research secondary data types

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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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.

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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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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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What Is Secondary Data? A Complete Guide

What is secondary data, and why is it important? Find out in this post.

Within data analytics, there are many ways of categorizing data. A common distinction, for instance, is that between qualitative and quantitative data . In addition, you might also distinguish your data based on factors like sensitivity. For example, is it publicly available or is it highly confidential?  

Probably the most fundamental distinction between different types of data is their source. Namely, are they primary, secondary, or third-party data? Each of these vital data sources supports the data analytics process in its own way. In this post, we’ll focus specifically on secondary data. We’ll look at its main characteristics, provide some examples, and highlight the main pros and cons of using secondary data in your analysis.  

We’ll cover the following topics:  

What is secondary data?

• What’s the difference between primary, secondary, and third-party data?
• What are some examples of secondary data?
• How to analyse secondary data
• Advantages of secondary data
• Disadvantages of secondary data
• Wrap-up and further reading

Ready to learn all about secondary data? Then let’s go.

1. What is secondary data?

Secondary data (also known as second-party data) refers to any dataset collected by any person other than the one using it.  

Secondary data sources are extremely useful. They allow researchers and data analysts to build large, high-quality databases that help solve business problems. By expanding their datasets with secondary data, analysts can enhance the quality and accuracy of their insights. Most secondary data comes from external organizations. However, secondary data also refers to that collected within an organization and then repurposed.

Secondary data has various benefits and drawbacks, which we’ll explore in detail in section four. First, though, it’s essential to contextualize secondary data by understanding its relationship to two other sources of data: primary and third-party data. We’ll look at these next.

2. What’s the difference between primary, secondary, and third-party data?

To best understand secondary data, we need to know how it relates to the other main data sources: primary and third-party data.

What is primary data?

‘Primary data’ (also known as first-party data) are those directly collected or obtained by the organization or individual that intends to use them. Primary data are always collected for a specific purpose. This could be to inform a defined goal or objective or to address a particular business problem. 

For example, a real estate organization might want to analyze current housing market trends. This might involve conducting interviews, collecting facts and figures through surveys and focus groups, or capturing data via electronic forms. Focusing only on the data required to complete the task at hand ensures that primary data remain highly relevant. They’re also well-structured and of high quality.

As explained, ‘secondary data’ describes those collected for a purpose other than the task at hand. Secondary data can come from within an organization but more commonly originate from an external source. If it helps to make the distinction, secondary data is essentially just another organization’s primary data. 

Secondary data sources are so numerous that they’ve started playing an increasingly vital role in research and analytics. They are easier to source than primary data and can be repurposed to solve many different problems. While secondary data may be less relevant for a given task than primary data, they are generally still well-structured and highly reliable.

What is third-party data?

‘Third-party data’ (sometimes referred to as tertiary data) refers to data collected and aggregated from numerous discrete sources by third-party organizations. Because third-party data combine data from numerous sources and aren’t collected with a specific goal in mind, the quality can be lower. 

Third-party data also tend to be largely unstructured. This means that they’re often beset by errors, duplicates, and so on, and require more processing to get them into a usable format. Nevertheless, used appropriately, third-party data are still a useful data analytics resource. You can learn more about structured vs unstructured data here . 

OK, now that we’ve placed secondary data in context, let’s explore some common sources and types of secondary data.

3. What are some examples of secondary data?

External secondary data.

Before we get to examples of secondary data, we first need to understand the types of organizations that generally provide them. Frequent sources of secondary data include:  

• Government departments
• Public sector organizations
• Industry associations
• Trade and industry bodies
• Educational institutions
• Private companies
• Market research providers

While all these organizations provide secondary data, government sources are perhaps the most freely accessible. They are legally obliged to keep records when registering people, providing services, and so on. This type of secondary data is known as administrative data. It’s especially useful for creating detailed segment profiles, where analysts hone in on a particular region, trend, market, or other demographic.

Types of secondary data vary. Popular examples of secondary data include:

• Tax records and social security data
• Census data (the U.S. Census Bureau is oft-referenced, as well as our favorite, the U.S. Bureau of Labor Statistics )
• Electoral statistics
• Health records
• Books, journals, or other print media
• Social media monitoring, internet searches, and other online data
• Sales figures or other reports from third-party companies
• Libraries and electronic filing systems
• App data, e.g. location data, GPS data, timestamp data, etc.

Internal secondary data 

As mentioned, secondary data is not limited to that from a different organization. It can also come from within an organization itself.  

Sources of internal secondary data might include:

• Sales reports
• Annual accounts
• Quarterly sales figures
• Customer relationship management systems
• Emails and metadata
• Website cookies

In the right context, we can define practically any type of data as secondary data. The key takeaway is that the term ‘secondary data’ doesn’t refer to any inherent quality of the data themselves, but to how they are used. Any data source (external or internal) used for a task other than that for which it was originally collected can be described as secondary data.

4. How to analyse secondary data

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

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

• Statement of purpose: Before collecting secondary data, you need to know your statement of purpose. This means you should have a clear awareness of the goal of the research work and how this data will help achieve it. This will guide you to collect the right data, then choosing the best data source and method of analysis.
• Research design: This is a plan on how the research activities will be carried out. It describes the kind of data to be collected, the sources of data collection, the method of data collection, tools used, and method of analysis. Once the purpose of the research has been identified, the researcher should design a research process that will guide the data analysis process.
• Developing the research questions: Once you’ve identified the research purpose, an analyst should also prepare research questions to help identify secondary data. For example, if a researcher is looking to learn more about why working adults are increasingly more interested in the “gig economy” as opposed to full-time work, they may ask, “What are the main factors that influence adults decisions to engage in freelance work?” or, “Does education level have an effect on how people engage in freelance work?
• Identifying secondary data: Using the research questions as a guide, researchers will then begin to identify relevant data from the sources provided. If the kind of data to be collected is qualitative, a researcher can filter out qualitative data—for example.
• Evaluating secondary data: Once relevant data has been identified and collates, it will be evaluated to ensure it fulfils the criteria of the research topic. Then, it is analyzed either using the quantitative or qualitative method, depending on the type of data it is.

You can learn more about secondary data analysis in this post .  

5. Advantages of secondary data

Secondary data is suitable for any number of analytics activities. The only limitation is a dataset’s format, structure, and whether or not it relates to the topic or problem at hand. 

When analyzing secondary data, the process has some minor differences, mainly in the preparation phase. Otherwise, it follows much the same path as any traditional data analytics project. 

More broadly, though, what are the advantages and disadvantages of using secondary data? Let’s take a look.

Advantages of using secondary data

It’s an economic use of time and resources: Because secondary data have already been collected, cleaned, and stored, this saves analysts much of the hard work that comes from collecting these data firsthand. For instance, for qualitative data, the complex tasks of deciding on appropriate research questions or how best to record the answers have already been completed. Secondary data saves data analysts and data scientists from having to start from scratch.  

It provides a unique, detailed picture of a population: Certain types of secondary data, especially government administrative data, can provide access to levels of detail that it would otherwise be extremely difficult (or impossible) for organizations to collect on their own. Data from public sources, for instance, can provide organizations and individuals with a far greater level of population detail than they could ever hope to gather in-house. You can also obtain data over larger intervals if you need it., e.g. stock market data which provides decades’-worth of information.  

Secondary data can build useful relationships: Acquiring secondary data usually involves making connections with organizations and analysts in fields that share some common ground with your own. This opens the door to a cross-pollination of disciplinary knowledge. You never know what nuggets of information or additional data resources you might find by building these relationships.

Secondary data tend to be high-quality: Unlike some data sources, e.g. third-party data, secondary data tends to be in excellent shape. In general, secondary datasets have already been validated and therefore require minimal checking. Often, such as in the case of government data, datasets are also gathered and quality-assured by organizations with much more time and resources available. This further benefits the data quality , while benefiting smaller organizations that don’t have endless resources available.

It’s excellent for both data enrichment and informing primary data collection: Another benefit of secondary data is that they can be used to enhance and expand existing datasets. Secondary data can also inform primary data collection strategies. They can provide analysts or researchers with initial insights into the type of data they might want to collect themselves further down the line.

6. Disadvantages of secondary data

They aren’t always free: Sometimes, it’s unavoidable—you may have to pay for access to secondary data. However, while this can be a financial burden, in reality, the cost of purchasing a secondary dataset usually far outweighs the cost of having to plan for and collect the data firsthand.  

The data isn’t always suited to the problem at hand: While secondary data may tick many boxes concerning its relevance to a business problem, this is not always true. For instance, secondary data collection might have been in a geographical location or time period ill-suited to your analysis. Because analysts were not present when the data were initially collected, this may also limit the insights they can extract.

The data may not be in the preferred format: Even when a dataset provides the necessary information, that doesn’t mean it’s appropriately stored. A basic example: numbers might be stored as categorical data rather than numerical data. Another issue is that there may be gaps in the data. Categories that are too vague may limit the information you can glean. For instance, a dataset of people’s hair color that is limited to ‘brown, blonde and other’ will tell you very little about people with auburn, black, white, or gray hair.  

You can’t be sure how the data were collected: A structured, well-ordered secondary dataset may appear to be in good shape. However, it’s not always possible to know what issues might have occurred during data collection that will impact their quality. For instance, poor response rates will provide a limited view. While issues relating to data collection are sometimes made available alongside the datasets (e.g. for government data) this isn’t always the case. You should therefore treat secondary data with a reasonable degree of caution.

Being aware of these disadvantages is the first step towards mitigating them. While you should be aware of the risks associated with using secondary datasets, in general, the benefits far outweigh the drawbacks.

7. Wrap-up and further reading

In this post we’ve explored secondary data in detail. As we’ve seen, it’s not so different from other forms of data. What defines data as secondary data is how it is used rather than an inherent characteristic of the data themselves. 

To learn more about data analytics, check out this free, five-day introductory data analytics short course . You can also check out these articles to learn more about the data analytics process:

• What is data cleaning and why is it important?
• What is data visualization? A complete introductory guide
• 10 Great places to find free datasets for your next project

Secondary Research Guide: Definition, Methods, Examples

Apr 3, 2024

8 min. read

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

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

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

Secondary Research Definition: What Is Secondary Research?

Primary vs Secondary Market Research

What Are Secondary Research Methods?

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

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

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

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

• Journal articles
• Research papers

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

Primary vs. Secondary Market Research

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

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

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

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

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

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

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

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

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

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

Cost and time efficiency

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

Access to large datasets

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

Ability to analyze trends and patterns

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

Historical context

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

Basis for further research

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

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

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

Reliability concerns

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

Potential bias

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

Outdated information

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

Limitations in customization

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

A lack of relevance

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

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

Identify credible sources for secondary research

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

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

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

Evaluate the quality and relevance of secondary data

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

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

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

Document your sources 

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

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

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

Continue Reading

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

• Data Collection

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

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

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

What is Secondary Data?  

