empirical research steps

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

Create a single source of real data with a built-for-insights platform. Store past data, add nuggets of insights, and import research data from various sources into a CRM for insights. Build on ever-growing research with a real-time dashboard in a unified research management platform to turn insights into knowledge.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Life@QuestionPro: The Journey of Kristie Lawrence

Jun 7, 2024

How Can I Help You? — Tuesday CX Thoughts

Jun 5, 2024

Why Multilingual 360 Feedback Surveys Provide Better Insights

Jun 3, 2024

Raked Weighting: A Key Tool for Accurate Survey Results

May 31, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Get science-backed answers as you write with Paperpal's Research feature

Empirical Research: A Comprehensive Guide for Academics 

Empirical research relies on gathering and studying real, observable data. The term ’empirical’ comes from the Greek word ’empeirikos,’ meaning ‘experienced’ or ‘based on experience.’ So, what is empirical research? Instead of using theories or opinions, empirical research depends on real data obtained through direct observation or experimentation. 

Why Empirical Research?

Empirical research plays a key role in checking or improving current theories, providing a systematic way to grow knowledge across different areas. By focusing on objectivity, it makes research findings more trustworthy, which is critical in research fields like medicine, psychology, economics, and public policy. In the end, the strengths of empirical research lie in deepening our awareness of the world and improving our capacity to tackle problems wisely. 1,2  

Qualitative and Quantitative Methods

There are two main types of empirical research methods – qualitative and quantitative. 3,4 Qualitative research delves into intricate phenomena using non-numerical data, such as interviews or observations, to offer in-depth insights into human experiences. In contrast, quantitative research analyzes numerical data to spot patterns and relationships, aiming for objectivity and the ability to apply findings to a wider context. 

Steps for Conducting Empirical Research

When it comes to conducting research, there are some simple steps that researchers can follow. 5,6  

• Create Research Hypothesis:  Clearly state the specific question you want to answer or the hypothesis you want to explore in your study. 
• Examine Existing Research:  Read and study existing research on your topic. Understand what’s already known, identify existing gaps in knowledge, and create a framework for your own study based on what you learn. 
• Plan Your Study:  Decide how you’ll conduct your research—whether through qualitative methods, quantitative methods, or a mix of both. Choose suitable techniques like surveys, experiments, interviews, or observations based on your research question. 
• Develop Research Instruments:  Create reliable research collection tools, such as surveys or questionnaires, to help you collate data. Ensure these tools are well-designed and effective. 
• Collect Data:  Systematically gather the information you need for your research according to your study design and protocols using the chosen research methods. 
• Data Analysis:  Analyze the collected data using suitable statistical or qualitative methods that align with your research question and objectives. 
• Interpret Results:  Understand and explain the significance of your analysis results in the context of your research question or hypothesis. 
• Draw Conclusions:  Summarize your findings and draw conclusions based on the evidence. Acknowledge any study limitations and propose areas for future research. 

Advantages of Empirical Research

Empirical research is valuable because it stays objective by relying on observable data, lessening the impact of personal biases. This objectivity boosts the trustworthiness of research findings. Also, using precise quantitative methods helps in accurate measurement and statistical analysis. This precision ensures researchers can draw reliable conclusions from numerical data, strengthening our understanding of the studied phenomena. 4  

Disadvantages of Empirical Research

While empirical research has notable strengths, researchers must also be aware of its limitations when deciding on the right research method for their study.4 One significant drawback of empirical research is the risk of oversimplifying complex phenomena, especially when relying solely on quantitative methods. These methods may struggle to capture the richness and nuances present in certain social, cultural, or psychological contexts. Another challenge is the potential for confounding variables or biases during data collection, impacting result accuracy.  

Tips for Empirical Writing

In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7   

• Define Your Objectives:  When you write about your research, start by making your goals clear. Explain what you want to find out or prove in a simple and direct way. This helps guide your research and lets others know what you have set out to achieve. 
• Be Specific in Your Literature Review:  In the part where you talk about what others have studied before you, focus on research that directly relates to your research question. Keep it short and pick studies that help explain why your research is important. This part sets the stage for your work. 
• Explain Your Methods Clearly : When you talk about how you did your research (Methods), explain it in detail. Be clear about your research plan, who took part, and what you did; this helps others understand and trust your study. Also, be honest about any rules you follow to make sure your study is ethical and reproducible. 
• Share Your Results Clearly : After doing your empirical research, share what you found in a simple way. Use tables or graphs to make it easier for your audience to understand your research. Also, talk about any numbers you found and clearly state if they are important or not. Ensure that others can see why your research findings matter. 
• Talk About What Your Findings Mean:  In the part where you discuss your research results, explain what they mean. Discuss why your findings are important and if they connect to what others have found before. Be honest about any problems with your study and suggest ideas for more research in the future. 
• Wrap It Up Clearly:  Finally, end your empirical research paper by summarizing what you found and why it’s important. Remind everyone why your study matters. Keep your writing clear and fix any mistakes before you share it. Ask someone you trust to read it and give you feedback before you finish. 

References:  

• Empirical Research in the Social Sciences and Education, Penn State University Libraries. Available online at  https://guides.libraries.psu.edu/emp  
• How to conduct empirical research, Emerald Publishing. Available online at  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research  
• Empirical Research: Quantitative & Qualitative, Arrendale Library, Piedmont University. Available online at  https://library.piedmont.edu/empirical-research  
• Bouchrika, I.  What Is Empirical Research? Definition, Types & Samples  in 2024. Research.com, January 2024. Available online at  https://research.com/research/what-is-empirical-research  
• Quantitative and Empirical Research vs. Other Types of Research. California State University, April 2023. Available online at  https://libguides.csusb.edu/quantitative  
• Empirical Research, Definitions, Methods, Types and Examples, Studocu.com website. Available online at  https://www.studocu.com/row/document/uganda-christian-university/it-research-methods/emperical-research-definitions-methods-types-and-examples/55333816  
• Writing an Empirical Paper in APA Style. Psychology Writing Center, University of Washington. Available online at  https://psych.uw.edu/storage/writing_center/APApaper.pdf  

Paperpal is an AI writing assistant that help academics write better, faster with real-time suggestions for in-depth language and grammar correction. Trained on millions of research manuscripts enhanced by professional academic editors, Paperpal delivers human precision at machine speed.  

Try it for free or upgrade to  Paperpal Prime , which unlocks unlimited access to premium features like academic translation, paraphrasing, contextual synonyms, consistency checks and more. It’s like always having a professional academic editor by your side! Go beyond limitations and experience the future of academic writing.  Get Paperpal Prime now at just US$19 a month!  

Related Reads:

• How to Write a Scientific Paper in 10 Steps 
• What is a Literature Review? How to Write It (with Examples)
• What is an Argumentative Essay? How to Write It (With Examples)
• Ethical Research Practices For Research with Human Subjects

Ethics in Science: Importance, Principles & Guidelines 

Presenting research data effectively through tables and figures, you may also like, how paperpal can boost comprehension and foster interdisciplinary..., what is the importance of a concept paper..., how to write the first draft of a..., mla works cited page: format, template & examples, how to ace grant writing for research funding..., powerful academic phrases to improve your essay writing , how to write a high-quality conference paper, how paperpal’s research feature helps you develop and..., how paperpal is enhancing academic productivity and accelerating..., how to write a successful book chapter for....

