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Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

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

Secondary Data Types

Types of secondary data are as follows:

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 This can include census data, historical documents, and archival records.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

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

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

Secondary Data Formats

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

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

Secondary Data Analysis Methods

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

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

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

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

Examples of Secondary Data

Here are some examples of secondary data from different fields:

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

Purpose of Secondary Data

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

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

When to use Secondary Data

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

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

Characteristics of Secondary Data

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

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

Advantages of Secondary Data

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

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

Limitations of Secondary Data

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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

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

LEARN ABOUT: Research Process Steps

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

What is Secondary Research: Definition

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

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

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

LEARN ABOUT: Level of Analysis

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

LEARN ABOUT: Data Analytics Projects

Secondary Research Methods with Examples

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

The following are popularly used secondary research methods and examples:

1. Data Available on The Internet

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

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

2. Government and Non-Government Agencies

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

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

3. Public Libraries

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

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

4. Educational Institutions

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

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

5. Commercial Information Sources

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

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

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

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

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

What is Secondary Research?

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

Benefits of Secondary Research

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

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

Primary Research vs. Secondary Research

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

Primary Research

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

Secondary Research

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

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

Types of Secondary Research

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

Here are the primary types of secondary research:

Internal Sources

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

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

External Sources

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

These sources offer a broader perspective on various subjects.

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

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

How to Conduct Secondary Research?

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

1. Define Your Research Objectives

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

2. Identify Relevant Sources

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

3. Evaluate Source Credibility

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

4. Extract and Analyze Information

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

5. Synthesize Findings

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

6. Address Limitations and Gaps

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

7. Contextualize Your Findings

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

8. Cite Your Sources

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

9. Integrate Secondary and Primary Research (If Applicable)

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

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

10. Communicate Your Findings

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

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

Secondary Research Examples

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

Market Analysis and Trend Forecasting

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

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

Academic Research and Literature Reviews

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

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

Competitive Landscape Assessment for Business Strategy

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

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

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

Secondary Research Limitations

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

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

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

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

How to Conduct Secondary Research in Minutes?

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

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

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4 Chapter 5 Secondary Research

Learning Objectives

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

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

What is Secondary Research?

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

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

Marketing textbook © 2022  Western Sydney University taken by   Sally Tsoutas Western Sydney University Photographer  is licensed under an   Attribution-NonCommercial-NoDerivatives 4.0 International

Sources of secondary data.

The two main sources of secondary data are:

• Internal sources
• External sources

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

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

Benefits of using Secondary Data

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

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

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

Evaluation of Secondary Data

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

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

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

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

Table 2: differences between primary and secondary research.

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

About the author

name: Aila Khan

institution: Western Sydney University

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

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

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

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

What is Secondary  Research?

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

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

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

What are Secondary Research Methods?

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

• Online Data

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

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

• Data from Government and Non-government Archives

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

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

• Data from Libraries

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

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

• Data from Institutions of Learning

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

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

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

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

When Do We Conduct Secondary Research?

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

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

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

Questions to ask before conducting Secondary Research

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

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

Advantages of Secondary Research

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

Disadvantages of Secondary Research  

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

How to Conduct Online Surveys with Formplus 

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

• Sign into Formplus

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

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

• Edit Form Title

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

• Click on the edit button to edit the form.

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

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

• Multiple Sharing Options

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

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

Why Use Formplus as a Secondary Research Tool?

• Simple Form Builder Solution

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

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

• Over 25 Form Fields

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

• Offline Form feature

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

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

• Team and Collaboration

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

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

• Embeddable Form

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

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

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

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

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

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

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

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What is secondary research?

Last updated

7 February 2023

Reviewed by

Cathy Heath

In this guide, we explain in detail what secondary research is, including the difference between this research method and primary research, the different sources for secondary research, and how you can benefit from this research method.

Analyze your secondary research

Bring your secondary research together inside Dovetail, tag PDFs, and uncover actionable insights

• Overview of secondary research

Secondary research is a method by which the researcher finds existing data, filters it to meet the context of their research question, analyzes it, and then summarizes it to come up with valid research conclusions.

This research method involves searching for information, often via the internet, using keywords or search terms relevant to the research question. The goal is to find data from internal and external sources that are up-to-date and authoritative, and that fully answer the question.

Secondary research reviews existing research and looks for patterns, trends, and insights, which helps determine what further research, if any, is needed.

• Secondary research methods

Secondary research is more economical than primary research, mainly because the methods for this type of research use existing data and do not require the data to be collected first-hand or by a third party that you have to pay.

Secondary research is referred to as ‘desk research’ or ‘desktop research,’ since the data can be retrieved from behind a desk instead of having to host a focus group and create the research from scratch.

Finding existing research is relatively easy since there are numerous accessible sources organizations can use to obtain the information they need. These  include:

The internet:  This data is either free or behind a paywall. Yet, while there are plenty of sites on the internet with information that can be used, businesses need to be careful to collect information from trusted and authentic websites to ensure the data is accurate.

Government agencies: Government agencies are typically known to provide valuable, trustworthy information that companies can use for their research.

The public library: This establishment holds paper-based and online sources of reliable information, including business databases, magazines, newspapers, and government publications. Be mindful of any copyright restrictions that may apply when using these sources.

Commercial information: This source provides first-hand information on politics, demographics, and economic developments through information aggregators, newspapers, magazines, radio, blogs, podcasts, and journals. This information may be free or behind a paywall.

Educational and scientific facilities: Universities, colleges, and specialized research facilities carry out significant amounts of research. As a result, they have data that may be available to the public and businesses for use.

• Key differences between primary research and secondary research

Both primary and secondary research methods provide researchers with vital, complementary information, despite some major differences between the two approaches.

Primary research involves gathering first-hand information by directly working with the target market, users, and interviewees. Researchers ask questions directly using surveys , interviews, and focus groups.

Through the primary research method, researchers obtain targeted responses and accurate results directly related to their overall research goals.

Secondary research uses existing data, such as published reports, that have already been completed through earlier primary and secondary research. Researchers can use this existing data to support their research goals and preliminary research findings.

Other notable differences between primary and secondary research  include:

Relevance: Primary research uses raw data relevant to the investigation's goals. Secondary research may contain irrelevant data or may not neatly fit the parameters of the researcher's goals.

Time: Primary research takes a lot of time. Secondary research can be done relatively quickly.

Researcher bias: Primary research can be subject to researcher bias.

Cost: Primary research can be expensive. Secondary research can be more affordable because the data is often free. However, valuable data is often behind a paywall. The piece of secondary research you want may not exist or be very expensive, so you may have to turn to primary research to fill the information gap.

• When to conduct secondary research

Both primary and secondary research have roles to play in providing a holistic and accurate understanding of a topic. Generally, secondary research is done at the beginning of the research phase, especially if the topic is new.

Secondary research can provide context and critical background information to understand the issue at hand and identify any gaps, that could then be filled by primary research.

• How to conduct secondary research

Researchers usually follow several steps for secondary research.

1. Identify and define the research topic

Before starting either of these research methods, you first need to determine the following:

Topic to be researched

Purpose of this research

For instance, you may want to explore a question, determine why something happened, or confirm whether an issue is true.

At this stage, you also need to consider what search terms or keywords might be the most effective for this topic. You could do this by looking at what synonyms exist for your topic, the use of industry terms and acronyms, as well as the balance between statistical or quantitative data and contextual data to support your research topic.

It’s also essential to define what you don’t want to cover in your secondary research process. This might be choosing only to use recent information or only focusing on research based on a particular country or type of consumer. From there, once you know what you want to know and why you can decide whether you need to use both primary and secondary research to answer your questions.

2. Find research and existing data sources

Once you have determined your research topic , select the information sources that will provide you with the most appropriate and relevant data for your research. If you need secondary research, you want to determine where this information can likely be found, for example:

Trade associations

Government sources

Create a list of the relevant data sources , and other organizations or people that can help you find what you need.

3. Begin searching and collecting the existing data

Once you have narrowed down your sources, you will start gathering this information and putting it into an organized system. This often involves:

Checking the credibility of the source

Setting up meetings with research teams

Signing up for accounts to access certain websites or journals

One search result on the internet often leads to other pieces of helpful information, known as ‘pearl gathering’ or ‘pearl harvesting.’ This is usually a serendipitous activity, which can lead to valuable nuggets of information you may not have been aware of or considered.

4. Combine the data and compare the results

Once you have gathered all the data, start going through it by carefully examining all the information and comparing it to ensure the data is usable and that it isn’t duplicated or corrupted. Contradictory information is useful—just make sure you note the contradiction and the context. Be mindful of copyright and plagiarism when using secondary research and always cite your sources.

Once you have assessed everything, you will begin to look at what this information tells you by checking out the trends and comparing the different datasets. You will also investigate what this information means for your research, whether it helps your overall goal, and any gaps or deficiencies.

5. Analyze your data and explore further

In the final stage of conducting secondary research, you will analyze the data you have gathered and determine if it answers the questions you had before you started researching. Check that you understand the information, whether it fills in all your gaps, and whether it provides you with other insights or actions you should take next.

If you still need further data, repeat these steps to find additional information that can help you explore your topic more deeply. You may also need to supplement what you find with primary research to ensure that your data is complete, accurate, transparent, and credible.

• The advantages of secondary research

There are numerous advantages to performing secondary research. Some key benefits are:

Quicker than primary research: Because the data is already available, you can usually find the information you need fairly quickly. Not only will secondary research help you research faster, but you will also start optimizing the data more quickly.

Plenty of available data: There are countless sources for you to choose from, making research more accessible. This data may be already compiled and arranged, such as statistical information,  so you can quickly make use of it.

Lower costs:  Since you will not have to carry out the research from scratch, secondary research tends to be much more affordable than primary research.

Opens doors to further research:  Existing research usually identifies whether more research needs to be done. This could mean follow-up surveys or telephone interviews with subject matter experts (SME) to add value to your own research.

• The disadvantages of secondary research

While there are plenty of benefits to secondary research are plenty, there are some issues you should be aware of. These include:

Credibility issues: It is important to verify the sources used. Some information may be biased and not reflect or hide, relevant issues or challenges. It could also be inaccurate.

No recent information:  Even if data may seem accurate, it may not be up to date, so the information you gather may no longer be correct. Outdated research can distort your overall findings.

Poor quality: Because secondary research tends to make conclusions from primary research data, the success of secondary research will depend on the quality and context of the research that has already been completed. If the research you are using is of poor quality, this will bring down the quality of your own findings.

Research doesn’t exist or is not easily accessible, or is expensive: Sometimes the information you need is confidential or proprietary, such as sales or earnings figures. Many information-based businesses attach value to the information they hold or publish, so the costs to access this information can be prohibitive.

Should you complete secondary research or primary research first?

Due to the costs and time involved in primary research, it may be more beneficial to conduct secondary market research first. This will save you time and provide a picture of what issues you may come across in your research. This allows you to focus on using more expensive primary research to get the specific answers you want.

What should you ask yourself before using secondary research data?

Check the date of the research to make sure it is still relevant. Also, determine the data source so you can assess how credible and trustworthy it is likely to be. For example, data from known brands, professional organizations, and even government agencies are usually excellent sources to use in your secondary research, as it tends to be trustworthy.

Be careful when using some websites and personal blogs as they may be based on opinions rather than facts. However, these sources can be useful for determining sentiment about a product or service, and help direct any primary research.

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Secondary Analysis Research

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

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

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

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

SECONDARY ANALYSIS PROCESS

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

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

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

Example of a Secondary Data Analysis

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

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

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

ADVANTAGES OF SECONDARY DATA ANALYSIS

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

Secondary Data Analysis to Answer Additional Research Questions

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

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

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

Secondary Data Analysis to Avoid Study Repetition and Over-Research

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

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

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

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

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

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

DISADVANTAGES OF SECONDARY DATA ANALYSIS

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

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

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

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

READING SECONDARY ANALYSIS RESEARCH

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

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

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

The author has no conflicts of interest to disclose.

• Ascierto P. A., Del Vecchio M., Robert C., Mackiewicz A., Chiarion-Sileni V., Arance A.,…Maio M. (2017). Ipilimumab 10 mg/kg versus ipilimumab 3 mg/kg in patients with unresectable or metastatic melanoma: A randomised, double-blind, multicentre, phase 3 trial . Lancet Oncology , 18 ( 5 ), 611–622. 10.1016/S1470-2045(17)30231-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burkhalter J. E., Margolies L., Sigurdsson H. O., Walland J., Radix A., Rice D.,…Maingi S. (2016). The National LGBT Cancer Action Plan: A white paper of the 2014 National Summit on Cancer in the LGBT Communities . LGBT Health , 3 ( 1 ), 19–31. 10.1089/lgbt.2015.0118 [ CrossRef ] [ Google Scholar ]
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Integrated Primary & Secondary Research

5 Types of Secondary Research Data

Secondary sources allow you to broaden your research by providing background information, analyses, and unique perspectives on various elements for a specific campaign. Bibliographies of these sources can lead to the discovery of further resources to enhance research for organizations.

There are two common types of secondary data: Internal data and External data. Internal data is the information that has been stored or organized by the organization itself. External data is the data organized or collected by someone else.