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

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

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

Sources of Secondary Data

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

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

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

• Published Sources

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

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

• Unpublished Personal Sources

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

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

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

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

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

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

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

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

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

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

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

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

• Government Records

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

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

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

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

Some other sources of data collection include:

• Radio stations
• Public sector records.

What are the Secondary Data Collection Tools?

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

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

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

• Internet-Enabled Devices

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

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

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

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

Secondary Data Analysis  

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

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

How to Analyse Secondary Data

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

• Statement of Purpose

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

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

• Research Design

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

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

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

• Developing the Research Questions

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

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

• Identifying Secondary Data

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

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

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

• Evaluating Secondary Data

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

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

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

Advantages of Secondary Data

• Ease of Access

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

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

• Inexpensive

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

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

• Time-Saving

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

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

• Longitudinal and Comparative Studies

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

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

• Generating new insights

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

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

Disadvantages of Secondary Data  

• Data Quality:

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

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

• Irrelevant Data:

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

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

• Exaggerated Data

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

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

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

• Outdated Information

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

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

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

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

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

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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 ]
• Castle J. E. (2003). Maximizing research opportunities: Secondary data analysis . Journal of Neuroscience Nursing , 35 ( 5 ), 287–290. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/14593941 [ PubMed ] [ Google Scholar ]
• Cheng H. G., & Phillips M. R. (2014). Secondary analysis of existing data: Opportunities and implementation . Shanghai Archives of Psychiatry , 26 ( 6 ), 371–375. https://dx.doi.org/10.11919%2Fj.issn.1002-0829.214171 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Heaton J. (2008). Secondary analysis of qualitative data: An overview . Historical Social Research , 33 ( 3 ), 33–45. [ Google Scholar ]
• Hulbert-Williams N. J., Plumpton C. O., Flowers P., McHugh R., Neal R. D., Semlyen J., & Storey L. (2017). The cancer care experiences of gay, lesbian and bisexual patients: A secondary analysis of data from the UK Cancer Patient Experience Survey . European Journal of Cancer Care , 26 ( 4 ). 10.1111/ecc.12670 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnston M. P. (2014). Secondary data analysis: A method of which the time has come . Qualitative and Quantitative Methods in Libraries (QQML) , 3 , 619–626.r [ Google Scholar ]
• Office of Disease Prevention and Health Promotion. (2019). Lesbian, gay, bisexual, and transgender health . Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/lesbian-gay-bisexual-and-transgender-health
• Rew L., Koniak-Griffin D., Lewis M. A., Miles M., & O’Sullivan A. (2000). Secondary data analysis: New perspective for adolescent research . Nursing Outlook , 48 ( 5 ), 223–239. 10.1067/mno.2000.104901 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schadendorf D., Hodi F. S., Robert C., Weber J. S., Margolin K., Hamid O.,…Wolchok J. D. (2015). Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma . Journal of Clinical Oncology , 33 ( 17 ), 1889–1894. 10.1200/JCO.2014.56.2736 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Silver J. K., & Gilchrist L. S. (2011). Cancer rehabilitation with a focus on evidence-based outpatient physical and occupational therapy interventions . American Journal of Physical Medicine & Rehabilitation , 90 ( 5 Suppl 1 ), S5–S15. 10.1097/PHM.0b013e31820be4ae [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith A. K., Ayanian J. Z., Covinsky K. E., Landon B. E., McCarthy E. P., Wee C. C., & Steinman M. A. (2011). Conducting high-value secondary dataset analysis: An introductory guide and resources . Journal of General Internal Medicine , 26 ( 8 ), 920–929. 10.1007/s11606-010-1621-5 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stout N. L. (2017). Expanding the perspective on chemotherapy-induced peripheral neuropathy management . Journal of Clinical Oncology , 35 ( 23 ), 2593–2594. 10.1200/JCO.2017.73.6207 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tripathy J. P. (2013). Secondary data analysis: Ethical issues and challenges (letter) . Iranian Journal of Public Health , 42 ( 12 ), 1478–1479. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Winters-Stone K. M., Horak F., Jacobs P. G., Trubowitz P., Dieckmann N. F., Stoyles S., & Faithfull S. (2017). Falls, functioning, and disability among women with persistent symptoms of chemotherapy-induced peripheral neuropathy . Journal of Clinical Oncology , 35 ( 23 ) , 2604–2612. 10.1200/JCO.2016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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Research Methods

Secondary research.

• Primary Research

What is Secondary Research?

Advantages and disadvantages of secondary research, secondary research in literature reviews, secondary research - going beyond literature reviews, main stages of secondary research, useful resources, using material on this page.

• Quantitative Research This link opens in a new window
• Qualitative Research This link opens in a new window
• Being Critical This link opens in a new window
• Subject LibGuides This link opens in a new window

Secondary research

Secondary research uses research and data that has already been carried out. It is sometimes referred to as desk research. It is a good starting point for any type of research as it enables you to analyse what research has already been undertaken and identify any gaps. 

You may only need to carry out secondary research for your assessment or you may need to use secondary research as a starting point, before undertaking your own primary research .

Searching for both primary and secondary sources can help to ensure that you are up to date with what research has already been carried out in your area of interest and to identify the key researchers in the field.

"Secondary sources are the books, articles, papers and similar materials written or produced by others that help you to form your background understanding of the subject. You would use these to find out about experts’ findings, analyses or perspectives on the issue and decide whether to draw upon these explicitly in your research." (Cottrell, 2014, p. 123).

Examples of secondary research sources include:.

• journal articles
• official statistics, such as government reports or organisations which have collected and published data

Primary research  involves gathering data which has not been collected before. Methods to collect it can include interviews, focus groups, controlled trials and case studies. Secondary research often comments on and analyses this primary research.

Gopalakrishnan and Ganeshkumar (2013, p. 10) explain the difference between primary and secondary research:

"Primary research is collecting data directly from patients or population, while secondary research is the analysis of data already collected through primary research. A review is an article that summarizes a number of primary studies and may draw conclusions on the topic of interest which can be traditional (unsystematic) or systematic".

Secondary Data

As secondary data has already been collected by someone else for their research purposes, it may not cover all of the areas of interest for your research topic. This research will need to be analysed alongside other research sources and data in the same subject area in order to confirm, dispute or discuss the findings in a wider context.

"Secondary source data, as the name infers, provides second-hand information. The data come ‘pre-packaged’, their form and content reflecting the fact that they have been produced by someone other than the researcher and will not have been produced specifically for the purpose of the research project. The data, none the less, will have some relevance for the research in terms of the information they contain, and the task for the researcher is to extract that information and re-use it in the context of his/her own research project." (Denscombe, 2021, p. 268)

In the video below Dr. Benedict Wheeler (Senior Research Fellow at the European Center for Environment and Human Health at the University of Exeter Medical School) discusses secondary data analysis. Secondary data was used for his research on how the environment affects health and well-being and utilising this secondary data gave access to a larger data set.

As with all research, an important part of the process is to critically evaluate any sources you use. There are tools to help with this in the  Being Critical  section of the guide.

Louise Corti, from the UK Data Archive, discusses using secondary data  in the video below. T he importance of evaluating secondary research is discussed - this is to ensure the data is appropriate for your research and to investigate how the data was collected.

There are advantages and disadvantages to secondary research:

Advantages:

• Usually low cost
• Easily accessible
• Provides background information to clarify / refine research areas
• Increases breadth of knowledge
• Shows different examples of research methods
• Can highlight gaps in the research and potentially outline areas of difficulty
• Can incorporate a wide range of data
• Allows you to identify opposing views and supporting arguments for your research topic
• Highlights the key researchers and work which is being undertaken within the subject area
• Helps to put your research topic into perspective

Disadvantages

• Can be out of date
• Might be unreliable if it is not clear where or how the research has been collected - remember to think critically
• May not be applicable to your specific research question as the aims will have had a different focus

Literature reviews 

Secondary research for your major project may take the form of a literature review . this is where you will outline the main research which has already been written on your topic. this might include theories and concepts connected with your topic and it should also look to see if there are any gaps in the research., as the criteria and guidance will differ for each school, it is important that you check the guidance which you have been given for your assessment. this may be in blackboard and you can also check with your supervisor..

The videos below include some insights from academics regarding the importance of literature reviews.

Secondary research which goes beyond literature reviews

For some dissertations/major projects there might only be a literature review (discussed above ). For others there could be a literature review followed by primary research and for others the literature review might be followed by further secondary research. 

You may be asked to write a literature review which will form a background chapter to give context to your project and provide the necessary history for the research topic. However, you may then also be expected to produce the rest of your project using additional secondary research methods, which will need to produce results and findings which are distinct from the background chapter t o avoid repetition .

Remember, as the criteria and guidance will differ for each School, it is important that you check the guidance which you have been given for your assessment. This may be in Blackboard and you can also check with your supervisor.

Although this type of secondary research will go beyond a literature review, it will still rely on research which has already been undertaken. And,  "just as in primary research, secondary research designs can be either quantitative, qualitative, or a mixture of both strategies of inquiry" (Manu and Akotia, 2021, p. 4).

Your secondary research may use the literature review to focus on a specific theme, which is then discussed further in the main project. Or it may use an alternative approach. Some examples are included below.  Remember to speak with your supervisor if you are struggling to define these areas.

Some approaches of how to conduct secondary research include:

• A systematic review is a structured literature review that involves identifying all of the relevant primary research using a rigorous search strategy to answer a focused research question.
• This involves comprehensive searching which is used to identify themes or concepts across a number of relevant studies. 
• The review will assess the q uality of the research and provide a summary and synthesis of all relevant available research on the topic.
• The systematic review  LibGuide goes into more detail about this process (The guide is aimed a PhD/Researcher students. However, students on other levels of study may find parts of the guide helpful too).
• Scoping reviews aim to identify and assess available research on a specific topic (which can include ongoing research). 
• They are "particularly useful when a body of literature has not yet been comprehensively reviewed, or exhibits a complex or heterogeneous nature not amenable to a more precise systematic review of the evidence. While scoping reviews may be conducted to determine the value and probable scope of a full systematic review, they may also be undertaken as exercises in and of themselves to summarize and disseminate research findings, to identify research gaps, and to make recommendations for the future research."  (Peters et al., 2015) .
• This is designed to  summarise the current knowledge and provide priorities for future research.
• "A state-of-the-art review will often highlight new ideas or gaps in research with no official quality assessment." ( MacAdden, 2020).
• "Bibliometric analysis is a popular and rigorous method for exploring and analyzing large volumes of scientific data." (Donthu et al., 2021)
• Quantitative methods and statistics are used to analyse the bibliographic data of published literature. This can be used to measure the impact of authors, publications, or topics within a subject area.

The bibliometric analysis often uses the data from a citation source such as Scopus or Web of Science .

