of survey bias creeping in, in the sense that research samples may not be representative of the wider group.

Secondary research 

Primary market research means you collect the data your business needs, whereas the types of market research known as secondary market research use information that’s already been gathered for other purposes but can still be valuable. Examples include published market studies, white papers, analyst reports, customer emails, and customer surveys/feedback.

For many small businesses with limited budgets, secondary market research is their first choice because it’s easier to acquire and far more affordable than primary research.

Secondary research can still answer specific business questions, but with limitations. The data collected from that audience may not match your targeted audience exactly, resulting in skewed outcomes. 

A big benefit of secondary market research is helping lay the groundwork and get you ready to carry out primary market research by making sure you’re focused on what matters most.

research methodology secondary data example

Qualitative research

Qualitative research is one of the two fundamental types of market research. Qualitative research is about people and their opinions. Typically conducted by asking questions either one-on-one or in groups, qualitative research can help you define problems and learn about customers’ opinions, values, and beliefs.

Classic examples of qualitative research are long-answer questions like “Why do you think this product is better than competitive products? Why do you think it’s not?”, or “How would you improve this new service to make it more appealing?”

Because qualitative research generally involves smaller sample sizes than its close cousin quantitative research, it gives you an anecdotal overview of your subject, rather than highly detailed information that can help predict future performance.

Qualitative research is particularly useful if you’re developing a new product, service, website or ad campaign and want to get some feedback before you commit a large budget to it.

Quantitative research

If qualitative research is all about opinions, quantitative research is all about numbers, using math to uncover insights about your audience. 

Typical quantitative research questions are things like, “What’s the market size for this product?” or “How long are visitors staying on this website?”. Clearly the answers to both will be numerical.

Quantitative research usually involves questionnaires. Respondents are asked to complete the survey, which marketers use to understand consumer needs, and create strategies and marketing plans.

Importantly, because quantitative research is math-based, it’s statistically valid, which means you’re in a good position to use it to predict the future direction of your business.

Consumer research 

As its name implies, consumer research gathers information about consumers’ lifestyles, behaviors, needs and preferences, usually in relation to a particular product or service. It can include both quantitative and qualitative studies.

Examples of consumer research in action include finding ways to improve consumer perception of a product, or creating buyer personas and market segments, which help you successfully market your product to different types of customers.

Understanding consumer trends , driven by consumer research, helps businesses understand customer psychology and create detailed purchasing behavior profiles. The result helps brands improve their products and services by making them more customer-centric, increasing customer satisfaction, and boosting bottom line in the process.

Product research 

Product research gives a new product (or indeed service, we don’t judge) its best chance of success, or helps an existing product improve or increase market share.

It’s common sense: by finding out what consumers want and adjusting your offering accordingly, you gain a competitive edge. It can be the difference between a product being a roaring success or an abject failure.

Examples of product research include finding ways to develop goods with a higher value, or identifying exactly where innovation effort should be focused. 

Product research goes hand-in-hand with other strands of market research, helping you make informed decisions about what consumers want, and what you can offer them.

Brand research  

Brand research is the process of gathering feedback from your current, prospective, and even past customers to understand how your brand is perceived by the market.

It covers things like brand awareness, brand perceptions, customer advocacy, advertising effectiveness, purchase channels, audience profiling, and whether or not the brand is a top consideration for consumers.

The result helps take the guesswork out of your messaging and brand strategy. Like all types of market research, it gives marketing leaders the data they need to make better choices based on fact rather than opinion or intuition.

Market research methods 

So far we’ve reviewed various different types of market research, now let’s look at market research methods, in other words the practical ways you can uncover those all-important insights.

Consumer research platform 

A consumer research platform like GWI is a smart way to find on-demand market research insights in seconds.

In a world of fluid markets and changing attitudes, a detailed understanding of your consumers, developed using the right research platform, enables you to stop guessing and start knowing.

As well as providing certainty, consumer research platforms massively accelerate speed to insight. Got a question? Just jump on your consumer research platform and find the answer – job done.

The ability to mine data for answers like this is empowering – suddenly you’re in the driving seat with a world of possibilities ahead of you. Compared to the most obvious alternative – commissioning third party research that could take weeks to arrive – the right consumer research platform is basically a magic wand.

Admittedly we’re biased, but GWI delivers all this and more. Take our platform for a quick spin and see for yourself.

And the downside of using a consumer research platform? Well, no data set, however fresh or thorough, can answer every question. If you need really niche insights then your best bet is custom market research , where you can ask any question you like, tailored to your exact needs.

Face-to-face interviews 

Despite the rise in popularity of online surveys , face-to-face survey interviewing – using mobile devices or even the classic paper survey – is still a popular data collection method.

In terms of advantages, face-to-face interviews help with accurate screening, in the sense the interviewee can’t easily give misleading answers about, say, their age. The interviewer can also make a note of emotions and non-verbal cues. 

