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Primary vs Secondary Research: Differences, Methods, Sources, and More

Table of Contents

Primary vs Secondary Research – What’s the Difference?

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

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

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

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

When to Use Primary and Secondary Research

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

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

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

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

Primary vs Secondary Research Methods

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

Primary Research Methods:

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

Secondary Research Methods:

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

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

Primary vs Secondary Sources in Research

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

Primary Research Data Sources

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

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

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

Secondary Research Data Sources

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

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

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

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

Examples of Primary and Secondary Research in Marketing

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

Example 1: New Product Development

Primary Research: Direct Consumer Feedback through Surveys and Focus Groups

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

Secondary Research: Market Analysis Reports

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

Example 2: Brand Positioning

Primary Research: Brand Perception Analysis through Surveys

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

Secondary Research: Social Media Sentiment Analysis

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

Example 3: Market Expansion Strategy

Primary Research: Consumer Demand Studies in New Markets

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

Secondary Research: Economic and Demographic Analysis

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

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

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

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

Navigating the Pros and Cons

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

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• What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

• The government
• Government agencies
• Non-governmental organizations
• Educational institutions
• Businesses or consultancies
• Libraries or archives
• Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

• Secondary data is very easy to source and readily available .
• It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
• As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
• Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

• Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
• Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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Research bias

• Rosenthal effect
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• Selection bias
• Negativity bias
• Status quo bias

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/secondary-research/

Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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Primary Research vs Secondary Research: A Comparative Analysis

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

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

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

What is Primary Research?

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

Types of Primary Research

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

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

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

Observations

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

Data analysis

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

Focus groups

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

Benefits of Primary Research

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

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

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

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

Challenges of Primary Research

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

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

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

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

What is Secondary Research?

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

Sources of Secondary Research

Published studies and academic journals.

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

Reports and white papers

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

Books and reference materials

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

Online databases

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

Benefits of Secondary Research

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

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

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

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

Challenges of Secondary Research

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

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

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

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

Comparing Primary and Secondary Research

Primary research vs secondary research.

Examples of Primary and Secondary Research

Examples of primary research.

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

Examples of Secondary Research

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

How to Use Primary and Secondary Research Together

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

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

Ethical Considerations for Primary and Secondary Research

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

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

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• v.10(Suppl 2); 2020

Acquiring data in medical research: A research primer for low- and middle-income countries

Vicken totten.

a Kaweah Delta Health Care District (KDHCD), KDHCD Department of Emergency Medicine, Visalia, CA, USA

Erin L. Simon

b Cleveland Clinic Akron General, Department of Emergency Medicine, Akron, OH, USA

Mohammad Jalili

c Department of Emergency Medicine, Tehran University of Medical Sciences, Tehran, Iran

Hendry R. Sawe

d Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania

Without data, there is no new knowledge generated. There may be interesting speculation, new paradigms or theories, but without data gathered from the universe, as representative of the truth in the universe as possible, there will be no new knowledge. Therefore, it is important to become excellent at collecting, collating and correctly interpreting data. Pre-existing and new data sources are discussed; variables are discussed, and sampling methods are covered. The importance of a detailed protocol and research manual are emphasized. Data collectors and data collection forms, both electronic and paper-based are discussed. Ensuring subject privacy while also ensuring appropriate data retention must be balanced.

African relevance

• • To get good quality information you first need good quality data
• • Data collection systematically and reproducibly gathers and measures variables to answer research questions.
• • Good data is a result of a well thought out study protocol

The International Federation for Emergency Medicine global health research primer

This paper forms part 9 of a series of ‘how to’ papers, commissioned by the International Federation for Emergency Medicine. It describes data sources, variables, sampling methods, data collection and the value of a clear data protocol. We have also included additional tips and pitfalls that are relevant to emergency medicine researchers.

Data collection is the process of systematically and reproducibly gathering and measuring variables in order to answer research questions, test hypotheses, or evaluate outcomes.

Data is not information. To get good quality information you first need good quality data, then you must curate, analyse and interpret it. Data is comprised of variables. Data collection begins with determining which variables are required, followed by the selection of a sample from a certain population. After that, a data collection tool is used to collect the variables from the selected sample, which is then converted into a data spreadsheet or database. The analysis is done on the database.

Sometimes you gather data yourself. Sometimes you analyse data others collected for different purposes. Ideally, you collect a universal sample, that is, 100%. In real life, you get a limited sample. Preferably, it will be a truly random sample with enough power to answer your question. Unfortunately, you may have to settle for consecutive or convenience sampling. Ideally, your data collectors would be blinded to the outcome of interest, to prevent bias. However, real life is full of biases. Imperfect data may be better than no data; you can often get useful information from imperfect data. Remember the enemy of good is perfect.

Why is good data important?

Acquiring data is the most important step in a research study. The best design with bad data is useless. Bad design produces bad data. The most sophisticated analysis cannot be performed without data; analysing bad data produces erroneous results. Analysis can never be better than the quality of the data on which it was run. Good data has integrity. Data integrity is paramount to learning “Truth in the Universe”. Good data is as complete and as clean, as you can reasonably make it. Clean data ‘has integrity’ when the variables access as much relevant information as possible, and in the same way for each subject.

Some information is very hard to get. You may have to use proxy variables for what you really want to know. A proxy variable is a variable that is not in itself directly relevant, but that serves in place of an unobservable or immeasurable variable. In order for a variable to be a good proxy, it must have a close correlation, not necessarily linear, with the variable of interest. One example for the variable of a specific illness might be a medication list.

Consequences of bad data include an inability to answer the research question; inability to replicate or validate the study; distorted findings and wasted resources; compromised knowledge and even harm to subjects.

Ensure data quality

Good data is a result of a well-thought-out study protocol, which is the written plan for the study. Good planning is the most cost-effective way to ensure data integrity. Good planning is documented by a thorough and detailed protocol, with a comprehensive procedures manual. Poorly written manuals risk incomplete or inconsistent collection of data, in other words, ‘bad data’. The manual should include rigorous, step-by-step instructions on how to administer tests or collect the data. It should cover the ‘who’ (the subject and the researcher); the ‘when’ (the timing), the ‘how’ (methods), and the ‘what’ (a complete listing of variables to be collected). There should also be an identified mechanism to document any changes in procedures that may evolve over the course of the investigation. The study design should be reproducible: so that the protocol can be followed by any other researcher. All data needs to be gathered in the same way. Test (trial-run) your manual before you start your study. If data is collected by several people, make sure there is a sufficient degree of inter-rater reliability.

To get good data, your sample needs to be representative of the population. For others to apply your results, you need to characterize your population, so others can decide if your conclusions are relevant to their population (see Sampling section, below).

Data integrity demands you supervise your study, making sure it is complete and accurate. You may wish to do interim analyses. Keep copies! Keep both the raw data and the data sheets, for the length of time required by law or by Good Research Practice in your country. This will protect you from accusations of falsification of data.

In real life, you may have to deal with any number of sampling and data collection biases. Some of these biases can be measured statistically. Regardless, all the limitations you can think of should be written in your limitations section. The best design you can practically use gives you the best data you can reasonably get. Remember, “you cannot fix with statistics what you fouled up by design.”

Before you acquire your first datum, consider: Do you have a developed protocol and a research manual? Have you sought Ethics Board approval? Do you have an informed consent? Do you have a plan to protect the subject's confidentiality? Do you have a plan for data analysis? Where will you safely store and protect the data? If you have collaborators, have you established, in writing, who owns the data, and who has the right to analyse and publish it?

Types of data: qualitative vs. quantitative data

Numerical data is generally called quantitative; if in words or sentences, it is qualitative. Medical research historically has focused on quantitative methods. Generally, quantitative research is cheaper, easier to gather and easier to analyse. For purposes of this chapter, we will focus on quantitative research.

Qualitative research is about words, sentences, sounds, feeling, emotions, colours and other elements that are non-quantifiable. It requires human intellect to extract themes from the sentences, evaluate the fit of the data to the themes, and to draw the implications of the themes. Primary sources for qualitative data include open ended surveys, interviews, and public meetings. Qualitative research is more common in politics and the social sciences, and will not be further discussed here, except to refer you to other sources.

Quantitative research can include questionnaires with closed-ended questions (open ended questions belong in qualitative research). The data is transformed into numbers and will be analysed with parametric and non-parametric statistical tests. In general, you will derive a mean, mode and median; you will calculate probabilities, make correlation and regressions in order to draw conclusions.

Sources of data: primary vs secondary data

To answer a research question, there are many potential sources of data. Two main categories are primary data and secondary data. Primary data is newly collected data; it can be gathered directly from people's responses (surveys), or from their biometrics (blood pressure, weight, blood tests, etc.). It is still considered primary data if you gather data that was collected for other (medical) purposes by extracting the data from medical records. Medical records can be a rich source of data, but data extraction by hand takes a lot of time.

Secondary data already exists; it has already been published or complied. There are extant local, regional, national and international databases such as Trauma Registries, Disease-specific Registries, Public Health Data, government statistics, and World Health Organization data. Locally, your hospital or clinic may already keep statistics on any number of topics. Combining information from disparate databases may sometimes yield interesting results. For example, in the US, the Centers for Disease Control and Prevention keeps databases of reportable diseases, accidents, causes of death and much more. The US Geographic Survey reports the average elevation of American cities. Combining the two databases revealed that, even when gun ownership, drug and alcohol use were statistically controlled for, there was a linear correlation between altitude and suicide rates [ 2 ]. Reno et al., reviewed the existing medical literature (also secondary data), and confirmed the correlation and concluded that the mechanisms have yet to be elucidated [ 3 ].

Collecting good data is often the hardest part of research. Ideally, you would want to collect 100% of the data (universal sampling to reflect target population). One example would be ‘all elderly persons with gout’. In real life, you have access to only a subset of the target population (the accessible population). Further, in your study you will be limited to a subset of the accessible population (the study population). Again, in the ideal world, that limited sample would be truly random, and have enough power to answer your question. You can find free random number generators online. In real life, you may have to settle for consecutive or convenience sampling. Of the two, consecutive sampling has less bias. Sometimes it is important to balance your groups. You may have 2 or 3 treatments (or interventions) and want to have an equal number of each kind. So, you create blocks — of a few times the number of treatments. You randomized within the block. Each time a block is filled, you are assured that you have the right balance of subjects. Blocks are often in groups of six, eight or 12. This is called balanced allocation .

If you must get only a convenience sample – for example because you only have a single data gatherer and can get data only when that person is available – you should, at a minimum, try to get some simple demographics from times when the data gatherer is not available, to see if subjects at that other time are systematically different. For example, if you are looking at injuries, people who are injured when drinking on a Friday night might be systematically different from people who are injured on their way to work on a Monday morning. If you can only collect injury data in the morning, your results will be biased.

Variables are the bits of data you collect. They change from subject to subject and describe the subject numerically. Age (or year of birth); gender; ethnic group or tribe; and geographic location are commonly called simple demographic variables and should be collected and reported for most populations.

Continuous variables are quantified on a continuous scale, such as body weight. Discrete variables use a scale whose units are limited to integers (such as the number of cigarettes smoked per day). Discrete variables have a number of possible values and can resemble continuous variables in statistical analysis and be equivalent for the purpose of designing measurements. A good general rule is to prefer continuous variables because the information they contain provides additional information and improves statistical efficiency (more study power and smaller sample size).

Categorical variables are those not suitable for quantification. They are often measured by classifying them into categories. If there are two possible values (dead or alive), they are dichotomous. If there are more than two categories, they can be classified according to the type of information they provide (polytomous).

Research variables are either predictor (independent) or outcome (dependent) variables. The predictor variables might include such things as “Diabetes, Yes/No”, “Age over 65 — Yes/No”, and “diagnosis of hypertension” (again, Yes/No). The respective outcome might be “lower limb amputation” or “death within 10 years”. Your question might have been, “How much additional risk of amputation does a diagnosis of hypertension add in a person with diabetes?”

Before analysis, variables are coded into numbers and entered into a database. Your Research Manual should describe how to code all the data. When the variables are binary, (male/female; alive/dead) coding them into “0” and “1” makes analysing the data much easier (“1” versus “2” makes it harder). The easiest variables for computers to analyse are binary. In other words, “0” or “1”. Such variables are Yes/No; True/False; Male/Female; 65 or over / under 65, etc. The next easiest are ordinal integers: 1, 2, 3, etc. You might create ordinal numbers from categories (0–9; 10–19; 20–29 years of age, etc.), but in order to be ordinal, they require an obvious sequence. Categorical variables do not have an intrinsic order. “Green” “Brown” and “Orange” are non-ordinal, categorical variables. It is possible to transform categorical variables into binary variables, by making columns where only one of the answers is marked with a “1” (if that variable is present) and all the others are marked “0”. The form of the variables and their distribution will determine the type of statistical analysis possible. Data which must be transformed or cleaned is more prone to error in the cleaning or transformation process.