• This is a technique used to combine the statistic results of prior quantitative studies in order to increase precision and validity.
• "It goes beyond the parameters of a literature review, which assesses existing literature, to actually perform calculations based on the results collated, thereby coming up with new results" (Curtis and Curtis, 2011, p. 220)

(Adapted from: Grant and Booth, 2009, cited in Sarhan and Manu, 2021, p. 72)

• Grounded Theory is used to create explanatory theory from data which has been collected.
• "Grounded theory data analysis strategies can be used with different types of data, including secondary data." (Whiteside, Mills and McCalman, 2012)
• This allows you to use a specific theory or theories which can then be applied to your chosen topic/research area.
• You could focus on one case study which is analysed in depth, or you could examine more than one in order to compare and contrast the important aspects of your research question.
• "Good case studies often begin with a predicament that is poorly comprehended and is inadequately explained or traditionally rationalised by numerous conflicting accounts. Therefore, the aim is to comprehend an existent problem and to use the acquired understandings to develop new theoretical outlooks or explanations."  (Papachroni and Lochrie, 2015, p. 81)

Main stages of secondary research for a dissertation/major project

In general, the main stages for conducting secondary research for your dissertation or major project will include:

Click on the image below to access the reading list which includes resources used in this guide as well as some additional useful resources.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License .

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Pros and Cons of Secondary Data Analysis

A Review of the Advantages and Disadvantages in Social Science Research

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

Secondary data analysis is the analysis of data that was collected by someone else. Below, we’ll review the definition of secondary data, how it can be used by researchers, and the pros and cons of this type of research.

Key Takeaways: Secondary Data Analysis

• Primary data refers to data that researchers have collected themselves, while secondary data refers to data that was collected by someone else.
• Secondary data is available from a variety of sources, such as governments and research institutions.
• While using secondary data can be more economical, existing data sets may not answer all of a researcher’s questions.

Comparison of Primary and Secondary Data

In social science research, the terms primary data and secondary data are common parlance. Primary data is collected by a researcher or team of researchers for the specific purpose or analysis under consideration. Here, a research team conceives of and develops a research project, decides on a sampling technique , collects data designed to address specific questions, and performs their own analyses of the data they collected. In this case, the people involved in the data analysis are familiar with the research design and data collection process.

Secondary data analysis , on the other hand, is the use of data that was collected by someone else for some other purpose . In this case, the researcher poses questions that are addressed through the analysis of a data set that they were not involved in collecting. The data was not collected to answer the researcher’s specific research questions and was instead collected for another purpose. This means that the same data set can actually be a primary data set to one researcher and a secondary data set to a different one.

Using Secondary Data

There are some important things that must be done before using secondary data in an analysis. Since the researcher did not collect the data, it's important for them to become familiar with the data set: how the data was collected, what the response categories are for each question, whether or not weights need to be applied during the analysis, whether or not clusters or stratification need to be accounted for, who the population of study was, and more.

A great deal of secondary data resources and data sets are available for sociological research , many of which are public and easily accessible. The United States Census , the General Social Survey , and the American Community Survey are some of the most commonly used secondary data sets available.

Advantages of Secondary Data Analysis

The biggest advantage of using secondary data is that it can be more economical. Someone else has already collected the data, so the researcher does not have to devote money, time, energy and resources to this phase of research. Sometimes the secondary data set must be purchased, but the cost is almost always lower than the expense of collecting a similar data set from scratch, which usually entails salaries, travel and transportation, office space, equipment, and other overhead costs. In addition, since the data is already collected and usually cleaned and stored in electronic format, the researcher can spend most of their time analyzing the data instead of getting the data ready for analysis.

A second major advantage of using secondary data is the breadth of data available. The federal government conducts numerous studies on a large, national scale that individual researchers would have a difficult time collecting. Many of these data sets are also longitudinal , meaning that the same data has been collected from the same population over several different time periods. This allows researchers to look at trends and changes of phenomena over time.

A third important advantage of using secondary data is that the data collection process often maintains a level of expertise and professionalism that may not be present with individual researchers or small research projects. For example, data collection for many federal data sets is often performed by staff members who specialize in certain tasks and have many years of experience in that particular area and with that particular survey. Many smaller research projects do not have that level of expertise, as a lot of data is collected by students working part-time.

Disadvantages of Secondary Data Analysis

A major disadvantage of using secondary data is that it may not answer the researcher’s specific research questions or contain specific information that the researcher would like to have. It also may not have been collected in the geographic region or during the years desired, or with the specific population that the researcher is interested in studying. For example, a researcher who is interested in studying adolescents may find that the secondary data set only includes young adults. 

Additionally, since the researcher did not collect the data, they have no control over what is contained in the data set. Often times this can limit the analysis or alter the original questions the researcher sought to answer. For example, a researcher who is studying happiness and optimism might find that a secondary data set only includes one of these variables , but not both.

A related problem is that the variables may have been defined or categorized differently than the researcher would have chosen. For example, age may have been collected in categories rather than as a continuous variable, or race may be defined as “white” and “other” instead of containing categories for every major race.

Another significant disadvantage of using secondary data is that the researcher doesn't know exactly how the data collection process was done or how well it was carried out. The researcher is not usually privy to information about how seriously the data is affected by problems such as low response rate or respondent misunderstanding of specific survey questions. Sometimes this information is readily available, as is the case with many federal data sets. However, many other secondary data sets are not accompanied by this type of information and the analyst must learn to read between the lines in order to uncover any potential limitations of the data.

• Understanding Secondary Data and How to Use It in Research
• Secondary Sources in Research
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• The Differences Between Indexes and Scales
• What Is a Primary Source?
• Pilot Study in Research
• What Is Panel Data?
• An Overview of Qualitative Research Methods
• Research in Essays and Reports
• Convenience Samples for Research
• How to Conduct a Sociology Research Interview
• Definition and Overview of Grounded Theory
• A Step-By-Step Guide to Writing a Ph.D. Dissertation
• Content Analysis: Method to Analyze Social Life Through Words, Images
• What Is Naturalistic Observation? Definition and Examples

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Market research definition

Market research – in-house or outsourced, market research in the age of data, when to use market research.

• Types of market research 

Different types of primary research

How to do market research (primary data), how to do secondary market research, communicating your market research findings, choose the right platform for your market research, try qualtrics for free, the ultimate guide to market research: how to conduct it like a pro.

27 min read Wondering how to do market research? Or even where to start learning about it? Use our ultimate guide to understand the basics and discover how you can use market research to help your business.

Market research is the practice of gathering information about the needs and preferences of your target audience – potential consumers of your product.

When you understand how your target consumer feels and behaves, you can then take steps to meet their needs and mitigate the risk of an experience gap – where there is a shortfall between what a consumer expects you to deliver and what you actually deliver. Market research can also help you keep abreast of what your competitors are offering, which in turn will affect what your customers expect from you.

Market research connects with every aspect of a business – including brand , product , customer service , marketing and sales.

Market research generally focuses on understanding:

• The consumer (current customers, past customers, non-customers, influencers))
• The company (product or service design, promotion, pricing, placement, service, sales)
• The competitors (and how their market offerings interact in the market environment)
• The industry overall (whether it’s growing or moving in a certain direction)

Free eBook: 2024 market research trends report

Why is market research important?

A successful business relies on understanding what like, what they dislike, what they need and what messaging they will respond to. Businesses also need to understand their competition to identify opportunities to differentiate their products and services from other companies.

Today’s business leaders face an endless stream of decisions around target markets, pricing, promotion, distribution channels, and product features and benefits . They must account for all the factors involved, and there are market research studies and methodologies strategically designed to capture meaningful data to inform every choice. It can be a daunting task.

Market research allows companies to make data-driven decisions to drive growth and innovation.

What happens when you don’t do market research?

Without market research, business decisions are based at best on past consumer behavior, economic indicators, or at worst, on gut feel. Decisions are made in a bubble without thought to what the competition is doing. An important aim of market research is to remove subjective opinions when making business decisions. As a brand you are there to serve your customers, not personal preferences within the company. You are far more likely to be successful if you know the difference, and market research will help make sure your decisions are insight-driven.

Traditionally there have been specialist market researchers who are very good at what they do, and businesses have been reliant on their ability to do it. Market research specialists will always be an important part of the industry, as most brands are limited by their internal capacity, expertise and budgets and need to outsource at least some aspects of the work.

However, the market research external agency model has meant that brands struggled to keep up with the pace of change. Their customers would suffer because their needs were not being wholly met with point-in-time market research.

Businesses looking to conduct market research have to tackle many questions –

• Who are my consumers, and how should I segment and prioritize them?
• What are they looking for within my category?
• How much are they buying, and what are their purchase triggers, barriers, and buying habits?
• Will my marketing and communications efforts resonate?
• Is my brand healthy ?
• What product features matter most?
• Is my product or service ready for launch?
• Are my pricing and packaging plans optimized?

They all need to be answered, but many businesses have found the process of data collection daunting, time-consuming and expensive. The hardest battle is often knowing where to begin and short-term demands have often taken priority over longer-term projects that require patience to offer return on investment.

Today however, the industry is making huge strides, driven by quickening product cycles, tighter competition and business imperatives around more data-driven decision making. With the emergence of simple, easy to use tools , some degree of in-house market research is now seen as essential, with fewer excuses not to use data to inform your decisions. With greater accessibility to such software, everyone can be an expert regardless of level or experience.

How is this possible?

The art of research hasn’t gone away. It is still a complex job and the volume of data that needs to be analyzed is huge. However with the right tools and support, sophisticated research can look very simple – allowing you to focus on taking action on what matters.

If you’re not yet using technology to augment your in-house market research, now is the time to start.

The most successful brands rely on multiple sources of data to inform their strategy and decision making, from their marketing segmentation to the product features they develop to comments on social media. In fact, there’s tools out there that use machine learning and AI to automate the tracking of what’s people are saying about your brand across all sites.

The emergence of newer and more sophisticated tools and platforms gives brands access to more data sources than ever and how the data is analyzed and used to make decisions. This also increases the speed at which they operate, with minimal lead time allowing brands to be responsive to business conditions and take an agile approach to improvements and opportunities.

Expert partners have an important role in getting the best data, particularly giving access to additional market research know-how, helping you find respondents , fielding surveys and reporting on results.

How do you measure success?

Business activities are usually measured on how well they deliver return on investment (ROI). Since market research doesn’t generate any revenue directly, its success has to be measured by looking at the positive outcomes it drives – happier customers, a healthier brand, and so on.

When changes to your products or your marketing strategy are made as a result of your market research findings, you can compare on a before-and-after basis to see if the knowledge you acted on has delivered value.

Regardless of the function you work within, understanding the consumer is the goal of any market research. To do this, we have to understand what their needs are in order to effectively meet them. If we do that, we are more likely to drive customer satisfaction , and in turn, increase customer retention .