What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

• Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
• Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
• Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
• Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
• Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
• Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
• Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

• Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
• Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
• Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
• Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
• Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
• Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
• Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
• Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

• Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
• Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
• Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
• Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

• Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
• Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
• Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
• Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

• Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
• Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
• Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
• Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

• Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
• Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
• Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
• Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

• Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
• Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
• Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
• Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
• Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
• Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

• Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
• Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
• Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

• Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
• Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
• Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

• Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
• Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
• Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

• Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
• Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

• Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
• Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

• Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
• Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
• Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
• Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
• Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

• Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
• Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
• Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
• Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
• Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

To streamline your process and gather insights with precision and efficiency, consider leveraging innovative tools like Appinio . With Appinio's intuitive platform, you can harness the power of real-time consumer data to inform your research decisions effectively. Whether you're conducting surveys, interviews, or observations, Appinio empowers you to define your target audience, collect data from diverse demographics, and analyze results seamlessly.

By incorporating Appinio into your data collection toolkit, you can unlock a world of possibilities and elevate the impact of your empirical research. Ready to revolutionize your approach to data collection?

Book a Demo

Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

• Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
• Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
• Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
• Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
• Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

• Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
• Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
• Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
• Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
• Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
• Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

• Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
• Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
• Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
• Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
• Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

• Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
• Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
• Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

• Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
• Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
• Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
• Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

• Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
• Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
• Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
• Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

• Bar Charts and Histograms : Used to display the distribution of categorical data or discrete data .
• Line Charts : Ideal for showing trends and changes in data over time.
• Scatter Plots : Visualize relationships and correlations between two variables.
• Pie Charts : Display the composition of a whole in terms of its parts.
• Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
• Box Plots : Provide a summary of the data distribution, including outliers.
• Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

• Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
• Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
• Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
• Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
• Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
• Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
• References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
• Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

• Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
• Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
• Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
• Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
• Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
• Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

• Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
• Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
• Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
• Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
• Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys , focus groups , and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

Introducing Appinio , the real-time market research platform revolutionizing how companies gather consumer insights for their empirical research endeavors. With Appinio, you can conduct your own market research in minutes, gaining valuable data to fuel your data-driven decisions.

Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

Here's why Appinio is the go-to solution for empirical research:

• From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
• Intuitive Platform for Everyone : No need for a PhD in research; Appinio's platform is designed to be intuitive and user-friendly, ensuring that anyone can navigate and utilize it effectively.
• Rapid Response Times : With an average field time of under 23 minutes for 1,000 respondents, Appinio delivers rapid results, allowing you to gather data swiftly and efficiently.
• Global Reach with Targeted Precision : With access to over 90 countries and the ability to define target groups based on 1200+ characteristics, Appinio empowers you to reach your desired audience with precision and ease.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

30.05.2024 | 29min read

Pareto Analysis: Definition, Pareto Chart, Examples

28.05.2024 | 32min read

What is Systematic Sampling? Definition, Types, Examples

16.05.2024 | 30min read

Time Series Analysis: Definition, Types, Techniques, Examples

Summer is here, and so is the sale. Get a yearly plan with up to 65% off today! 🌴🌞

• Form Builder
• Survey Maker
• AI Form Generator
• AI Survey Tool
• AI Quiz Maker
• Store Builder
• WordPress Plugin

HubSpot CRM

Google Sheets

Google Analytics

Microsoft Excel

• Popular Forms
• Job Application Form Template
• Rental Application Form Template
• Hotel Accommodation Form Template
• Online Registration Form Template
• Employment Application Form Template
• Application Forms
• Booking Forms
• Consent Forms
• Contact Forms
• Donation Forms
• Customer Satisfaction Surveys
• Employee Satisfaction Surveys
• Evaluation Surveys
• Feedback Surveys
• Market Research Surveys
• Personality Quiz Template
• Geography Quiz Template
• Math Quiz Template
• Science Quiz Template
• Vocabulary Quiz Template

Try without registration Quick Start

Read engaging stories, how-to guides, learn about forms.app features.

Inspirational ready-to-use templates for getting started fast and powerful.

Spot-on guides on how to use forms.app and make the most out of it.

See the technical measures we take and learn how we keep your data safe and secure.

• Integrations
• Help Center
• Sign In Sign Up Free
• What is empirical research: Methods, types & examples

Defne Çobanoğlu

Having opinions on matters based on observation is okay sometimes. Same as having theories on the subject you want to solve. However, some theories need to be tested. Just like Robert Oppenheimer says, “Theory will take you only so far .” 

In that case, when you have your research question ready and you want to make sure it is correct, the next step would be experimentation. Because only then you can test your ideas and collect tangible information. Now, let us start with the empirical research definition:

• What is empirical research?

Empirical research is a research type where the aim of the study is based on finding concrete and provable evidence . The researcher using this method to draw conclusions can use both quantitative and qualitative methods. Different than theoretical research, empirical research uses scientific experimentation and investigation. 

Using experimentation makes sense when you need to have tangible evidence to act on whatever you are planning to do. As the researcher, you can be a marketer who is planning on creating a new ad for the target audience, or you can be an educator who wants the best for the students. No matter how big or small, data gathered from the real world using this research helps break down the question at hand. 

• When to use empirical research?

Empirical research methods are used when the researcher needs to gather data analysis on direct, observable, and measurable data. Research findings are a great way to make grounded ideas. Here are some situations when one may need to do empirical research:

1. When quantitative or qualitative data is needed

There are times when a researcher, marketer, or producer needs to gather data on specific research questions to make an informed decision. And the concrete data gathered in the research process gives a good starting point.

2. When you need to test a hypothesis

When you have a hypothesis on a subject, you can test the hypothesis through observation or experiment. Making a planned study is a great way to collect information and test whether or not your hypothesis is correct.

3. When you want to establish causality

Experimental research is a good way to explore whether or not there is any correlation between two variables. Researchers usually establish causality by changing a variable and observing if the independent variable changes accordingly.

• Types of empirical research

The aim of empirical research is to collect information about a subject from the people by doing experimentation and other data collection methods. However, the methods and data collected are divided into two groups: one collects numerical data, and the other one collects opinion-like data. Let us see the difference between these two types:

Quantitative research

Quantitative research methods are used to collect data in a numerical way. Therefore, the results gathered by these methods will be numbers, statistics, charts, etc. The results can be used to quantify behaviors, opinions, and other variables. Quantitative research methods are surveys, questionnaires, and experimental research.

Qualitiative research

Qualitative research methods are not used to collect numerical answers, instead, they are used to collect the participants’ reasons, opinions, and other meaningful aspects. Qualitative research methods include case studies, observations, interviews, focus groups, and text analysis.

• 5 steps to conduct empirical research

Necessary steps for empirical research

When you want to collect direct and concrete data on a subject, empirical research is a great way to go. And, just like every other project and research, it is best to have a clear structure in mind. This is even more important in studies that may take a long time, such as experiments that take years. Let us look at a clear plan on how to do empirical research:

1. Define the research question

The very first step of every study is to have the question you will explore ready. Because you do not want to change your mind in the middle of the study after investing and spending time on the experimentation.