There are alternative ways to get similar information. For example, if you wanted to know the HIV status of each of your subjects, you could either test each one, or you could ask them. The tests cost more, however; they are less likely to give biased results. How you gather each variable will depend on your resources and will inform the limitations of your study.

Precision of a variable is the degree to which it is reproducible with nearly the same value each time it is measured. Precision has a very important influence on the power of a study. The more precise a measurement, the greater the statistical power of a given sample size to estimate mean values and test your hypotheses. In order to minimize random error in your data, and increase the precision of measurements, you should standardize your measurement methods; train your observers; refine any instruments you may use (such as calibrating instruments); automate instruments when possible (automated blood pressure cuff instead of manual); and repeat your measurements.

Accuracy of the variable is the degree to which it actually represents what it is intended to (Truth in the Universe). This influences the validity of the study. Accuracy is impacted by systemic error (bias). The greater the error, the less accurate the variable. Three common biases are: observer bias (how the measurement is reported); instrument bias (faulty function of an instrument); and subject bias (bad reporting or recall of the measurement by the study subject).

Validity is the degree to which a measurement represents the phenomenon of interest. When validating an abstract concept, search the literature or consult with experts so you can find an already validated data collection instrument (such as a questionnaire). This allows your results to be comparable to prior studies in the same area and strengthens your study methods.

Research manual

Simple research with limited resources does not need a research manual, just a protocol. Nor is there much need if the primary investigator is the only data gatherer and analyser. However, if several persons gather data, it is important that the data be gathered the same way each time.

Prevention is the most cost-effective activity that will ensure the integrity of data collection. A detailed and comprehensive research manual will standardize data collection. Poorly written manuals are vague and ambiguous.

The research manual is based off your protocol. The manual should spell out every step of the data collection process. It should include the name of each variable and specific details about how each variable should be collected. Contingents should be written. For example: “If the patient does not have a left arm, the blood pressure may be taken on the right arm. If the patient has no arms, leg blood pressures may be recorded, but put an ‘*’ beside the reading.” The manual should also include every step of the coding process. The coding manual should describe the name of each variable, and how it should be coded. Both the coder and the statistician will want to refer to that section. The coding section should describe how each variable will be entered into the database. Test the manual to make sure everyone understands it the same way.

Think about various ways a plan can go wrong. Write them down, with preferred solutions. There will always be unexpected changes. They should be added into the manual on a continuing basis. An on-going section where questions, problems and their solutions are all recorded will increase the integrity of your research.

Data collection methods

Before you start data collection, you need to ask yourself what data you are going to collect and how you are going to collect them. Which data, and the amount of data to be collected needs to be defined clearly. Different people (including several data collectors) should have a similar understanding of each variable and how it is measured. Otherwise, the data cannot be relied on. Furthermore, the decision to collect a piece of data needs to be justified. The amount of data collected for the study should be sufficient. A common mistake is to collect too much data without actually knowing what will be done with it. Researchers should identify essential data elements and eliminate those that may seem interesting but are not central to the study hypothesis. Collection of the latter type of data places an unnecessary burden on both the study participants and data collectors.

Different data collection approaches which are commonly used in the conduct of clinical research include questionnaire surveys, patient self-reported data, proxy/informant information, hospital and ambulatory medical records, as well as the collection and analysis of biologic samples. Each of these methods has its own advantages and disadvantages.

Surveys are conducted through administration of standardized or home-grown questionnaires, where participants are asked to respond to a set of questions as yes/no, or perhaps on a Likert type scale. Sometimes open-ended responses are elicited.

Medical records can be important sources of high-quality data and may be used either as the only source of data, or as a complement to information collected through other instruments. Unfortunately, due to the non-standardized nature of data collection, information contained in the medical records may be conflicting or of questionable accuracy. Moreover, the extent of documentation by different providers can vary significantly. These issues can make the construction or use of key study variables very difficult.

Collection of biological materials, as well as various imaging modalities, from the study participants are increasingly being used in clinical research. They need to be performed under standardized conditions, and ethical implications should be considered.

Data collection tool

You may need to collect information on paper. If you do, it is useful to have the actual code which should be entered into the computerized database written on the forms themselves (as well as in the manual). If you have access to an electronic database such as REDcap [a web-based application developed by Vanderbilt University to capture data for clinical research and create databases and projects [ 4 ], you can enter the data directly as you get them ( male ; female ) and the database will automatically convert the data into code. This reduces transcribing errors. Another common electronic database is Excel, which can also be used to manipulate the data. In spite of the advantages of recording data electronically, such as directly into REDcap or Excel, there are advantages to collecting and keeping the original data on paper. Paper data collection forms can be saved for audit or quality control. Furthermore, paper records cannot be remotely hacked. Moreover, if the anonymous electronic database is compromised or corrupted, you can re-create your database.

Data collectors

Good data collectors are worth gold. If they are thorough and ethical, you will get great data. If not, your data may be unusable. Make sure they understand research ethics, the need for protection of human subjects, and the privacy of data. Ideally, your data collectors would be blinded to the outcome of interest, to prevent bias. It is ok to blind data collectors to the research question, but they need to understand that collecting every variable the same way for each subject is essential to data integrity.

Data gatherers should be trained in advance of collecting any data. They need to understand informed consent and have the time to explain a study to the satisfaction of the subjects. The importance of conducting a dry run in an attempt to anticipate and address issues that can arise during data collection cannot be over-stated. It would even be worthwhile to pilot the research manual, to learn if everyone understands it the same way.

Data storage

Data collection, done right, protects the confidentiality of the subject as well as the data. Data must also be properly stored safely and securely. It is reasonable to back up your data in a different, secure, location. You do not want to go to all the trouble of creating a protocol, collecting your data, only to lose it, or have no way to analyse it!

There are many reasons to keep your data safe and secure. Obviously, you do not want to lose your data. You may wish to use the data again. For example, you may wish to combine it with other data for a different study. An additional reason is that you do not want your subjects to risk a ‘loss of privacy’. Still another reason is that institutions and governments may require you to store data for a specified number of years. Know how long you must keep your data. Keep it in a locked cabinet in a secure room, or behind an institutional firewall.

Furthermore, if you keep a cipher , that is, a connector between a subject and their study number, keep that cipher separate from the research data. That way, even if someone learns that subject 302 has an embarrassing condition, they will not know who subject 302 really is.

These days, almost everyone has access to computers and programs, locally or ‘in the cloud’. For statistical analysis, you will need to have your data in electronic form. If you started with paper, consider double entry (two data extractors for each record, then compare the two) for greater accuracy.

Tips on this topic and pitfalls to avoid

Hazard: no research manual.

• • No identified mechanism to document changes in procedures that may evolve over the course of the investigation.
• • Vague description of data collection instruments to be used in lieu of rigorous step-by-step instructions on administering tests
• • Only a partial listing of variables to be collected
• • Forgetting to put instructions on the data collection sheet about how to code the data when transferring to an electronic medium.

Hazard: no assistant training

• • Failure to adequately train data collectors
• • Failure to do a Dry Run/Failure to try enrolling a mock subject
• • Uncertainty about when, how and who should review gathered data.

Hazard: failure to understand data management

• • Data should be easy to understand, and the protocol good enough that another researcher can repeat the study.
• • Data audit: keep raw data and collected data
• • Failure to keep backups

Annotated bibliography

• 1. RCR Data Acquisition and Management. This online book is pretty comprehensive. http://ccnmtl.columbia.edu/projects/rcr/rcr_data/foundation/ (Accessed 2019 June 23)
• 2. Qualitative research – Wikipedia: en.wikipedia.org/wiki/Qualitative_research (Accessed 2019 June 23) – this is a good overview with references so you can delve deeper if you wish.
• 3. Qualitative Research: Definition, Types, Methods and Examples: https://www.questionpro.com/blog/qualitative-research-methods/ (Accessed 2019 June 23) – this is a good overview with references so you can delve deeper if you wish.
• 4. Qualitative Research Methods: A Data Collector's Field Guide: https://course.ccs.neu.edu/is4800sp12/resources/qualmethods.pdf (Accessed 2019 June 23) – another on-line resource about data collection.

Additional reading about statistical variables

• 1. Types of Variables in Statistics and Research: A List of Common and Uncommon Types of Variables. https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/types-of-variables/
• 2. Research Variables: Dependent, Independent, Control, Extraneous & Moderator. https://study.com/academy/lesson/research-variables-dependent-independent-control-extraneous-moderator.html
• 3. Knatterud GL. Rockhold FW. George SL. Barton FB. Davis CE. Fairweather WR. Honohan, T. Mowery R. O'Neill R. (1998). Guidelines for quality assurance in multicenter trials: a position paper. Controlled Clinical Trials, 19:477–493.
• 4. Whitney CW. Lind BK. Wahl PW. (1998). Quality assurance and quality control in longitudinal studies. Epidemiologic Reviews, 20 [ 1 ]: 71–80.

Additional relevant information to consider

Consider who owns the data before and after collection (this brings up questions of consent, privacy, sponsorship and data-sharing, most of which are beyond the scope of this paper).

Authors' contribution

Authors contributed as follow to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content: ES contributed 70%; VT, MJ and HS contributed 10% each. All authors approved the version to be published and agreed to be accountable for all aspects of the work.

Declaration of competing interest

The authors declared no conflicts of interest.

• Primary vs Secondary Data:15 Key Differences & Similarities

• Data Collection

In a time when data is becoming easily accessible to researchers all over the world, the practicality of utilizing secondary data for research is becoming more prevalent, same as its questionable authenticity when compared with primary data. 

These 2 types of data, when considered for research is a double-edged sword because it can equally make a research project as well as it can mar it.

In a nutshell, primary data and secondary data both have their advantages and disadvantages. Therefore, when carrying out research, it is left for the researcher to weigh these factors and choose the better one.

It is therefore important for one to study the similarities and differences between these data types so as to make proper decisions when choosing a better data type for research work.

What is Primary Data?

Primary data is the kind of data that is collected directly from the data source without going through any existing sources. It is mostly collected specially for a research project and may be shared publicly to be used for other research

Primary data is often reliable, authentic, and objective in as much as it was collected with the purpose of addressing a particular research problem. It is noteworthy that primary data is not commonly collected because of the high cost of implementation.

A common example of primary data is the data collected by organizations during market research, product research, and competitive analysis. This data is collected directly from its original source which in most cases are the existing and potential customers.

Most of the people who collect primary data are government-authorized agencies, investigators, research-based private institutions, etc. 

Read More: Primary Data: Definition, Examples & Collection Techniques

• Primary data is specific to the needs of the researcher at the moment of data collection. The researcher is able to control the kind of data that is being collected.
• It is accurate compared to secondary data. The data is not subjected to personal bias and as such the authenticity can be trusted.
• The researcher exhibits ownership of the data collected through primary research . He or she may choose to make it available publicly, patent it, or even sell it.
• Primary data is usually up to date because it collects data in real-time and does not collect data from old sources.
• The researcher has full control over the data collected through primary research . He can decide which design, method, and data analysis techniques to be used.
• Primary data is very expensive compared to secondary data. Therefore, it might be difficult to collect primary data.
• It is time-consuming.
• It may not be feasible to collect primary data in some cases due to its complexity and required commitment.

What is Secondary Data?  

Secondary data is data that has been collected in the past by someone else but made available for others to use. They are usually once primary data but become secondary when used by a third party.

Secondary data are usually easily accessible to researchers and individuals because they are mostly shared publicly. This, however, means that the data are usually general and not tailored specifically to meet the researcher’s needs as primary data does.

For example, when conducting a research thesis, researchers need to consult past works done in this field and add findings to the literature review. Some other things like definitions and theorems are secondary data that are added to the thesis to be properly referenced and cited accordingly.

Some common sources of secondary data include trade publications, government statistics, journals, etc. In most cases, these sources cannot be trusted as authentic.

Read More: What is Secondary Data? + [Examples, Sources, & Analysis]

• Secondary data is easily accessible compared to primary data. Secondary data is available on different platforms that can be accessed by the researcher.
• Secondary data is very affordable. It requires little to no cost to acquire them because they are sometimes given out for free.
• The time spent on collecting secondary data is usually very little compared to that of primary data.
• Secondary data makes it possible to carry out longitudinal studies without having to wait for a long time to draw conclusions.
• It helps to generate new insights into existing primary data.
• Secondary data may not be authentic and reliable. A researcher may need to further verify the data collected from the available sources.
• Researchers may have to deal with irrelevant data before finally finding the required data.
• Some of the data is exaggerated due to the personal bias of the data source.
• Secondary data sources are sometimes outdated with no new data to replace the old ones.