Several metrics and KPIs are used to gauge the success of decisions made from market research results, including

• Brand awareness within the target market
• Share of wallet
• CSAT (customer satisfaction)
• NPS (Net Promoter Score)

You can use market research for almost anything related to your current customers, potential customer base or target market. If you want to find something out from your target audience, it’s likely market research is the answer.

Here are a few of the most common uses:

Buyer segmentation and profiling

Segmentation is a popular technique that separates your target market according to key characteristics, such as behavior, demographic information and social attitudes. Segmentation allows you to create relevant content for your different segments, ideally helping you to better connect with all of them.

Buyer personas are profiles of fictional customers – with real attributes. Buyer personas help you develop products and communications that are right for your different audiences, and can also guide your decision-making process. Buyer personas capture the key characteristics of your customer segments, along with meaningful insights about what they want or need from you. They provide a powerful reminder of consumer attitudes when developing a product or service, a marketing campaign or a new brand direction.

By understanding your buyers and potential customers, including their motivations, needs, and pain points, you can optimize everything from your marketing communications to your products to make sure the right people get the relevant content, at the right time, and via the right channel .

Attitudes and Usage surveys

Attitude & Usage research helps you to grow your brand by providing a detailed understanding of consumers. It helps you understand how consumers use certain products and why, what their needs are, what their preferences are, and what their pain points are. It helps you to find gaps in the market, anticipate future category needs, identify barriers to entry and build accurate go-to-market strategies and business plans.

Marketing strategy

Effective market research is a crucial tool for developing an effective marketing strategy – a company’s plan for how they will promote their products.

It helps marketers look like rock stars by helping them understand the target market to avoid mistakes, stay on message, and predict customer needs . It’s marketing’s job to leverage relevant data to reach the best possible solution  based on the research available. Then, they can implement the solution, modify the solution, and successfully deliver that solution to the market.

Product development

You can conduct market research into how a select group of consumers use and perceive your product – from how they use it through to what they like and dislike about it. Evaluating your strengths and weaknesses early on allows you to focus resources on ideas with the most potential and to gear your product or service design to a specific market.

Chobani’s yogurt pouches are a product optimized through great market research . Using product concept testing – a form of market research – Chobani identified that packaging could negatively impact consumer purchase decisions. The brand made a subtle change, ensuring the item satisfied the needs of consumers. This ability to constantly refine its products for customer needs and preferences has helped Chobani become Australia’s #1 yogurt brand and increase market share.

Pricing decisions

Market research provides businesses with insights to guide pricing decisions too. One of the most powerful tools available to market researchers is conjoint analysis, a form of market research study that uses choice modeling to help brands identify the perfect set of features and price for customers. Another useful tool is the Gabor-Granger method, which helps you identify the highest price consumers are willing to pay for a given product or service.

Brand tracking studies

A company’s brand is one of its most important assets. But unlike other metrics like product sales, it’s not a tangible measure you can simply pull from your system. Regular market research that tracks consumer perceptions of your brand allows you to monitor and optimize your brand strategy in real time, then respond to consumer feedback to help maintain or build your brand with your target customers.

Advertising and communications testing

Advertising campaigns can be expensive, and without pre-testing, they carry risk of falling flat with your target audience. By testing your campaigns, whether it’s the message or the creative, you can understand how consumers respond to your communications before you deploy them so you can make changes in response to consumer feedback before you go live.

Finder, which is one of the world’s fastest-growing online comparison websites, is an example of a brand using market research to inject some analytical rigor into the business. Fueled by great market research, the business lifted brand awareness by 23 percent, boosted NPS by 8 points, and scored record profits – all within 10 weeks.

Competitive analysis

Another key part of developing the right product and communications is understanding your main competitors and how consumers perceive them. You may have looked at their websites and tried out their product or service, but unless you know how consumers perceive them, you won’t have an accurate view of where you stack up in comparison. Understanding their position in the market allows you to identify the strengths you can exploit, as well as any weaknesses you can address to help you compete better.

Customer Story

See How Yamaha Does Product Research

Types of market research

Although there are many types market research, all methods can be sorted into one of two categories: primary and secondary.

Primary research

Primary research is market research data that you collect yourself. This is raw data collected through a range of different means – surveys , focus groups,  , observation and interviews being among the most popular.

Primary information is fresh, unused data, giving you a perspective that is current or perhaps extra confidence when confirming hypotheses you already had. It can also be very targeted to your exact needs. Primary information can be extremely valuable. Tools for collecting primary information are increasingly sophisticated and the market is growing rapidly.

Historically, conducting market research in-house has been a daunting concept for brands because they don’t quite know where to begin, or how to handle vast volumes of data. Now, the emergence of technology has meant that brands have access to simple, easy to use tools to help with exactly that problem. As a result, brands are more confident about their own projects and data with the added benefit of seeing the insights emerge in real-time.

Secondary research

Secondary research is the use of data that has already been collected, analyzed and published – typically it’s data you don’t own and that hasn’t been conducted with your business specifically in mind, although there are forms of internal secondary data like old reports or figures from past financial years that come from within your business. Secondary research can be used to support the use of primary research.

Secondary research can be beneficial to small businesses because it is sometimes easier to obtain, often through research companies. Although the rise of primary research tools are challenging this trend by allowing businesses to conduct their own market research more cheaply, secondary research is often a cheaper alternative for businesses who need to spend money carefully. Some forms of secondary research have been described as ‘lean market research’ because they are fast and pragmatic, building on what’s already there.

Because it’s not specific to your business, secondary research may be less relevant, and you’ll need to be careful to make sure it applies to your exact research question. It may also not be owned, which means your competitors and other parties also have access to it.

Primary or secondary research – which to choose?

Both primary and secondary research have their advantages, but they are often best used when paired together, giving you the confidence to act knowing that the hypothesis you have is robust.

Secondary research is sometimes preferred because there is a misunderstanding of the feasibility of primary research. Thanks to advances in technology, brands have far greater accessibility to primary research, but this isn’t always known.

If you’ve decided to gather your own primary information, there are many different data collection methods that you may consider. For example:

• Customer surveys
• Focus groups
• Observation

Think carefully about what you’re trying to accomplish before picking the data collection method(s) you’re going to use. Each one has its pros and cons. Asking someone a simple, multiple-choice survey question will generate a different type of data than you might obtain with an in-depth interview. Determine if your primary research is exploratory or specific, and if you’ll need qualitative research, quantitative research, or both.

Qualitative vs quantitative

Another way of categorizing different types of market research is according to whether they are qualitative or quantitative.

Qualitative research

Qualitative research is the collection of data that is non-numerical in nature. It summarizes and infers, rather than pin-points an exact truth. It is exploratory and can lead to the generation of a hypothesis.

Market research techniques that would gather qualitative data include:

• Interviews (face to face / telephone)
• Open-ended survey questions

Researchers use these types of market research technique because they can add more depth to the data. So for example, in focus groups or interviews, rather than being limited to ‘yes’ or ‘no’ for a certain question, you can start to understand why someone might feel a certain way.

Quantitative research

Quantitative research is the collection of data that is numerical in nature. It is much more black and white in comparison to qualitative data, although you need to make sure there is a representative sample if you want the results to be reflective of reality.

Quantitative researchers often start with a hypothesis and then collect data which can be used to determine whether empirical evidence to support that hypothesis exists.

Quantitative research methods include:

• Questionnaires
• Review scores

Exploratory and specific research

Exploratory research is the approach to take if you don’t know what you don’t know. It can give you broad insights about your customers, product, brand, and market. If you want to answer a specific question, then you’ll be conducting specific research.

• Exploratory . This research is general and open-ended, and typically involves lengthy interviews with an individual or small focus group.
• Specific . This research is often used to solve a problem identified in exploratory research. It involves more structured, formal interviews.

Exploratory primary research is generally conducted by collecting qualitative data. Specific research usually finds its insights through quantitative data.

Primary research can be qualitative or quantitative, large-scale or focused and specific. You’ll carry it out using methods like surveys – which can be used for both qualitative and quantitative studies – focus groups, observation of consumer behavior, interviews, or online tools.

Step 1: Identify your research topic

Research topics could include:

• Product features
• Product or service launch
• Understanding a new target audience (or updating an existing audience)
• Brand identity
• Marketing campaign concepts
• Customer experience

Step 2: Draft a research hypothesis

A hypothesis is the assumption you’re starting out with. Since you can disprove a negative much more easily than prove a positive, a hypothesis is a negative statement such as ‘price has no effect on brand perception’.

Step 3: Determine which research methods are most effective

Your choice of methods depends on budget, time constraints, and the type of question you’re trying to answer. You could combine surveys, interviews and focus groups to get a mix of qualitative and quantitative data.

Step 4: Determine how you will collect and analyze your data.

Primary research can generate a huge amount of data, and when the goal is to uncover actionable insight, it can be difficult to know where to begin or what to pay attention to.

The rise in brands taking their market research and data analysis in-house has coincided with the rise of technology simplifying the process. These tools pull through large volumes of data and outline significant information that will help you make the most important decisions.

Step 5: Conduct your research!

This is how you can run your research using Qualtrics CoreXM

• Pre-launch – Here you want to ensure that the survey/ other research methods conform to the project specifications (what you want to achieve/research)
• Soft launch – Collect a small fraction of the total data before you fully launch. This means you can check that everything is working as it should and you can correct any data quality issues.
• Full launch – You’ve done the hard work to get to this point. If you’re using a tool, you can sit back and relax, or if you get curious you can check on the data in your account.
• Review – review your data for any issues or low-quality responses. You may need to remove this in order not to impact the analysis of the data.

A helping hand

If you are missing the skills, capacity or inclination to manage your research internally, Qualtrics Research Services can help. From design, to writing the survey based on your needs, to help with survey programming, to handling the reporting, Research Services acts as an extension of the team and can help wherever necessary.

Secondary market research can be taken from a variety of places. Some data is completely free to access – other information could end up costing hundreds of thousands of dollars. There are three broad categories of secondary research sources:

• Public sources – these sources are accessible to anyone who asks for them. They include census data, market statistics, library catalogs, university libraries and more. Other organizations may also put out free data from time to time with the goal of advancing a cause, or catching people’s attention.
• Internal sources – sometimes the most valuable sources of data already exist somewhere within your organization. Internal sources can be preferable for secondary research on account of their price (free) and unique findings. Since internal sources are not accessible by competitors, using them can provide a distinct competitive advantage.
• Commercial sources – if you have money for it, the easiest way to acquire secondary market research is to simply buy it from private companies. Many organizations exist for the sole purpose of doing market research and can provide reliable, in-depth, industry-specific reports.

No matter where your research is coming from, it is important to ensure that the source is reputable and reliable so you can be confident in the conclusions you draw from it.

How do you know if a source is reliable?

Use established and well-known research publishers, such as the XM Institute , Forrester and McKinsey . Government websites also publish research and this is free of charge. By taking the information directly from the source (rather than a third party) you are minimizing the risk of the data being misinterpreted and the message or insights being acted on out of context.