2. Go through relevant literature

This is the step where you sit down and do a desk research where you gather relevant data and see if other researchers have tried to explore similar research questions. If so, you can see how well they were able to answer the question or what kind of difficulties they faced during the research process.

3. Decide on the methodology

Once you are done going through the relevant literature, you can decide on which method or methods you can use. The appropriate methods are observation, experimentation, surveys, interviews, focus groups, etc.

4. Do data analysis

When you get to this step, it means you have successfully gathered enough data to make a data analysis. Now, all you need to do is look at the data you collected and make an informed analysis.

5. Conclusion

This is the last step, where you are finished with the experimentation and data analysis process. Now, it is time to decide what to do with this information. You can publish a paper and make informed decisions about whatever your goal is.

• Empirical research methodologies

Some essential methodologies to conduct empirical research

The aim of this type of research is to explore brand-new evidence and facts. Therefore, the methods should be primary and gathered in real life, directly from the people. There is more than one method for this goal, and it is up to the researcher to use which one(s). Let us see the methods of empirical research: 

• Observation

The method of observation is a great way to collect information on people without the effect of interference. The researcher can choose the appropriate area, time, or situation and observe the people and their interactions with one another. The researcher can be just an outside observer or can be a participant as an observer or a full participant.

• Experimentation

The experimentation process can be done in the real world by intervening in some elements to unify the environment for all participants. This method can also be done in a laboratory environment. The experimentation process is good for being able to change the variables according to the aim of the study.

The case study method is done by making an in-depth analysis of already existing cases. When the parameters and variables are similar to the research question at hand, it is wise to go through what was researched before.

• Focus groups

The case study method is done by using a group of individuals or multiple groups and using their opinions, characteristics, and responses. The scientists gather the data from this group and generalize it to the whole population.

Surveys are an effective way to gather data directly from people. It is a systematic approach to collecting information. If it is done in an online setting as an online survey , it would be even easier to reach out to people and ask their opinions in open-ended or close-ended questions.

Interviews are similar to surveys as you are using questions to collect information and opinions of the people. Unlike a survey, this process is done face-to-face, as a phone call, or as a video call.

• Advantages of empirical research

Empirical research is effective for many reasons, and helps researchers from numerous fields. Here are some advantages of empirical research to have in mind for your next research:

• Empirical research improves the internal validity of the study.
• Empirical evidence gathered from the study is used to authenticate the research question.
• Collecting provable evidence is important for the success of the study.
• The researcher is able to make informed decisions based on the data collected using empirical research.
• Disadvantages of empirical research

After learning about the positive aspects of empirical research, it is time to mention the negative aspects. Because this type may not be suitable for everyone and the researcher should be mindful of the disadvantages of empirical research. Here are the disadvantages of empirical research:

• As it is similar to other research types, a case study where experimentation is included will be time-consuming no matter what. It has more steps and variables than concluding a secondary research.
• There are a lot of variables that need to be controlled and considered. Therefore, it may be a challenging task to be mindful of all the details.
• Doing evidence-based research can be expensive if you need to complete it on a large scale.
• When you are conducting an experiment, you may need some waivers and permissions.
• Frequently asked questions about empirical research

Empirical research is one of the many research types, and there may be some questions in mind about its similarities and differences to other research types.

Is empirical research qualitative or quantitative?

The data collected by empirical research can be qualitative, quantitative, or a mix of both. It is up to the aim of researcher to what kind of data is needed and searched for.

Is empirical research the same as quantitative research?

As quantitative research heavily relies on data collection methods of observation and experimentation, it is, in nature, an empirical study. Some professors may even use the terms interchangeably. However, that does not mean that empirical research is only a quantitative one.

What is the difference between theoretical and empirical research?

Empirical studies are based on data collection to prove theories or answer questions, and it is done by using methods such as observation and experimentation. Therefore, empirical research relies on finding evidence that backs up theories. On the other hand, theoretical research relies on theorizing on empirical research data and trying to make connections and correlations.

What is the difference between conceptual and empirical research?

Conceptual research is about thoughts and ideas and does not involve any kind of experimentation. Empirical research, on the other hand, works with provable data and hard evidence.

What is the difference between empirical vs applied research?

Some scientists may use these two terms interchangeably however, there is a difference between them. Applied research involves applying theories to solve real-life problems. On the other hand, empirical research involves the obtaining and analysis of data to test hypotheses and theories.

• Final words

Empirical research is a good means when the goal of your study is to find concrete data to go with. You may need to do empirical research when you need to test a theory, establish causality, or need qualitative/quantitative data. For example, you are a scientist and want to know if certain colors have an effect on people’s moods, or you are a marketer and want to test your theory on ad places on websites. 

In both scenarios, you can collect information by using empirical research methods and make informed decisions afterward. These are just the two of empirical research examples. This research type can be applied to many areas of work life and social sciences. Lastly, for all your research needs, you can visit forms.app to use its many useful features and over 1000 form and survey templates!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

• Form Features
• Data Collection

Table of Contents

Related posts.

15 Best Survicate alternatives to create surveys online (Pros, Cons & Prices)

Emre Eltemur

Top 11 survio alternatives and their pros & cons

How to generate leads in sales - with lead generation forms

forms.app Team

• Ask a Librarian

Research: Overview & Approaches

• Getting Started with Undergraduate Research
• Planning & Getting Started
• Building Your Knowledge Base
• Locating Sources
• Reading Scholarly Articles
• Creating a Literature Review
• Productivity & Organizing Research
• Scholarly and Professional Relationships

Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

• Interpretive Research
• Action-Based Research
• Creative & Experimental Approaches

Your Librarian

• Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

• Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

• Study on radiation transfer in human skin for cosmetics
• Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
• Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
• Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
• Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

• << Previous: Scholarly and Professional Relationships
• Next: Interpretive Research >>
• Last Updated: May 29, 2024 3:30 PM
• URL: https://guides.lib.purdue.edu/research_approaches

Penn State University Libraries

Empirical research in the social sciences and education.

• What is Empirical Research and How to Read It
• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Ethics, Cultural Responsiveness, and Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions to be answered
• Definition of the population, behavior, or   phenomena being studied
• Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
• Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Feb 18, 2024 8:33 PM
• URL: https://guides.libraries.psu.edu/emp

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

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

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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 analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are 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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 7 June 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism. Run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Table of Content

What is empirical research definition, types, and more.

Navigate data's complexities with empirical research, distinguishing truth from speculation. Explore types, methods, and more.

Research is crucial in many fields, involving a systematic exploration to confirm facts or draw specific conclusions. Empirical research, widely applied in different areas, aims to validate new facts. Grasping the significance of empirical research and knowing how to carry it out can aid in making decisions backed by a thorough investigation. 

What Do You Mean by Empirical Research?

The empirical research method is a study based on observation and direct experience to understand phenomena and draw conclusions based on real-world observations.