Here are 15 Differences between Primary and Secondary Data

Primary data is the type of data that is collected by researchers directly from main sources while 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.

The main difference between these 2 definitions is the fact that primary data is collected from the main source of data, while secondary data is not.

The secondary data made available to researchers from existing sources are formerly primary data that was collected for research in the past. The availability of secondary data is highly dependent on the primary researcher’s decision to share their data publicly or not.

An example of primary data is the national census data collected by the government while an example of secondary data is the data collected from online sources. The secondary data collected from an online source could be the primary data collected by another researcher.

For example, the government, after successfully the national census, shares the results in newspapers, online magazines, press releases, etc. Another government agency that is trying to allocate the state budget for healthcare, education, etc. may need to access the census results.

With access to this information, the number of children who needs education can be analyzed, and hard to determine the amount that should be allocated to the education sector. Similarly, knowing the number of old people will help in allocating funds for them in the health sector.

The type of data provided by primary data is real-time, while the data provided by secondary data is stale. Researchers are able to have access to the most recent data when conducting primary research , which may not be the case for secondary data.

Secondary data have to depend on primary data that has been collected in the past to perform research. In some cases, the researcher may be lucky that the data is collected close to the time that he or she is conducting research.

Therefore, reducing the amount of difference between the secondary data being used and the recent data.

Researchers are usually very involved in the primary data collection process, while secondary data is quick and easy to collect. This is due to the fact that primary research is mostly longitudinal.

Therefore, researchers have to spend a long time performing research, recording information, and analyzing the data. This data can be collected and analyzed within a few hours when conducting secondary research.

For example, an organization may spend a long time analyzing the market size for transport companies looking to talk into the ride-hailing sector. A potential investor will take this data and use it to inform his decision of investing in the sector or not. 

• Availability

Primary data is available in crude form while secondary data is available in a refined form. That is, secondary data is usually made available to the public in a simple form for a layman to understand while primary data are usually raw and will have to be simplified by the researcher.

Secondary data are this way because they have previously been broken down by researchers who collected the primary data afresh. A good example is the Thomson Reuters annual market reports that are made available to the public.

When Thomson Reuters collect this data afresh, they are usually raw and may be difficult to understand. They simplify the results of this data by visualizing it with graphs, charts, and explanations in words.

• Data Collection Tools

Primary data can be collected using surveys and questionnaires while secondary data are collected using the library, bots, etc. The differences between these data collection tools are glaring and can it be interchangeably used.

When collecting primary data, researchers lookout for a tool that can be easily used and can collect reliable data. One of the best primary data collection tools that satisfy this condition is Formplus.

Formplus is a web-based primary data collection tool that helps researchers collect reliable data while simultaneously increasing the response rate from respondents.

Primary data sources include; Surveys, observations, experiments, questionnaires, focus groups, interviews, etc., while secondary data sources include; books, journals, articles, web pages, blogs, etc. These sources vary explicitly and there is no intersection between the primary and secondary data sources.

Primary data sources are sources that require a deep commitment from researchers and require interaction with the subject of study. Secondary data, on the other hand, do not require interaction with the subject of study before it can be collected.

In most cases, secondary researchers do not have any interaction with the subject of research.

Primary data is always specific to the researcher’s needs, while secondary data may or may not be specific to the researcher’s needs. It depends solely on the kind of data the researcher was able to lay hands on.

Secondary researchers may be lucky to have access to data tailored specifically to meet their needs, which mag is not the case in some cases. For example, a market researcher researching the purchasing power of people from a particular community may not have access to the data of the subject community.

Alternatively, there may be another community with a similar standard of living to the subject community whose data is available. The researcher mag uses to settle for this data and use it to inform his conclusion on the subject community.

Some common advantages of primary data are its authenticity, specific nature, and up to date information while secondary data is very cheap and not time-consuming. 

Primary data is very reliable because it is usually objective and collected directly from the original source. It also gives up-to-date information about a research topic compared to secondary data.

Secondary day, on the other hand, is not expensive making it easy for people to conduct secondary research. It doesn’t take so much time and most of the secondary data sources can be accessed for free.

• Disadvantage

The disadvantage of primary data is the cost and time spent on data collection while secondary data may be outdated or irrelevant. Primary data incur so much cost and takes time because of the processes involved in carrying out primary research.

For example, when physically interviewing research subjects, one may need one or  more professionals, including the interviewees, videographers who will make a record of the interview in some cases and the people involved in preparing for the interview. Apart from the time required, the cost of doing this may be relatively high.

Secondary data may be outdated and irrelevant. In fact, researchers have to surf through irrelevant data before finally having access to the data relevant to the research purpose.

• Accuracy and Reliability

 Primary data is more accurate and reliable while secondary data is relatively less reliable and accurate. This is mainly because the secondary data sources are not regulated and are subject to personal bias.

A good example of this is business owners who lay bloggers to write good reviews about their products just to gain more customers. This is not the case with primary data which is collected by the researcher himself. 

One of the researcher’s aims when gathering primary data for research will be gathering accurate data so as to arrive at correct conclusions. Therefore, biases will be avoided at all costs (e.g. same businesses when collecting feedback from customers).  

• Cost-effectiveness

Primary data is very expensive while secondary data is economical. When working on a low budget, it is better for researchers to work with secondary data, then analyze it to uncover new trends.

In fact, a researcher might work with both primary data and secondary data for one research. This is usually very advisable in cases whereby the available secondary data does not fully meet the research needs.

Therefore, a little extension on the available data will be done and cost will also be saved. For example, a researcher may require a market report from 2010 to 2019 while the available reports stop at 2018.

• Collection Time

The time required to collect primary data is usually long while that required to collect secondary data is usually short. The primary data collection process is sometimes longitudinal in nature.

Therefore, researchers may need to observe the research subject for some time while taking down important data. For example, when observing the behavior of a group of people or particular species, researchers have to observe them for a while.

Secondary data can, however, be collected in a matter of minutes and analyzed to dead conclusions—taking a shorter time when compared to primary data. In some rare cases, especially when collecting little data, secondary data may take a longer time because of difficulty consulting different data sources to find the right data. 

Read Also – What is Secondary Data? + [Examples, Sources & Analysis]

Similarities Between Primary & Secondary Data

• Contains the Same Content:

Secondary data was once primary data when it was newly collected by the first researcher. The content of the data collected does not change and therefore has the same content as primary data.

It doesn’t matter if it was further visualized in the secondary form, the content does not change. A common example of these are definitions, theorems, and postulates that were made years ago but still remain the same.

Primary data and secondary data are both used in research and statistics. They can be used to carry out the same kind of research in these fields depending on data availability. This is because secondary data and primary data have the same content. The only difference is the method by which they are collected.

Since the method of collection does not directly affect the uses of data, they can be used to perform similar research. For example, whether collected directly or from an existing database, the demography of a particular target market can be used to inform similar business decisions.

When performing research, it is important to consider the available data options so as to ensure that the right type of data is used to arrive at a feasible conclusion. A good understanding of the different data types, similarities, and differences is however required to do this.

Primary data and secondary data both have applications in business and research. They may, however, differ from each other in the way in which they are collected, used, and analyzed.

The most common setback with primary data is that it is very expensive, which is not the case for secondary data. Secondary data, on the other hand, has authenticity issues.

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Ultimate guide on Primary Data, it’s meaning, collection methods, examples, advantages and disadvantage.

Primary vs Secondary Sources: Meaning, Examples & Differences

In this article, we will discuss the two types of sources; Primary and Secondary sources, their importance, and their uses.

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Primary Research: Methods and Best Practices

Introduction

What is the definition of primary research, what are examples of primary research, primary vs. secondary research, types of primary research, when to use primary research.

Conducting research involves two types of data: primary data and secondary data . While secondary research deals with existing data, primary research collects new data . Ultimately, the most appropriate type of research depends on which method is best suited to your research question .

While this article discusses the difference between primary and secondary research, the main focus is on primary research, the types of data collected through primary research, and considerations for researchers who conduct primary research.

Simply put, researchers conduct primary research to gather new information. When existing data cannot address the research inquiry at hand, the researcher usually needs to collect new data to meet their research objectives.

How do you identify primary research?

Primary research uses collected data that hasn't been previously documented. Primary research typically means collecting data straight from the source (e.g., interviewing a research participant, observing a cultural practice or phenomenon firsthand).

Note that other divides that you should also consider include that of collecting quantitative or qualitative data , and of conducting basic or applied research . Each of these dimensions informs and is informed by your research inquiry.

What are the advantages of primary research?

New data, particularly that which addresses a research gap, can contribute to a novel inquiry and prove compelling to the research audience. When a researcher conducts a literature review and generates a problem statement for their research, they can identify what new data needs to be collected and what primary research method can be used to collect it.

Primary research studies ultimately contribute to theoretical developments and novel insights that an analysis of existing data might not have identified. Research publications in some fields may place a premium on primary research for its potential to generate new scientific knowledge as a result.

What are the disadvantages of primary research?

Primary research is time-consuming and potentially expensive to conduct, considering the equipment and resources needed to collect new data as well as the time required to engage with the field and collect data.

Moreover, primary research relies on new data that has yet to be documented elsewhere, meaning that the research audience is less familiar with the primary data being presented. This might raise issues of transparency and research rigor (e.g., how does the audience know that the data they are shown is trustworthy?).

Primary research is common in various fields of research. Let's look at some typical examples of primary research in three different areas.

Education research

Teaching and learning is a field that relies on evidence-based data to make policy recommendations affecting teachers, learning materials, and even classroom requirements. As a result, there are countless methods for collecting relevant data on the various aspects of education.

Observations , interviews , and assessments are just some of the primary research methods that are employed when studying education contexts. Education research acknowledges the full variety of situated differences found in the diversity of learners and their schooling contexts. This makes collecting data that is relevant to the given context and research inquiry crucial to understanding teaching and learning.

Market research

Businesses often rely on primary research to understand the target market for their products and services. Since competing businesses tend not to share research on customer insights with each other, primary research collecting original data can be a necessity.

Focus groups , surveys , and user research are typical research tools employed by businesses. Within market research, the goal is typically to understand customers' preferences and use cases for specific products and services.

Cultural studies

Fields such as anthropology and sociology count on primary research for understanding cultures and communities. Ethnographic research acknowledges that thick description of cultures and phenomena is more meaningful than only generating universal theories, making the collection of primary data essential to understanding the full diversity of the social world.

Researchers examining culture often collect data through interviews, observations, and photovoice, among other research methods. These methods look at the social world through the eyes of the research participants to generate an immersive view of cultures and groups with which audiences may not be familiar.

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Primary research data stands in contrast to secondary research data, which is any data that has been previously collected and documented. In some situations, existing data may be abundant and available, making secondary research a more feasible approach to generating theory and identifying key insights.

Secondary research methods are employed in all fields of research. Market researchers conduct secondary research when there is already existing data about a target market. In particular, secondary market research might look at previous trends in the popularity of products to make predictions about the demand for new products.

Scholarly researchers can use secondary sources such as corpora, news articles, and online videos to make assertions about language and culture. Analytical approaches such as discourse analysis and content analysis can be well suited to analyzing data collected through secondary research methods.

Ultimately, primary and secondary research go hand in hand. The main function of research in building knowledge does not necessarily depend on the use of primary data collection . Rather, it is a matter of whether data needs to be collected in order to address your research inquiry, or relevant data already exists and you can access it.

There are many research methods used to collect data for primary research. The research method that works best for you depends on what you are looking to do with your research project.

This section lists some of the common primary data collection methods that researchers rely on.

One-on-one interviews are useful for capturing perspectives from research participants. Direct interactions can tell researchers what perspectives their research participants have and the thinking behind those perspectives.

Interview research is a complex and detailed methodology that includes several types of interviews to suit various research inquiries. Researchers can choose between structured interviews , semi-structured interviews , and unstructured interviews , depending on the nature of interaction they are looking to establish.

Focus groups

Focus groups are discussions that involve multiple research participants and are led by a moderator. Similar to interviews, the primary goal is to gather information about people's perspectives. Yet focus groups are distinct, because they can capture how people interact and build meaning when discussing a particular topic.

Market researchers may consider conducting a focus group discussion when they want to know more about how a particular group feels about a product or service. Researchers in linguistics and anthropology might be interested in observing how a group of people construct meaning with each other.