How to apply secondary research

The purpose and application of secondary research will vary depending on your circumstances. Often, secondary research is used to support primary research and therefore give you greater confidence in your conclusions. However, there may be circumstances that prevent this – such as the timeframe and budget of the project.

Keep an open mind when collecting all the relevant research so that there isn’t any collection bias. Then begin analyzing the conclusions formed to see if any trends start to appear. This will help you to draw a consensus from the secondary research overall.

Market research success is defined by the impact it has on your business’s success. Make sure it’s not discarded or ignored by communicating your findings effectively. Here are some tips on how to do it.

• Less is more – Preface your market research report with executive summaries that highlight your key discoveries and their implications
• Lead with the basic information – Share the top 4-5 recommendations in bullet-point form, rather than requiring your readers to go through pages of analysis and data
• Model the impact – Provide examples and model the impact of any changes you put in place based on your findings
• Show, don’t tell – Add illustrative examples that relate directly to the research findings and emphasize specific points
• Speed is of the essence – Make data available in real-time so it can be rapidly incorporated into strategies and acted upon to maximize value
• Work with experts – Make sure you’ve access to a dedicated team of experts ready to help you design and launch successful projects

Trusted by 8,500 brands for everything from product testing to competitor analysis, Our Strategic Research software is the world’s most powerful and flexible research platform . With over 100 question types and advanced logic, you can build out your surveys and see real-time data you can share across the organization. Plus, you’ll be able to turn data into insights with iQ, our predictive intelligence engine that runs complicated analysis at the click of a button.

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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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.

Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

• Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
• Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

• Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
• Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
• Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

• Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
• Regression Analysis : This method examines the relationship between dependent and independent variables.
• Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

• Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
• Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

• Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

• Experiments : Controlled experiments are designed to test the causality.
• Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

• Statistical Analysis : Includes various statistical tests and measures.
• Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
• Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

• Content Analysis : Systematic coding and categorizing of textual information.
• Thematic Analysis : Identifying themes and patterns within qualitative data.
• Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

• Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

• Clustering : Grouping data points with similar characteristics.
• Association Rule Learning : Identifying interesting relations between variables in large databases.
• Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

• Hadoop and Spark : Frameworks for processing and analyzing large datasets.
• NoSQL Databases : Designed to handle unstructured data.
• Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

• Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
• Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
• Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
• investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
• Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
• Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
• Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
• Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

• Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
• Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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Long COVID or Post-COVID Conditions

Some people who have been infected with the virus that causes COVID-19 can experience long-term effects from their infection, known as Long COVID or Post-COVID Conditions (PCC). Long COVID is broadly defined as signs, symptoms, and conditions that continue or develop after acute COVID-19 infection. This definition  of Long COVID was developed by the Department of Health and Human Services (HHS) in collaboration with CDC and other partners.

People call Long COVID by many names, including Post-COVID Conditions, long-haul COVID, post-acute COVID-19, long-term effects of COVID, and chronic COVID. The term post-acute sequelae of SARS CoV-2 infection (PASC) is also used to refer to a subset of Long COVID.

What You Need to Know

• Long COVID is a real illness and can result in chronic conditions that require comprehensive care. There are resources available .
• Long COVID can include a wide range of ongoing health problems; these conditions can last weeks, months, or years.
• Long COVID occurs more often in people who had severe COVID-19 illness, but anyone who has been infected with the virus that causes COVID-19 can experience it.
• People who are not vaccinated against COVID-19 and become infected may have a higher risk of developing Long COVID compared to people who have been vaccinated.
• People can be reinfected with SARS-CoV-2, the virus that causes COVID-19, multiple times. Each time a person is infected or reinfected with SARS-CoV-2, they have a risk of developing Long COVID.
• While most people with Long COVID have evidence of infection or COVID-19 illness, in some cases, a person with Long COVID may not have tested positive for the virus or known they were infected.
• CDC and partners are working to understand more about who experiences Long COVID and why, including whether groups disproportionately impacted by COVID-19 are at higher risk.

In July 2021, Long COVID was added as a recognized condition that could result in a disability under the Americans with Disabilities Act (ADA). Learn more: Guidance on “Long COVID” as a Disability Under the ADA .

About Long COVID

Long COVID is a wide range of new, returning, or ongoing health problems that people experience after being infected with the virus that causes COVID-19. Most people with COVID-19 get better within a few days to a few weeks after infection, so at least 4 weeks after infection is the start of when Long COVID could first be identified. Anyone who was infected can experience Long COVID. Most people with Long COVID experienced symptoms days after first learning they had COVID-19, but some people who later experienced Long COVID did not know when they got infected.

There is no test that determines if your symptoms or condition is due to COVID-19. Long COVID is not one illness. Your healthcare provider considers a diagnosis of Long COVID based on your health history, including if you had a diagnosis of COVID-19 either by a positive test or by symptoms or exposure, as well as based on a health examination.

Science behind Long COVID

RECOVER: Researching COVID to Enhance Recovery

People with Long COVID may experience many symptoms.

People with Long COVID can have a wide range of symptoms that can last weeks, months, or even years after infection. Sometimes the symptoms can even go away and come back again. For some people, Long COVID can last weeks, months, or years after COVID-19 illness and can sometimes result in disability.

Long COVID may not affect everyone the same way. People with Long COVID may experience health problems from different types and combinations of symptoms that may emerge, persist, resolve, and reemerge over different lengths of time. Though most patients’ symptoms slowly improve with time, speaking with your healthcare provider about the symptoms you are experiencing after having COVID-19 could help determine if you might have Long COVID.

People who experience Long COVID most commonly report:

General symptoms ( Not a Comprehensive List)

• Tiredness or fatigue that interferes with daily life
• Symptoms that get worse after physical or mental effort (also known as “ post-exertional malaise ”)

Respiratory and heart symptoms

• Difficulty breathing or shortness of breath
• Fast-beating or pounding heart (also known as heart palpitations)

Neurological symptoms

• Difficulty thinking or concentrating (sometimes referred to as “brain fog”)
• Sleep problems
• Dizziness when you stand up (lightheadedness)
• Pins-and-needles feelings
• Change in smell or taste
• Depression or anxiety

Digestive symptoms

• Stomach pain

Other symptoms

• Joint or muscle pain
• Changes in menstrual cycles

Symptoms that are hard to explain and manage

Some people with Long COVID have symptoms that are not explained by tests or easy to manage.

People with Long COVID may develop or continue to have symptoms that are hard to explain and manage. Clinical evaluations and results of routine blood tests, chest X-rays, and electrocardiograms may be normal. The symptoms are similar to those reported by people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and other poorly understood chronic illnesses that may occur after other infections. People with these unexplained symptoms may be misunderstood by their healthcare providers, which can result in a delay in diagnosis and receiving the appropriate care or treatment.

Review these tips to help prepare for a healthcare provider appointment for Long COVID.

Health conditions

Some people experience new health conditions after COVID-19 illness.

Some people, especially those who had severe COVID-19, experience multiorgan effects or autoimmune conditions with symptoms lasting weeks, months, or even years after COVID-19 illness. Multi-organ effects can involve many body systems, including the heart, lung, kidney, skin, and brain. As a result of these effects, people who have had COVID-19 may be more likely to develop new health conditions such as diabetes, heart conditions, blood clots, or neurological conditions compared with people who have not had COVID-19.

People experiencing any severe illness may develop health problems

People experiencing any severe illness, hospitalization, or treatment may develop problems such as post-intensive care syndrome (PICS).

PICS refers to the health effects that may begin when a person is in an intensive care unit (ICU), and which may persist after a person returns home. These effects can include muscle weakness, problems with thinking and judgment, and symptoms of post-traumatic stress disorder  (PTSD), a long-term reaction to a very stressful event. While PICS is not specific to infection with SARS-CoV-2, it may occur and contribute to the person’s experience of Long COVID. For people who experience PICS following a COVID-19 diagnosis, it is difficult to determine whether these health problems are caused by a severe illness, the virus itself, or a combination of both.

People More Likely to Develop Long COVID

Some people may be more at risk for developing Long COVID.

Researchers are working to understand which people or groups of people are more likely to have Long COVID, and why. Studies have shown that some groups of people may be affected more by Long COVID. These are examples and not a comprehensive list of people or groups who might be more at risk than other groups for developing Long COVID:

• People who have experienced more severe COVID-19 illness, especially those who were hospitalized or needed intensive care.
• People who had underlying health conditions prior to COVID-19.
• People who did not get a COVID-19 vaccine.

Health Inequities May Affect Populations at Risk for Long COVID

Some people are at increased risk of getting sick from COVID-19 because of where they live or work, or because they can’t get health care. Health inequities may put some people from racial or ethnic minority groups and some people with disabilities at greater risk for developing Long COVID. Scientists are researching some of those factors that may place these communities at higher risk of getting infected or developing Long COVID.

Preventing Long COVID

The best way to prevent Long COVID is to protect yourself and others from becoming infected. For people who are eligible, CDC recommends staying up to date on COVID-19 vaccination , along with improving ventilation, getting tested for COVID-19 if needed, and seeking treatment for COVID-19 if eligible. Additional preventative measures include avoiding close contact with people who have a confirmed or suspected COVID-19 illness and washing hands  or using alcohol-based hand sanitizer.

Research suggests that people who get a COVID-19 infection after vaccination are less likely to report Long COVID, compared to people who are unvaccinated.

CDC, other federal agencies, and non-federal partners are working to identify further measures to lessen a person’s risk of developing Long COVID. Learn more about protecting yourself and others from COVID-19 .

Living with Long COVID

Living with Long COVID can be hard, especially when there are no immediate answers or solutions.

People experiencing Long COVID can seek care from a healthcare provider to come up with a personal medical management plan that can help improve their symptoms and quality of life. Review these tips  to help prepare for a healthcare provider appointment for Long COVID. In addition, there are many support groups being organized that can help patients and their caregivers.

Although Long COVID appears to be less common in children and adolescents than in adults, long-term effects after COVID-19 do occur in children and adolescents .

Talk to your doctor if you think you or your child has Long COVID. Learn more: Tips for Talking to Your Healthcare Provider about Post-COVID Conditions

Data for Long COVID

Studies are in progress to better understand Long COVID and how many people experience them.

CDC is using multiple approaches to estimate how many people experience Long COVID. Each approach can provide a piece of the puzzle to give us a better picture of who is experiencing Long COVID. For example, some studies look for the presence of Long COVID based on self-reported symptoms, while others collect symptoms and conditions recorded in medical records. Some studies focus only on people who have been hospitalized, while others include people who were not hospitalized. The estimates for how many people experience Long COVID can be quite different depending on who was included in the study, as well as how and when the study collected information.  Estimates of the proportion of people who had COVID-19 that go on to experience Long COVID can vary.