Empirical Research Examples

Consider a scenario where a study aims to determine if people add a product to their online cart due to product ratings. To investigate this, an experiment is carried out using an online shopping attitude survey . One group of participants is exposed to ratings, while another group is not exposed to any product ratings. The researchers then observe the behavior of these groups. The findings from this research will provide concrete evidence on whether product ratings impact the decision to purchase.

Types of Methodologies for Empirical Research

Quantitative research.

Quantitative research collects numerical data to analyze specific behaviors, opinions, or defined variables . Here are some methods used in quantitative empirical research:

This calls for collecting information from a group of people using a questionnaire. When conducting surveys, it's essential to pose straightforward, brief, and easy questions for participants to respond to. Survey participants can provide their answers through various channels, whether it be on paper, online through emails, or on social media. Administering surveys is generally a straightforward approach to obtaining information, whether from the general public or a specific audience.

Experimental Research

This process includes forming an idea and checking it through experimentation. Researchers can change one variable and see how it impacts other variables, helping them figure out if there's a clear connection. They can then examine the findings to confirm if their initial idea is correct.

Longitudinal Study

A longitudinal study involves observing a subject's characteristics or actions by testing them repeatedly over a period. The data collected from this method can be either qualitative or quantitative. For instance, marketers could track the buying patterns of a particular demographic, such as young adults, over several years. By repeatedly collecting data on their product design preferences, brand loyalty, and spending habits, researchers can gain insights into how these factors evolve over time. 

Cross-Sectional Research

Cross-sectional research is a way of studying people by looking at them during a particular time. In this method, researchers pick a group of individuals with similar characteristics, excluding the ones they are studying. This helps ensure that any findings are likely caused by the variable under investigation. For instance, researchers assess consumer preferences for different packaging designs at a specific time. Participants from the target market evaluate various options, providing immediate feedback. This approach offers a quick snapshot of consumer opinions on packaging, helping companies make informed decisions based on current preferences.

Correlational Research

Correlational research is a method used to find connections and prevalence among different factors. It often uses regression as a statistical tool to predict outcomes, showing whether there's a negative, neutral, or positive correlation between variables. For example, researchers might explore the relationship between how much time individuals spend watching television and their overall well-being. By collecting data on both variables from a diverse group of participants, the researchers can analyze whether there is a correlation between the time spent watching TV and factors like happiness or stress levels.

Qualitative Research

Qualitative research is useful for collecting information that isn't in numbers or can't be measured easily. It usually involves semi-structured or unstructured approaches, letting researchers uncover personal meanings, reasons, and opinions from participants. Qualitative empirical research often involves a small group of people and conversational methods to get detailed information and deeper insights into a problem. Examples of methods used in qualitative research include:

Observational Method

It involves watching and collecting descriptive information about a subject. The observational method gives researchers personal insights, helping them form detailed opinions about their studies. It's commonly used in ethnographic research, which looks at the culture of different groups of people.

One-on-One Interview

This is an entirely qualitative method that includes directly talking to a subject. Researchers often use it to get accurate and meaningful information about a subject. It's a conversational approach where specific questions are asked to guide the discussion. 

Focus Group

Focus groups are employed when researchers seek answers to questions of why, what, and how. A small group is typically chosen, and in-person interaction may not be necessary. If an in-person discussion is involved, a moderator is usually required. This method is commonly utilized by product companies to gather information about their brands and products.

For instance, in media/ad testing with focus groups, a company may evaluate a new soft drink advertisement. A small group views different ad versions and discusses their impressions, preferences, and memorable elements. This feedback helps the company refine its advertising strategy before a wider campaign launch.

Text Analysis

This qualitative empirical research method enables the analysis of an individual's social life . It's a contemporary approach leveraging the growing importance of social media and technology. Researchers can examine the specific words and images an individual uses to draw meaningful conclusions.

How to Conduct Empirical Research?

Empirical research relies on observation and experiences, so planning and analysis are crucial. Let’s take an example of media/ad/shopper testing as the research base to understand the steps to conduct empirical research - 

Step 1: Define the Research Objective

Clearly outline the study's goal, such as evaluating the effectiveness of a new packaging design for a consumer product or an advertisement of a new series. Consider potential issues with the resources schedule and ensure the study's benefits justify the costs.

Step 2: Review Relevant Literature and Theories

Identify theories or previous studies on consumer responses to packaging changes or new series ad releases. Understand how these insights can inform the study's outcomes.

Step 3: Formulate Hypothesis and Measurements

Develop an initial hypothesis, considering variables like consumer perception, brand appeal, and market competitiveness. Define units of measurement, such as consumer preferences and purchasing behavior, ensuring they align with industry standards.

Step 4: Define Research Design, Methodology, and Data Collection Techniques

Choose an appropriate research approach, whether qualitative research or quantitative research , to assess consumer reactions to the new packaging. Consider using focus groups and one-on-one interviews for in-depth insights and gather data on consumer reactions.

Step #5: Conduct Data Analysis and Frame the Results

Analyze the collected data, considering both quantitative metrics and qualitative feedback from focus groups and interviews. Assess whether the new packaging positively influences consumer perceptions and purchasing decisions. 

Evaluate consumer research tools powered by Insights AI that are powered by AI to give you unbiased feedback considering the emotions and behaviour of the respondent. 

Step 6: Draw Conclusions

Prepare a comprehensive report presenting the findings, including the impact of the new packaging or advertisement on consumer behavior. If sharing the results widely, convert the report into an article for publication and recommend further research areas in the packaging and media testing domain. Use a plagiarism checker to ensure the originality and credibility of the research.

You can also utilize the Gen AI feature in Decode to draw conclusions from your studies by just asking the Decode co-pilot, a virtual assistant. 

{{cta-button}}

Empirical Research Cycle

Observation

A media researcher observes audience reactions to a new television show by monitoring social media comments, ratings, and viewership numbers. This initial data collection serves as the basis for forming hypotheses about the show's popularity.

Based on the observations, the researcher may induce a hypothesis that suggests the show's popularity is linked to its engaging storyline and relatable characters. This assumption is then examined and tested against the collected data.

Using deductive reasoning, the researcher concludes that if the show's popularity is consistently associated with positive audience engagement and high ratings, it can be inferred that engaging content is a significant factor.

To test the hypothesis, the researcher designs a survey asking viewers about their reasons for liking the show and analyzes the responses. Statistical methods are employed to determine if there's a significant correlation between positive viewer feedback and the show's popularity.

In the final stage, the researcher evaluates the survey results, considering the empirical data, viewer comments, and any challenges encountered during the research. The findings are used to draw conclusions about the factors contributing to the show's success, and this information becomes the basis for further media testing or content development.

{{cta-case}}

Advantages and Disadvantages of Empirical Research

Advantages of empirical research.