Internal Secondary Sources

Internal secondary sources include databases containing reports from individuals or prior research. This is often an overlooked resource—it’s amazing how much useful information collects dust on an organization’s shelves! Other individuals may have conducted research of their own or bought secondary research that could be useful to the task at hand. This prior research would still be considered secondary even if it were performed internally because it was conducted for a different purpose.

External Secondary Sources

A wide range of information can be obtained from secondary research. Reliable databases for secondary sources include Government Sources, Business Source Complete, ABI, IBISWorld, Statista, and CBCA Complete. This data is generated by others but can be considered useful when conducting research into a new scope of the study. It also means less work for a non-for-profit organization as they would not have to create their own data and instead can piggyback off the data of others.

Examples of Secondary Sources

Government sources.

A lot of secondary data is available from the government, often for free, because it has already been paid for by tax dollars. Government sources of data include the Census Bureau, the Bureau of Labor Statistics, and the National Centre for Health Statistics.

For example, through the Census Bureau, the Bureau of Labor Statistics regularly surveys individuals to gain information about them (Bls.gov, n.d). These surveys are conducted quarterly, through an interview survey and a diary survey, and they provide data on expenditures, income, and household information (families or single). Detailed tables of the Expenditures Reports include the age of the reference person, how long they have lived in their place of residence and which geographic region they live in.

Syndicated Sources

A syndicated survey is a large-scale instrument that collects information about a wide variety of people’s attitudes and capital expenditures. The Simmons Market Research Bureau conducts a National Consumer Survey by randomly selecting families throughout the country that agree to report in great detail what they eat, read, watch, drive, and so on. They also provide data about their media preferences.

Other Types of Sources

Gallup, which has a rich tradition as the world’s leading public opinion pollster, also provides in-depth reports based on its proprietary probability-based techniques (called the Gallup Panel), in which respondents are recruited through a random digit dial method so that results are more reliably generalizable. The Gallup organization operates one of the largest telephone research data-collection systems in the world, conducting more than twenty million interviews over the last five years and averaging ten thousand completed interviews per day across two hundred individual survey research questionnaires (GallupPanel, n.d).

Attribution

This page contains materials taken from:

Bls.gov. (n.d). U.S Bureau of Labor Statistics. Retrieved from https://www.bls.gov/

Define Quantitative and Qualitative Evidence. (2020). Retrieved July 23, 2020, from http://sgba-resource.ca/en/process/module-8-evidence/define-quantitative-and-qualitative-evidence/

GallupPanel. (n.d). Gallup Panel Research. Retrieved from http://www.galluppanel.com

Secondary Data. (2020). Retrieved July 23, 2020, from https://2012books.lardbucket.org/books/advertising-campaigns-start-to-finish/s08-03-secondary-data.html

An Open Guide to Integrated Marketing Communications (IMC) Copyright © by Andrea Niosi and KPU Marketing 4201 Class of Summer 2020 is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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?

Library Guides

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.  

Definitions  

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   

Use  

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.

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  

validity  

feasibility  

recording  

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 

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 . 

Observations 

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  

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. 

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. 

Bibliography

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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• Last Updated: Sep 14, 2022 12:58 PM
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Primary vs Secondary Research: Differences, Methods, Sources, and More

Table of Contents

Primary vs Secondary Research – What’s the Difference?

In the search for knowledge and data to inform decisions, researchers and analysts rely on a blend of research sources. These sources are broadly categorized into primary and secondary research, each serving unique purposes and offering different insights into the subject matter at hand. But what exactly sets them apart?

Primary research is the process of gathering fresh data directly from its source. This approach offers real-time insights and specific information tailored to specific objectives set by stakeholders. Examples include surveys, interviews, and observational studies.

Secondary research , on the other hand, involves the analysis of existing data, most often collected and presented by others. This type of research is invaluable for understanding broader trends, providing context, or validating hypotheses. Common sources include scholarly articles, industry reports, and data compilations.

The crux of the difference lies in the origin of the information: primary research yields firsthand data which can be tailored to a specific business question, whilst secondary research synthesizes what's already out there. In essence, primary research listens directly to the voice of the subject, whereas secondary research hears it secondhand .

When to Use Primary and Secondary Research

Selecting the appropriate research method is pivotal and should be aligned with your research objectives. The choice between primary and secondary research is not merely procedural but strategic, influencing the depth and breadth of insights you can uncover.

Primary research shines when you need up-to-date, specific information directly relevant to your study. It's the go-to for fresh insights, understanding consumer behavior, or testing new theories. Its bespoke nature makes it indispensable for tailoring questions to get the exact answers you need.

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Secondary research is your first step into the research world. It helps set the stage by offering a broad understanding of the topic. Before diving into costly primary research, secondary research can validate the need for further investigation or provide a solid background to build upon. It's especially useful for identifying trends, benchmarking, and situating your research within the existing body of knowledge.

Combining both methods can significantly enhance your research. Starting with secondary research lays the groundwork and narrows the focus, whilst subsequent primary research delves deep into specific areas of interest, providing a well-rounded, comprehensive understanding of the topic.

Primary vs Secondary Research Methods

In the landscape of market research, the methodologies employed can significantly influence the insights and conclusions drawn. Let's delve deeper into the various methods underpinning both primary and secondary research, shedding light on their unique applications and the distinct insights they offer.

Primary Research Methods:

• Surveys: Surveys are a cornerstone of primary research, offering a quantitative approach to gathering data directly from the target audience. By employing structured questionnaires, researchers can collect a vast array of data ranging from customer preferences to behavioral patterns. This method is particularly valuable for acquiring statistically significant data that can inform decision-making processes and strategy development. The application of statistical approaches for analysing this data, such as key drivers analysis, MaxDiff or conjoint analysis can also further enhance any collected data.
• One on One Interviews: Interviews provide a qualitative depth to primary research, allowing for a nuanced exploration of participants' attitudes, experiences, and motivations. Conducted either face-to-face or remotely, interviews enable researchers to delve into the complexities of human behavior, offering rich insights that surveys alone may not uncover. This method is instrumental in exploring new areas of research or obtaining detailed information on specific topics.
• Focus Groups: Focus groups bring together a small, diverse group of participants to discuss and provide feedback on a particular subject, product, or idea. This interactive setting fosters a dynamic exchange of ideas, revealing consumers' perceptions, experiences, and preferences. Focus groups are invaluable for testing concepts, exploring market trends, and understanding the factors that influence consumer decisions.
• Ethnographic Studies: Ethnographic studies involve the systematic watching, recording, and analysis of behaviors and events in their natural setting. This method offers an unobtrusive way to gather authentic data on how people interact with products, services, or environments, providing insights that can lead to more user-centered design and marketing strategies.

Secondary Research Methods:

• Literature Reviews: Literature reviews involve the comprehensive examination of existing research and publications on a given topic. This method enables researchers to synthesize findings from a range of sources, providing a broad understanding of what is already known about a subject and identifying gaps in current knowledge.
• Meta-Analysis: Meta-analysis is a statistical technique that combines the results of multiple studies to arrive at a comprehensive conclusion. This method is particularly useful in secondary research for aggregating findings across different studies, offering a more robust understanding of the evidence on a particular topic.
• Content Analysis: Content analysis is a method for systematically analyzing texts, media, or other content to quantify patterns, themes, or biases . This approach allows researchers to assess the presence of certain words, concepts, or sentiments within a body of work, providing insights into trends, representations, and societal norms. This can be performed across a range of sources including social media, customer forums or review sites.
• Historical Research: Historical research involves the study of past events, trends, and behaviors through the examination of relevant documents and records. This method can provide context and understanding of current trends and inform future predictions, offering a unique perspective that enriches secondary research.

Each of these methods, whether primary or secondary, plays a crucial role in the mosaic of market research, offering distinct pathways to uncovering the insights necessary to drive informed decisions and strategies.

Primary vs Secondary Sources in Research

Both primary and secondary sources of research form the backbone of the insight generation process, when both are utilized in tandem it can provide the perfect steppingstone for the generation of real insights. Let’s explore how each category serves its unique purpose in the research ecosystem.

Primary Research Data Sources

Primary research data sources are the lifeblood of firsthand research, providing raw, unfiltered insights directly from the source. These include:

• Customer Satisfaction Survey Results: Direct feedback from customers about their satisfaction with a product or service. This data is invaluable for identifying strengths to build on and areas for improvement and typically renews each month or quarter so that metrics can be tracked over time.
• NPS Rating Scores from Customers: Net Promoter Score (NPS) provides a straightforward metric to gauge customer loyalty and satisfaction. This quantitative data can reveal much about customer sentiment and the likelihood of referrals.
• Ad-hoc Surveys: Ad-hoc surveys can be about any topic which requires investigation, they are typically one off surveys which zero in on one particular business objective. Ad-hoc projects are useful for situations such as investigating issues identified in other tracking surveys, new product development, ad testing, brand messaging, and many other kinds of projects.
• A Field Researcher’s Notes: Detailed observations from fieldwork can offer nuanced insights into user behaviors, interactions, and environmental factors that influence those interactions. These notes are a goldmine for understanding the context and complexities of user experiences.
• Recordings Made During Focus Groups: Audio or video recordings of focus group discussions capture the dynamics of conversation, including reactions, emotions, and the interplay of ideas. Analyzing these recordings can uncover nuanced consumer attitudes and perceptions that might not be evident in survey data alone.

These primary data sources are characterized by their immediacy and specificity, offering a direct line to the subject of study. They enable researchers to gather data that is specifically tailored to their research objectives, providing a solid foundation for insightful analysis and strategic decision-making.

Secondary Research Data Sources

In contrast, secondary research data sources offer a broader perspective, compiling and synthesizing information from various origins. These sources include:

• Books, Magazines, Scholarly Journals: Published works provide comprehensive overviews, detailed analyses, and theoretical frameworks that can inform research topics, offering depth and context that enriches primary data.
• Market Research Reports: These reports aggregate data and analyses on industry trends, consumer behavior, and market dynamics, providing a macro-level view that can guide primary research directions and validate findings.
• Government Reports: Official statistics and reports from government agencies offer authoritative data on a wide range of topics, from economic indicators to demographic trends, providing a reliable basis for secondary analysis.
• White Papers, Private Company Data: White papers and reports from businesses and consultancies offer insights into industry-specific research, best practices, and market analyses. These sources can be invaluable for understanding the competitive landscape and identifying emerging trends.

Secondary data sources serve as a compass, guiding researchers through the vast landscape of information to identify relevant trends, benchmark against existing data, and build upon the foundation of existing knowledge. They can significantly expedite the research process by leveraging the collective wisdom and research efforts of others.

By adeptly navigating both primary and secondary sources, researchers can construct a well-rounded research project that combines the depth of firsthand data with the breadth of existing knowledge. This holistic approach ensures a comprehensive understanding of the research topic, fostering informed decisions and strategic insights.

Examples of Primary and Secondary Research in Marketing

In the realm of marketing, both primary and secondary research methods play critical roles in understanding market dynamics, consumer behavior, and competitive landscapes. By comparing examples across both methodologies, we can appreciate their unique contributions to strategic decision-making.

Example 1: New Product Development

Primary Research: Direct Consumer Feedback through Surveys and Focus Groups

• Objective: To gauge consumer interest in a new product concept and identify preferred features.
• Process: Surveys distributed to a target demographic to collect quantitative data on consumer preferences, and focus groups conducted to dive deeper into consumer attitudes and desires.
• Insights: Direct insights into consumer needs, preferences for specific features, and willingness to pay. These insights help in refining product design and developing a targeted marketing strategy.

Secondary Research: Market Analysis Reports

• Objective: To understand the existing market landscape, including competitor products and market trends.
• Process: Analyzing published market analysis reports and industry studies to gather data on market size, growth trends, and competitive offerings.
• Insights: Provides a broader understanding of the market, helping to position the new product strategically against competitors and align it with current trends.

Example 2: Brand Positioning

Primary Research: Brand Perception Analysis through Surveys

• Objective: To understand how the brand is perceived by consumers and identify potential areas for repositioning.
• Process: Conducting surveys that ask consumers to describe the brand in their own words, rate it against various attributes, and compare it to competitors.
• Insights: Direct feedback on brand strengths and weaknesses from the consumer's perspective, offering actionable data for adjusting brand messaging and positioning.

Secondary Research: Social Media Sentiment Analysis

• Objective: To analyze public sentiment towards the brand and its competitors.
• Process: Utilizing software tools to analyze mentions, hashtags, and discussions related to the brand and its competitors across social media platforms.
• Insights: Offers an overview of public perception and emerging trends in consumer sentiment, which can validate findings from primary research or highlight areas needing further investigation.

Example 3: Market Expansion Strategy

Primary Research: Consumer Demand Studies in New Markets

• Objective: To assess demand and consumer preferences in a new geographic market.
• Process: Conducting surveys and interviews with potential consumers in the target market to understand their needs, preferences, and cultural nuances.
• Insights: Provides specific insights into the new market’s consumer behavior, preferences, and potential barriers to entry, guiding market entry strategies.

Secondary Research: Economic and Demographic Analysis

• Objective: To evaluate the economic viability and demographic appeal of the new market.
• Process: Reviewing existing economic reports, demographic data, and industry trends relevant to the target market.
• Insights: Offers a macro view of the market's potential, including economic conditions, demographic trends, and consumer spending patterns, which can complement insights gained from primary research.