CDC posts data on Long COVID and provides analyses, the most recent of which can be found on the U.S. Census Bureau’s Household Pulse Survey .

CDC and other federal agencies, as well as academic institutions and research organizations, are working to learn more about the short- and long-term health effects associated with COVID-19 , who gets them and why.

Scientists are also learning more about how new variants could potentially affect Long COVID. We are still learning to what extent certain groups are at higher risk, and if different groups of people tend to experience different types of Long COVID. CDC has several studies that will help us better understand Long COVID and how healthcare providers can treat or support patients with these long-term effects. CDC will continue to share information with healthcare providers to help them evaluate and manage these conditions.

CDC is working to:

• Better identify the most frequent symptoms and diagnoses experienced by patients with Long COVID.
• Better understand how many people are affected by Long COVID, and how often people who are infected with COVID-19 develop Long COVID
• Better understand risk factors and protective factors, including which groups might be more at risk, and if different groups experience different symptoms.
• Help understand how Long COVID limit or restrict people’s daily activity.
• Help identify groups that have been more affected by Long COVID, lack access to care and treatment for Long COVID, or experience stigma.
• Better understand the role vaccination plays in preventing Long COVID.
• Collaborate with professional medical groups to develop and offer clinical guidance and other educational materials for healthcare providers, patients, and the public.

Related Pages

• Caring for People with Post-COVID Conditions
• Preparing for Appointments for Post-COVID Conditions
• Researching COVID to Enhance Recovery
• Guidance on “Long COVID” as a Disability Under the ADA

For Healthcare Professionals

• Post-COVID Conditions: Healthcare Providers

Search for and find historical COVID-19 pages and files. Please note the content on these pages and files is no longer being updated and may be out of date.

• Visit archive.cdc.gov for a historical snapshot of the COVID-19 website, capturing the end of the Federal Public Health Emergency on June 28, 2023.
• Visit the dynamic COVID-19 collection  to search the COVID-19 website as far back as July 30, 2021.

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biblioverlap : an R package for document matching across bibliographic datasets

• Published: 08 June 2024

Cite this article

• Gabriel Alves Vieira   ORCID: orcid.org/0000-0002-5529-7628 1 &
• Jacqueline Leta   ORCID: orcid.org/0000-0002-3271-7749 1  

Bibliographic databases have long been a cornerstone of scientometrics research, and new information sources have prompted several comparative studies between them. Such studies often employ document-level matching procedures to identify overlaps in the corpus of each database and assess their coverage. However, despite being increasingly relevant in comparative studies, such a type of analysis still lacks an open-source tool to automate it. To fill this gap, we have developed an R package called biblioverlap, which implements a hybrid matching approach using a unique identifier and a selection of ubiquitous bibliographic fields to establish document co-occurrence. It supports data analysis from a broad range of secondary sources and can be used for comparing databases and assessing document overlap in virtually any bibliographic dataset, which can be insightful for various research questions. This paper presents the biblioverlap tool, details the matching procedure’s implementation, and uses an example dataset containing records from the Federal University of Rio de Janeiro to illustrate the package’s built-in functionality.

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Data availability

All the data analyzed has been downloaded from The Lens database and we are compliant with its terms of use ( https://about.lens.org/policies/#acceptableuse ). The data can be accessed as an example dataset named ufrj_bio_0122 after loading the biblioverlap package in R.

Code availability

The source code of the biblioverlap R package is available at https://github.com/gavieira/biblioverlap .

Archambault, É., Campbell, D., Gingras, Y., & Larivière, V. (2009). Comparing bibliometric statistics obtained from the Web of Science and Scopus. Journal of the American Society for Information Science and Technology, 60 (7), 1320–1326. https://doi.org/10.1002/asi.21062

Article   Google Scholar  

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11 (4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Baker, M. (2017). Scientific computing: code alert. Nature . https://doi.org/10.1038/nj7638-563a

Basson, I., Simard, M.-A., Ouangré, Z. A., Sugimoto, C. R., & Larivière, V. (2022). The effect of data sources on the measurement of open access: a comparison of Dimensions and the Web of Science. PLoS One, 17 (3), e0265545. https://doi.org/10.1371/journal.pone.0265545

Bornmann, L., Haunschild, R., & Mutz, R. (2021). Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications . https://doi.org/10.1057/s41599-021-00903-w

Boudry, C., & Chartron, G. (2017). Availability of digital object identifiers in publications archived by PubMed. Scientometrics, 110 (3), 1453–1469. https://doi.org/10.1007/s11192-016-2225-6

Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A comparison between two main academic literature collections: web of science and Scopus databases. Asian Social Science . https://doi.org/10.5539/ass.v9n5p18

Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A & Borges B (2023) shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny

Conway, J. R., Lex, A., & Gehlenborg, N. (2017). UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics, 33 (18), 2938–2940. https://doi.org/10.1093/bioinformatics/btx364

Delgado-Quirós, L., Aguillo, I. F., Martín-Martín, A., López-Cózar, E. D., Orduña-Malea, E., & Ortega, J. L. (2023). Why are these publications missing? Uncovering the reasons behind the exclusion of documents in free-access scholarly databases. Journal of the Association for Information Science and Technology . https://doi.org/10.1002/asi.24839

Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2015). Errors in DOI indexing by bibliometric databases. Scientometrics, 102 (3), 2181–2186. https://doi.org/10.1007/s11192-014-1503-4

Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2016). Empirical analysis and classification of database errors in Scopus and Web of Science. Journal of Informetrics, 10 (4), 933–953. https://doi.org/10.1016/j.joi.2016.07.003

Gagolewski, M. (2011). Bibliometric impact assessment with R and the CITAN package. Journal of Informetrics, 5 (4), 678–692. https://doi.org/10.1016/j.joi.2011.06.006

Gao, C.-H., Yu, G., & Cai, P. (2021). ggVennDiagram: an intuitive, easy-to-use, and highly customizable R package to generate Venn diagram. Frontiers in Genetics . https://doi.org/10.3389/fgene.2021.706907

Gorraiz, J., Melero-Fuentes, D., Gumpenberger, C., & Valderrama-Zurián, J.-C. (2016). Availability of digital object identifiers (DOIs) in Web of Science and Scopus. Journal of Informetrics, 10 (1), 98–109. https://doi.org/10.1016/j.joi.2015.11.008

Grindlay, D. J. C., Brennan, M. L., & Dean, R. S. (2012). Searching the veterinary literature: A Comparison of the coverage of veterinary journals by nine bibliographic databases. Journal of Veterinary Medical Education, 39 (4), 404–412. https://doi.org/10.3138/jvme.1111.109R

Guerrero-Bote, V. P., Chinchilla-Rodríguez, Z., Mendoza, A., & de Moya-Anegón, F. (2021). Comparative analysis of the bibliographic data sources dimensions and scopus: an approach at the country and institutional levels. Frontiers in Research Metrics and Analytics . https://doi.org/10.3389/frma.2020.593494

Gusenbauer, M. (2019). Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases. Scientometrics, 118 (1), 177–214. https://doi.org/10.1007/s11192-018-2958-5

Gusenbauer, M. (2022). Search where you will find most: Comparing the disciplinary coverage of 56 bibliographic databases. Scientometrics, 127 (5), 2683–2745. https://doi.org/10.1007/s11192-022-04289-7

Hackenberger, B. K. (2020). R software: Unfriendly but probably the best. Croatian Medical Journal, 61 (1), 66–68. https://doi.org/10.3325/cmj.2020.61.66

Huang, C.-K., Neylon, C., Brookes-Kenworthy, C., Hosking, R., Montgomery, L., Wilson, K., & Ozaygen, A. (2020). Comparison of bibliographic data sources: Implications for the robustness of university rankings. Quantitative Science Studies, 1 (2), 445–478. https://doi.org/10.1162/qss_a_00031

Kokol, P., Blažun Vošner, H., & Završnik, J. (2021). Application of bibliometrics in medicine: A historical bibliometrics analysis. Health Information & Libraries Journal, 38 (2), 125–138. https://doi.org/10.1111/hir.12295

Lai, J., Lortie, C. J., Muenchen, R. A., Yang, J., & Ma, K. (2019). Evaluating the popularity of R in ecology. Ecosphere, 10 (1), e02567. https://doi.org/10.1002/ecs2.2567

Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014). UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics, 20 (12), 1983–1992. https://doi.org/10.1109/TVCG.2014.2346248

Liang, Z., Mao, J., Lu, K., & Li, G. (2021). Finding citations for PubMed: A large-scale comparison between five freely available bibliographic data sources. Scientometrics, 126 (12), 9519–9542. https://doi.org/10.1007/s11192-021-04191-8

López-Illescas, C., de Moya-Anegón, F., & Moed, H. F. (2008). Coverage and citation impact of oncological journals in the Web of Science and Scopus. Journal of Informetrics, 2 (4), 304–316. https://doi.org/10.1016/j.joi.2008.08.001

Madhan, M., Chandrasekar, G., & Arunachalam, S. (2010). Highly cited papers from India and China. Current Science, 99 (6), 738–749.

Google Scholar  

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google scholar, microsoft academic, Scopus, dimensions, web of science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics, 126 (1), 871–906. https://doi.org/10.1007/s11192-020-03690-4

Masuadi, E., Mohamud, M., Almutairi, M., Alsunaidi, A., Alswayed, A. K., & Aldhafeeri, O. F. (2021). Trends in the usage of statistical software and their associated study designs in health sciences research: a bibliometric analysis. Cureus . https://doi.org/10.7759/cureus.12639

Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106 (1), 213–228. https://doi.org/10.1007/s11192-015-1765-5

Purnell, P. J. (2022). The prevalence and impact of university affiliation discrepancies between four bibliographic databases—Scopus, Web of Science, Dimensions, and Microsoft Academic. Quantitative Science Studies, 3 (1), 99–121. https://doi.org/10.1162/qss_a_00175

R Core Team. (2023). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing. https://www.R-project.org/

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126 (6), 5113–5142. https://doi.org/10.1007/s11192-021-03948-5

Uddin, A., Bhoosreddy, J., Tiwari, M., & Singh, V. K. (2016). A Sciento-text framework to characterize research strength of institutions at fine-grained thematic area level. Scientometrics, 106 (3), 1135–1150. https://doi.org/10.1007/s11192-016-1836-2

Valderrama-Zurián, J.-C., Aguilar-Moya, R., Melero-Fuentes, D., & Aleixandre-Benavent, R. (2015). A systematic analysis of duplicate records in Scopus. Journal of Informetrics, 9 (3), 570–576. https://doi.org/10.1016/j.joi.2015.05.002

van der Loo, M. P. J. (2014). The stringdist package for approximate string matching. The R Journal, 6 (1), 111–122.