Empirical research is widely used for several reasons, and here are some of its advantages:

• Authentication of Traditional Research: It validates traditional research through experiments and observations.
• Enhanced Competence and Authenticity: This methodology enhances the competency and authenticity of the conducted research.
• Adaptability to Dynamic Changes: Researchers can understand and adapt to dynamic changes by utilizing empirical research and adjusting their strategies accordingly.
• High Control Level: Empirical research offers a high level of control, allowing researchers to manage multiple variables.
• Increased Internal Validity: It plays a crucial role in boosting internal validity, ensuring the accuracy of the research outcomes.

Disadvantages of Empirical Research

While empirical research brings competency and authenticity, it also has some drawbacks:

• Time-Consuming Nature: Collecting data from various sources and dealing with numerous parameters can make this research time-consuming requiring patience.
• Costly Endeavor: Conducting research in different locations or environments may lead to increased expenses.
• Permission Challenges: Obtaining consent for certain experimental methods can be difficult, as there are strict rules governing their execution.
• Data Collection Challenges: Collecting data from various sources through different methods can be problematic at times.

Bottom Line

In a world full of data, empirical research is crucial for finding out what's true. It involves carefully observing and experiencing things to draw conclusions based on real-world evidence. This type of research uses both numbers (quantitative) and descriptions (qualitative) to understand various topics.

To conduct empirical research, you need a step-by-step plan. This includes setting clear goals, looking at existing research, making educated guesses (hypotheses), picking the right methods, analyzing data, and reaching sensible conclusions.

The research cycle involves watching, making guesses, drawing logical conclusions, testing those guesses, and finally evaluating everything.

While empirical research has benefits like proving traditional research, increasing competence, and adapting to changes, it also has challenges like being time-consuming, expensive, and dealing with permission and data collection issues.

In summary, understanding and using empirical research helps us make informed decisions in different fields by carefully studying and validating information through a systematic process.

Frequently Asked Questions:

What do you mean by empirical research.

Empirical research is a type of study that relies on observing and measuring real-life phenomena as directly witnessed by the researcher. The collected data can be analyzed in relation to a theory or hypothesis, but the conclusions are grounded in actual experiences.

Theoretical vs Empirical Research

Empirical refers to information derived from observations or personal experiences, while theoretical is associated with ideas and hypotheses. In research contexts, these terms are commonly used to describe data, methods, or probabilities.

What are the benefits of Empirical Research?

Empirical research strives to understand the significance of a specific phenomenon. In simpler terms, it seeks to uncover how and why something operates the way it does. By pinpointing the reasons behind occurrences, it becomes feasible to reproduce or avoid similar events.

Is Empirical quantitative or qualitative?

Empirical research is often thought of as the same as quantitative research, but to be precise, it's any research that relies on direct observation.

Empirical Method Psychology Example

Suppose a researcher aims to investigate the impact of listening to happy music on promoting prosocial behavior. In this scenario, an empirical analysis could involve conducting an experiment where one group of participants is exposed to happy music while another group is not exposed to any music at all.

Get your Product Pack Design tested against competitors

Got a question? Check out our FAQ’s

Maximize your research potential.

Experience why teams worldwide trust our Consumer & User Research solutions.

Book a Demo

Understanding the Empirical Method in Research Methodology

Table of Contents

Have you ever wondered how scientists gather evidence to support their theories? Or what steps researchers take to ensure that their findings are reliable and not just based on speculation? The answer lies in a cornerstone of scientific investigation known as the empirical method . This approach to research is all about collecting data and observing the world to form solid, evidence-based conclusions. Let’s dive into the empirical method’s fascinating world and understand why it’s so critical in research methodology.

What is the empirical method?

The empirical method is a way of gaining knowledge by means of direct and indirect observation or experience. It’s fundamentally based on the idea that knowledge comes from sensory experience and can be acquired through observation and experimentation. This method stands in contrast to approaches that rely solely on theoretical or logical means.

The role of observation in the empirical method

Observation is at the heart of the empirical method. It involves using your senses to gather information about the world. This could be as simple as noting the color of a flower or as complex as using advanced technology to observe the behavior of microscopic organisms. The key is that the observations must be systematic and replicable, providing reliable data that can be used to draw conclusions.

Data collection: qualitative and quantitative

Different types of data can be collected using the empirical method:

• Qualitative data – This data type is descriptive and conceptual, often collected through interviews, observations, and case studies.
• Quantitative data – This involves numerical data collected through methods like surveys, experiments, and statistical analysis.

Empirical vs. experimental methods

While the empirical method is often associated with experimentation, it’s important to distinguish between the two. Experimental methods involve controlled tests where the researcher manipulates one variable to observe the effect on another. In contrast, the empirical method doesn’t necessarily involve manipulation. Instead, it focuses on observing and collecting data in natural settings, offering a broader understanding of phenomena as they occur in real life.

Why the distinction matters

Understanding the difference between empirical and experimental methods is crucial because it affects how research is conducted and how results are interpreted. Empirical research can provide a more naturalistic view of the subject matter, whereas experimental research can offer more control over variables and potentially more precise outcomes.

The significance of experiential learning

The empirical method has deep roots in experiential learning, which emphasizes learning through experience. This connection is vital because it underlines the importance of engaging with the subject matter at a practical level, rather than just theoretically. It’s a hands-on approach to knowledge that has been valued since the time of Aristotle.

Developing theories from empirical research

One of the most significant aspects of the empirical method is its role in theory development . Researchers collect and analyze data, and from these findings, they can formulate or refine theories. Theories that are supported by empirical evidence tend to be more robust and widely accepted in the scientific community.

Applying the empirical method in various fields

The empirical method is not limited to the natural sciences. It’s used across a range of disciplines, from social sciences to humanities, to understand different aspects of the world. For instance:

• In psychology , researchers might use the empirical method to observe and record behaviors to understand the underlying mental processes.
• In sociology , it could involve studying social interactions to draw conclusions about societal structures.
• In economics , empirical data might be used to test the validity of economic theories or to measure market trends.

Challenges and limitations

Despite its importance, the empirical method has its challenges and limitations. One major challenge is ensuring that observations and data collection are unbiased. Additionally, not all phenomena are easily observable, and some may require more complex or abstract approaches.

The empirical method is a fundamental aspect of research methodology that has stood the test of time. By relying on observation and data collection, it allows researchers to ground their theories in reality, providing a solid foundation for knowledge. Whether it’s used in the hard sciences, social sciences, or humanities, the empirical method continues to be a critical tool for understanding our complex world.

How do you think the empirical method affects the credibility of research findings? And can you think of a situation where empirical methods might be difficult to apply but still necessary for advancing knowledge? Let’s discuss these thought-provoking questions and consider the breadth of the empirical method’s impact on the pursuit of understanding.

How useful was this post?

Click on a star to rate it!