By leveraging both primary and secondary research, marketers can form a comprehensive understanding of their market, consumers, and competitors, facilitating informed decision-making and strategic planning. Each method brings its strengths to the table, with primary research offering direct consumer insights and secondary research providing a broader context within which to interpret those insights.

What Are the Pros and Cons of Primary and Secondary Research?

When it comes to market research, both primary and secondary research offer unique advantages and face certain limitations. Understanding these can help researchers and businesses make informed decisions on which approach to utilize for their specific needs. Below is a comparative table highlighting the pros and cons of each research type.

Navigating the Pros and Cons

• Balance Your Research Needs: Consider starting with secondary research to gain a broad understanding of the subject matter, then delve into primary research for specific, targeted insights that are tailored to your precise needs.
• Resource Allocation: Evaluate your budget, time, and resource availability. Primary research can offer more specific and actionable data but requires more resources. Secondary research is more accessible but may lack the specificity or recency you need.
• Quality and Relevance: Assess the quality and relevance of available secondary sources before deciding if primary research is necessary. Sometimes, the existing data might suffice, especially for preliminary market understanding or trend analysis.
• Combining Both for Comprehensive Insights: Often, the most effective research strategy involves a combination of both primary and secondary research. This approach allows for a more comprehensive understanding of the market, leveraging the broad perspective provided by secondary sources and the depth and specificity of primary data.

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5 Data: Types and Sources

Subhrangsu Santra

1.      Objective

2.      Introduction

3.      Learning Outcome

4.      Types of Data

4.1    Quantitative Data

4.3   Quantitative vs. Qualitative Data

  Self-Check Exercise 1

5.       Sources of Data: Primary and Secondary

5.1.    Advantages of use of Primary Data

5.2.    Advantages of use of Secondary Data

5.3.    Choice between Primary and Secondary Data

5.4.    Publications containing Primary and Secondary Data

6.       Methods of Collection of Primary Data

7.       Steps of Collection of Primary Data

   Self-Check Exercise 2

8.       Summary

1.        Objective

In this module, an attempt has been made to explain the meaning and concept of data with its types, sources and different methods of data collection to conduct any type of social research. A brief introduction of some quantitative and qualitative techniques of data collection including PRA is also given to allow students to develop a comparative approach.

Data is a plural form of a Latin word datum , meaning ‘facts or statics used for reference or analysis’ . In social sciences, it is treated as a plural noun and used with a plural verb, for instance, we write ‘the data were classified’ and use the term datum in its singular form. According to Hicks (1993: 668), data is “a representation of facts, concepts or instructions in a formalised manner suitable for communication, interpretation, or processing by humans or by automatic means” . Data is basically the combination of different kinds of observation either in quantitative or in qualitative form which is needed to express any problem of the society as well as to find out possible solution of that problem through using appropriate methods of calculation in a research study. Data consists of full information regarding a particular aspect; it summarise a set of facts. Reading, scores, points, goals, measurements obtained from observation of salient features of a certain reality and systematically recorded in a standard form, are, therefore, called data (Singh 2011).

The procurement of observation from different sources for a single object is known as collection of data. So, data is the only leader, whose systematic arrangement can help to reach the actual objective of any study. In any research, data are used to draw conclusions, which help us to understand our environment better. We use data to adjust ourselves to the environment or to change it.

This module will help the students to get basic idea of various types of data and their sources. Students will also learn about various methods of collection of data, steps to be followed to collect those. The module would also provide some basic idea about the use of a qualitative technique called Participatory Rural Appraisal (PRA) – a tool or a method to collect primary data in a very short time with the active participation of people of that area.

4. Types of Data

There are many ways of classifying data. A common classification is based upon who collected the data . The term ‘primary data’ is used to refer to data collected by the investigator himself/herself for a specific purpose. For instance, a scholar carrying out field research is bound to generate primary data from his/her point of view. In many instance agency/organization collecting primary data allow others to read and use them in original form. Census data, for instance, are primary data. Similarly, National Sample Survey Organization (NSSO) or National Crime Record Bureau (NCRB) do publish their original data and make those available to researchers. As against this, the term ‘secondary data’ is used to mean data collected by someone else for some other purpose, but being utilized by the investigator for his/her own research purpose. For example, we all use research findings of other scholars to analyse a situation or to prove a point. While using such secondary data, the original data is not cited; rather the analysis of the concerned  author is used. It is therefore called ‘secondary’. There are several advantages and disadvantages of both these types of data. As primary and secondary data also refer to two different sources of data, we would discuss these in some detail later.

Another classification of data is based on objective and subjective points of view. Objective data are those that are external to the individual and to which the researcher has assigned meaning a priori . Dimensions of a house, age of a respondent, education of the family members, etc. are examples of objective data. Subjective data are those relating to the subjective makeup of the individuals studied. Attitudes and opinions are typical illustrations of subjective data.

This objective point of view accepts that the data are validated in the sense that they must be measured and recorded accurately. Data can be shown to be true if they correspond to reality. The objective view makes the following assumptions about data:

•  They are factual, resulting from recording of measurable events, or objects.

•  Data represent information and are the only way we can make information explicit.

•  They record particular instances of reality.

•  They are explicit as they are in a fixed in recorded form.

•  Hence, they can be communicated digitally.

On the other hand, the subjective view makes the following assumptions about data.

•   The data are not necessarily true or accurate as not all errors can be detected automatically and not everyone will necessarily agree that they are a true representation of a particular fact.

•  Some data record subjective opinion, not facts. If data can represent opinions and concepts, they are not truly objective.

•   Data have absolutely no meaning. They acquire meaning only when appropriated and analysed by a human recipient in a particular context.

Another popular way to classify data is to refer to their numerical and non-numerical characters. We prefer to call them quantitative (numerical) and qualitative (non-numerical) data. Information about the age of respondents is quantitative because age of the respondents is expressed in number, say 38 year. It is possible to quantify many aspects of social living. For instance, apart from age, we can also quantify height, weight, literacy, income or distance. We also further divide such quantitative data into two more groups – discrete and continuous. As compared to numerical data, religion of a group of people is qualitative because religion cannot be stated in numerical terms, a person should either be Hindu or Muslim or Buddhist or Christian. There are many non-numerical concepts like love, hatred, relationship, sentiment, emotion, feeling that are difficult to quantify. Even though it is often possible to numerically present a qualitative aspect of social living, say measuring maturity of a person by his age and work experience, the depth of meaning attached to the concept is often lost in the process.

Let us discuss the quantitative and qualitative types of data in some detail to reveal their nuances, advantages and limitations.

4.1 Quantitative Data

The quantitative character of data is technically called numerical variable. Numerical variable is a quantitative character of an object/matter and its values can always be measured. For example, the students of a department may be classified according to their weight as follows:

Table 1: Weight wise distribution of Students

Such a distribution is known as frequency distribution. In this type of classification, there are two elements, namely (i) the numerical variable, i.e. the weight of the students in the above table in five groups or classes, and (ii) frequency, i.e. the number of students in each group or class. There were 12 students having weight between 35 to 45 Kilograms, 109 students having weight between 45 to 55 Kilograms and so on. Thus, we can find out the ways in which the frequencies (i.e. the number of students) are distributed.

Continuous and Discrete Variables

A frequency distribution refers to data classified on the basis of some variable that can be measured, such as price, wage, age, number of unit produced or consumed. The term variable refers to the characteristic that varies in amount or magnitude in a frequency distribution. A variable may be either continuous or discrete .

A Continuous Variable , also called continuous random variable, is capable of manifesting every conceivable fractional value within the range of possibilities, such as the height of the children of an ICDS centre or weight of persons donating blood in a camp. In a continuous variable, data are obtained by numerical measurements rather than counting. For example, the height of an individual may have any value between, say, from 60 inches to 74 inches. It may be 66.4 inches, 66.47 inches or even 66.46589 inches provided we could measure the height so precisely. So a continuous variate can take any infinite number of values within a given interval, however small it may be. Height, weight, temperature, density etc., are example of a continuous variate. Generally, the continuous data are obtained through measurement. Series which can be described by a continuous variable are called continuous series.

A Discrete Variable is that which can vary only by finite “jumps” and cannot manifest every conceivable fraction value. The number of children per family, the number of members per Self Help Group (SHG), the number of classrooms per primary school are example of this type. The number of classrooms per primary school can take only the values 1, 2, 3, 4, ……, i.e. whole numbers; it cannot take any value, e.g. a fractional value. Similarly, the number of members in SHG is a discrete variable. Such kinds of data are derived through counting. Series represented by discrete variables are called discrete series. The following are two examples of discrete and continuous frequency distribution:

Table 2: Classroom wise distribution of Primary School in Raina – I block of Burdwan District

Example of Continuous Frequency Distribution

Let us discuss some other examples:

(a)  Family Size is a discrete variable. Because, it can take only some isolated values. The family size may be either 1 or 2, or 3, or 4 etc. It cannot take any value like we cannot speak of 3.46 members in a family.

(b)   Family Income per Month is also a discrete variable. It can take values like Rs. 6890.50, or Rs. 6890.75, etc., but not any value as income in fraction of Paisa.

Although the theoretical distinction between continuous and discrete variation is clear and precise, in practical numerical work it is only an approximation. The reason is that even the most precise instruments of measurement can be used only to a finite number of places. Thus, every theoretically continuous series can never be expected to flow continuously with one measurement touching another without any break in actual observation.

4.2 Qualitative Data

In qualitative form, data are classified on the basis of some attribute or quality like sex, literacy, colour of hair, religion, etc. In this type of classification, the attribute under any study cannot be measured; it can be classified into different groups and one can find out whether it is present or absent in that unit under study. For example, if the attribute under a study is human population, one can find out how many persons are male and how many persons are female. Thus, when only one attribute is studied, two1 classes are found. This type of classification is known as simple classification.

The type of classification where only two classes are found is also called twofold or dichotomous classification. If instead of forming only two classes, we farther divide the data on the basis of some attribute or attributes so as to form several classes, the classification is known as manifold classification. For example, we may first divide the population into males and females, on the basis of the attribute sex ; each of these classes may be further subdivided in to literate and illiterate on the basis of the attribute literacy . Farther classification can be made on the basis of some other attribute, say employment. The flow chart stated below tries to make the classification clear:

4.3 Quantitative vs. Qualitative Data

Glesne and Peshkin (1992) have made a useful comparison between the characteristics of quantitative and qualitative modes of enquiry as under:

Self Check Exercise 1 :

Q 1. What is quantitative data?

Information about the age of respondents is quantitative because age of the respondents is expressed in number, say 38 year. It is possible to quantify many aspects of social living. For instance, apart from age, we can also quantify height, weight, literacy, income or distance.

Q 2. What purpose do qualitative data serve?

The qualitative data give us in depth knowledge about the problem where quantitative data only can give us the numerical value.

Q 3. How can qualitative data be classified?

In qualitative form, data are classified on the basis of some attribute or quality like sex, literacy, colour of hair, religion, etc. For example, if the attribute under a study is population, one can find out how many persons are male and how many persons are female. Thus, when only one attribute is studied, two classes are found. This type of classification is known as simple classification.

In a study it is found that the total population in the area is 468 out of which 238 are male and 230 are female. The figures are stated in tabular form bellow:

5. Sources of Data: Primary and Secondary

The investigator, while collecting data is faced with one of the most difficult problem of obtaining or gathering the desired information or data. Utmost care must be taken while collecting data because data constitute the foundation on which the superstructure of analysis is built and policy decisions are taken. So if the data are incorrect or inadequate, the whole endeavour becomes useless.

Data may be obtained either from the Primary Source or from the Secondary Source . Primary data are original in character and are generated through surveys/field work conducted by the Government, individuals, institutions and research bodies. Primary source usually has more detail information particularly on the procedures followed in collecting and compilation of the data as compared to the secondary data. For example, data obtained in a population census by the Office of the Registrar General and Census Commissioner, Ministry of Home Affairs, are primary data. Such data may be both quantitative and qualitative in nature. Any ethnographic data collected through participant observation, for instance, is also primary data.

By comparison, secondary data are those that were previously been collected by some person/agency for one purpose and these were merely complied from that source for use in different research. For example, a person/agency conducting a research might use the findings and analysis of any other researcher to argue a point. In the chapter on Review of Literature, in particular, scholars writing a thesis or article use arguments/findings of other scholars to arrive at certain assessment of situation. These data/findings/analysis are secondary for any one carrying out research. It appears that primary data lose their ‘primary’ character when subjective assessment and analysis are carried out on them after their collection. All researchers collecting primary data through field research do so as they have to generalise on those and link those with the existing body of literature. It may be argued that even raw field data supplied by the Census, NSSO or NCRB officials may become ‘secondary’ if these are compiled again or reorganised for the purpose of any research. Such transformation of data signifies increasing use of primary data for the purpose of analysis and understanding of social situation.

5.1 Advantages for use of Primary data

It is preferable to make use of primary source wherever possible for the following reasons:

(i)   The secondary source may contain mistake due to errors in transcription made when the figures were copied from the primary source.

(ii)  The Primary source frequently includes definitions of terms and units used.

(iii)   Primary source often includes a copy of the schedule and a description of the procedure used in selecting and in collecting the data.

(iv)  Primary source usually shows data in greater detail.