Vieira, E. S., & Gomes, J. A. N. F. (2009). A comparison of Scopus and Web of Science for a typical university. Scientometrics, 81 (2), 587–600. https://doi.org/10.1007/s11192-009-2178-0

Visser, M., van Eck, N. J., & Waltman, L. (2021). Large-scale comparison of bibliographic data sources: Scopus, web of science, dimensions, crossref, and microsoft academic. Quantitative Science Studies, 2 (1), 20–41. https://doi.org/10.1162/qss_a_00112

Wallace, B. C., Dahabreh, I. J., Trikalinos, T. A., Lau, J., Trow, P., & Schmid, C. H. (2012). Closing the gap between methodologists and end-users: R as a computational back-end. Journal of Statistical Software, 49 , 1–15. https://doi.org/10.1863/jss.v049.i05

Waltman, L., & Larivière, V. (2020). Special issue on bibliographic data sources. Quantitative Science Studies, 1 (1), 360–362. https://doi.org/10.1162/qss_e_00026

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (2nd ed. 2016). Springer International Publishing : Imprint: Springer. https://doi.org/10.1007/978-3-319-24277-4

Yeung, A. W. K., Goto, T. K., & Leung, W. K. (2017). At the leading front of neuroscience: a bibliometric study of the 100 most-cited articles. Frontiers in Human Neuroscience . https://doi.org/10.3389/fnhum.2017.00363

Zhu, J., Hu, G., & Liu, W. (2019). DOI errors and possible solutions for Web of Science. Scientometrics, 118 (2), 709–718. https://doi.org/10.1007/s11192-018-2980-7

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We are grateful to Carolina Dias for her comprehensive revision of the manuscript, which has made it easier to understand and hopefully will allow us to reach a broader audience.

G.A.V. received financial support from CNPq through doctoral scholarship and J.L. coordinates a research project with CNPq funding (project n. 434.146/2018-8).

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• Disaster recovery planning and management

Downtime can do serious damage to an organization's bottom line and reputation. Business continuity and disaster recovery -- two closely related practices -- help keep an organization running even in the wake of disaster. This guide explains how BCDR works, why you need it and how to build a BCDR plan for your organization to protect it today and into the future.

Disaster recovery (dr).

• Kinza Yasar, Technical Writer
• Erin Sullivan, Senior Site Editor
• Paul Crocetti, Executive Editor

What is disaster recovery (DR)?

Disaster recovery (DR) is an organization's ability to respond to and recover from an event that negatively affects business operations.

The goal of DR is to reduce downtime, data loss and operational disruptions while maintaining business continuity by restoring critical applications and infrastructure ideally within minutes after an outage. To prepare for this, organizations often perform an in-depth analysis of their systems and IT infrastructure and create a formal document to follow in times of crisis. This document is known as a disaster recovery plan .

What is a disaster?

The practice of DR revolves around serious events. These events are often thought of in terms of natural disasters, but they can also be caused by systems or technical failures, human errors or intentional attacks. These events are significant enough to disrupt or completely stop critical systems and business operations for a period of time. Types of disasters include the following:

• Cyberattacks, such as malware, distributed denial-of-service and ransomware .
• Power outages.
• Hardware failures.
• Equipment failures.
• Epidemics or pandemics, such as COVID-19.
• Terrorist attacks or biochemical threats.
• Industrial accidents.
• Hurricanes.
• Earthquakes.

Why is disaster recovery important?

Disasters can inflict damage with varying levels of severity, depending on the scenario. A brief network outage could result in frustrated customers and some loss of business to an e-commerce system. A hurricane or tornado could destroy an entire manufacturing facility, data center or office.

Also, the shift to public, private, hybrid and multi-cloud systems and the rise of remote workforces are making IT infrastructures more complex and potentially risky. An effective disaster recovery plan lets organizations respond promptly to disruptive events, offering the following benefits in return:

This article is part of

What is BCDR? Business continuity and disaster recovery guide

• Which also includes:
• 7 top business continuity certifications to consider in 2024
• ITGC audit checklist: 6 controls you need to address
• 12 key points a disaster recovery plan checklist must include
• Business continuity. Disasters can significantly harm business operations, incurring costs and disrupting productivity. A DR plan enables automation and the swift restart of backup systems and data, ensuring a prompt resumption of scheduled operations.
• Data loss reduction. A well-designed disaster recovery plan aims to reduce the amount of data lost by using methods such as frequent backups, quick recovery and redundancy checks. The probability of data loss increases with the length of time an organization experiences a system outage, but effective DR planning reduces this risk.
• Cost reduction. The monetary costs of disasters and outages can be significant. According to results from Uptime Institute's "Annual outage analysis 2023" survey , 25% of respondents reported in 2022 that their latest outage incurred more than $1 million in direct and indirect costs, indicating a consistent upward trend in expenses. In addition, 45% reported that the cost of their most recent outage ranged between $100,000 and $1 million. With disaster recovery procedures in place, companies can get back on their feet quickly after outages, reducing recovery and operational costs.
• Help with compliance regulations. Many businesses are required to create and follow plans for disaster recovery, business continuity and data protection to meet compliance regulations. This is particularly important for organizations operating in the financial, healthcare, manufacturing and government sectors. Failure to have DR procedures in place can result in legal or regulatory penalties, so understanding how to comply with resilience standards is important.
• System security. A business can reduce the detrimental effects of ransomware, malware and other security threats by incorporating data protection, backup and restoration procedures into a disaster recovery plan. For instance, several built-in security mechanisms in cloud data backups can minimize questionable activity before it affects the company.
• Improved customer retention. When a disaster strikes, customer confidence in an organization's security and services can be questioned and easily lost. A solid disaster recovery plan, including employee training for handling inquiries, can boost customer assurance by demonstrating that the company is prepared for any disaster.
• Emergency preparedness. Thinking about disasters before they happen and creating a response plan can provide many benefits. It raises awareness about potential disruptions and helps an organization prioritize its mission-critical functions. It also provides a forum for discussing these topics and making careful decisions about how to best respond in a low-pressure setting. While preparing for every potential disaster might seem extreme, the COVID-19 pandemic illustrated that even scenarios that seem farfetched can happen. For example, businesses with emergency measures to support remote work had a clear advantage over unprepared companies when stay-at-home orders were enacted during the pandemic.

DR initiatives are more attainable by businesses of all sizes today due to widespread cloud adoption and the high availability of virtualization technologies that make backup and replication easier. However, much of the terminology and best practices developed for DR were based on enterprise efforts to re-create large-scale physical data centers. This involved plans to transfer, or failover , workloads from a primary data center to a secondary location or DR site to restore data and operations.

What is the difference between disaster recovery and business continuity?

On a practical level, DR and business continuity are often combined into a single corporate initiative and even abbreviated together as BCDR , but they aren't the same thing. While the two disciplines have similar goals relating to an organization's resilience, they differ greatly in scope.

Key points of DR and business continuity include the following:

• BC is a proactive discipline intended to minimize risk and help ensure the business can continue to deliver its products and services no matter the circumstances. It focuses especially on how employees continue to work and how the business continues operations while a disaster is occurring.
• DR is a subset of business continuity that focuses on the IT systems that enable business functions. It addresses the specific steps an organization must take to recover and resume technology operations following an event.
• BC is also closely related to business resilience , crisis management and risk management, but each of these disciplines has different goals and parameters.
• DR measures could typically include developing extra safety precautions for employees, such as buying emergency supplies or holding fire drills.
• A business continuity plan helps guarantee that communication channels, including phones and network servers, stay operational during a disaster.
• DR is also a reactive process by nature. While planning for it must be done in advance, DR activity isn't kicked off until a disaster actually occurs.
• Business continuity ensures the overall functioning and resilience of an organization throughout the entirety of an event, rather than solely focusing on the immediate aftermath.
• The disaster recovery process is complete once systems fail over to backup systems and are finally restored. With business continuity, plans stay in place for the entirety of the event and even after the systems are back up following the disaster.
• Top of Form

Elements of a disaster recovery strategy

Organizations should consider several factors while developing a disaster recovery strategy. Common elements of a DR strategy include the following:

Risk analysis

Risk analysis, or risk assessment , is an evaluation of all the potential risks the business could face, as well as their outcomes. Risks can vary greatly depending on the industry the organization is in and its geographic location. The assessment should identify potential hazards, determine whom or what these hazards would harm, and use the findings to create procedures that take these risks into account.

Business impact analysis

A business impact analysis ( BIA ) evaluates the effects of the identified risks on business operations. A BIA can help predict and quantify costs, both financial and nonfinancial. It also examines the effects of different disasters on an organization's safety, finances, marketing, business reputation, legal compliance and quality assurance.

Understanding the difference between risk analysis and BIA and conducting the assessments can also help an organization define its goals when it comes to data protection and the need for backup. Organizations generally quantify these using measurements called recovery point objective ( RPO ) and recovery time objective ( RTO ).

• RPO. RPO is the maximum age of files that an organization must recover from backup storage for normal operations to resume after a disaster. The RPO determines the minimum frequency of backups. For example, if an organization has an RPO of four hours, the system must back up at least every four hours.
• RTO. RTO refers to the amount of time an organization estimates its systems can be down without causing significant or irreparable damage to the business. In some cases, applications can be down for several days without severe consequences. In others, seconds can do substantial harm to the business.

RPO and RTO are both important elements in disaster recovery, but the metrics have different uses. RPO is acted on before a disruptive event takes place to ensure data is backed up, while RTO comes into play after an event occurs.

Incident response

This encompasses detecting, containing, analyzing and resolving a disruptive event. Incident response includes activating the disaster recovery plan, evaluating the incident's scope and effect, executing the recovery strategy, restoring normal operations and deactivating the plan. To maintain accountability and promote ongoing improvement, it's also essential to record and report incident response actions and results.

The components of a DR strategy can vary depending on the size, industry and particular demands of an organization. Therefore, these plans should be customized to meet the unique requirements of each business.

What's in a disaster recovery plan?

Once an organization has thoroughly reviewed its risk factors , recovery goals and technology environment, it can write a disaster recovery plan. The DR plan is the formal document that specifies these elements and outlines how the organization will respond when disruption or disaster occurs. The plan details recovery goals including RTO and RPO, as well as the steps the organization will take to minimize the effects of the disaster.

A DR plan should include the following components:

• A DR policy statement, plan overview and main goals of the plan.
• Key personnel and DR team contact information.
• A risk assessment and BIA to identify potential threats, vulnerabilities and negative effects on business.
• An updated IT inventory that includes details on hardware, software assets and essential cloud computing services, specifying their business-critical status and ownership, such as owned, leased or utilized as a service.
• A plan outlining how backups will be carried out along with an RPO that states the frequency of backups and an RTO that defines the maximum downtime that's acceptable after a disaster.
• A step-by-step description of disaster response actions immediately following an incident.
• A diagram of the entire network and recovery site.
• Directions for how to get to the recovery site.
• A list of software and systems that staff will use in the recovery.
• Sample templates for a variety of technology recoveries, including technical documentation from vendors.
• A communication that includes internal and external contacts, as well as a boilerplate for dealing with the media.
• A summary of insurance coverage.
• Proposed actions for dealing with financial and legal issues.