Average rating / 5. Vote count:

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Submit Comment

Research Methodology

1 Introduction to Research in General

• Research in General
• Research Circle
• Tools of Research
• Methods: Quantitative or Qualitative
• The Product: Research Report or Papers

2 Original Unity of Philosophy and Science

• Myth Philosophy and Science: Original Unity
• The Myth: A Spiritual Metaphor
• Myth Philosophy and Science
• The Greek Quest for Unity
• The Ionian School
• Towards a Grand Unification Theory or Theory of Everything
• Einstein’s Perennial Quest for Unity

3 Evolution of the Distinct Methods of Science

• Definition of Scientific Method
• The Evolution of Scientific Methods
• Theory-Dependence of Observation
• Scope of Science and Scientific Methods
• Prevalent Mistakes in Applying the Scientific Method

4 Relation of Scientific and Philosophical Methods

• Definitions of Scientific and Philosophical method
• Philosophical method
• Scientific method
• The relation
• The Importance of Philosophical and scientific methods

5 Dialectical Method

• Introduction and a Brief Survey of the Method
• Types of Dialectics
• Dialectics in Classical Philosophy
• Dialectics in Modern Philosophy
• Critique of Dialectical Method

6 Rational Method

• Understanding Rationalism
• Rational Method of Investigation
• Descartes’ Rational Method
• Leibniz’ Aim of Philosophy
• Spinoza’ Aim of Philosophy

7 Empirical Method

• Common Features of Philosophical Method
• Empirical Method
• Exposition of Empiricism
• Locke’s Empirical Method
• Berkeley’s Empirical Method
• David Hume’s Empirical Method

8 Critical Method

• Basic Features of Critical Theory
• On Instrumental Reason
• Conception of Society
• Human History as Dialectic of Enlightenment
• Substantive Reason
• Habermasian Critical Theory
• Habermas’ Theory of Society
• Habermas’ Critique of Scientism
• Theory of Communicative Action
• Discourse Ethics of Habermas

9 Phenomenological Method (Western and Indian)

• Phenomenology in Philosophy
• Phenomenology as a Method
• Phenomenological Analysis of Knowledge
• Phenomenological Reduction
• Husserl’s Triad: Ego Cogito Cogitata
• Intentionality
• Understanding ‘Consciousness’
• Phenomenological Method in Indian Tradition
• Phenomenological Method in Religion

10 Analytical Method (Western and Indian)

• Analysis in History of Philosophy
• Conceptual Analysis
• Analysis as a Method
• Analysis in Logical Atomism and Logical Positivism
• Analytic Method in Ethics
• Language Analysis
• Quine’s Analytical Method
• Analysis in Indian Traditions

11 Hermeneutical Method (Western and Indian)

• The Power (Sakti) to Convey Meaning
• Three Meanings
• Pre-understanding
• The Semantic Autonomy of the Text
• Towards a Fusion of Horizons
• The Hermeneutical Circle
• The True Scandal of the Text
• Literary Forms

12 Deconstructive Method

• The Seminal Idea of Deconstruction in Heidegger
• Deconstruction in Derrida
• Structuralism and Post-structuralism
• Sign Signifier and Signified
• Writing and Trace
• Deconstruction as a Strategic Reading
• The Logic of Supplement
• No Outside-text

13 Method of Bibliography

• Preparing to Write
• Writing a Paper
• The Main Divisions of a Paper
• Writing Bibliography in Turabian and APA
• Sample Bibliography

14 Method of Footnotes

• Citations and Notes
• General Hints for Footnotes
• Writing Footnotes
• Examples of Footnote or Endnote
• Example of a Research Article

15 Method of Notes Taking

• Methods of Note-taking
• Note Book Style
• Note taking in a Computer
• Types of Note-taking
• Notes from Field Research
• Errors to be Avoided

16 Method of Thesis Proposal and Presentation

• Preliminary Section
• Presenting the Problem of the Thesis
• Design of the Study
• Main Body of the Thesis
• Conclusion Summary and Recommendations
• Reference Material

Share on Mastodon

• What is Empirical Research Study? [Examples & Method]

The bulk of human decisions relies on evidence, that is, what can be measured or proven as valid. In choosing between plausible alternatives, individuals are more likely to tilt towards the option that is proven to work, and this is the same approach adopted in empirical research. 

In empirical research, the researcher arrives at outcomes by testing his or her empirical evidence using qualitative or quantitative methods of observation, as determined by the nature of the research. An empirical research study is set apart from other research approaches by its methodology and features hence; it is important for every researcher to know what constitutes this investigation method. 

What is Empirical Research? 

Empirical research is a type of research methodology that makes use of verifiable evidence in order to arrive at research outcomes. In other words, this  type of research relies solely on evidence obtained through observation or scientific data collection methods. 

Empirical research can be carried out using qualitative or quantitative observation methods , depending on the data sample, that is, quantifiable data or non-numerical data . Unlike theoretical research that depends on preconceived notions about the research variables, empirical research carries a scientific investigation to measure the experimental probability of the research variables 

Characteristics of Empirical Research

• Research Questions

An empirical research begins with a set of research questions that guide the investigation. In many cases, these research questions constitute the research hypothesis which is tested using qualitative and quantitative methods as dictated by the nature of the research.

In an empirical research study, the research questions are built around the core of the research, that is, the central issue which the research seeks to resolve. They also determine the course of the research by highlighting the specific objectives and aims of the systematic investigation. 

• Definition of the Research Variables

The research variables are clearly defined in terms of their population, types, characteristics, and behaviors. In other words, the data sample is clearly delimited and placed within the context of the research. 

• Description of the Research Methodology

 An empirical research also clearly outlines the methods adopted in the systematic investigation. Here, the research process is described in detail including the selection criteria for the data sample, qualitative or quantitative research methods plus testing instruments. 

An empirical research is usually divided into 4 parts which are the introduction, methodology, findings, and discussions. The introduction provides a background of the empirical study while the methodology describes the research design, processes, and tools for the systematic investigation. 

The findings refer to the research outcomes and they can be outlined as statistical data or in the form of information obtained through the qualitative observation of research variables. The discussions highlight the significance of the study and its contributions to knowledge. 

Uses of Empirical Research

Without any doubt, empirical research is one of the most useful methods of systematic investigation. It can be used for validating multiple research hypotheses in different fields including Law, Medicine, and Anthropology. 

• Empirical Research in Law : In Law, empirical research is used to study institutions, rules, procedures, and personnel of the law, with a view to understanding how they operate and what effects they have. It makes use of direct methods rather than secondary sources, and this helps you to arrive at more valid conclusions.
• Empirical Research in Medicine : In medicine, empirical research is used to test and validate multiple hypotheses and increase human knowledge.
• Empirical Research in Anthropology : In anthropology, empirical research is used as an evidence-based systematic method of inquiry into patterns of human behaviors and cultures. This helps to validate and advance human knowledge.

Discover how Extrapolation Powers statistical research: Definition, examples, types, and applications explained.

The Empirical Research Cycle

The empirical research cycle is a 5-phase cycle that outlines the systematic processes for conducting and empirical research. It was developed by Dutch psychologist, A.D. de Groot in the 1940s and it aligns 5 important stages that can be viewed as deductive approaches to empirical research. 

In the empirical research methodological cycle, all processes are interconnected and none of the processes is more important than the other. This cycle clearly outlines the different phases involved in generating the research hypotheses and testing these hypotheses systematically using the empirical data. 