(v)   Through Primary survey one can get any data to express the problem better and to find better correlation among certain factors or to notice change in field situation; but in secondary sources all relevant data may not be available. There is, therefore, every need to continuously collect primary data to assess the current field situation.

5.2 Advantage for use of Secondary data

(i)  It is highly convenient to use information which someone else has compiled. There is no need for printing data collection forms, hiring enumerators, editing and tabulating the result, etc. Researcher alone or with some clerical assistance may obtain information from published records complied by somebody else.

(ii)  If secondary data are available they are much quicker to obtain than primary data.

(iii)  Secondary data may be available on all most all subjects where it would be impossible to collect primary data. For example, census data cannot be collected by an individual or research organisation, but can only be obtained from Government publication.

(iv)   In almost all research, scholars have to take note of research findings that are already been done on the chosen theme. Such a review of literature is required to make assessment of “what is already being known” (for details on this read module RMS 4). We all know that research is not ‘reproduction’ of opinions of other scholars, yet, use of such literature to develop an argument or to develop a distinctive position on the subject is essential. This is because, the basic objective of any research endeavour is to a) reject an established explanation/theory, b) modify them, and/or c) strengthen them. A social research must also have relevance, a depth of concern for social issues. How would one prove the relevance of any research or the uniqueness of its objectives? It is mainly done by relating the research questions with the broad body of existing literature in the field. Hence, secondary data are useful for conducting any research.

5.3 Choice between Primary and Secondary Data

Selection of use of primary data and secondary data depends on following factors:

(i)  Nature and Scope of the enquiry: The nature and scope of a study dictates to a large extent whether the study would be based on primary or secondary data. As for example, if it is an evaluation study like the impact of MGNREGA for improving the standard of living of the population of a village, then the sources of data must be primary. On the other hand, if we are involved in assessing the increase in per capita income in India in the last one decade, then we have to rely mostly on secondary data.

(ii)   Availability of financial resources: As the financial involvement is high in the collection of primary data, one might prefer to conduct a secondary data based study with limited financial support.

(iii)  Availability of time: Availability of time is an important determinant. Collection of primary data requires much time. Contrarily, a review based on secondary literature may be done within a stipulated period. If we have to conduct a study to prepare the personal profile of the members of the SHGs formed under SGSY scheme in West Bengal within one month, then we have to depend on secondary sources of data.

(iv)  Degree of accuracy desired: The main barrier to the use of secondary data is degree of accuracy. As the society is changing very fast, a study based only on secondary data might not reflect the field reality.

(v)   The collecting agency: Sources of data for conducting any study also depends on the collecting agency. It is possible for Government body or reputed institutions to carry our large and extended field based project with proper budget, involvement of a team of researchers and time allocation. By comparison, an individual can collect field data only from a limited number of respondents.

5.4 Publications containing Primary and Secondary data

The following lists of primary and secondary data would allow one to look for appropriate sources of data most easily. It should however be noted that these lists are inconclusive.

a)  Primary data

(i)   “Census of India” published by the Office of the Registrar General and Census Commissioner, Ministry of Home Affairs ( censusindia.gov.in/)

(ii)     Report Published by National Sample Survey Organisation, Government of India, Kolkata (mospi.nic.in)

(iii)   “Reserve Bank of India Bulletin” issued monthly by Reserve Bank of India, Mumbai ( https://m.rbi.org.in)

(iv)   “Indian   Textile   Bulletin”,   issued   monthly   by   the   Textile   Commissioner,   Mumbai

( www.textileconnect.com)

(v) “Crime in India” published by National Crime record Bureau of the Ministry of Home Affairs ( ncrb.gov.in/).

(vi) Central Statistical Organisation, New Delhi (mospi.nic.in)

(viii)     Directorate of Economics and Statistics, Ministry of Agriculture, Government of India ( eands.dacnet.nic.in)

b ) Secondary Data

(i)   Official documents like “Statistical Abstract of the Indian Union” issued by the Central Statistical Organisation (C.S.O), New Delhi or “Monthly Abstract of Statistics” issued by C.S.O.

(ii)   Doctoral Thesis made available by University Grants Commission (UGC) at Shodhganga website ( shodhgangotri.inflibnet.ac.in) . Interestingly, keeping pace with advances in the electronic world, many reputed universities across the globe have allowed awarded doctoral thesis to be read and used by other researcher as “unpublished manuscripts”.

(iii)  Research articles available at several journals many of which are available on line (find a list of social science journal in Module no RMS 4).

(iv)  Books (even though most important source of secondary data is library, some online libraries like Library Genesis: Scientific Articles ( libgen.org/scimag ), Libgen ( libgen.info ), JSTOR ( www.jstor.org ), Bookzz.org; booksfi.org are helpful to locate books).

(v)  Mass Media Output (all newspapers do publish their online versions).

(vi)  Encyclopaedia and Dictionaries (also available on line).

c)  Issues Concerning Use of Secondary Data

Any scholar using secondary literature should also be aware of certain important issues. Thus, to begin with, scholars should be aware of the theoretical framework used to arrive at any conclusion at a particular document. Second, the methodological tool used to collect data might also generate a particular variety of responses. Hence the data source and type should be clearly mentioned while stating the findings of a research. Third, the time-frame of any data used and analysed in any document should be noticed. It is expected that a document based on data collected 15 or 20 years ago may be different from the one published recently. Fourth, a literature may express the personal opinion or interpretation of the author(s) instead of any broad based findings. This should be clearly stated. Fifth, the usefulness of any document for any particular research should also be determined before using it. Finally, all secondary (and primary) sources used in any work should be cited properly to avoid the crime of plagiarism (read Module RMS 4 for details).

6. Methods of Collection of Primary Data

Like secondary data, students should also take serious note of various issues concerning the collection and use of primary data. We all know that different methods are adopted mainly to collect the primary data to fulfil the objective of the study. The following are the most widely used methods2.

(a)    Direct personal observation or interview,

(b)   Indirect oral interview/investigation

(c)    Questionnaire sent by post or mail,

(d)   Schedules filled up by investigators

(e)    Case Study method

(f)    Participatory Rural Appraisal method

(a) Direct Personal Interview

In this method the investigator (or interviewer) collects the required information in a face-to-face contact with the respondent. The investigator asks them questions pertaining to the survey and collects the desired information. Thus, in order to study the infrastructural facilities available for the delivery of quality education in the department of Sociology, University of Burdwan, the investigator has to meet the students of the department of Sociology, University of Burdwan personally and has to collect necessary information. The information is first hand or original in character.

When the researcher and the respondent are present in the same location, they face each other, the interview is called face-to-face interview . But in some cases the researcher and the respondents are separated by the distance and the researcher uses telephone for communication. It is called telephone interview.

Focus group interview or focus group discussion (FDG) is a type of interview that facilitates collection of qualitative data. Even though FGD is a form of ‘group interview’, the difference between the focus group method and the group interview is by no means clear and the two are frequently employed interchangeably. In focus group interview, the researcher interviews a group of respondents at the same time. Focus groups typically emphasize a specific theme that is explored in depth in an unstructured setting as compared to any formal individual interview. Alan Bryman (2009: 346) argues that the focus group is a form of group interview in which a) there are several participants in addition to the moderator/facilitator, b) there is emphasis in the questioning on a particular fairly/tightly defined topic, c) the accent is upon interaction within the group, and d) joint construction of meaning as individuals discuss a certain issue as members of a group, rather than simply as individuals. In other words, FGD allows the participants to respond to each other’s views to build up a view out of interaction within the group.

 (b) Indirect oral interview/investigation

In this method, the investigator contacts third parties to get information. The method is generally adopted in those cases where the information to be obtained is of complex nature and the informants are not inclined to respond if approached directly. For example, drugs addicts may be reluctant to provide correct information about their own habit. As a corollary, most of the Commissions of Enquiry or committees appointed by the Government collect primary data by this method. The accuracy of the method depends largely upon the type of persons interviewed and hence these persons have to be selected very carefully.

(c) Questionnaire sent by post or mail

Under this method questionnaire is sent to different respondents by post or mail. A request is made to the respondents through a covering letter and possible guideline for how to fill up the questionnaire and send it back within a given time period. This method can be adopted where the field of investigation is very large and the respondents are distributed over a wide geographical area. It is also relatively cheap.

But this method can be adopted where only all respondents are well educated. It involves some uncertainty. Cooperation on the part of respondents may be difficult to presume. It is also very difficult to verify the accuracy.

(d) Schedules filled up by investigators

It is most widely used method of collection of primary data. Here investigators are employed for data collection. The investigators carry with them printed schedules specially developed for the purpose. They fill up these schedules themselves on spot based on answers received from the respondents. The method is very popular and many a time it yields satisfactory result. Much of the accuracy of the collected data however depends on the ability and tactfulness of the investigators, who are given special training as to how they should elicit the correct information by developing rapport and through friendly discussions. This method is adopted during the decennial census of population in this country.

(e) Case Study method

Case study method enables a researcher to closely examine the data within a specific context. In most cases, a case study method selects a small geographical area or a very limited number of individuals as the subjects of study. Case studies, in their true essence, explore and investigate contemporary real-life phenomenon through detailed contextual analysis of a limited number of events or conditions, and their relationships. Yin (1984) defines the case study research method as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.

Yin (1994) has mentioned that the case study design must have five components:

(i)  The research question(s),

(ii)  The research propositions,

(iii)  Unit of analysis,

(iv)  How the data are linked to the propositions, and

(v) Criteria to interpret the findings

According to Stake (1994), types of case studies depend upon the purpose of the inquiry: an instrumental case study is used to provide insight into the issue; an intrinsic case study is conducted to get in-depth knowledge regarding the case; and the collective case study is the study of a number of cases in order to inquire into a particular phenomenon. Stake has also put emphasis that many other types of case studies based on their specific purpose, such as the teaching case study or the biography. Yin (1994) points out that case studies are the preferred strategy when “how” and “why” questions are posed.

According to Yin, a good case study report must have following components:

(i)  The report itself should make sufficient citation to the relevant portions of the case study database,

(ii) The database, upon inspection, should reveal the actual evidence and also indicate the circumstances under which the evidence was collected,

(iii)  It should be consistent that the specific procedures and questions contained in the case study protocol, to show that the data collection followed the procedures stipulated by the protocol,

(iv)  A reading of the protocol should indicate the link between the content of the protocol and the initial study questions.

Again Yin has mentioned that one can move from one part of the case study process to another, with clear cross-referencing to methodological procedures and to the resulting evidence. This is the ultimate “chain of evidence” that is desired (see chart below).

(f) Participatory Rural Appraisal Method

Participatory Rural Appraisal (PRA) approaches have become increasingly popular among qualitative researchers in recent times. Such approaches are based on post-positivism and combine the strengths of “constructivist” paradigm and that of “critical realism”. From the “constructivist” paradigm, PRA approaches seek to understand human experience as it is lived and felt by the participants, the context of such experience in an interdisciplinary framework. PRA as a methodology puts emphasis on the people’s capabilities, field-based learning and innovations.

According to Kumar (2002), there are four pillars of PRA: methods and tools, process, sharing and attitude and behaviour. Again, he considers that attitude and behaviour in PRA is the most important pillars and it is central to PRA and more important than methods. The attitude and behaviour in PRA includes:

·         Self-awareness of one’s behaviour

·         Accountability to the poor

·         Self-respect and respecting others

·         Good Listeners

·         Ability to ‘handing over the stick’

PRA as a tool or a method, or a tool and method is very useful to collect primary data from the field in a very short period of time with the active participation of local people of the area. Initially to apply this method and tools, the researcher has to build up a sound rapport with the participants. Then she/he has to explain the purpose of the exercise to the participants. PRA is conducted mostly during the leisure time of the respondents.

A wide range of applications of PRA have evolved and still is it continuing. According to Chambers (1997), most application of PRA are in three areas, namely a) topical investigation, b) research, training, and c) an empowering process of appraisal, analysis, planning, action, monitoring and evaluation. Five sectors like, natural resources management; agriculture; people poverty and livelihood; health and nutrition; and urban attracted more PRA.

As per the need of the application of tools it is categorised under sectors, like, Space-related PRA methods, Time-related PRA methods and PRA relation methods.

Here we would cite one example of a Venn Diagram under PRA relation methods.

Venn Diagram

Venn diagram is one of the commonly used methods in PRA to study institutional relationships and is sometimes also referred to as institutional diagram. It is, however, popularly known as chapati diagram as this method uses circles of various sizes to represent institutions or individuals. The bigger the circle, the more important is the institution or individual. The distance between circles represents, for example, the degree of influence or contact between institutions or individuals. Overlapping circles indicate interactions and the extent of overlap can indicate the level of interaction.

Applications

The Venn diagram method in PRA has been found very useful to study and understand local people’s perceptions about local institutions, individuals, programmes, etc. The method provides valuable insights into and analyses of the power structure, the decision-making process, etc. The need to strengthen the community’s institutions can also be ascertained. The relative importance of services and programmes has also been studied using the Venn diagram.

The suggested steps in the process of doing a Venn diagram are as follows:

·         Explain the purpose of the exercise to the participants.

·         Ask them to list the various institutions, individuals, etc., as per the objectives of the exercise.

·         Ask them to write and/or depict them on small cards. Visual depiction becomes necessary if there are non-literate participants.