An organization should consider its DR plan a living document. It should schedule regular disaster recovery testing to ensure the plan is accurate and will work when a recovery is required. The plan should also be evaluated against consistent criteria whenever there are changes in the business or IT systems that could affect disaster recovery.

How to build a disaster recovery team

A DR team is entrusted with creating, documenting and carrying out processes and procedures for an organization's data recovery and business continuity in the event of a disaster or failure.

The key steps and considerations for building a disaster recovery team include the following:

• Identify the key stakeholders. Determine who within the organization should be involved in the disaster recovery planning process. A DR team typically includes cross-departmental employees and executives, such as the chief information officer , IT personnel, department heads, business continuity experts, impact assessment and recovery advisors and crisis management coordinators.
• Define roles and responsibilities. Once the members of the DR team are determined, the next step is to assign them specific roles and responsibilities to ensure effective management of the recovery process. Common roles include team leaders, IT experts, business continuity experts, disaster recovery coordinators and department liaisons.
• Assess expertise. If the organization lacks internal expertise, it can outsource or engage a service provider. These providers can offer external expertise to aid the team, deliver disaster recovery as a service ( DRaaS ), or provide consulting services to bolster the capabilities of the internal team.
• Develop a recovery plan. The team should outline a detailed disaster recovery plan that outlines procedures for responding to various types of disasters. This plan should include steps for data backup and recovery, system restoration, communication protocols and employee safety procedures.
• Train team members. It's important to teach and train team members on their responsibilities within the disaster recovery strategy. This could entail doing frequent drills and simulations to evaluate the plan's efficacy and pinpointing areas in need of development. For example, this could include testing all apps and finding ways to access the critical ones in the event of a disaster.
• Regularly revise the DR plan. The disaster recovery plan needs to be reviewed and updated regularly to reflect organizational changes and how they affect the recovery process.
• Document the procedures. All procedures and protocols within the DR plan should be documented in a clear and accessible format. This ensures that team members can easily reference and follow the necessary steps during a crisis.

Disaster recovery sites

An organization uses a DR site to recover and restore its data, technology infrastructure and operations when its primary data center is unavailable. DR sites can be internal, external or cloud-based.

An organization sets up and maintains an internal DR site. Organizations with large information requirements and aggressive RTOs are more likely to use an internal DR site, which is typically a second data center. When building an internal site, the business must consider hardware configuration, supporting equipment, power maintenance, heating and cooling of the site, layout design, location and staff.

An external disaster recovery site is owned and operated by a third-party provider. External sites can be hot, warm or cold.

• Hot site. A hot site is a fully functional data center with hardware and software, personnel and customer data, which is typically staffed 24/7 and operationally ready in the event of a disaster.
• Warm site. A warm site is an equipped data center that doesn't have customer data. An organization can install additional equipment and introduce customer data following a disaster.
• Cold site. This type of site has infrastructure to support IT systems and data, but no technology until an organization activates DR plans and installs equipment. These sites are sometimes used to supplement hot and warm sites during a long-term disaster.

A cloud-based disaster recovery site is another option, which is also scalable. An organization should consider site proximity, internal and external resources, operational risks, service-level agreements (SLAs) and cost when contracting with cloud providers to host their DR assets or outsourcing additional services .

Disaster recovery tiers

In addition to choosing the most appropriate DR site, it can be helpful for organizations to consult the tiers of disaster recovery identified by the Share Technical Steering Committee and IBM in the 1980s. The tiers feature a variety of recovery options organizations can use as a blueprint to help determine the best DR approach depending on their business needs.

The recognized disaster recovery tiers include the following:

• Tier 7. Tier 7 is a highly advanced level of disaster recovery capability. At this level, artificial intelligence and automation are likely to play a key part in the recovery process.
• Tier 6. Tier 6 disaster recovery capabilities are comparable to Tier 5's, but they often include even more sophisticated technology and techniques for rapid recovery and minimal data loss.
• Tier 5. Tier 5 often implies advanced disaster recovery capabilities beyond a hot site. This can include capabilities such as real-time data replication , automated failover and enhanced monitoring and administration tools.
• Tier 4. This tier includes a hot site, which is a DR site that's fully functioning and ready to use. Hot sites replicate the primary data center's systems and operations in real time, enabling quick failover and minimal downtime. They provide the maximum availability and recovery speed, but they're also the most expensive alternative.
• Tier 3. By electronically vaulting mission-critical data, Tier 3 options improve upon the capabilities of Tier 2. Electronic vaulting of data involves electronically transferring data to a backup site, in contrast to the traditional method of physically shipping backup tapes or disks. After a disaster, there's less chance of data loss or re-creation because the electronically vaulted data is usually more recent than data sent through conventional means.
• Tier 2. This tier improves upon Tier 1 with the addition of a hot site, which are disaster recovery locations that have hardware and network infrastructure already set up to facilitate faster recovery times. There might still be a need for additional setup and configuration.
• Tier 1. This level consists of cold sites that provide basic infrastructure but lack preinstalled systems. Businesses in this category have data backups, but recovery involves manual intervention and hardware configuration, which lengthens recovery times.
• Tier 0. This tier denotes the lowest preparedness level and is usually associated with organizations that don't have disaster recovery or off-site data backups . Because recovery in this tier is entirely dependent on on-site technologies, recovery times can be unpredictable.

Another type of DR tiering involves assigning levels of importance to different types of data and applications and treating each tier differently based on the tolerance for data loss. This approach recognizes that some mission-critical functions might not be able to tolerate any data loss or downtime, while others can be offline for longer or have smaller sets of data restored.

Types of disaster recovery

In addition to choosing a DR site and considering DR tiers, IT and business leaders must evaluate the best way to put their DR plan into action. This will depend on the IT environment and the technology the business chooses to support its DR strategy.

Types of disaster recovery can vary, based on the IT infrastructure and assets that need protection, as well as the method of backup and recovery the organization decides to use. Depending on the size and scope of the organization, it might have separate DR plans and response and resilience teams specific to different departments.

Major types of DR include the following:

• Data center disaster recovery. Organizations that house their own data centers must have a DR strategy that considers all the IT infrastructure within the data center as well as the physical facility. Backup to a failover site at a secondary data center or a colocation facility is often a large part of the plan. IT and business leaders should also document and make alternative arrangements for a wide range of facilities-related components, including power systems, heating and cooling, fire safety, and physical security.
• Network disaster recovery. Network connectivity is essential for internal and external communication, data sharing, and application access during a disaster. A network DR strategy must provide a plan for restoring network services, especially in terms of access to backup sites and data.
• Virtualized disaster recovery. Virtualization provides disaster recovery by letting organizations replicate workloads in an alternate location or the cloud. The benefits of virtual DR include flexibility, ease of deployment, efficiency and speed. Since virtualized workloads have a small IT footprint, replication can be done frequently, and failover can be initiated quickly.
• Cloud disaster recovery. The widespread acceptance of cloud services lets organizations, typically reliant on alternate or on-premises DR locations, host their disaster recovery in the cloud. Cloud DR goes beyond simple backup to the cloud. It requires an IT team to set up automatic failover of workloads to a public cloud platform in the event of a disruption.
• DRaaS. DRaaS is the commercially available version of cloud DR. In DRaaS, a third party provides replication and hosting of an organization's physical and virtual machines. The provider assumes responsibility for deploying the DR plan when a crisis arises, based on an SLA. In the event of a disaster, the DRaaS provider shifts an organization's computer processing to its cloud infrastructure. This enables uninterrupted business operations to be carried out seamlessly from the provider's location, even if the organization's servers are offline.
• Point-in-time snapshots. Point-in-time snapshots or copies generate a precise replica of the database at a specific time. Data recovery from these backups is possible, provided they're stored offsite or on an external machine unaffected by the catastrophe.

Disaster recovery services and vendors

Disaster recovery providers can take many forms, as DR is more than just an IT issue, and business continuity affects the entire organization. DR vendors include those selling backup and recovery software, as well as those offering hosted or managed services. Because disaster recovery is also an element of organizational risk management, some vendors couple it with other aspects of security planning, such as incident response and emergency planning.

Examples of options for DR services and vendors include the following:

• Backup and data protection platforms.
• DRaaS providers.
• Add-on services from data center and colocation providers.
• Infrastructure-as-a-service providers.

Choosing the best option for an organization ultimately depends on top-level business continuity plans and data protection goals, as well as which option best meets those needs and budgetary goals.

Examples of DR software and DRaaS providers include the following:

• Acronis Cyber Protect Cloud.
• Carbonite Disaster Recovery.
• Dell EMC RecoverPoint.
• Druva Data Resiliency Cloud.
• IBM Storage Protect Plus.
• Microsoft Azure Site Recovery.
• Unitrends Backup and Recovery.
• Veeam Backup & Replication.
• VMware Live Cyber Recovery (formerly known as VMware Cloud DR).

Emergency communication vendors are also a key part of the disaster recovery process, as they help keep employees informed during a crisis by sending them notifications and communications. Examples of vendors and their systems include AlertMedia, BlackBerry AtHoc, Cisco Emergency Responder, Everbridge Crisis Management and Rave Alert.

Download a free SLA template for use with disaster recovery products and services .

While some organizations might find it challenging to invest in comprehensive disaster recovery planning, none can afford to ignore the concept when planning for long-term growth and sustainability. In addition, if the worst were to happen, organizations that have prioritized DR would experience less downtime and be able to resume normal operations faster.

Businesses often prepare for minor disruptions, but it's easy to overlook larger and more intricate disasters. Examine the top scenarios for IT disasters that disaster recovery teams should test vigorously.

Continue Reading About disaster recovery (DR)

• SME disaster recovery: Key points to consider
• Game-changing disaster recovery trends
• Maximize the benefits of virtual disaster recovery
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Unveiling the dynamics of educational equity: exploring the third type of digital divide for primary and secondary schools in china.

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Wang, P.; Li, Z.; Wang, Y.; Wang, F. Unveiling the Dynamics of Educational Equity: Exploring the Third Type of Digital Divide for Primary and Secondary Schools in China. Sustainability 2024 , 16 , 4868. https://doi.org/10.3390/su16114868

Wang P, Li Z, Wang Y, Wang F. Unveiling the Dynamics of Educational Equity: Exploring the Third Type of Digital Divide for Primary and Secondary Schools in China. Sustainability . 2024; 16(11):4868. https://doi.org/10.3390/su16114868

Wang, Ping, Zhiyuan Li, Yujing Wang, and Feiye Wang. 2024. "Unveiling the Dynamics of Educational Equity: Exploring the Third Type of Digital Divide for Primary and Secondary Schools in China" Sustainability 16, no. 11: 4868. https://doi.org/10.3390/su16114868

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