• Observation: This is the process of gathering empirical data for the research. At this stage, the researcher gathers relevant empirical data using qualitative or quantitative observation methods, and this goes ahead to inform the research hypotheses.
• Induction: At this stage, the researcher makes use of inductive reasoning in order to arrive at a general probable research conclusion based on his or her observation. The researcher generates a general assumption that attempts to explain the empirical data and s/he goes on to observe the empirical data in line with this assumption.
• Deduction: This is the deductive reasoning stage. This is where the researcher generates hypotheses by applying logic and rationality to his or her observation.
• Testing: Here, the researcher puts the hypotheses to test using qualitative or quantitative research methods. In the testing stage, the researcher combines relevant instruments of systematic investigation with empirical methods in order to arrive at objective results that support or negate the research hypotheses.
• Evaluation: The evaluation research is the final stage in an empirical research study. Here, the research outlines the empirical data, the research findings and the supporting arguments plus any challenges encountered during the research process.

This information is useful for further research. 

Learn about qualitative data: uncover its types and examples here.

Examples of Empirical Research 

• An empirical research study can be carried out to determine if listening to happy music improves the mood of individuals. The researcher may need to conduct an experiment that involves exposing individuals to happy music to see if this improves their moods.

The findings from such an experiment will provide empirical evidence that confirms or refutes the hypotheses. 

• An empirical research study can also be carried out to determine the effects of a new drug on specific groups of people. The researcher may expose the research subjects to controlled quantities of the drug and observe research subjects to controlled quantities of the drug and observe the effects over a specific period of time to gather empirical data.
• Another example of empirical research is measuring the levels of noise pollution found in an urban area to determine the average levels of sound exposure experienced by its inhabitants. Here, the researcher may have to administer questionnaires or carry out a survey in order to gather relevant data based on the experiences of the research subjects.
• Empirical research can also be carried out to determine the relationship between seasonal migration and the body mass of flying birds. A researcher may need to observe the birds and carry out necessary observation and experimentation in order to arrive at objective outcomes that answer the research question.

Empirical Research Data Collection Methods

Empirical data can be gathered using qualitative and quantitative data collection methods. Quantitative data collection methods are used for numerical data gathering while qualitative data collection processes are used to gather empirical data that cannot be quantified, that is, non-numerical data. 

The following are common methods of gathering data in empirical research

• Survey/ Questionnaire

A survey is a method of data gathering that is typically employed by researchers to gather large sets of data from a specific number of respondents with regards to a research subject. This method of data gathering is often used for quantitative data collection , although it can also be deployed during quantitative research.

A survey contains a set of questions that can range from close-ended to open-ended questions together with other question types that revolve around the research subject. A survey can be administered physically or with the use of online data-gathering platforms like Formplus. 

Empirical data can also be collected by carrying out an experiment. An experiment is a controlled simulation in which one or more of the research variables is manipulated using a set of interconnected processes in order to confirm or refute the research hypotheses.

An experiment is a useful method of measuring causality; that is cause and effect between dependent and independent variables in a research environment. It is an integral data gathering method in an empirical research study because it involves testing calculated assumptions in order to arrive at the most valid data and research outcomes. 

T he case study method is another common data gathering method in an empirical research study. It involves sifting through and analyzing relevant cases and real-life experiences about the research subject or research variables in order to discover in-depth information that can serve as empirical data.

• Observation

The observational method is a method of qualitative data gathering that requires the researcher to study the behaviors of research variables in their natural environments in order to gather relevant information that can serve as empirical data.

How to collect Empirical Research Data with Questionnaire

With Formplus, you can create a survey or questionnaire for collecting empirical data from your research subjects. Formplus also offers multiple form sharing options so that you can share your empirical research survey to research subjects via a variety of methods.

Here is a step-by-step guide of how to collect empirical data using Formplus:

Sign in to Formplus

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

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

Unlock the secrets of Quantitative Data: Click here to explore the types and examples.

Edit Form Title

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

Edit Form  

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

Customize Form

Formplus allows you to add unique features to your empirical research survey form. You can personalize your survey using various customization options. Here, you can add background images, your organization’s logo, and use other styling options. You can also change the display theme of your form. 

• Share your Form Link with Respondents

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

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

Empirical vs Non-Empirical Research

Empirical and non-empirical research are common methods of systematic investigation employed by researchers. Unlike empirical research that tests hypotheses in order to arrive at valid research outcomes, non-empirical research theorizes the logical assumptions of research variables. 

Definition: Empirical research is a research approach that makes use of evidence-based data while non-empirical research is a research approach that makes use of theoretical data. 

Method: In empirical research, the researcher arrives at valid outcomes by mainly observing research variables, creating a hypothesis and experimenting on research variables to confirm or refute the hypothesis. In non-empirical research, the researcher relies on inductive and deductive reasoning to theorize logical assumptions about the research subjects.

The major difference between the research methodology of empirical and non-empirical research is while the assumptions are tested in empirical research, they are entirely theorized in non-empirical research. 

Data Sample: Empirical research makes use of empirical data while non-empirical research does not make use of empirical data. Empirical data refers to information that is gathered through experience or observation. 

Unlike empirical research, theoretical or non-empirical research does not rely on data gathered through evidence. Rather, it works with logical assumptions and beliefs about the research subject. 

Data Collection Methods : Empirical research makes use of quantitative and qualitative data gathering methods which may include surveys, experiments, and methods of observation. This helps the researcher to gather empirical data, that is, data backed by evidence.  

Non-empirical research, on the other hand, does not make use of qualitative or quantitative methods of data collection . Instead, the researcher gathers relevant data through critical studies, systematic review and meta-analysis. 

Advantages of Empirical Research 

• Empirical research is flexible. In this type of systematic investigation, the researcher can adjust the research methodology including the data sample size, data gathering methods plus the data analysis methods as necessitated by the research process.
• It helps the research to understand how the research outcomes can be influenced by different research environments.
• Empirical research study helps the researcher to develop relevant analytical and observation skills that can be useful in dynamic research contexts.
• This type of research approach allows the researcher to control multiple research variables in order to arrive at the most relevant research outcomes.
• Empirical research is widely considered as one of the most authentic and competent research designs.
• It improves the internal validity of traditional research using a variety of experiments and research observation methods.

Disadvantages of Empirical Research 

• An empirical research study is time-consuming because the researcher needs to gather the empirical data from multiple resources which typically takes a lot of time.
• It is not a cost-effective research approach. Usually, this method of research incurs a lot of cost because of the monetary demands of the field research.
• It may be difficult to gather the needed empirical data sample because of the multiple data gathering methods employed in an empirical research study.
• It may be difficult to gain access to some communities and firms during the data gathering process and this can affect the validity of the research.
• The report from an empirical research study is intensive and can be very lengthy in nature.

Conclusion 

Empirical research is an important method of systematic investigation because it gives the researcher the opportunity to test the validity of different assumptions, in the form of hypotheses, before arriving at any findings. Hence, it is a more research approach. 

There are different quantitative and qualitative methods of data gathering employed during an empirical research study based on the purpose of the research which include surveys, experiments, and various observatory methods. Surveys are one of the most common methods or empirical data collection and they can be administered online or physically. 