·         Ask the participants to place the cards on one of the variables of study, e.g., perceived importance of the institutions, in a descending order. Once the cards are arranged in an order, confirm the order. Encourage them to make changes, if they are interested.

·         Ask them to assign paper circles of different sizes (cut and kept ready) to the institutions or individuals in such a way that the bigger the circle, the higher that institution or individual ranks on that variable. Paste on the circles the cards with names of institutions or individuals. If you want, you can simply note down or depict the institutions or individuals on the circles.

·         After placing all the cards, confirm the placement. Encourage them to make changes, if needed.

·         In case, there are certain institutions/individuals who interact or work closely, they could be placed with an overlap. The degree of overlap indicates the degree of interaction.

·         Ask them to discuss and explain why they placed the cards in such a manner. Note down the points of discussion and explanation.

·         Copy the output onto a sheet of paper. Record the name of the village, participants, date, legends, what the size of the circle represents and what the distance represents.

·         Triangulate the diagram and the major findings with others knowledgeable about the situation to ensure that your information is correct.

In order to facilitate easy making of this diagram, you should follow a step by step approach and need not explain the whole process to the participants at the outset. For instance, ask the participants first to list the institutions. Once the list has been made, ask them to put them in descending order based on each variable. Next, ask them to assign paper circles of different sizes and so on. Also ask them what they mean by the two variables. Make sure that the participants are clear on which dimension represents what variable. One simple way is to write it down legibly in bold letters and keep it in front when the exercise is on.

Materials Required

Paper circles are the most frequently used materials in Venn diagramming. It can also be drawn directly on the ground or on paper, but that does not allow the size or location of circles to be changed. Sometimes, after the circles are drawn, participants discuss the diagram and want to change the size or location. They hesitate to do so if the Venn diagram has been drawn, but if the circles are cut from paper, they find making modifications easy at any point in the process.

Time Required

Time required for a Venn diagram may vary considerably depending upon the details that are being represented. However, you should plan to spend 2-3 hours on the Venn diagram and the subsequent discussion.

Limitations of Venn Diagram

There are certain limitations of this method. Venn diagram generally becomes difficult and complex when the number of items increases. Relatively inexperienced facilitators find it difficult to explain the Venn diagram process to the participants. Another practical problem with Venn diagramming is that sometimes it can become sensitive. In the presence of some of the individuals or representatives of institutions that are being rated in the Venn diagram, the participants may play safe. Hence the output in such cases may not reflect the realities.

7. Steps for Collection of Primary Data

The different steps of collection of primary data are as follows:

(1)  Planning the Study: The planning is an essential component to conduct any research study without which the data collected may not be found suitable. The following points should be considered at the planning stage:

(a)  Objective of the study should be clearly mentioned.

(b)  Sources of data, whether primary or secondary, should be identified.

(c)  Type of study, whether census or sample survey.

(d)  Definition of the unit of the study, whether individual or household.

(e)  Degree of accuracy.

(2)    Collection of Data: The collection of accurate data is the most important part in the whole investigation. The method of collection depends to a large extant on the nature, objective and scope of the study and the availability of time and money. We have already discussed several methods that can be applied singularly or in mixed form to collect field data.

(3)  Editing the data and its tabulation: Soon after the collection of data, arrangements should be made to scrutinise them. If they are available in written form, those should be checked to limit inconsistency, errors and omission. After such check, the numerical data should be classified and tabulated, if required. Qualitative researchers also carefully read their diary or notebook to organise and classify their material under certain themes.

(4)  Analysis of data and Interpretation of Result : Data analysis is a method of abstracting significant facts from the large mass of data collected during the field work. If the given sets of data are numerical, then it involved determination of various statistical measures, the estimation of statistical constants and subsequent test of significance. The results of analysis are then interpreted and conclusions are drawn. Ethnographers also follow certain standard practice to organise their field data. Availability of computer aided qualitative programmes has made such analysis very easy these days (read module RMS 30 for details).

(5)  Preparation of Report: After completion of the total process, it is necessary that a detail report is to be published. It should contain a detail description of all the stages of the survey. Charts and tables are also included in the report to represent/classify data rationally and consistently.

Self Check Exercise 2 :

Q 1. What is the importance of secondary data?

It is highly convenient to use. There is no need for printing data collection forms, hiring enumerators, editing and tabulating the result, etc. Researcher alone or with some clerical assistance may obtain information from published records complied by somebody else. They are also used to justify the primary data that has been collected from the field.

Q 2. What are the differences between schedule and questionnaire?

Questionnaire refers to a device for securing answers to questions by using a form which the respondents themselves fill. Schedule is a set of questions which are asked and filled up in a face-to-face situation by the interviewer.

Q 3. Why planning is essential to conduct any research study?

Planning is essential to collect reliable and valid data. It would be complete wastage of time and money if a study fails to generate useful conclusions. Hence, in the planning process the following units of a study should be clearly defined and linked to one another:

(a)    Objective of the study

(b)   Sources of data

(c)    Type of the study

(d)   Definition of statistical unit

(e)    Degree of accuracy

The quality of any research report depends on the quality of data collected and analysed. In fact, all research endeavours uniformly rely on collection of suitable data for proper analysis. In social science research, however, the stress is primarily put on collection of primary data and link them with the existing body of secondary literature. Obviously, a mixture of both primary and secondary sources of data makes a study perfect and much more reliable. This is not to deny the fact that research can also be done by just  reviewing secondary literature. Such a review very often is essential to identify trend in research and develop general arguments. But in contemporary times, change is ubiquitous and we can’t deny that each and every aspect of our social life is changing very fast. Hence, there is every need to upgrade our knowledge base by going to the field. There is no denying the fact that it is the field that dictates the contours of social science research. It should also be recognised that both quantitative and qualitative types of data are required to analyse social life and hence we often prefer to triangulate them. Conducting research, therefore, requires knowledge, sensitivity and training. It is, therefore, prescribed that before beginning any research, a student should try to focus on the types of data that are to be explored, the methods that are most appropriate for their collection and steps to be followed in completing the study successfully. While, there are several methods or tools available for such an endeavour, the epistemological and ontological concerns become crucial to select the appropriate one or mix them.

• The category of ‘third sex’ is also legally recognised today.
• Scholars conducting research particularly on secondary literature (content or documentary research) might not look for field data. Hence, for them, these documents may serve as primary source of data. For instance, a scholar working on Rabindranath Tagore or Mahatma Gandhi would consider the personal writings of these scholars as primary data. Similarly, a scholar working on newspaper advertisements would consider such newspaper as his/her primary source of data though there might be secondary literature by others on the topic.
• Bryman, Alan. Social Research Methods (3rd Edition). Oxford: Oxford University Press, 2009.
• Das, Lal, D.K and Vanila Bhaskaran (Eds.). Research Methods for Social Work.Rawat Publications, Jaipur, 2008.
• ——-.Statistical Methods (Volume Two). McGraw Hill Education (India) Private Limited, New Delhi, 2014.
• Feagsin, J.R., Orum, A.M., and Sjoberg, G. (Eds). A Case for the Case Study.University of North Carolina Press, 1991.
• Glesne, C and Peshkin, A. Becoming Qualitative Researchers: An Introduction.Sage Publication, 1992. Gun M.A, M.K Gupta, B. Dasgupta. Fundamentals of Statistics (Volume One and Two). World Press Private Limited, Kolkata, 2014.
• Gupta, P.S. Statistical Methods.Sultan Chand & Sons Publishers, New Delhi, 1991.
• Hicks, J. O. Management Information Systems: A New perspectives. West Publishing, 1993.
• Kothari C.R. Research Methodology (Second Edition).New Delhi, 1998.
• Kumar, S. Methods for Community Participation. Vistaar Publication, New Delhi, 2002.
• Singh, Jaspal. Instruments of social Research. Rawat Publications, Jaipur, 2011.
• Singh, P.S. Research Methods in Social Sciences.Kanishka Publishers, New Delhi, 2002.
• Stake, R.E. “Case Studies”, in Denzin, N.K. and Lincoln, Y.S. (Eds), Hand Book of Qualitative Research.Sage Publication, 1994.
• V.V. Khanzode. Research Methodology. APH Publishing Corporation, New Delhi, 1995.
• Yin, R.K. Case Study Research: Design and Methods (2nded).Sage Publication, 1984, 1994.
• Young, V. Pauline. Scientific social surveys and research (4th Edition). Prentice Hall of India Limited, New Delhi, 1992.

Secondary Data Collection Methods

Data is physical or digital information; information is knowledge and knowledge is power! But to leverage that powerful data and…

Data is physical or digital information; information is knowledge and knowledge is power! But to leverage that powerful data and execute a successful strategy, businesses need to first gather the data—simply known as data collection.

Collecting data is more than just searching on Google. Although our society is heavily dependent on data, the importance of collecting it still eludes many. Accurately collecting data is crucial for ensuring quality assurance, keeping research integrity and making informed business decisions. There are methods, goals, time and money involved. Researchers have to have a data-driven approach and achieve the desired end results. Only after having a clear picture of the objective can a researcher decide whether to use primary or secondary data and where the primary or s econdary data can be collected from.

But before we learn about the sources of secondary data in research methodology , we must first understand the meaning of data collection. 

What Is Data Collection?

What is secondary data collection, various methods of collecting secondary data, how to use sources of secondary data in research methodology, advantages of secondary data collection methods, disadvantages of secondary data collection methods, secondary data collection examples.

Data collection is a crucial element of statistical research. The process involves collecting information from available sources to come up with solutions for a problem. The process evaluates the outcome and predicts trends and possibilities of the future. Researchers start by collecting the most basic data related to the problem and then progress with the volume and type of data to be collected.

There are two methods of data collection—primary data collection methods and secondary data collection methods. Data collection involves identifying data types, their sources and the methods being used. There are different collection methods that are used across commercial, governmental and research fields, and various sources are accessed where primary and secondary data can be collected from . Whether it’s for academic research or promoting a new product, data collection helps us make better choices and get better results. 

In this article, we’ll discuss secondary data collection, the various methods of collecting secondary data , its advantages, disadvantages, secondary data collection examples and sources of secondary data in research methodology .

Secondary data collection refers to gathering information that’s already available. The data was previously collected, has undergone necessary statistical analysis and isn’t owned by the researcher. This data is usually one that was collected from primary sources and later made available for everyone to access. In other words, secondary data is second-hand information that’s collected by third parties. A researcher may ask others to collect data or obtain it from other sources. Existing data is typically collated and summarized to boost the overall effectiveness of a research.

There are two t ypes of secondary data collection —qualitative secondary data collection and quantitative secondary data collection. Qualitative data deals with the intangibles and covers factors such as quality, color, preference or appearance. Quantitative data deals with numbers, statistics and percentages. Although the end goal determines which of the two types of secondary data collection a researcher chooses, secondary data collection is mostly concerned with quantitative data.

Let’s look at the common secondary data collection methods :

Collecting Information Available On The Internet 

One of the most popular methods of collecting secondary data is by using the internet. Readily available data can be accessed with the click of a button, which makes the internet one of the best places where secondary data can be collected from . It’s practically free of cost, although some websites may charge money—usually low prices. However, organizations and individuals must look out for inauthentic and untrustworthy sources of information.

Collecting Data Available In Government And Non-Government Agencies 

Government and non-government agencies such as Census bureaus, government printing offices and business development centers store relevant data and valuable information that both individuals and organizations can access.

Accessing Public Libraries 

Public libraries house copies of research, public documents and statistical information. Although services may vary, libraries usually have a vast collection of publications highlighting market statistics, business directories and newsletters. 

Using Data From Educational Institutions

Educational institutions are often overlooked when deciding a method of collection. Educational institutions conduct more research than any other sector. Universities have a plethora of primary data that can act as vital information for secondary research.

Using Sources Of Commercial Information 

Secondary data collection methods are cost-effective and hence quite popular among businesses and individuals. Small businesses that can’t afford expensive research have to resort to a cheaper method of data collection. They can request and obtain data from anywhere it’s available to identify prospective clients and have a wider reach when promoting products and services.

Here are the steps to conduct research using sources of secondary data collection :

• Identify the topic of research, make a list of research attributes and define the purpose of research. 
• Information sources have to be narrowed down and identified to access the most relevant data applicable to the research. 
• Once the secondary data sources are narrowed down, check and collect all existing data related to the research from similar sources. 
• After collecting the data, check for duplication before assembling it into a usable format. 
• Analyze the collected data and check if it answers all questions crucial to meet the objective. 

The most important aspect of secondary research is looking out for any inauthentic source or incorrect data that may hamper the research.

These are the advantages of secondary data collection: Most of the data and information is readily available and there are plenty of sources of secondary data collection .  

• The process is less expensive compared to the primary method. There’s minimum expenditure associated with obtaining data from authentic sources. 
• Data collected for secondary research can give a fair idea about how effective the primary research was. Businesses can hypothesize and evaluate the cost of primary research. 
• Re-evaluating data from another person’s point of view can uncover things that may have been overlooked. This may lead to discovering new features or fixing a bug in an app. 
• Secondary data collection is less time-consuming as the data doesn’t need to be collected from the root. Hence, data collection time is significantly lower than primary methods. 
• Longitudinal and comparative studies are easier to conduct with secondary data as we don’t have to wait to draw conclusions. For example, to compare the population difference in a country across five years, we can simply compare the present census with that of five years back. 