On the other hand, face-to-face interviews can be costly, while the quality of data you get back often depends on the ability of the interviewer. Also, the size of the sample is limited to the size of your interviewing staff, the area in which the interviews are conducted, and the number of qualified respondents within that area.

Social listening 

Social listening is a powerful solution for brands who want to keep an ear to the ground, gathering unfiltered thoughts and opinions from consumers who are posting on social media. 

Many social listening tools store data for up to a couple of years, great for trend analysis that needs to compare current and past conversations.

Social listening isn’t limited to text. Images, videos, and emojis often help us better understand what consumers are thinking, saying, and doing better than more traditional research methods. 

Perhaps the biggest downside is there are no guarantees with social listening, and you never know what you will (or won’t) find. It can also be tricky to gauge sentiment accurately if the language used is open to misinterpretation, for example if a social media user describes something as “sick”.

There’s also a potential problem around what people say vs. what they actually do. Tweeting about the gym is a good deal easier than actually going. The wider problem – and this may shock you – is that not every single thing people write on social media is necessarily true, which means social listening can easily deliver unreliable results.

Public domain data 

Public domain data comes from think tanks and government statistics or research centers like the UK’s National Office for Statistics or the United States Census Bureau and the National Institute of Statistical Sciences. Other sources are things like research journals, news media, and academic material.

Its advantages for market research are it’s cheap (or even free), quick to access, and easily available. Public domain datasets can be huge, so potentially very rich.

On the flip side, the data can be out of date, it certainly isn’t exclusive to you, and the collection methodology can leave much to be desired. But used carefully, public domain data can be a useful source of secondary market research.

Telephone interviews 

You know the drill – you get a call from a researcher who asks you questions about a particular topic and wants to hear your opinions. Some even pay or offer other rewards for your time.

Telephone surveys are great for reaching niche groups of consumers within a specific geographic area or connected to a particular brand, or who aren’t very active in online channels. They’re not well-suited for gathering data from broad population groups, simply because of the time and labor involved.

How to use market research 

Data isn’t an end in itself; instead it’s a springboard to make other stuff happen. So once you’ve drawn conclusions from your research, it’s time to think of what you’ll actually do based on your findings.

While it’s impossible for us to give a definitive list (every use case is different), here are some suggestions to get you started.

Leverage it . Think about ways to expand the use – and value – of research data and insights, for example by using research to support business goals and functions, like sales, market share or product design.

Integrate it . Expand the value of your research data by integrating it with other data sources, internal and external. Integrating data like this can broaden your perspective and help you draw deeper insights for more confident decision-making.

Justify it . Enlist colleagues from areas that’ll benefit from the insights that research provides – that could be product management, product development, customer service, marketing, sales or many others – and build a business case for using research.

How to choose the right type of market research 

Broadly speaking, choosing the right research method depends on knowing the type of data you need to collect. To dig into ideas and opinions, choose qualitative; to do some testing, it’s quantitative you want.

There are also a bunch of practical considerations, not least cost. If a particular approach sounds great but costs the earth then clearly it’s not ideal for any brand on a budget.

Then there’s how you intend to use the actual research, your level of expertise with research data, whether you need access to historical data or just a snapshot of today, and so on.

The point is, different methods suit different situations. When choosing, you’ll want to consider what you want to achieve, what data you’ll need, the pros and cons of each method, the costs of conducting the research, and the cost of analyzing the results. 

Market research examples

Independent agency Bright/Shift used GWI consumer insights to shape a high-impact go-to-market strategy for their sustainable furniture client, generating £41K in revenue in the first month. Here’s how they made the magic happen .

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  1. What is Secondary Research?

    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. Example: Secondary research.

  2. Secondary Data

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    Common secondary research methods include data collection through the internet, libraries, archives, schools and organizational reports. Online Data. Online data is data that is gathered via the internet. In recent times, this method has become popular because the internet provides a large pool of both free and paid research resources that can ...

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    conceptualize primary and secondary data by considering two extreme cases. In the first, which is an example of primary data, a research team conceives of and develops a research project, collects data designed to address specific questions posed by the project, and performs and pub-lishes their own analyses of the data they have collected.

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    5. Advantages of secondary data. Secondary data is suitable for any number of analytics activities. The only limitation is a dataset's format, structure, and whether or not it relates to the topic or problem at hand. When analyzing secondary data, the process has some minor differences, mainly in the preparation phase.

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    Sources of Secondary Data. Sources of secondary data include books, personal sources, journals, newspapers, websitess, government records etc. Secondary data are known to be readily available compared to that of primary data. It requires very little research and needs for manpower to use these sources.

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    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.

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    A notable marker of primary research is the inclusion of a "methods" section, where the authors describe how the data was generated. Common examples of secondary research include textbooks, encyclopedias, news articles, review articles, and meta analyses.

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