Observations

In research involving naturalistic inquiry and the social world, the researcher can gather information directly from the field through observational research methods . Primary data takes the form of field notes , audio and video recordings , their resulting transcripts , and even images of objects of interest.

For quantitative research inquiries, observation entails measuring the amount of activity or the frequency of particular phenomena. Qualitative observations look for patterns in cultural or social practices and document significant events in the field.

When the objective is to capture perspectives from large numbers of people, surveys are a good research method for collecting novel data. In-person questionnaires and online surveys can be used to quickly collect data at scale.

Surveys are used for conducting primary research in both quantitative and qualitative research . The structure of survey questions provide data that can be measured quantitatively, while open-ended survey responses require qualitative data analysis .

Experiments

While the above methods emphasize or are involved with naturalistic inquiry, experiments are a different form of primary research that is far more controlled. When you want to understand the relationship between various elements in a certain context (e.g., the effect of water and fertilizer on plant growth), a controlled experiment is a typical research approach to empirically establish scientific knowledge.

Experiments focus on a specific set of factors from the research phenomenon to understand causal relationships between variables. Experiments are a common primary research method in physical sciences, but they are also extensively used in psychology, education, and political science, among other areas.

The decision to conduct a primary or secondary study is a question of whether existing data is sufficient to satisfy the research inquiry at hand. Where data does not exist, primary research should be conducted.

Consider an example research study regarding ideal teaching methods in elementary school contexts in a developing country in Asia. Just because there is abundant data on the same topic in elementary schools in Western countries does not preclude the possibility of novel theoretical developments in schools in Asia. This becomes particularly important if insights based on existing data from other contexts may not be applicable to the present context.

Note that this does not mean that a secondary research study is any less novel than a primary study. Indeed, many fields and methodologies rely extensively on analyzing existing data. For example, studies that employ discourse analysis and content analysis typically (though not always) rely on existing sources of data to facilitate understanding of language use in real-world situations.

As a result, the choice between primary and secondary research can be seen as more of a practical consideration than a matter of a study's potential contribution to scientific knowledge. Novelty in research is as much about the data collection as it is about the resulting analysis. If you require data for your study where none exists, then data from primary research is your best option.

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Secondary research: definition, methods, & examples.

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

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

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

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What is secondary research?

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

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

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

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

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

How to conduct secondary research

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

1.    Identify and define the research topic

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

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

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

2.    Find research and existing data sources

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

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

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

3.    Begin searching and collecting the existing data

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

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

4.    Combine the data and compare the results

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

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

5.    Analyze your data and explore further

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

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

Primary vs secondary research

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

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

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

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

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

Sources of Secondary Research

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

Internal data

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

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

External data

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

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

Three examples of secondary research methods in action

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

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

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

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

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

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

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

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

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

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

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

Advantages of secondary research

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

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

Disadvantages of secondary research

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

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

When do we conduct secondary research?

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

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

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

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

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

Questions to ask before conducting secondary research

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

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

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

• What am I trying to achieve with this research?

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

• How credible will my research be?

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

• What is the date of the secondary research?

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

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

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

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

In it, you’ll learn more about:

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

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

Related resources

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

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Public Health Research Guide: Primary & Secondary Data Definitions

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Primary & Secondary Data Definitions

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Primary Data: Data that has been generated by the researcher himself/herself, surveys, interviews, experiments, specially designed for understanding and solving the research problem at hand.

Secondary Data:  Using existing data generated by large government Institutions, healthcare facilities etc. as part of organizational record keeping. The data is then extracted from more varied datafiles. 

Supplementary Data : A few years ago the Obama Administration judged that any research that is done using Federal Public funds should be available for free to the public. Moreover Data Management Plans should be in place to store and preserve the data for almost eternity. These data sets are published as Supplementary Materials in the journal lliterature, and data sets can downloaded and manipulated for research. 

NOTE: Even though the research is Primary source, the supplemental files downloaded by others becomes Secondary Source.

 Pros and Cons for each. 

Comparison Chart

Quantitative & Qualitative Research Methods

Quantitative Research Definition:  Data that can be measured, quantified. Basically Descriptive Statistics.

Read:  Introduction to Quantitative Methods

Qualitative Research Definition: Data collected that is not numerical, hence cannot be quantified. It measures other characteristics through interviews, observation and focused groups among a few methods. It can also be termed as  " Categorical Statistics ". 

Read:  Qualitative methods in public health

Mixed methods research. When quantitative and qualitative research methods are used.

Qualitative Research Methods:

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Difference Between Primary and Secondary Data

There are many differences between primary and secondary data, which are discussed in this article. But the most important difference is that primary data is factual and original whereas secondary data is just the analysis and interpretation of the primary data. While primary data is collected with an aim for getting solution to the problem at hand, secondary data is collected for other purposes.

Content: Primary Data Vs Secondary Data

Comparison chart.

Definition of Primary Data

Primary data is data originated for the first time by the researcher through direct efforts and experience, specifically for the purpose of addressing his research problem. Also known as the first hand or raw data. Primary data collection is quite expensive, as the research is conducted by the organisation or agency itself, which requires resources like investment and manpower. The data collection is under direct control and supervision of the investigator.

The data can be collected through various methods like surveys, observations, physical testing, mailed questionnaires, questionnaire filled and sent by enumerators, personal interviews, telephonic interviews, focus groups, case studies, etc.

Definition of Secondary Data

Secondary data implies second-hand information which is already collected and recorded by any person other than the user for a purpose, not relating to the current research problem. It is the readily available form of data collected from various sources like censuses, government publications, internal records of the organisation, reports, books, journal articles, websites and so on.

Secondary data offer several advantages as it is easily available, saves time and cost of the researcher. But there are some disadvantages associated with this, as the data is gathered for the purposes other than the problem in mind, so the usefulness of the data may be limited in a number of ways like relevance and accuracy.

Moreover, the objective and the method adopted for acquiring data may not be suitable to the current situation. Therefore, before using secondary data, these factors should be kept in mind.

Key Differences Between Primary and Secondary Data

The fundamental differences between primary and secondary data are discussed in the following points:

• The term primary data refers to the data originated by the researcher for the first time. Secondary data is the already existing data, collected by the investigator agencies and organisations earlier.
• Primary data is a real-time data whereas secondary data is one which relates to the past.
• Primary data is collected for addressing the problem at hand while secondary data is collected for purposes other than the problem at hand.
• Primary data collection is a very involved process. On the other hand, secondary data collection process is rapid and easy.
• Primary data collection sources include surveys, observations, experiments, questionnaire, personal interview, etc. On the contrary, secondary data collection sources are government publications, websites, books, journal articles, internal records etc.
• Primary data collection requires a large amount of resources like time, cost and manpower. Conversely, secondary data is relatively inexpensive and quickly available.
• Primary data is always specific to the researcher’s needs, and he controls the quality of research. In contrast, secondary data is neither specific to the researcher’s need, nor he has control over the data quality.
• Primary data is available in the raw form whereas secondary data is the refined form of primary data. It can also be said that secondary data is obtained when statistical methods are applied to the primary data.
• Data collected through primary sources are more reliable and accurate as compared to the secondary sources.

Video: Primary Vs Seconday Data

As can be seen from the above discussion that primary data is an original and unique data, which is directly collected by the researcher from a source according to his requirements. As opposed to secondary data which is easily accessible but are not pure as they have undergone through many statistical treatments.

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Primary Research: What It Is, Purpose & Methods + Examples

As we continue exploring the exciting research world, we’ll come across two primary and secondary data approaches. This article will focus on primary research – what it is, how it’s done, and why it’s essential. 

We’ll discuss the methods used to gather first-hand data and examples of how it’s applied in various fields. Get ready to discover how this research can be used to solve research problems , answer questions, and drive innovation.

What is Primary Research: Definition

Primary research is a methodology researchers use to collect data directly rather than depending on data collected from previously done research. Technically, they “own” the data. Primary research is solely carried out to address a certain problem, which requires in-depth analysis .

There are two forms of research:

• Primary Research
• Secondary Research

Businesses or organizations can conduct primary research or employ a third party to conduct research. One major advantage of primary research is this type of research is “pinpointed.” Research only focuses on a specific issue or problem and on obtaining related solutions.

For example, a brand is about to launch a new mobile phone model and wants to research the looks and features they will soon introduce. 

Organizations can select a qualified sample of respondents closely resembling the population and conduct primary research with them to know their opinions. Based on this research, the brand can now think of probable solutions to make necessary changes in the looks and features of the mobile phone.

Primary Research Methods with Examples

In this technology-driven world, meaningful data is more valuable than gold. Organizations or businesses need highly validated data to make informed decisions. This is the very reason why many companies are proactive in gathering their own data so that the authenticity of data is maintained and they get first-hand data without any alterations.

Here are some of the primary research methods organizations or businesses use to collect data:

1. Interviews (telephonic or face-to-face)

Conducting interviews is a qualitative research method to collect data and has been a popular method for ages. These interviews can be conducted in person (face-to-face) or over the telephone. Interviews are an open-ended method that involves dialogues or interaction between the interviewer (researcher) and the interviewee (respondent).

Conducting a face-to-face interview method is said to generate a better response from respondents as it is a more personal approach. However, the success of face-to-face interviews depends heavily on the researcher’s ability to ask questions and his/her experience related to conducting such interviews in the past. The types of questions that are used in this type of research are mostly open-ended questions . These questions help to gain in-depth insights into the opinions and perceptions of respondents.

Personal interviews usually last up to 30 minutes or even longer, depending on the subject of research. If a researcher is running short of time conducting telephonic interviews can also be helpful to collect data.

2. Online surveys

Once conducted with pen and paper, surveys have come a long way since then. Today, most researchers use online surveys to send to respondents to gather information from them. Online surveys are convenient and can be sent by email or can be filled out online. These can be accessed on handheld devices like smartphones, tablets, iPads, and similar devices.

Once a survey is deployed, a certain amount of stipulated time is given to respondents to answer survey questions and send them back to the researcher. In order to get maximum information from respondents, surveys should have a good mix of open-ended questions and close-ended questions . The survey should not be lengthy. Respondents lose interest and tend to leave it half-done.

It is a good practice to reward respondents for successfully filling out surveys for their time and efforts and valuable information. Most organizations or businesses usually give away gift cards from reputed brands that respondents can redeem later.

3. Focus groups

This popular research technique is used to collect data from a small group of people, usually restricted to 6-10. Focus group brings together people who are experts in the subject matter for which research is being conducted.

Focus group has a moderator who stimulates discussions among the members to get greater insights. Organizations and businesses can make use of this method, especially to identify niche markets to learn about a specific group of consumers.

4. Observations

In this primary research method, there is no direct interaction between the researcher and the person/consumer being observed. The researcher observes the reactions of a subject and makes notes.

Trained observers or cameras are used to record reactions. Observations are noted in a predetermined situation. For example, a bakery brand wants to know how people react to its new biscuits, observes notes on consumers’ first reactions, and evaluates collective data to draw inferences .

Primary Research vs Secondary Research – The Differences

Primary and secondary research are two distinct approaches to gathering information, each with its own characteristics and advantages. 

While primary research involves conducting surveys to gather firsthand data from potential customers, secondary market research is utilized to analyze existing industry reports and competitor data, providing valuable context and benchmarks for the survey findings.

Find out more details about the differences: 

1. Definition

• Primary Research: Involves the direct collection of original data specifically for the research project at hand. Examples include surveys, interviews, observations, and experiments.
• Secondary Research: Involves analyzing and interpreting existing data, literature, or information. This can include sources like books, articles, databases, and reports.

2. Data Source

• Primary Research: Data is collected directly from individuals, experiments, or observations.
• Secondary Research: Data is gathered from already existing sources.

3. Time and Cost

• Primary Research: Often time-consuming and can be costly due to the need for designing and implementing research instruments and collecting new data.
• Secondary Research: Generally more time and cost-effective, as it relies on readily available data.

4. Customization

• Primary Research: Provides tailored and specific information, allowing researchers to address unique research questions.
• Secondary Research: Offers information that is pre-existing and may not be as customized to the specific needs of the researcher.
• Primary Research: Researchers have control over the research process, including study design, data collection methods , and participant selection.
• Secondary Research: Limited control, as researchers rely on data collected by others.

6. Originality

• Primary Research: Generates original data that hasn’t been analyzed before.
• Secondary Research: Involves the analysis of data that has been previously collected and analyzed.

7. Relevance and Timeliness

• Primary Research: Often provides more up-to-date and relevant data or information.
• Secondary Research: This may involve data that is outdated, but it can still be valuable for historical context or broad trends.