Researchers can look to collect data from both internal and external sources, which prevents relying on any special or specific data collection method. 

Let’s discuss the disadvantages of secondary data collection:

• Data may be readily available but the credibility of sources is under constant scrutiny. Research can break down due to a lack of credible and authentic information
• Most secondary data sources don’t offer the latest statistics, studies or reports. Accurate data doesn’t necessarily mean updated data
• As a researcher has no control over the primary source or quality of information, the success of secondary research heavily depends on the quality of the primary research that was conducted 

Primary data collection may often be expensive but the credibility, accuracy and quality of information is seldom questionable. 

Here are some secondary data collection examples :

• Journals and blogs are popular examples of secondary sources of data collection today. They’re both regularly updated but blogs run the risk of being less authentic than journals as the latter is backed by periodically updated information with new publications.
• Newspapers have been at the top of the most reliable and authentic sources of secondary data collection for centuries. Although they mostly cover economic, educational and political information, there is specialized content available with newspapers dedicated to covering topics such as science, environment and sports. 
• Podcasts are the new-age alternative to radio and are widely becoming a common source of secondary information. Presenters talk to the audience about specific topics or conduct interviews on the show. With the digital media boom, interactive podcasts have become wildly common and popular.

Some other examples of secondary data collection are letters, books, government records and columns.

Secondary data finds use across the fields of business, research and statistics. Researchers may choose secondary data due to finance issues, availability, research needs or time. Due to various factors, secondary data may sometimes be the only data available. In such cases, collecting authentic and relevant data and coming up with solutions to meet the objective may come down to a manager’s ability of CRITICAL THINKING . 

Using secondary data has its drawbacks and data collection is concerned with finding solutions. Managers need to go behind the scenes to fully understand the process of problem-solving. Learn to make research foolproof and analyze scenarios error-free with Harappa’s Create New Solutions pathway. Continuously seek, absorb and interpret new information. Lay down insightful questions, look for relevant data and use smart analyses to create working solutions. Strive to get all available information first and then make the best possible decision. Make well-reasoned and clearly articulated arguments that are backed by logic and evidence. 

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Secondary Research

What is secondary research.

Secondary research is research with specimens/data initially collected for purposes other than the planned (or primary) research. In this case, the specimens/data were collected to answer a different research question or to test a different scientific hypothesis.

Examples of secondary research vs. primary research are included in the presentation given on 1/19/2021, “ Secondary Research: Fact, Fiction, Fears and Fantasies ” that is available on the Office of Human Subjects Research Protections (OHSRP) website in the Presentation Archive . 

FAQs about Secondary Research

Download this section as a PDF or click on the answers below.

1. When does secondary research not require IRB review?

There are two circumstances in which secondary research with specimens/data collected from human subjects may not require prospective IRB review.

• All the individuals from which the specimens/data were collected are deceased, AND/OR
• The specimens/data are not identifiable to the research team (see below for further discussion on this).

Subjects are all deceased

If all the individuals from whom the specimens/data were collected are now deceased, the research with these materials does not meet the definition of human subjects research and does not require prospective IRB review.  However, if the specimens/data were collected under another research protocol, the terms of the original consent still apply.  For example, if the consent form contained any limitations on the future use of the specimens/data, those limitations must be honored.  The investigator is responsible for ensuring that any proposed research is consistent with the original consent. 

Specimens/data are not identifiable to the research team

Research, with specimens/data which are not identifiable to the research team, is not considered human subjects research and does not require IRB review and approval.  For example, the specimens/data have been fully anonymized by removing all identifiers, or they have been coded, and the investigator(s) conducting the research do not have access to the code key and cannot otherwise re-identify the subjects. The term “coded” means that all identifying information has been replaced with a number, letter, symbol, or combination thereof (i.e., the code) and a key to decipher the code exists, enabling linkage of the identifying information to the specimens/data. See the Guidance for Determining Whether Data Constitutes Individually Identifiable Information Under 45 CFR 46 on the OHSRP website.  Investigators should consult with OHSRP if they are unsure whether data or biospecimens being used in a specific research project would be considered individually identifiable.

2. When does secondary research require IRB review?

Secondary research is considered human subjects research that requires IRB review when the specimens/data are identifiable to the researchers and were collected for another purpose than the planned research.  The following is an example of secondary research:

• An investigator learns of preliminary data from a study that suggests cigarette smoking leads to specific epigenetic changes that increase susceptibility to certain infections. She also has a large number of pre-treatment samples from cigarette smokers that would be ideal to initially test this hypothesis. The specimens were collected under protocols focused on the study of lung cancer. The samples are coded, and the investigator holds the code key with identifiers. Her planned research with the specimens/data is not described in the objectives of the original lung cancer protocols.  The planned activity meets the definition of secondary research because the specimens/data were collected under another protocol for a different research purpose.

Note that if the planned research is related to the existing primary, secondary, or exploratory objectives described in the IRB-approved protocol (under which the specimens/data were originally collected), then it is not considered to be secondary research.  This is research that should be conducted under the primary protocol (i.e., primary research).  If the investigator is unsure whether the protocol should be amended to address the planned activities or whether the current description in the protocol is adequate to allow the new research to move forward, the investigator should contact the IRB.  The following is an example of a proposed activity that does not meet the definition of secondary research:

• An investigator has collected samples as part of an IRB approved phase 1 protocol to determine the maximum tolerated dose of new checkpoint inhibitor drug XYZ123 for the treatment of metastatic lung cancer refractory to standard therapies. The protocol includes an exploratory objective to determine changes in immunologic profiles of subjects receiving the drug.  After the trial is already complete, the investigator decides to have all the previously stored samples analyzed for T cell subsets.  Since this additional analysis meets the exploratory objective of the original protocol, the planned activities would be considered primary research.     

If your research involves the use of specimens/data to test a device, please see Question 4.

3. Does my research involving de-identified specimens or data (e.g. images) and a device require IRB review and approval?

If your research involves the use of specimens or data from one or more humans to test the safety or effectiveness of an investigational medical device (e.g. AI/machine learning, in vitro diagnostic (IVD), etc.), the study is considered a clinical investigation under the FDA regulations (see 21 CFR 812.3(h)). This research likely requires creation of a protocol, prospective IRB review and approval (21 CFR 56) and a device determination and either IRB consent or waiver of consent (21 CFR 50).  This is because under the FDA regulations, a human subject is not defined based on identifiability.   For more information, please consult with the IRB.

4. How do I know if the secondary research I am interested in performing requires IRB review?

To determine if your planned activity with specimens/data is secondary research that will require IRB review, ask yourself the following four questions in order.  If the answer to all four of these questions is “yes,” then you are performing secondary research that requires review by the IRB.

• Research (2018 Common Rule) means a systematic investigation, including research development, testing, and evaluation, designed to develop or contribute to generalizable knowledge. (45 CFR 46.102(l))
• Clinical investigation (FDA Regulations) means any experiment that involves a test article and one or more human subjects [1] , and that either must meet the requirements for prior submission to the Food and Drug Administration under section 505(i) or 520(g) of the act, or need not meet the requirements for prior submission to the Food and Drug Administration under these sections of the act, but the results of which are intended to be later submitted to, or held for inspection by, the Food and Drug Administration as part of an application for a research or marketing permit. The term does not include experiments that must meet the provisions of part 58, regarding nonclinical laboratory studies. The terms research, clinical research, clinical study, study, and clinical investigation are deemed to be synonymous for purposes of this part. (21 CFR 56.102(c))
• Does the planned activity meet the definition human subjects research? Consider the following regulatory definitions of human subject:
• Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
• Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.
• Human Subject  (FDA Regulations) means an individual who is or becomes a participant in research, either as a recipient of the test article or as a control. A subject may be either a healthy human or a patient. ( 21 CFR 50.3(g) )
• Subject (FDA regulations related it use of investigational devices) means a human who participates in an investigation, either as an individual on whom or on whose specimen an investigational device is used or as a control. A subject may be in normal health or may have a medical condition or disease. ( 812.3.(p))

       3. Are you using specimens/data that were collected for other purposes (either research or non-research) for the planned research?

       4. Is the planned research use of these specimens/data unrelated to the aims or objectives of the current IRB-approved protocol, under which they were originally collected?

1.  Please note that the FDA considers research involving human specimens and the use of a medical device to be “clinical investigations”, if  data from the research will be used to support an IDE (including IVDs), device marketing application, or another submission to the FDA.  In this regard, research involving leftover human specimens that are de-identified is included.

5. What type of IRB review process is required for secondary research?

Secondary research is subject to the same regulations and reviewed using the same processes as all other human subjects research.  However, depending on the specifics of the study, it may be able to be determined to be exempt from IRB review. Refer to Exempt Research on the OHSRP website. 

• Exempt: Submit an exempt protocol via the electronic IRB submission system. Exempt protocol templates and instructions for submitting for an exemption are available at the link above.  
• Non-exempt: Submit a secondary research protocol via the electronic IRB submission system. Non-exempt research needs to go through either an expedited or full board IRB review process.  Secondary research studies are typically considered minimal risk and are usually eligible for review and approval using expedited procedures.  The secondary research protocol template is available on the OHSRP website.

6. Why does my secondary research need IRB review?

Secondary research that meets the definition of human subjects research is subject to the same regulatory requirements as all other human subjects research; therefore, it must undergo IRB review.  The ethical underpinning of this expectation is to assure that the proposed use of the specimens/data meets regulatory requirements and does not violate subjects’ rights.  When subjects, as part of an IRB approved protocol, provided their specimens/data, it was with an understanding they would be used for a specific named purpose. IRB review of a secondary research protocol is conducted to assure that the new use is not counter to that intent nor likely to introduce new risks, not previously disclosed to the subject or considered by the IRB.  The IRB will also ensure that appropriate privacy and confidentiality protections are put in place.

7. What is the IRB considering during the review process of secondary research?

The original protocol title(s) and protocol number(s) from which the specimens/data were collected, when applicable, should be listed in the new protocol. When investigators will be using existing specimens/data collected under other research protocols, the IRB will look for information about how the investigator has access to the materials. Furthermore, the new protocol must include a summary of the consent language in all applicable consent versions (for all the original protocols) as it applies to sharing and use of specimens/data for future research.  This information could be provided in a table or in summary form in the protocol or the previous consent document versions could be uploaded as part of an Appendix. This will allow the IRB to review the original consent language to understand what information was conveyed to subjects about the use of their research specimens/data.  Any promises made in the consent regarding sharing and the future use of specimens/data must be honored.

Some examples of circumstances that could be considered include:

• If a previous subject(s) opted out of the use of their specimens/data for future research as part of the original consent, the specimens/data should not be used as part of the secondary research.
• If the original consent stated that the specimens/data would not be used for future research or would only be used for a specific type of future research (i.e., other than what is planned for this study), then the specimens/data cannot be used for secondary research without re-consent.
• If using the specimens/data for secondary research appears consistent with the terms of the original consent, then IRB can waive consent for the secondary research.
• If consent was silent on secondary research, then IRB will consider whether the proposed use is acceptable with a waiver, or alternatively, if re-consent is required.
• Please note that under the revised Common Rule (which applies to new protocols approved on or after January 21, 2019), there are new consent requirements which could affect what is allowable within a secondary research protocol. Please see FAQ 9.
• The IRB will consider if the secondary research would impose new or significantly greater risks (including privacy risks) not described in the original consent form.
• The IRB will also consider if the study population(s) may have known concerns about the proposed secondary use. An example of this would be Native American or Alaskan Native populations who have distinct culture, beliefs and values and have concerns for potential community harms based on past abuses and violations related to clinical research. Secondary research may require additional ethical review by an Indian Health Service or Tribal IRB and permission from a tribal government.

8. Doesn’t the future use language in the original consent document allow the research team to perform broad secondary research without new IRB approval or consent?

As with all human subjects research, either the specific consent of the subject to participate in the research must be obtained, or the IRB must waive consent. The future use language in consent forms does not contain all the required elements of consent. Typically, the future use language is very broad, so it does not adequately describe the purpose of the planned study. In addition, secondary research is a new research project that the IRB has not previously reviewed, so a determination that the project meets the IRB approval criteria must be made.  Any permission granted for future use is best thought of as a statement of intent by the subject, that allows the IRB to waive consent for the future use of the specimens/data for research that is compatible with the original consent.

9. Is the language about storage, use and sharing of specimens/data for future research in the current consent templates required?

Under the revised Common Rule (which applies to the consent forms associated with new protocols approved on or after January 21, 2019), the original consent form must include:

• A statement that clarifies whether or not identifiers might be removed from the data or biospecimens and that, after such removal, the information or biospecimens could be used for future research studies or distributed to another investigator for future research studies without additional informed consent
• A statement that the subject’s biospecimens (even if identifiers are removed) may be used for commercial profit and whether the subject will or will not share in this commercial profit;
• For research involving biospecimens, a statement that the research will (if known) or might include whole genome sequencing (i.e., sequencing of a human germline or somatic specimen with the intent to generate the genome or exome sequence of that specimen), if applicable.

Furthermore, for new studies approved on or after the compliance date, OHSRP advises that if the investigator intends to share coded or identifiable specimens/data for future research, there should be language in the original consent form informing the subject of this.  See the current Consent Templates for Use at NIH Sites .