Advantages of Primary Research

Primary research has several advantages over other research methods, making it an indispensable tool for anyone seeking to understand their target market, improve their products or services, and stay ahead of the competition. So let’s dive in and explore the many benefits of primary research.

• One of the most important advantages is data collected is first-hand and accurate. In other words, there is no dilution of data. Also, this research method can be customized to suit organizations’ or businesses’ personal requirements and needs .
• I t focuses mainly on the problem at hand, which means entire attention is directed to finding probable solutions to a pinpointed subject matter. Primary research allows researchers to go in-depth about a matter and study all foreseeable options.
• Data collected can be controlled. I T gives a means to control how data is collected and used. It’s up to the discretion of businesses or organizations who are collecting data how to best make use of data to get meaningful research insights.
• I t is a time-tested method, therefore, one can rely on the results that are obtained from conducting this type of research.

Disadvantages of Primary Research

While primary research is a powerful tool for gathering unique and firsthand data, it also has its limitations. As we explore the drawbacks, we’ll gain a deeper understanding of when primary research may not be the best option and how to work around its challenges.

• One of the major disadvantages of primary research is it can be quite expensive to conduct. One may be required to spend a huge sum of money depending on the setup or primary research method used. Not all businesses or organizations may be able to spend a considerable amount of money.
• This type of research can be time-consuming. Conducting interviews and sending and receiving online surveys can be quite an exhaustive process and require investing time and patience for the process to work. Moreover, evaluating results and applying the findings to improve a product or service will need additional time.
• Sometimes, just using one primary research method may not be enough. In such cases, the use of more than one method is required, and this might increase both the time required to conduct research and the cost associated with it.

Every research is conducted with a purpose. Primary research is conducted by organizations or businesses to stay informed of the ever-changing market conditions and consumer perception. Excellent customer satisfaction (CSAT) has become a key goal and objective of many organizations.

A customer-centric organization knows the importance of providing exceptional products and services to its customers to increase customer loyalty and decrease customer churn. Organizations collect data and analyze it by conducting primary research to draw highly evaluated results and conclusions. Using this information, organizations are able to make informed decisions based on real data-oriented insights.

QuestionPro is a comprehensive survey platform that can be used to conduct primary research. Users can create custom surveys and distribute them to their target audience , whether it be through email, social media, or a website.

QuestionPro also offers advanced features such as skip logic, branching, and data analysis tools, making collecting and analyzing data easier. With QuestionPro, you can gather valuable insights and make informed decisions based on the results of your primary research. Start today for free!

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• Primary Data vs. Secondary Data: Market Research Methods

by Lee Steinbock , on October 25, 2022

Not only can you simply plug your search term into any web browser, but you will also get an almost innumerable number of results, many of which circularly source from one another. As such, how can you avoid bad data and ensure that you’re relying on the best information to make strategic decisions?

The experts here at Freedonia Custom Research (FCR) are here to help you navigate all of these data sources.

At the highest level, market research data can be split into primary and secondary data sources, although from a best practices perspective, secondary research should always be performed first.

What Is Secondary Data?

Secondary data is publicly available or relatively inexpensive to obtain and can be used as the foundation for any analysis or business decision — so long as you can feel comfortable regarding the source, something FCR can assist with.

Secondary Data Examples

Sources of secondary data include, but are not limited to, the following:

Company Websites

• Advantages — Public companies tend to have investor relations sections on their websites that are full of reports and other investor documents that can provide insights on both the company, as well as the industry overall.
• Disadvantages — Private companies are not required to report sales or other types of financial information, while public companies only have to report certain information, so if you are seeking information on a specific segment or product, details may be lacking.
• Applications  — Company websites can be used across a number of research workflows. Revenue information can be used for market sizing, as well as market share analyses, while investor information can offer insights on sales splits and expected sales.

Government Statistics

• Advantages — This data is widely available and easily accessible online. Topics range from product shipments and trade, to patents, pricing inflation, and building trends, to name a few.
• Disadvantages — Data is organized in very specific manner, meaning it is often not presented specifically as needed, so further manipulation/analysis tends to be required.
• Applications  — Government statistics tend to be utilized in market sizing exercises and the identification of market trends overall.

Industry Associations

• Advantages — These groups often share lots of valuable information on their websites — including industry overviews, industry participants, product and company news, and other details regarding industry trends.
• Disadvantages — Industry associations tend to compile industry data for their members, so access to this information is likely not available to everyone. Additionally, industry data typically only accounts for a certain share of overall activity. As such, further investigation as to the portion of the market represented is required.
• Applications  — Industry association data can be a great initial source of information when performing a market sizing analysis, as well as identification of industry trends. The information contained in these sources can also be used to identify key players in the market and areas of focus.

Published Market Research Reports

• Advantages — These reports, for a fee, can provide a great overview of an industry, including quantitative data you might not find elsewhere related to market size, growth rates, and industry participant market share.
• Disadvantages — Because they are targeting as broad an audience as possible, these reports tend to be rather broad, with relatively standard segmentation. For those interested in greater detail on certain product segments or geographies, these reports may not be the best option.
• Applications  — As noted, published market research often includes high-level quantitative information, which can assist in market size and share analyses. Depending on the report, details on major manufacturers involved in the industry may be available.

Trade Publications

• Advantages — Sources like periodicals and news articles, many of which are available online, are a great industry-specific source for in-depth product, industry, and competitor data. Oftentimes, these sources include commentary from leading executives about new technologies, industry trends, and future plans.
• Disadvantages — While certain periodicals are known for their data collection, the data from these organizations has similar disadvantages to those outlined above for industry associations. Quantitative details from newspaper articles tend to be derived from other secondary sources.
• Applications  — Information from trade publications can be used for both qualitative and quantitative analyses. While the information might not get you as far as you need — in either instance — it can serve as a strong starting point.

As noted, there are often limitations to relying solely on secondary sources, especially if you are interested in a niche product or a new technology. In these cases, the information may be outdated or not accurately reflect the industry situation as a whole. You might be asking a question that no one has tried to answer before. So what should you do now? Primary research.

What Is Primary Data?

A primary resource is information that is collected specifically for your purposes, directly from people who are involved in the industry that is being examined. Methods of primary data collection will vary based on the goals of the research and the level of detail being sought.

Primary Data Examples

Examples of primary data sources include:

Focus Groups

• Advantages — Focus groups allow companies to get very specific demographic end-user thoughts (qualitative data) on a new product or service that are still in the early stages of development.
• Disadvantages — Feedback comes from small groups, so unless multiple focus groups are conducted and responses are compared, there may be sampling error issues.
• Applications  — Focus groups can provide qualitative feedback on what end-users like, dislike, are confused by, or would do differently, in regard to the new offering.

In-Depth Interviews

• Advantages — In-depth interviews allow for the targeting of key industry experts who can offer both quantitative and qualitative details that can help provide clarity to your current analysis. Not only do these allow for conversation regarding the research objectives, but they often can result in new paths of examination that arise organically from the conversation.
• Disadvantages — The more detailed the information you are seeking, the fewer people there are that can provide the desired information. As such, not just anyone can perform in-depth interviews. They require significant contacts within the industry being studied, as well as extensive experience engaging experts on their level.
• Applications  — In-depth interviews present the opportunity to gather detailed qualitative and quantitative insights from leading industry players about their business, competitors, and the industry at large.

Social Media Monitoring

• Advantages — Social media monitoring shows that you don’t have to always participate in the conversation to learn from it.
• Disadvantages — Without engaging with the market, you are often left to draw inferences from the information that is uncovered. If you have questions about why a group is saying something, you’re left to wonder.
• Applications  — Social media monitoring allows for passive data gathering in the form of questions like: How much are people talking about your brand compared to competitive brands? Is what they’re saying positive or negative? Is the public clamoring for something the industry currently doesn’t provide? How are your competitors portraying themselves via social media, and what does that say about their strategy?
• Advantages — Surveys are a great way to collect significant amounts of representative quantitative data via primary research methods.
• Disadvantages — Whereas in-depth interviews are dynamic in nature, surveys are static, and don’t allow for follow-up or further probing as to the “why” someone does something. Additionally, the development of the questions used in a survey is critical to the success of the research, meaning that it’s more complex than simply saying, “This is what I want to know.”
• Applications  — Surveys can be used to describe a given population in terms of who they are, what they do, what they like, and if they’re happy, among many other characteristics.

Freedonia Custom Research Is Here to Help

Overwhelmed at how to put all of the pieces together? Here are a few ways that the FCR team can help, based on our long track record of conducting secondary and primary market research for corporations across a wide array of industries:

• Access to trusted secondary data — FCR has access to The Freedonia Group’s catalog of over 4,000 industry studies and focus reports, including Packaged Facts and Simba Information. This gives us a head start on secondary data collection on market sizing, growth, and competitive intelligence. Additionally, because of the Freedonia brand name, many industry participants are familiar with us and eager to share their perspectives.
• Long list of industry contacts — FCR has been in business for nearly 20 years, throughout which we have been conducting primary research with decision-makers and industry influencers we can reach out to for in-depth interviews and surveys. We know what sorts of questions to ask various constituent groups (such as manufacturers, distributors, end-users, industry associations, and regulatory bodies), and because we are an independent, third-party firm, you can trust that their answers will be candid and unbiased.
• Analytical experience — Not only do we know where and how to find all of the information that you are seeking, we also have the expertise to synthesize all of the information into meaningful and actionable insights for your business.

If you’d like to learn more about how Freedonia Custom Research can help you navigate a sea of data, please contact us at 440-684-9600 or request more information on our website .

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What Is Primary Data and Secondary Data in Research Methodology

When it comes to research methodology, primary data and secondary data are essential components of the process. What is primary data and secondary data in research methodology?

Primary data is information collected through direct observation or experimentation, while secondary data is existing knowledge obtained from sources such as books, reports, and surveys. Understanding how to collect both primary and secondary data can be a challenge for R&D teams looking for insights into their projects.

In this blog post, we will explore what exactly these two types of research entail, how they should be collected in order to get the best results possible, how to analyze your findings, and how to apply those results to your project.

By understanding more about what is primary data and secondary data in research methodology, you can ensure that any decisions made regarding an innovation project are well-informed ones!

Table of Contents

What is Primary Data?

Types of primary data, advantages of primary data, disadvantages of primary data, how to collect primary and secondary data, methods for collecting primary and secondary data, challenges in collecting what is primary data and secondary data in research methodology, tips for collecting reliable primary and secondary data, analyzing primary and secondary research results, challenges in analyzing research results.

Primary data is information that has been collected directly from its original source. It is original and unique to the research project or study being conducted, as opposed to secondary data which has already been gathered and published by someone else.

Primary data can be collected through a variety of methods such as surveys, interviews, focus groups, observations, experiments, and more.

This type of data can be qualitative or quantitative in nature and provides insight into a particular issue or problem being studied. It is often used in research projects to gain an understanding of people’s opinions, behaviors, attitudes, and preferences on various topics.

The types of primary data depend on the method used for collecting it. Common types include survey responses (qualitative), interview transcripts (qualitative), observation notes (quantitative), and experiment results (quantitative).

Other examples include photographs taken during fieldwork trips or video recordings made during interviews with participants in a study.

Using primary data offers several advantages over relying solely on secondary sources when conducting research.

First off, it allows researchers to collect their own unique set of information that may not have been available before. This gives them greater control over what they are studying as well as how they interpret their findings.

Additionally, primary sources tend to provide more accurate results since there are fewer chances for errors due to human bias or misinterpretation.

Lastly, using primary sources also helps ensure that any potential ethical issues related to collecting personal information are addressed prior to the beginning of the project – something which isn’t always possible with secondary sources!

Despite all these benefits associated with using primary sources, there are some drawbacks too.

One major disadvantage is cost. Primary data collection can become quite expensive if done incorrectly!

Another downside relates to accuracy. Since much less time goes into verifying each data source, mistakes may occur more frequently — resulting in unreliable conclusions.

Key Takeaway: Primary data is a valuable source of information for research as it allows researchers to collect their own unique set of information that may not have been available before.

What is primary data and secondary data in research methodology?

Primary data can be gathered through surveys, interviews, focus groups, and experiments. It provides an accurate picture of the subject being studied since it has not been altered or influenced by other sources.

Secondary data is information that has already been collected and stored in a database. Examples of secondary data include census records, government statistics, published journal articles , and public opinion polls.

Secondary data can provide valuable insights into the topic being studied but may not always be up-to-date or reliable due to its age or source material.

There are several methods available for collecting primary and secondary data including surveys, interviews, focus groups, and experiments as well as online resources such as databases and archives.

Surveys are one of the most common methods used to collect primary data. They involve asking specific questions from a group of people who have agreed to participate in the survey process.

Interviews are another popular method used to gather primary information. They involve having an interviewer ask questions face-to-face with participants who have agreed to take part in the interview process.

Focus groups allow researchers to gain insight into specific topics by gathering together small groups of individuals who share similar interests or experiences so that their opinions can be discussed openly among each other during a moderated session.

Experiments are often used when conducting scientific research. They involve manipulating variables within controlled conditions while measuring results over time.

Online resources such as databases and archives offer access to large amounts of existing secondary information which can then be analyzed further if needed.

One challenge associated with collecting both primary and secondary data is obtaining accurate responses from participants.

Another issue could arise if there’s too much bias present within certain types of datasets (eg: political opinion polls) which makes it difficult for researchers to accurately interpret results.

Additionally, there might also exist some privacy concerns depending on the nature of personal details required while conducting research (eg: medical studies).

How to ensure reliable results when collecting both primary and secondary datasets?

First, make sure you have enough sample size.

Secondly, try to avoid using biased sources like political opinion polls.

Third, check all relevant privacy laws prior to starting any project involving the collection of personal details.

Lastly, double-check the accuracy and validity of all your findings before drawing final conclusions.

Key Takeaway: Collecting reliable primary and secondary data for research projects requires careful consideration of various factors. Researchers should ensure an adequate sample size, avoid biased sources, check relevant privacy laws, and double-check accuracy before drawing conclusions.

The first step in analyzing primary and secondary research results is to identify the key points from each study. This includes understanding what was studied, who participated in the study, how it was conducted, and any other relevant information about the study’s methodology.

Once this information has been gathered, it can be used to draw conclusions about the findings. Additionally, researchers should compare their own findings with those of other studies on similar topics to gain a more comprehensive understanding of their topic area.

Analyzing primary and secondary research results can be challenging due to sample size or methodology.

It is also difficult to determine which findings are reliable since some studies may have methodological flaws that could affect their accuracy or validity.

Additionally, interpreting qualitative data can be especially challenging since there is often no clear-cut answer when examining subjective responses from participants in a survey or interview setting.

Finally, researchers must take care not to make assumptions based on limited evidence as this could lead them astray from accurate interpretations of their results.

Primary data is collected through surveys, interviews, experiments, or observations while secondary data is obtained from existing sources such as books, journals, newspapers, and websites. Collecting both types of data requires careful planning and execution to ensure accuracy and reliability.

Analyzing the results of primary and secondary research can help identify trends in the industry that could be used to inform decisions or strategies for innovation teams.

Are you an R&D or innovation team looking for a solution to help centralize data sources and provide rapid time to insights? Look no further than Cypris . Our platform is designed specifically for teams like yours, providing easy access to primary and secondary data research so that your team can make the most informed decisions possible.

With our streamlined approach, there’s never been a better way to maximize efficiency in the pursuit of groundbreaking ideas!

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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

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

Secondary Data Types

Types of secondary data are as follows:

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

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

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

Secondary Data Formats

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

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

Secondary Data Analysis Methods

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

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

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

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

Examples of Secondary Data

Here are some examples of secondary data from different fields:

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

Purpose of Secondary Data

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

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

When to use Secondary Data

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

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

Characteristics of Secondary Data

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

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

Advantages of Secondary Data

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

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

Limitations of Secondary Data

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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1. Introduction

Data contains raw facts or figures that a researcher captures, stores, manipulates or analyzes to discern some meaning or make a decision. Data is not important for its own sake but because it helps us find an answer to a research question . Researchers use two categories of data: primary and secondary data .

Hence, it’s important to know the definition, purpose, advantages, and drawbacks of primary and secondary data and understand the context in which we can use them.

In this tutorial, we’ll explain the difference between these two data types.

2. Understanding Primary Data

Primary data represents raw findings from first-hand fieldwork, questionnaires, interview transcripts, focus groups, observational studies, or experimental data. It’s  unfiltered and unprocessed, so it’s in the form in which it was recorded.

Primary data is the foundational material from which researchers build theories, answer questions, and formulate hypotheses. So, gathering primary data is the first step of many research methodologies .

2.1. Example

Let’s say a company is market-testing a mobile app. It invited several users from a test group to use the app so they could provide feedback on improving features or usability. For example, the company may want to find out things such as: 

• how long does it take for users to complete the tasks they want
• whether there are missing features or UI elements to add
• which functionality are users most drawn to, and why

During the test sessions, participants complete the questionnaire, and the app records all the interactions with the user. So, we know the exact timesteps at which the users performed any action, such as entering data or clicking on a menu item. Additionally, we have their textual responses to the questions from our survey. In this example, the users’ textual responses we get from usability testing constitute our primary data.

2.2. Collecting Primary Data

Surveys are one of the most common and popular methods of collecting primary data. They are structured queries about people’s attitudes, experiences, or behavior.

Further, researchers can interview study participants to get data. Interviews may be in person, over the phone, or even online. Because of their interactive nature, interviews can provide more details than surveys, often revealing things that a survey can miss.

Another method is observational study. Observational studies collect data on events, interactions, or behaviors as they occur spontaneously in nature or society. For example, the researchers can conduct an observational usability study. They can track users’ activity through the app to note where they get stuck or confused. Observations bring researchers closer to understanding the inner life of social, cultural, or ecological systems. These observations enable the rhythms, deviations, and connections that quantitative methods may not reveal.

Finally, experimental designs allow researchers to systematically manipulate (or ‘test’) independent variables by random assignment. They observe the effects on dependent variables and model these effects in controlled conditions.

For example, when designing a mobile app, researchers might want to measure and test the usability of the app interface against an alternative one. They could randomly assign users to either use Design A or Design B and compare the results in terms of specific measures and indicators of usability. Examples include time to complete a task, error rate, and level of satisfaction. In so doing, they can isolate the effect of the interface design on the usability outcomes.

3. Understanding Secondary Data

Secondary data refers to the data previously gathered, organized, and stored by another individual or organization. Secondary data can also be defined as data derived from primary data. For example, raw recordings of interviews represent the primary data, and the transcripts derived from them are the secondary data:

Secondary data can contain many items without a clear structure, as the data can come from various internal and external databases, published works, non-published documents, maps, photographs, videos, and so forth. So, a researcher first has to organize all the data into a coherent structure suitable for answering the specified research question .

3.1. Secondary Data Sources

Published books contain a substantial body of secondary data. They usually contain references to other books and articles with data that can be relevant to our research question.

In addition to published sources, researchers can look into unpublished sources, which allow them to obtain information that is not readily published. These sources  can be found in government agencies, non-profits, or private research institutes and cover a wide range of highly focused topics.

Organizations also generate terabytes of internal data —financial data, customer data, corporate performance metrics, employee surveys, etc. Internal sources often contain private data that can reveal insights about organizational processes, market shifts, or consumer tastes.

External data sources are all data sources compiled in another institution or organization than ours. These include data produced by national and local government agencies, statistical bureaus, international organizations, research consortia, and others.

4. Comparative Analysis

Let’s compare the two data types:

While primary data offers researchers complete control over the type, volume, and style of data collected, collecting it from scratch may be costly.

On the other hand, secondary data, which is relatively inexpensive and easier to get, poses ethical and methodological issues that we need to consider. The researcher’s objectives and purposes will determine the choice of research method.

5. Conclusion

In this article, we compare primary and secondary data. The former helps in analysis with more precision and detail but demands more time and resources for collecting. In contrast, secondary data provides some advantages over primary data because researchers don’t have to gather them. This eliminates the costs of time and money needed to gather primary data. However, researchers have to be careful. They need to ensure that the secondary data they use are relevant to their work and that they have been collected properly and ethically .

Research Methods: How and When to Use Primary and Secondary Research

• Survey Tips

What is Primary Research?

Primary research is research conducted by you or your team that examines and collects information directly from the context of the design problem. 

Simply put, primary research is research that is your own original work.

For example, if a researcher is interested in learning about the dietary habits of people in a particular region, he or she could administer a survey to residents of that region inquiring about what types of food they typically eat. 

Here, the researcher would be performing primary research.

What is Secondary Research?

Contrary to primary research, secondary research is research that was originally conducted by someone else.

Using our example from above, if after doing some investigation the researcher learns that a similar study has already been performed, he or she could utilize the results and findings from that study to assist him with his overall goal. 

Here the researcher would be performing secondary research.

Related: Why You Should Consider Secondary Data Analysis for Your Next Study 

Secondary research can be performed by leveraging the following sources:

• Academic peer-reviewed journals

• Magazines

• Books

• Market research reports

• Any other form of publicly available and accessible information


When to Use Secondary Research

Use secondary research as a starting point for your research process. .

Imagine that you’ve been tasked with developing an exercise program for elderly people. 

The goal of the program is to outline and schedule exercises and workouts in order to promote healthy lifestyles amongst senior citizens. 

But there’s a catch — You don’t have any experience in exercise science or developing this kind of program. 

The best place to start in order to kick off the project would be to leverage existing research.

You could review publicly available materials on exercise regimens optimized for the age of your target audience. This could involve reading published research reports, books, or articles.

Your findings from this secondary research could then help you define your own approach for how you plan to create the fitness plan for senior citizens. Additionally, starting with secondary research gives you an understanding of what's already been done, and it alerts you of where there may be gaps. 

Secondary research can help you understand what you don’t know.

Continuing on with our example above, you may realize that after researching existing materials on senior citizens and exercising that you know very little about what will motivate elderly people to exercise. 

If you find yourself in a similar situation, continue to identify resources to educate yourself on the matter at hand. 

In this case, secondary research has already saved you some time. If you had opted to not perform secondary research, and instead had made an attempt to build the exercise program from scratch using gut instinct, you would have spent a considerable amount of time banging your head against a theoretical wall to no avail.

If after digging into the available secondary sources, you realize that you still don’t have the precise knowledge needed to develop an effective program, you might then decide that primary research is the only viable way for you to move forward. 

Using Primary Research and Secondary Research Together

Once you have a deep understanding of the problem at hand thanks to your secondary research, you can then plan your primary research efforts accordingly, so that you can fill in any gaps and obtain any information that was previously missing.

Both methods are most effective when they work together.

Surveys Are Great Tools for Performing Primary Research 

Surveys are one of the most commonly used ways in which original data not found through secondary research is collected. 

This is because surveys are context-specific, meaning that the data collected from the survey comes directly from your exact target audience. Plus, there are essentially limitless ways to customize and tailor your survey to resonate with your target audience, which allows you to collect only the most pertinent data for your project.

To start building and administering powerful surveys today, start a trial with Alchemer!

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  Ghana Journal of Development Studies Journal / Ghana Journal of Development Studies / Vol. 21 No. 1 (2024) / Articles (function() { function async_load(){ var s = document.createElement('script'); s.type = 'text/javascript'; s.async = true; var theUrl = 'https://www.journalquality.info/journalquality/ratings/2407-www-ajol-info-gjds'; s.src = theUrl + ( theUrl.indexOf("?") >= 0 ? "&" : "?") + 'ref=' + encodeURIComponent(window.location.href); var embedder = document.getElementById('jpps-embedder-ajol-gjds'); embedder.parentNode.insertBefore(s, embedder); } if (window.attachEvent) window.attachEvent('onload', async_load); else window.addEventListener('load', async_load, false); })();  

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The Ghana Journal of Development Studies is published twice a year (May & October) by the Faculty of Integrated Development Studies as a service to development related research.

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Main Article Content

Interrogating the effectiveness of statutory bodies and state enterprises committee on budget oversight in botswana: an exploratory study, thato queen lesole, christopher dick-sagoe, daniel odoom, lawrencia agyepong.

This study explored the effectiveness of the Statutory Bodies and Public Enterprises (SBPE) Committee on budget oversight in Botswana.  The accountability theorygrounded this research. The study predominantly embraced a qualitative approachand an exploratory research  design to interrogate the effectiveness of theCommittee’s oversight function. It used both primary and secondary data  collection methods. In terms of primary data collection, interviews were conducted withkeystakeholders involved in the budget oversight  function. Additionally, the studyconducted a documentary review of relevant government reports, budgets, andfinancial statements to  supplement the information gathered frominterviews. Thematic analysis was conducted based on the data obtained. The study  observedthat the SBPE Committee was perceived as ineffective in undertaking its oversight responsibility. Poor accountability manifested  in various ways, including untimelyand inaccurate financial reporting. Also, inadequate technical expertise, funding, logistics,  low autonomy, lack of enforcement capacity, and poor separation of power impeded the Committee’s ability to perform its oversight  responsibility effectively. The research recommended constitutional reforms in Botswana that wouldempower parliament to follow  through on its recommendations and emancipatethelegislative arm of government from executive control and manipulation.  