10. Why is the use of ‘yes’ or ‘no’ check boxes with the storage, use, and sharing for future research language in consent forms optional for some studies and not optional for others?

If the primary research protocol has therapeutic intent or a prospect of direct benefit to the subject, then agreement to unspecified future use should be optional.  This is to avoid any possible coercion of subjects who wishes to participate in research that has the prospect to benefit them but who otherwise might not wish to have their specimens/data used for future unspecified research.  If the primary research protocol does not have any prospect of direct benefit, then agreement to future use does not have to be optional. In other words, the team could remove the ‘yes’ or ‘no’ check boxes, and the language would then communicate that if the subject chooses to participate in the research study, then their specimens and data may be used or shared for future research.

11. Can I share specimens/data from my primary protocol with other investigators for secondary research?

The sharing of specimens/data with other investigators does not require IRB approval per se.  However, the new use of the specimens/data for research may require IRB approval. In order to share specimens/data from a protocol, the consent must allow for sharing, or at least not prohibit it.  The proposed research use of the shared specimens/data should be consistent with the terms of the consent under which it was collected.  Subjects agreed to participate in a specific study, not anyone's study. If the original consent says the specimens/data will never be shared, you must honor the terms of the consent.  This means that the specimens/data cannot be shared even if de-identified.  If you still would like to share the specimens/data, you would have to re-consent the applicable subjects with a consent document that is transparent about the plan for sharing. See the flow diagram below and FAQ 12 for examples.

In addition, please note that under the revised Common Rule (which applies to new protocols approved on or after January 21, 2019), there are new consent requirements which could limit the type of secondary research that is allowable as a result of the sharing.  Please see FAQ 9.

Can I Share Specimens or Data from My IRB-Approved Protocol for Secondary Research*?

Review the consent form associated with the protocol to determine which scenario below is true.  There is….

*Secondary research:  research use of biospecimens or data for other than the original purpose(s) for which the      biospecimens or data were initially collected through interaction or intervention with living individuals

1 If you will receive research results that you can link back to identifiers after sharing specimens or data, the project is considered to be human subjects research . You must submit a secondary research protocol to address the planned research and seek IRB approval prior to initiation of the activities. The protocol must include new consent or a justification of a waiver of consent for the planned research.

2 In some circumstances, it may be appropriate to re-consent the subject to allow their specimens and data to be shared.  Consult the IRB if you wish to proceed with sharing.

12. When might I need IRB approval to share specimens/data?

If the terms of the original consent prohibit sharing, then you should consult with the IRB to determine if there is a path forward.  If the IRB provides guidance that sharing might be allowed, they will require you to re-consent subjects prior to any sharing.

The other situation in which you should seek IRB approval is if you are getting identifiable results/data returned to you from your collaborator, and the research is not described in the primary protocol.  In this case, you are considered to be conducting new secondary research yourself.  In other words, if you are sharing coded specimens (for which you have the code key) with an investigator who does not have the code key, and you are getting individual level data back (not aggregate data), this is human subjects research.  The rationale is that you are receiving new information about your subjects that you can link back to identifiers.  This activity needs IRB approval not because the specimens are being shared, but because identifiable data is being returned to the NIH research team that will be used for research.

Note that if you are only sharing or collaborating in research involving specimens/data for which the NIH research team has no access to identifiers or the ability to re-identify, this is not considered to be human subjects research.  Furthermore, the investigator does not need to submit for a request for determination of "not human subjects research" as was required in the past.

If you wish to share or receive human biospecimens and/or human data outside NIH under a Material Transfer Agreement, a Data Transfer/Use Agreement or a Research Collaborator Agreement, an Investigator Attestation must be completed and provided to the appropriate Tech Transfer contact. Please see NIH Tech Transfer on the OHSRP website for more details and a copy of this document . 

13. What are some examples of language in the primary protocol consent that would prohibit the sharing of specimens/data and disallow them to be used in secondary research?

Sharing and secondary research with existing specimens/data must comply with the terms of the original informed consent document.  Researchers are expected to review all previous versions of the consent form to determine who consented to what.  If the original consent form addresses the use and sharing of specimens/data for future research, the new plan for sharing and research should be consistent with the language in the original consent document.  If there is language in the original consent form which is contrary to sharing or future research generally or conflicts with the specific sharing and research plan, the investigator cannot proceed.  This is true, even if the specimens/data are being used or shared in a de-identified manner, or if the subjects are deceased.

Some examples of prohibitive or restrictive language include:

• “Your specimens will be destroyed at the end of this study.” These means the specimens cannot be stored for future research.
• “Your specimens/data will be used for future research on cancer.” This would restrict any secondary research to only cancer-related research.
• If some subjects opt-out of future research with specimens/data by checking a box in the consent, then the NIH investigator must track this information over time. Failing to check any box is considered the same as not agreeing, i.e., opting out of future research.
• “Your data will never be shared outside of the NIH.” This would restrict the use to only NIH investigators. You would not be able to share even de-identified specimens/data outside of NIH.
• “Your data will never be shared outside of the NIH research team working on the protocol.” This would restrict the use to only the NIH research team members named on the primary protocol.

If the initial consent contained restrictive language and you wish to be able to do future research or share the specimens/data, consult with the IRB.  Depending on the type of limitations, the IRB may require you to re-consent the subjects to allow the sharing or research to go ahead.  After that point, the specimens/data, of those that provide consent, would now be able to be used.

14. What if the original consent document did not ask subjects’ permission to use their data and specimens for secondary research?

If the original consent form (for new protocols approved before January 21, 2019) was silent on the topic of sharing and future research, then IRB will consider whether the proposed use is acceptable with a waiver or if re-consent is required.

Per the revised Common Rule (for new protocols approved on or after January 21, 2019), there are new consent requirements which affect what is allowable as part of secondary research.  Please see FAQ 9.

Furthermore, for protocols approved on or after the revised Common Rule implementation date, OHSRP advises that if the investigator intends to use coded or identifiable specimens/data for future research, there should be language in the consent form informing the subject of this.  See the current Consent Templates for Use at NIH Sites .

15. If the primary consent document allows sharing, what is the next step to be able to perform secondary research?

Consent to use specimens/data for future research is not sufficient to allow the investigator to move forward with secondary research with identifiable specimens/data.  This type of consent language simply allows the investigator to store the materials for future research (or use the materials once completely anonymized (stripped of all identifiers)).  If the investigator will conduct new research using existing identifiable specimens/data, generally they are expected to submit a new research protocol and seek IRB approval.

16. What is a waiver of consent?

A waiver of consent is when the requirement to obtain informed consent for research is formally waived by the IRB.  The waiver applies to the proposed research activity, not to the sharing of specimens/data. The IRB will consider whether the proposed use is consistent with the terms of the original consent and whether there are any new risks.  If the conditions described below in the next FAQ are met, then the IRB may grant the waiver.  The IRB will not grant a waiver that is counter to the terms of the original consent, nor can the IRB grant a waiver for broad, unspecified future use.

17. What are the criteria for being granted a waiver of consent?

When requesting a waiver for a research protocol being reviewed and approved on or after January 21, 2019, address and provide justification in the protocol for the following specific regulatory criteria:

• Example of a justification: The only risk to subjects is a possible breach of confidentiality.
• Example of a justification: The required number of specimens to conduct the research is so large that it would impede scientific validity and introduce bias if only those who were willing to consent were included.
• Example of a justification: Many of the subjects are lost to follow-up, or the research team has not been in contact with them for many years.
• Example of a justification: The research involves specimens and different types of data (medical records, imaging, lab results) that all must be linked together by a subject identifier to allow analysis.
• Example of a justification: Conducting the planned research with existing specimens/data (originally collected under informed consent) will not cause any harm to subjects.
• Example of a justification: We do not intend to contact subjects to share the results of our research.
• Example of a justification: The results of this research will not generate new clinically actionable findings.

18. Do I have to keep my primary protocol open, so that I can keep the identifiable specimens/data for future secondary research?

No.  Identifiable specimens/data can be stored even after the primary protocol is closed; you do not have to discard or de-identify them.  The primary protocol must only remain open if you are using the specimens/data for research purposes described in the protocol.  The specimens/data can be stored unused until there is approval of a secondary research protocol.  However, although it is permissible to store them, you cannot access or use the identifiable specimens/data for research without an IRB-approved protocol in place.

19. Instead of submitting a secondary research protocol, can I just amend the primary protocol to include my new research objective(s)?

If the proposed research is directly related to the primary protocol’s aims/objectives, then you can consider adding the new research to the primary protocol.  However, if the new project is unrelated to the primary protocol’s aims/objectives, it would be considered new research and needs to be submitted as its own protocol.  A protocol needs to be cohesive, and disconnected ideas and experiments are not considered approvable research.

20. Is guidance available for writing a protocol for secondary research?

The OHSRP website has templates to guide you in writing your secondary research protocol. 

• There is a secondary research protocol template under the Observational Research Protocol Templates section on the OHSRP website. This template should be used for non-exempt research.
• If you think your secondary research may be exempt, refer to the protocol template for retrospective data or biospecimen review on the OHSRP website.
• The protocol template includes information about what types of secondary projects are eligible for an exemption. The webpage also includes instructions that will help guide you through requesting an exemption in the electronic system.

21. What if I anticipate having future research questions, and I would like to collect new specimens/data or retain existing specimens/data in a repository?

A repository is an organized system to collect, maintain and store specimens/data, most often for future research use. The materials may be prospectively collected from humans for inclusion in a repository, or the repository may house existing materials originally collected for other purposes, including non-research purposes.  Repositories can also be referred to as registries, data banks, databases, or biobanks.

Repositories that are designed to prospectively collect specimens/data from humans for research purposes and/or to maintain and distribute specimens/data (that are linked to identifiers) to researchers must have IRB approval and oversight.  Accordingly, a repository protocol should first be submitted for IRB review.  The repository protocol itself generally does not describe the details of the secondary research.  Any secondary research involving the identifiable specimens/data from the repository would require submission of a separate protocol to the IRB. Another option would be for the investigator conducting the secondary research to receive all of the specimens and data in an anonymized or coded and linked format, with no access to the code key. In this case, the investigator(s) overseeing the repository protocol would be acting as an “honest broker” and either permanently anonymize the specimens and data before sharing them or coding them and maintaining the code key, so that only they are in a position to re-link to the original identifiers.

22. Is guidance available for writing a repository protocol?

There is a repository protocol template on the OHSRP website under the Observational Research Protocol Templates.

Table of Contents

What is data collection, why do we need data collection, what are the different data collection methods, data collection tools, the importance of ensuring accurate and appropriate data collection, issues related to maintaining the integrity of data collection, what are common challenges in data collection, what are the key steps in the data collection process, data collection considerations and best practices, choose the right data science program, are you interested in a career in data science, what is data collection: methods, types, tools.

The process of gathering and analyzing accurate data from various sources to find answers to research problems, trends and probabilities, etc., to evaluate possible outcomes is Known as Data Collection. Knowledge is power, information is knowledge, and data is information in digitized form, at least as defined in IT. Hence, data is power. But before you can leverage that data into a successful strategy for your organization or business, you need to gather it. That’s your first step.

So, to help you get the process started, we shine a spotlight on data collection. What exactly is it? Believe it or not, it’s more than just doing a Google search! Furthermore, what are the different types of data collection? And what kinds of data collection tools and data collection techniques exist?

If you want to get up to speed about what is data collection process, you’ve come to the right place. 

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Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare.

Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity.

During data collection, the researchers must identify the data types, the sources of data, and what methods are being used. We will soon see that there are many different data collection methods . There is heavy reliance on data collection in research, commercial, and government fields.

Before an analyst begins collecting data, they must answer three questions first:

• What’s the goal or purpose of this research?
• What kinds of data are they planning on gathering?
• What methods and procedures will be used to collect, store, and process the information?

Additionally, we can break up data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Quantitative data, unsurprisingly, deals with numbers, such as statistics, poll numbers, percentages, etc.

Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. The best courses of action come from informed decisions, and information and data are synonymous.

The concept of data collection isn’t a new one, as we’ll see later, but the world has changed. There is far more data available today, and it exists in forms that were unheard of a century ago. The data collection process has had to change and grow with the times, keeping pace with technology.

Whether you’re in the world of academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices.

Now that you know what is data collection and why we need it, let's take a look at the different methods of data collection. While the phrase “data collection” may sound all high-tech and digital, it doesn’t necessarily entail things like computers, big data , and the internet. Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.

Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail:

1. Primary Data Collection:

Primary data collection involves the collection of original data directly from the source or through direct interaction with the respondents. This method allows researchers to obtain firsthand information specifically tailored to their research objectives. There are various techniques for primary data collection, including:

a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms.

b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational).

c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention.

d. Experiments: Experimental studies involve the manipulation of variables to observe their impact on the outcome. Researchers control the conditions and collect data to draw conclusions about cause-and-effect relationships.

e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding opinions, perceptions, and experiences shared by the participants.

2. Secondary Data Collection:

Secondary data collection involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including:

a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.

b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys.

c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes.

d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research.

e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.

Now that we’ve explained the various techniques, let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”).

Word Association

The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word.

Sentence Completion

Researchers use sentence completion to understand what kind of ideas the respondent has. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it.

Role-Playing

Respondents are presented with an imaginary situation and asked how they would act or react if it was real.