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• Intracoronary thrombolysis in ST-elevation myocardial infarction: a systematic review and meta-analysis
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• http://orcid.org/0000-0002-3678-2855 Rajan Rehan 1 , 2 ,
• http://orcid.org/0000-0002-7235-3919 Sohaib Virk 3 ,
• http://orcid.org/0000-0002-0180-0072 Christopher C Y Wong 4 , 5 ,
• Freda Passam 6 ,
• Jamie Layland 7 ,
• Anthony Keech 8 ,
• Andy Yong 4 ,
• http://orcid.org/0000-0001-7712-6750 Harvey D White 9 ,
• William Fearon 10 ,
• Martin Ng 1 , 11
• 1 Royal Prince Alfred Hospital , Camperdown , New South Wales , Australia
• 2 Concord Hospital , Concord , New South Wales , Australia
• 3 Systematic Reviews , CORE Group , Sydney , New South Wales , Australia
• 4 Cardiology , Concord Repatriation General Hospital , Concord , New South Wales , Australia
• 5 Stanford Hospital , Stanford , California , USA
• 6 Department of Hematology , University of Sydney , Sydney , New South Wales , Australia
• 7 Monash University , Melbourne , Victoria , Australia
• 8 NHMRC Clinical Trials Centre , The University of Sydney , Sydney , New South Wales , Australia
• 9 Cardiology Department , Green Lane Cardiovascular Service and Green Lane Cardiovascular Research Unit, Auckland City Hospital , Auckland , New Zealand
• 10 Stanford University , Stanford , California , USA
• 11 Department of Cardiology , The University of Sydney , Sydney , New South Wales , Australia
• Correspondence to Professor Martin Ng, Department of Cardiology, The University of Sydney, Sydney, New South Wales, Australia; Martin.ng{at}sydney.edu.au

Background Despite restoration of epicardial blood flow in acute ST-elevation myocardial infarction (STEMI), inadequate microcirculatory perfusion is common and portends a poor prognosis. Intracoronary (IC) thrombolytic therapy can reduce microvascular thrombotic burden; however, contemporary studies have produced conflicting outcomes.

Objectives This meta-analysis aims to evaluate the efficacy and safety of adjunctive IC thrombolytic therapy at the time of primary percutaneous coronary intervention (PCI) among patients with STEMI.

Methods Comprehensive literature search of six electronic databases identified relevant randomised controlled trials. The primary outcome was major adverse cardiac events (MACE). The pooled risk ratio (RR) and weighted mean difference (WMD) with a 95% CI were calculated.

Results 12 studies with 1915 patients were included. IC thrombolysis was associated with a significantly lower incidence of MACE (RR=0.65, 95% CI 0.51 to 0.82, I 2 =0%, p<0.0004) and improved left ventricular ejection fraction (WMD=1.87; 95% CI 1.07 to 2.67; I 2 =25%; p<0.0001). Subgroup analysis demonstrated a significant reduction in MACE for trials using non-fibrin (RR=0.39, 95% CI 0.20 to 0.78, I 2 =0%, p=0.007) and moderately fibrin-specific thrombolytic agents (RR=0.62, 95% CI 0.47 to 0.83, I 2 =0%, p=0.001). No significant reduction was observed in studies using highly fibrin-specific thrombolytic agents (RR=1.10, 95% CI 0.62 to 1.96, I 2 =0%, p=0.75). Furthermore, there were no significant differences in mortality (RR=0.91; 95% CI 0.48 to 1.71; I 2 =0%; p=0.77) or bleeding events (major bleeding, RR=1.24; 95% CI 0.47 to 3.28; I 2 =0%; p=0.67; minor bleeding, RR=1.47; 95% CI 0.90 to 2.40; I 2 =0%; p=0.12).

Conclusion Adjunctive IC thrombolysis at the time of primary PCI in patients with STEMI improves clinical and myocardial perfusion parameters without an increased rate of bleeding. Further research is needed to optimise the selection of thrombolytic agents and treatment protocols.

• Acute Coronary Syndrome
• Myocardial Infarction
• Meta-Analysis
• Atherosclerosis

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/heartjnl-2024-324078

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WHAT IS ALREADY KNOWN ON THIS TOPIC

ST-elevation myocardial infarction (STEMI) is a significant cause of morbidity and mortality worldwide. Microvascular obstruction affects about half of patients with STEMI, leading to adverse outcomes. Previous studies on adjunctive intracoronary thrombolysis have shown inconsistent results.

WHAT THIS STUDY ADDS

This meta-analysis demonstrates that adjunctive intracoronary thrombolysis during primary percutaneous coronary intervention (PCI) significantly reduces major adverse cardiac events and improves left ventricular ejection fraction. Furthermore, it significantly improves myocardial perfusion parameters without increasing bleeding risk.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Adjunctive intracoronary thrombolysis in patients with STEMI undergoing primary PCI shows promise for clinical benefit. Future studies should identify high-risk patients for microcirculatory dysfunction to optimise treatment strategies and clinical outcomes.

Introduction

Ischaemic heart disease remains a leading cause of morbidity and mortality worldwide. 1 2 ST-elevation myocardial infarction (STEMI) occurs due to coronary vessel occlusion causing transmural myocardial ischaemia and subsequent necrosis. 3 The cornerstone of contemporary management involves prompt reopening of the occluded coronary artery with percutaneous coronary intervention (PCI). 4 5 Despite restoring epicardial blood flow, roughly 50% of patients fail to achieve adequate microvascular perfusion. 6 This phenomenon, known as microvascular obstruction (MVO), is predictive of a poor cardiac prognosis driven by left ventricular remodelling and larger infarct size. 7–9

In patients with STEMI, MVO is characterised by distal embolisation of atherothrombotic debris and fibrin-rich microvascular thrombi. 10 A growing body of evidence supports the efficacy of adjunctive low-dose intracoronary (IC) thrombolysis in this population. Sezer et al performed the first randomised controlled trial (RCT), demonstrating an improvement in myocardial perfusion with low-dose IC streptokinase post-PCI. 11 Subsequent studies focused on newer fibrin-specific agents with a lower propensity for systemic bleeding. 12 Despite encouraging results, many studies were inadequately powered and yielded conflicting outcomes. This meta-analysis aims to evaluate the efficacy and safety of adjunctive IC thrombolytic therapy at the time of primary PCI in patients with STEMI.

The present study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. 13

Search strategy and study selection

Electronic searches were performed using PubMed, Ovid Medline, Cochrane Library, ProQuest, ACP Journal Club and Google Scholar from their dates of inception to January 2022. The search terms “STEMI” AND “intracoronary” AND (“thrombolysis” OR “tenecteplase” OR “alteplase” OR “prourokinase” OR “urokinase” OR “streptokinase”) were combined as both keywords and Medical Subject Headings terms, with filters for RCTs. This was supplemented by hand searching the bibliographies of review articles and all potentially relevant studies.

Two reviewers (RR and SV) independently screened the title and abstracts of articles identified in the search. Full-text publications were subsequently reviewed separately if either reviewer considered the manuscript as being potentially eligible. Any disagreements regarding final study inclusion were resolved by discussion and consensus with a third reviewer (CCYW).

Eligibility criteria

Studies were included if they met following inclusion criteria: (1) RCT, (2) STEMI population, (3) IC thrombolysis given to treatment group with comparison with a control group (CG) receiving no thrombolytic therapy, (4) major adverse cardiovascular event (MACE) was an outcome reported.

All publications were limited to those involving human subjects and no restrictions were based on language. Reviews, meta-analyses, abstracts, case reports, conference presentations, editorials and expert opinions were excluded. When institutions published duplicate studies with accumulating numbers of patients or increased lengths of follow-up, only the most complete reports were included for assessment.

Data extraction and quality assessment

Two investigators (RR and SV) independently extracted data from text, tables and figures. Any discrepancies were resolved by discussion and consensus with a third reviewer (CCYW). For each of the included trials, the following data were extracted: publication year, number of patients, baseline characteristics of participants, treatment details (including specific agents administered), follow-up duration and endpoints.

Study quality and risk of bias were critically appraised using the updated Cochrane Collaboration Risk-of-Bias Tool V.2. 14 Five domains of bias were evaluated: (1) randomisation process, (2) deviations from study protocol, (3) missing outcome data, (4) outcome measurement and (5) selective reporting of results.

The predetermined primary endpoint was MACE, which represented a composite outcome as defined by each individual study. While the individual components of MACE were generally consistent across studies, minor discrepancies existed ( online supplemental table 1 ). Secondary outcomes included clinical endpoints (mortality, heart failure (HF), major and minor bleeding), myocardial perfusion endpoints (thrombolysis in myocardial infarction (TIMI) flow grade 3, TIMI myocardial perfusion grade (TMPG), corrected TIMI frame count (CTFC), ST-resolution (STR)) and echocardiographic parameters (left ventricular ejection fraction (LVEF)). Subgroup analysis for MACE was conducted based on fibrin specificity of the thrombolytic agent. This classification comprised non-fibrin-specific agents (streptokinase and urokinase), moderately fibrin-specific agents (prourokinase) and highly fibrin-specific agents (alteplase and tenectaplase). Clinical outcomes were assessed at the end of the follow-up period, which ranged from 1 to 12 months, while echocardiographic parameters were evaluated within a time frame of 1–6 months.

Supplemental material

Statistical analysis.

The mean difference (MD) or relative risk (RR) was used as summary statistics and reported with 95% CIs. Meta-analyses were performed using random-effects models to take into account the anticipated clinical and methodological diversity between studies. The I 2 statistic was used to estimate the percentage of total variation across studies due to heterogeneity rather than chance, with values exceeding 50% indicative of considerable heterogeneity. For meta-analysis of continuous data, values presented as median and IQR were converted to mean and SD using the quantile method previously described by Wan et al . 15 For subgroup analyses, a standard test of heterogeneity was used to assess for significant difference between subgroups with p<0.05 considered statistically significant.

Meta-regression analyses were performed to explore potential heterogeneity with the following moderator variables individually assessed for significance: publication year, mean age, proportion of male participants, percentage of left anterior descending artery infarcts, proportion of smokers, as well as baseline prevalence of diabetes, hypertension and dyslipidaemia.

Publication bias was assessed for the primary endpoint of MACE using funnel plots comparing log of point estimates with their SE. Egger’s linear regression method and Begg’s rank correlation test were used to detect funnel plot asymmetry. 16 17 Statistical analysis was conducted with Review Manager V.5.3.5 (Cochrane Collaboration, Oxford, UK) and Comprehensive Meta-Analysis V.3.0 (Biostat, Englewood, New Jersey, USA). All p values were two sided, and values <0.05 were considered statistically significant.

A total of 245 unique records were identified through electronic searches using six online databases, from which 85 duplicates were removed. Of these, 120 were excluded based on title and abstract alone. After screening the full text of the remaining 40 articles, 12 studies 18–29 were found to meet the inclusion criteria, as summarised on the PRISMA flow chart in figure 1 .

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart of literature search and study selection.

IC thrombolysis was examined in 12 studies (n=1030 received IC thrombolysis and 885 no IC thrombolysis). Included studies used non-fibrin-specific (streptokinase, urokinase), moderately fibrin-specific (prourokinase) and highly fibrin-specific thrombolytic (alteplase, tenecteplase) agents. The timing and delivery of IC thrombolytic therapy varied between studies. A complete summary of study characteristics and baseline participant characteristics is presented in tables 1 and 2 , respectively. Primary and secondary outcomes are summarised in online supplemental table 2 . According to the revised Cochrane tool, the overall risk of bias assessment for procedural measures was judged to be ‘low risk’ in two studies, ‘some concerns’ in eight studies and ‘high risk’ in two studies ( online supplemental figure 1 ).

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Summary of studies investigating intracoronary thrombolysis for patients with STEMI

Summary of baseline patient characteristics in studies investigating intracoronary thrombolysis for patients with STEMI

Clinical outcomes

All 12 RCTs reported the incidence of MACE. Compared with the CG, IC thrombolysis treatment significantly improved the occurrence of MACE at the end of follow-up (RR=0.65, 95% CI 0.51 to 0.82, I 2 =0%, p<0.0004; figure 2 ). Subgroup analysis demonstrated a significant reduction in MACE for trials using non-fibrin (RR=0.39, 95% CI 0.20 to 0.78, I 2 =0%, p=0.007) and moderately fibrin-specific thrombolysis (RR=0.62, 95% CI 0.47 to 0.83, I 2 =0%, p=0.001). MACE was observed at a similar rate in studies using highly fibrin-specific thrombolysis (RR=1.10, 95% CI 0.62 to 1.96, I 2 =0%, p=0.75). Test for subgroup difference was not significant (p=0.07). Furthermore, IC thrombolysis was associated with an improvement of LVEF (weighted MD (WMD)=1.87; 95% CI, 1.07 to 2.67; I 2 =25%; p<0.0001; online supplemental figure 2 ). There was a trend towards lower incidence of HF hospitalisation (RR=0.66; 95% CI 0.42 to 1.05; I 2 =0%; p=0.08; online supplemental figure 3 ), though not statistically significant. No significant differences were observed in mortality (RR=0.95; 95% CI 0.50 to 1.81; I 2 =0%; p=0.88; online supplemental figure 4 ), major bleeding (RR=1.24; 95% CI 0.47 to 3.28; I 2 =0%; p=0.67; online supplemental figure 5 ) and minor bleeding events (RR=1.47; 95% CI 0.90 to 2.40; I 2 =0%; p=0.12; online supplemental figure 6 ) between the two groups.

Forest plot displaying relative risk for major adverse cardiovascular events with intracoronary (IC) thrombolysis (stratified by fibrin-specific and non-fibrin-specific agents) or placebo in ST-elevation myocardial infarction. Squares and diamonds=risk ratios. Lines=95% CIs.

Myocardial perfusion outcomes

In patients with STEMI, IC thrombolysis significantly improved TIMI flow grade 3 (RR=1.09; 95% CI 1.02 to 1.15; I 2 =63%; p=0.006), TMPG (RR=1.38; 95% CI 1.13 to 1.68; I 2 =54%; p=0.001), complete STR (RR=1.20; 95% CI 1.10 to 1.31; I 2 =51%; p<0.0001) and CTFC (WMD=−4.58; 95% CI −6.23 to –2.72; I 2 =41%; p<0.0001) when compared with the CG ( figure 3 ).

Forest plots of myocardial perfusion outcomes with intracoronary (IC) thrombolysis or placebo in ST-elevation myocardial infarction. (A) Thrombolysis in myocardial infarction (TIMI) flow grade 3. (B) TIMI myocardial perfusion grade 3. (C) ST-segment resolution. (D) Corrected TIMI frame count. Squares and diamonds=risk ratios/weighted mean difference. Lines=95% CIs.

Meta-regression results

For primary endpoint of MACE, meta-regression analyses did not identify the following moderator variables as significant effect modifiers: publication year (p=0.97), proportion of male (p=0.23), prevalence of diabetes (p=0.44), proportion of smokers (p=0.68), prevalence of dyslipidaemia (p=0.44) and prevalence of hypertension (p=0.21).

Publication bias

Both Egger’s linear regression method (p=0.73) and Begg’s rank correlation test (p=0.63) suggested that publication bias was not an influencing factor when MACE was selected as the primary endpoint.

The present meta-analysis examined 12 RCTs that included 1915 patients with STEMI undergoing primary PCI. All trials evaluated the efficacy and safety of IC thrombolytic agents compared with a CG. The main findings were that patients administered IC thrombolysis had: (1) significantly lower incidence of MACE, (2) improvement in LVEF and (3) superior myocardial perfusion parameters (TIMI flow grade 3, TMPG, CTFC and complete STR). Notably, there were no significant differences observed in mortality and bleeding events in both groups.

Mortality rates following STEMI remain high, with 30-day mortality rates ranging from 5.4% to 14% and 1-year mortality rates ranging from 6.6% to 17.5%. 30 Despite the increased availability of primary PCI facilities and advancements in reperfusion strategies, there has been limited improvement in STEMI mortality rates. 31 Moreover, complications such as HF, arrhythmia, repeat revascularisation and reinfarction continue to be prevalent. 32–34 Despite restoring epicardial blood flow through PCI, MVO is evident in almost half of patients with STEMI. 6 It is characterised by distal embolisation of atherothrombotic debris, de novo microvascular thrombosis formation and plugging of circulating blood cells. 35 Furthermore, the upregulation of inflammatory mediators leads to intramyocardial haemorrhage and further microvascular necrosis. 36 37 These mechanistic pathways contribute to a larger infarct size, adverse myocardial remodelling and worse prognosis. 7 8 38

Thrombolytic therapy is an effective treatment for acute coronary thrombosis. 39 It inhibits red blood cell aggregation and dissolves thrombi to facilitate adequate microvascular perfusion. 40 41 Thrombolytic agents are commonly classified based on their affinity for fibrin. Streptokinase and urokinase lack fibrin specificity, indiscriminately activating both circulating and clot-bound plasminogen. Prourokinase has moderate fibrin specificity with a propensity for activation on fibrin surfaces, although systemic fibrinogen degradation has been observed. Alteplase and tenectaplase are highly fibrin specific, activating fibrin-bound plasminogen with minimal impact on circulating free plasminogen.

Utilisation of a facilitated PCI strategy with adjunctive intravenous thrombolysis improves coronary flow acutely, 42 however, causes paradoxical activation of thrombin, leading to increased bleeding. 43 44 As a result, clinicians considered the administration of IC thrombolytic therapy. Encouraging results from an open-chest animal model 45 led to the first randomised trial using adjunctive IC streptokinase in 41 patients with STEMI undergoing primary PCI. 11 In the IC streptokinase group, patients demonstrated improved coronary flow reserve, index of microcirculatory resistance (IMR) and CTFC 2 days after primary PCI. 11 Further RCTs with moderately fibrin-specific thrombolytic agents (prourokinase) demonstrated similar results with improved myocardial perfusion parameters. 19 20 22 23 26–28 Notably, the T-TIME Study, a large RCT of 440 patients comparing a highly fibrin-specific thrombolytic agent (alteplase) against placebo, reported different outcomes. At 3-month follow-up, there were no significant differences in rates of death or HF hospitalisation between groups. In addition, microvascular obstruction (% left ventricular mass) on cardiac magnetic resonance (CMR) between groups at 2–7 days did not differ. The ICE T-TIMI trial, which also used a highly fibrin-specific thrombolytic agent (tenecteplase), investigated its efficacy in 40 patients. This small study administered two fixed doses of 4 mg of IC tenecteplase and evaluated the primary endpoint of culprit lesion per cent diameter stenosis after the first bolus of tenecteplase or placebo. The results indicated no significant difference in the primary endpoint between the two groups.

In an initial meta-analysis of six RCTs investigating the use of IC thrombolysis in patients with STEMI compared with placebo, findings revealed a reduction in MVO but no impact on MACE. 46 Subsequent analyses, including studies with larger sample sizes or focusing on specific thrombolytic agents, have since been conducted with varied results. 47 48 Our meta-analysis, which is the largest to date, demonstrates that adjunctive IC thrombolysis in patients with STEMI improves both clinical and microcirculation outcomes. Although bleeding events did not significantly increase, it is plausible that a tradeoff may exist for reducing MACE. Notably, subgroup analysis for MACE demonstrated no significant benefit for highly fibrin-specific agents ( figure 2 ).

Intuitively, fibrin-specific thrombolytics are presumed to offer inherent advantages over their less fibrin-specific counterparts. In vivo studies have revealed that administration of alteplase in patients with STEMI induced shorter periods of thrombin and kallikrein activation, less reduction in fibrinogen, and a decrease in D-dimer and plasmin–antiplasmin complexes compared with streptokinase. 49 In this regard, tenecteplase demonstrates superior performance relative to alteplase with almost no paradoxical procoagulant effect due to reduced activation of thrombin and the kallikrein–factor XII system. 50

Nonetheless, other variables may diminish the significance of fibrin specificity. It has been argued that administration of IC alteplase, a short-acting thrombolytic with a half-life of 4–6 min, before flow optimisation with stenting may have contributed to the negative results seen in T-TIME. Although prourokinase has a similarly short half-life and was also given before stenting in multiple studies, it was associated with better results. 19 20 22 23 26–28 The therapeutic efficacy of prourokinase predominantly relies on its conversion to urokinase, a non-fibrin-specific direct plasminogen activator, potentially resulting in a prolonged duration of action. Furthermore, inducing a systemic fibrinolytic state with a non-selective agent may be paradoxically desirable in patients receiving adjunctive IC thrombolytics during primary PCI. This approach can potentially prevent further thrombus reaccumulation and embolisation to the microcirculation, especially in a highly thrombogenic environment. In contrast, fibrin-specific agents may heighten the risk of rethrombosis and reocclusion due to their limited impact on systemic fibrinogen depletion. Nevertheless, such varied outcomes across these studies could be attributed to the heterogeneous methodologies used.

Despite encouraging results, future studies targeting patients at the highest risk of MVO with appropriately powered sample sizes are required. The ongoing RESTORE-MI (Restoring Microcirculatory Perfusion in STEMI) trial ( NCT03998319 ) has a unique approach in which all study participants will undergo assessment of microvascular integrity after primary PCI prior to inclusion. Only patients with objective evidence of microvascular dysfunction (IMR value >32) following reperfusion will be randomised to treatment with IC tenecteplase or placebo. The primary endpoint measured will be cardiovascular mortality and rehospitalisation for HF at 24 months, in addition to infarct size on CMR at 6 months post-PCI. This study may potentially support a novel therapeutic approach towards treating MVO in patients with STEMI in the future.

Limitations

Several key limitations should be considered when interpreting the findings of the present meta-analysis. First, several studies were subject to bias due to issues in randomisation and blinding, leading to an increased chance of type 1 (false-positive) error. In addition, the sample size of individual studies, except for the T-TIME trial, was relatively small. Second, inconsistencies in the duration of follow-up and the definition of clinical outcomes, such as MACE, were observed among the studies. Third, interventional protocols varied between RCTs. For example, IC thrombolytic therapy differed in agent, dosage, timing and route of administration. Initial studies used non-fibrin-specific agents, while contemporary studies moved towards newer fibrin-specific therapy. Besides Sezer et al , 25 all other studies administered IC thrombolysis therapy prior to stent implantation. 18–24 26–29 Within the latter group, some delivered before flow restoration, 19 21 29 though most did so after balloon dilation or thrombus aspiration. 18 20 22–24 26–28 Similarly, the methods of IC administration of the agents varied between non-selective delivery through guiding catheters 24 25 to selective delivery via IC catheters. 18–24 26–29 Furthermore, antiplatelet, anticoagulant and glycoprotein IIb/IIIa inhibitors (GPI) regimens also differed ( table 1 ). Finally, patient selection was diverse between studies. Though regression analysis did not detect any significant effect modifiers, total ischaemic time was omitted due to significant heterogeneity in reporting.

Impaired myocardial perfusion remains a clinical challenge in patients with STEMI. Despite its limitations, this meta-analysis favours the use of IC thrombolytic therapy during primary PCI. Overall, IC thrombolysis reduced the incidence of MACE and improved myocardial perfusion markers without increasing the risk of bleeding. Future clinical trials should be appropriately powered for clinical outcomes and focus on patients at high risk of microcirculatory dysfunction.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @RajanRehan23

Contributors RR—conceptualisation, methodology, data analysis, writing (original draft preparation), reviewing and editing the final manuscript. SV—methodology, data analysis. CCYW—conceptualisation, methodology, data analysis. FP—supervision, writing (reviewing and editing). JL—supervision, writing (reviewing and editing). AK—supervision, writing (reviewing and editing). AY—conceptualisation, methodology, writing (reviewing and editing). HDW—conceptualisation, methodology, writing (reviewing and editing). WF—conceptualisation, methodology, writing (reviewing and editing). MN—conceptualisation, methodology, supervision, writing (reviewing and editing), guarantor.

Funding This study is funded by the National Health and Medical Research Council (2022150).

Competing interests JL has received minor honoraria from Abbott Vascular, Boehringer Ingelheim and Bayer. AY has received minor honoraria and research support from Abbot Vascular and Philips Healthcare. WF has received research support from Abbott Vascular and Medtronic; and has minor stock options with HeartFlow. MN has received research support from Abbot Vascular. HDW has received grant support paid to the institution and fees for serving on Steering Committees of the ODYSSEY trial from Sanofi and Regeneron Pharmaceuticals, the ISCHEMIA and MINT Study from the National Institutes of Health, the STRENGTH trial from Omthera Pharmaceuticals, the HEART-FID Study from American Regent, the DAL-GENE Study from DalCor Pharma UK, the AEGIS-II Study from CSL Behring, the CLEAR OUTCOMES Study from Esperion Therapeutics, and the SOLIST-WHF and SCOREDS trials from Sanofi Aventis Australia. The remaining authors have nothing to disclose.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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