In-Person Surveys

The researcher asks questions in person.

Online/Web Surveys

These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all.

Mobile Surveys

These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps.

Phone Surveys

No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer.

Observation

Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, it’s only effective in small-scale situations.

Accurate data collecting is crucial to preserving the integrity of research, regardless of the subject of study or preferred method for defining data (quantitative, qualitative). Errors are less likely to occur when the right data gathering tools are used (whether they are brand-new ones, updated versions of them, or already available).

Among the effects of data collection done incorrectly, include the following -

• Erroneous conclusions that squander resources
• Decisions that compromise public policy
• Incapacity to correctly respond to research inquiries
• Bringing harm to participants who are humans or animals
• Deceiving other researchers into pursuing futile research avenues
• The study's inability to be replicated and validated

When these study findings are used to support recommendations for public policy, there is the potential to result in disproportionate harm, even if the degree of influence from flawed data collecting may vary by discipline and the type of investigation.

Let us now look at the various issues that we might face while maintaining the integrity of data collection.

In order to assist the errors detection process in the data gathering process, whether they were done purposefully (deliberate falsifications) or not, maintaining data integrity is the main justification (systematic or random errors).

Quality assurance and quality control are two strategies that help protect data integrity and guarantee the scientific validity of study results.

Each strategy is used at various stages of the research timeline:

• Quality control - tasks that are performed both after and during data collecting
• Quality assurance - events that happen before data gathering starts

Let us explore each of them in more detail now.

Quality Assurance

As data collecting comes before quality assurance, its primary goal is "prevention" (i.e., forestalling problems with data collection). The best way to protect the accuracy of data collection is through prevention. The uniformity of protocol created in the thorough and exhaustive procedures manual for data collecting serves as the best example of this proactive step. 

The likelihood of failing to spot issues and mistakes early in the research attempt increases when guides are written poorly. There are several ways to show these shortcomings:

• Failure to determine the precise subjects and methods for retraining or training staff employees in data collecting
• List of goods to be collected, in part
• There isn't a system in place to track modifications to processes that may occur as the investigation continues.
• Instead of detailed, step-by-step instructions on how to deliver tests, there is a vague description of the data gathering tools that will be employed.
• Uncertainty regarding the date, procedure, and identity of the person or people in charge of examining the data
• Incomprehensible guidelines for using, adjusting, and calibrating the data collection equipment.

Now, let us look at how to ensure Quality Control.

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Quality Control

Despite the fact that quality control actions (detection/monitoring and intervention) take place both after and during data collection, the specifics should be meticulously detailed in the procedures manual. Establishing monitoring systems requires a specific communication structure, which is a prerequisite. Following the discovery of data collection problems, there should be no ambiguity regarding the information flow between the primary investigators and staff personnel. A poorly designed communication system promotes slack oversight and reduces opportunities for error detection.

Direct staff observation conference calls, during site visits, or frequent or routine assessments of data reports to spot discrepancies, excessive numbers, or invalid codes can all be used as forms of detection or monitoring. Site visits might not be appropriate for all disciplines. Still, without routine auditing of records, whether qualitative or quantitative, it will be challenging for investigators to confirm that data gathering is taking place in accordance with the manual's defined methods. Additionally, quality control determines the appropriate solutions, or "actions," to fix flawed data gathering procedures and reduce recurrences.

Problems with data collection, for instance, that call for immediate action include:

• Fraud or misbehavior
• Systematic mistakes, procedure violations 
• Individual data items with errors
• Issues with certain staff members or a site's performance 

Researchers are trained to include one or more secondary measures that can be used to verify the quality of information being obtained from the human subject in the social and behavioral sciences where primary data collection entails using human subjects. 

For instance, a researcher conducting a survey would be interested in learning more about the prevalence of risky behaviors among young adults as well as the social factors that influence these risky behaviors' propensity for and frequency. Let us now explore the common challenges with regard to data collection.

There are some prevalent challenges faced while collecting data, let us explore a few of them to understand them better and avoid them.

Data Quality Issues

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

Inconsistent Data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data have a tendency to accumulate and reduce the value of data if they are not continually resolved. Organizations that have heavily focused on data consistency do so because they only want reliable data to support their analytics.

Data Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or not prepared. Customer complaints and subpar analytical outcomes are only two ways that this data unavailability can have a significant impact on businesses. A data engineer spends about 80% of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. In order to ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Data pipelines can be difficult due to their size and complexity. Data downtime must be continuously monitored, and it must be reduced through automation.

Ambiguous Data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. For data streaming at a fast speed, the issue becomes more overwhelming. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. This unclear data might cause a number of problems for reporting and analytics.

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Duplicate Data

Streaming data, local databases, and cloud data lakes are just a few of the sources of data that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. If certain prospects are ignored while others are engaged repeatedly, marketing campaigns suffer. The likelihood of biased analytical outcomes increases when duplicate data are present. It can also result in ML models with biased training data.

Too Much Data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data exists. There is a risk of getting lost in an abundance of data when searching for information pertinent to your analytical efforts. Data scientists, data analysts, and business users devote 80% of their work to finding and organizing the appropriate data. With an increase in data volume, other problems with data quality become more serious, particularly when dealing with streaming data and big files or databases.

Inaccurate Data

For highly regulated businesses like healthcare, data accuracy is crucial. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide you with a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to a number of things, including data degradation, human mistake, and data drift. Worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while being transferred between different systems, and data quality might deteriorate with time.

Hidden Data

The majority of businesses only utilize a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For instance, the customer service team might not receive client data from sales, missing an opportunity to build more precise and comprehensive customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Finding Relevant Data

Finding relevant data is not so easy. There are several factors that we need to consider while trying to find relevant data, which include -

• Relevant Domain
• Relevant demographics
• Relevant Time period and so many more factors that we need to consider while trying to find relevant data.

Data that is not relevant to our study in any of the factors render it obsolete and we cannot effectively proceed with its analysis. This could lead to incomplete research or analysis, re-collecting data again and again, or shutting down the study.

Deciding the Data to Collect

Determining what data to collect is one of the most important factors while collecting data and should be one of the first factors while collecting data. We must choose the subjects the data will cover, the sources we will be used to gather it, and the quantity of information we will require. Our responses to these queries will depend on our aims, or what we expect to achieve utilizing your data. As an illustration, we may choose to gather information on the categories of articles that website visitors between the ages of 20 and 50 most frequently access. We can also decide to compile data on the typical age of all the clients who made a purchase from your business over the previous month.

Not addressing this could lead to double work and collection of irrelevant data or ruining your study as a whole.

Dealing With Big Data

Big data refers to exceedingly massive data sets with more intricate and diversified structures. These traits typically result in increased challenges while storing, analyzing, and using additional methods of extracting results. Big data refers especially to data sets that are quite enormous or intricate that conventional data processing tools are insufficient. The overwhelming amount of data, both unstructured and structured, that a business faces on a daily basis. 

The amount of data produced by healthcare applications, the internet, social networking sites social, sensor networks, and many other businesses are rapidly growing as a result of recent technological advancements. Big data refers to the vast volume of data created from numerous sources in a variety of formats at extremely fast rates. Dealing with this kind of data is one of the many challenges of Data Collection and is a crucial step toward collecting effective data. 

Low Response and Other Research Issues

Poor design and low response rates were shown to be two issues with data collecting, particularly in health surveys that used questionnaires. This might lead to an insufficient or inadequate supply of data for the study. Creating an incentivized data collection program might be beneficial in this case to get more responses.

Now, let us look at the key steps in the data collection process.

In the Data Collection Process, there are 5 key steps. They are explained briefly below -

1. Decide What Data You Want to Gather

The first thing that we need to do is decide what information we want to gather. We must choose the subjects the data will cover, the sources we will use to gather it, and the quantity of information that we would require. For instance, we may choose to gather information on the categories of products that an average e-commerce website visitor between the ages of 30 and 45 most frequently searches for. 

2. Establish a Deadline for Data Collection

The process of creating a strategy for data collection can now begin. We should set a deadline for our data collection at the outset of our planning phase. Some forms of data we might want to continuously collect. We might want to build up a technique for tracking transactional data and website visitor statistics over the long term, for instance. However, we will track the data throughout a certain time frame if we are tracking it for a particular campaign. In these situations, we will have a schedule for when we will begin and finish gathering data. 

3. Select a Data Collection Approach

We will select the data collection technique that will serve as the foundation of our data gathering plan at this stage. We must take into account the type of information that we wish to gather, the time period during which we will receive it, and the other factors we decide on to choose the best gathering strategy.

4. Gather Information

Once our plan is complete, we can put our data collection plan into action and begin gathering data. In our DMP, we can store and arrange our data. We need to be careful to follow our plan and keep an eye on how it's doing. Especially if we are collecting data regularly, setting up a timetable for when we will be checking in on how our data gathering is going may be helpful. As circumstances alter and we learn new details, we might need to amend our plan.

5. Examine the Information and Apply Your Findings

It's time to examine our data and arrange our findings after we have gathered all of our information. The analysis stage is essential because it transforms unprocessed data into insightful knowledge that can be applied to better our marketing plans, goods, and business judgments. The analytics tools included in our DMP can be used to assist with this phase. We can put the discoveries to use to enhance our business once we have discovered the patterns and insights in our data.

Let us now look at some data collection considerations and best practices that one might follow.

We must carefully plan before spending time and money traveling to the field to gather data. While saving time and resources, effective data collection strategies can help us collect richer, more accurate, and richer data.

Below, we will be discussing some of the best practices that we can follow for the best results -

1. Take Into Account the Price of Each Extra Data Point

Once we have decided on the data we want to gather, we need to make sure to take the expense of doing so into account. Our surveyors and respondents will incur additional costs for each additional data point or survey question.

2. Plan How to Gather Each Data Piece

There is a dearth of freely accessible data. Sometimes the data is there, but we may not have access to it. For instance, unless we have a compelling cause, we cannot openly view another person's medical information. It could be challenging to measure several types of information.

Consider how time-consuming and difficult it will be to gather each piece of information while deciding what data to acquire.

3. Think About Your Choices for Data Collecting Using Mobile Devices

Mobile-based data collecting can be divided into three categories -

• IVRS (interactive voice response technology) -  Will call the respondents and ask them questions that have already been recorded. 
• SMS data collection - Will send a text message to the respondent, who can then respond to questions by text on their phone. 
• Field surveyors - Can directly enter data into an interactive questionnaire while speaking to each respondent, thanks to smartphone apps.

We need to make sure to select the appropriate tool for our survey and responders because each one has its own disadvantages and advantages.

4. Carefully Consider the Data You Need to Gather

It's all too easy to get information about anything and everything, but it's crucial to only gather the information that we require. 

It is helpful to consider these 3 questions:

• What details will be helpful?
• What details are available?
• What specific details do you require?

5. Remember to Consider Identifiers

Identifiers, or details describing the context and source of a survey response, are just as crucial as the information about the subject or program that we are actually researching.

In general, adding more identifiers will enable us to pinpoint our program's successes and failures with greater accuracy, but moderation is the key.

6. Data Collecting Through Mobile Devices is the Way to Go

Although collecting data on paper is still common, modern technology relies heavily on mobile devices. They enable us to gather many various types of data at relatively lower prices and are accurate as well as quick. There aren't many reasons not to pick mobile-based data collecting with the boom of low-cost Android devices that are available nowadays.

The Ultimate Ticket to Top Data Science Job Roles

1. What is data collection with example?

Data collection is the process of collecting and analyzing information on relevant variables in a predetermined, methodical way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative. Example: A company collects customer feedback through online surveys and social media monitoring to improve their products and services.

2. What are the primary data collection methods?

As is well known, gathering primary data is costly and time intensive. The main techniques for gathering data are observation, interviews, questionnaires, schedules, and surveys.

3. What are data collection tools?

The term "data collecting tools" refers to the tools/devices used to gather data, such as a paper questionnaire or a system for computer-assisted interviews. Tools used to gather data include case studies, checklists, interviews, occasionally observation, surveys, and questionnaires.

4. What’s the difference between quantitative and qualitative methods?

While qualitative research focuses on words and meanings, quantitative research deals with figures and statistics. You can systematically measure variables and test hypotheses using quantitative methods. You can delve deeper into ideas and experiences using qualitative methodologies.

5. What are quantitative data collection methods?

While there are numerous other ways to get quantitative information, the methods indicated above—probability sampling, interviews, questionnaire observation, and document review—are the most typical and frequently employed, whether collecting information offline or online.

6. What is mixed methods research?

User research that includes both qualitative and quantitative techniques is known as mixed methods research. For deeper user insights, mixed methods research combines insightful user data with useful statistics.

7. What are the benefits of collecting data?

Collecting data offers several benefits, including:

• Knowledge and Insight
• Evidence-Based Decision Making
• Problem Identification and Solution
• Validation and Evaluation
• Identifying Trends and Predictions
• Support for Research and Development
• Policy Development
• Quality Improvement
• Personalization and Targeting
• Knowledge Sharing and Collaboration

8. What’s the difference between reliability and validity?

Reliability is about consistency and stability, while validity is about accuracy and appropriateness. Reliability focuses on the consistency of results, while validity focuses on whether the results are actually measuring what they are intended to measure. Both reliability and validity are crucial considerations in research to ensure the trustworthiness and meaningfulness of the collected data and measurements.

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