You can use Formplus to create and administer your online empirical research survey. Formplus allows you to create survey forms that you can share with target respondents in order to obtain valuable feedback about your research context, question or subject. 

In the form builder, you can add different fields to your survey form and you can also modify these form fields to suit your research process. Sign up to Formplus to access the form builder and start creating powerful online empirical research survey forms. 

Connect to Formplus, Get Started Now - It's Free!

• advantage of empirical research
• disadvantages of empirical resarch
• empirical research characteristics
• empirical research cycle
• empirical research method
• example of empirical research
• uses of empirical research
• busayo.longe

You may also like:

Research Questions: Definitions, Types + [Examples]

A comprehensive guide on the definition of research questions, types, importance, good and bad research question examples

Extrapolation in Statistical Research: Definition, Examples, Types, Applications

In this article we’ll look at the different types and characteristics of extrapolation, plus how it contrasts to interpolation.

What is Pure or Basic Research? + [Examples & Method]

Simple guide on pure or basic research, its methods, characteristics, advantages, and examples in science, medicine, education and psychology

Recall Bias: Definition, Types, Examples & Mitigation

This article will discuss the impact of recall bias in studies and the best ways to avoid them during research.

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

How to... Conduct empirical research

Share this content

Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data from a literature review may form the theoretical background.

On this page

What is empirical research, the research question, the theoretical framework, sampling techniques, design of the research.

• Methods of empirical research
• Techniques of data collection & analysis
• Reporting the findings of empirical research
• Further information

Typically, empirical research embodies the following elements:

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalise  from the findings to a larger sample and to other situations.

The starting point for your research should be your research question. This should be a formulation of the issue which is at the heart of the area which you are researching, which has the right degree of breadth and depth to make the research feasible within your resources. The following points are useful to remember when coming up with your research question, or RQ:

• your doctoral thesis;
• reading the relevant literature in journals, especially literature reviews which are good at giving an overview, and spotting interesting conceptual developments;
• looking at research priorities of funding bodies, professional institutes etc.;
• going to conferences;
• looking out for calls for papers;
• developing a dialogue with other researchers in your area.
• To narrow down your research topic, brainstorm ideas around it, possibly with your colleagues if you have decided to collaborate, noting all the questions down.
• Come up with a "general focus" question; then develop some other more specific ones.
• they are not too broad;
• they are not so narrow as to yield uninteresting results;
• will the research entailed be covered by your resources, i.e. will you have sufficient time and money;
• there is sufficient background literature on the topic;
• you can carry out appropriate field research;
• you have stated your question in the simplest possible way.

Let's look at some examples:

Bisking et al. examine whether or not gender has an influence on disciplinary action in their article  Does the sex of the leader and subordinate influence a leader's disciplinary decisions?  ( Management Decision , Volume 41 Number 10) and come up with the following series of inter-related questions:

• Given the same infraction, would a male leader impose the same disciplinary action on male and female subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on male and female subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on female subordinates as a male leader would on male subordinates?
• Given the same infraction, would a female leader impose the same disciplinary action on male subordinates as a male leader would on female subordinates?
• Given the same infraction, would a male and female leader impose the same disciplinary action on male subordinates?
• Given the same infraction, would a male and female leader impose the same disciplinary action on female subordinates?
• Do female and male leaders impose the same discipline on subordinates regardless of the type of infraction?
• Is it possible to predict how female and male leaders will impose disciplinary actions based on their respective BSRI femininity and masculinity scores?

Motion et al. examined co-branding in  Equity in Corporate Co-branding  ( European Journal of Marketing , Volume 37 Number 7/8) and came up with the following RQs:

RQ1:  What objectives underpinned the corporate brand?

RQ2:  How were brand values deployed to establish the corporate co-brand within particular discourse contexts?

RQ3:  How was the desired rearticulation promoted to shareholders?

RQ4:  What are the sources of corporate co-brand equity?

Note, the above two examples state the RQs very explicitly; sometimes the RQ is implicit:

Qun G. Jiao, Anthony J. Onwuegbuzie are library researchers who examined the question:  "What is the relationship between library anxiety and social interdependence?"  in a number of articles, see  Dimensions of library anxiety and social interdependence: implications for library services   ( Library Review , Volume 51 Number 2).

Or sometimes the RQ is stated as a general objective:

Ying Fan describes outsourcing in British companies in  Strategic outsourcing: evidence from British companies  ( Marketing Intelligence & Planning , Volume 18 Number 4) and states his research question as an objective:

The main objective of the research was to explore the two key areas in the outsourcing process, namely:

• pre-outsourcing decision process; and
• post-outsourcing supplier management.

or as a proposition:

Karin Klenke explores issues of gender in management decisions in  Gender influences in decision-making processes in top management teams   ( Management Decision , Volume 41 Number 10).

Given the exploratory nature of this research, no specific hypotheses were formulated. Instead, the following general propositions are postulated:

P1.  Female and male members of TMTs exercise different types of power in the strategic decision making process.

P2.  Female and male members of TMTs differ in the extent in which they employ political savvy in the strategic decision making process.

P3.  Male and female members of TMTs manage conflict in strategic decision making situations differently.

P4.  Female and male members of TMTs utilise different types of trust in the decision making process.

Sometimes, the theoretical underpinning (see next section) of the research leads you to formulate a hypothesis rather than a question:

Martin et al. explored the effect of fast-forwarding of ads (called zipping) in  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  ( Marketing Intelligence & Planning , Volume 20 Number 1) and his research explores the following hypotheses:

The influence of zipping H1. Individuals viewing advertisements played at normal speed will exhibit higher ad recall and recognition than those who view zipped advertisements.

Ad repetition effects H2. Individuals viewing a repeated advertisement will exhibit higher ad recall and recognition than those who see an advertisement once.

Zipping and ad repetition H3. Individuals viewing zipped, repeated advertisements will exhibit higher ad recall and recognition than those who see a normal speed advertisement that is played once.

Empirical research is not divorced from theoretical considerations; and a consideration of theory should form one of the starting points of your research. This applies particularly in the case of management research which by its very nature is practical and applied to the real world. The link between research and theory is symbiotic: theory should inform research, and the findings of research should inform theory.

There are a number of different theoretical perspectives; if you are unfamiliar with them, we suggest that you look at any good research methods textbook for a full account (see Further information), but this page will contain notes on the following:

This is the approach of the natural sciences, emphasising total objectivity and independence on the part of the researcher, a highly scientific methodology, with data being collected in a value-free manner and using quantitative techniques with some statistical measures of analysis. Assumes that there are 'independent facts' in the social world as in the natural world. The object is to generalise from what has been observed and hence add to the body of theory.

Very similar to positivism in that it has a strong reliance on objectivity and quantitative methods of data collection, but with less of a reliance on theory. There is emphasis on data and facts in their own right; they do not need to be linked to theory.

Interpretivism

This view criticises positivism as being inappropriate for the social world of business and management which is dominated by people rather than the laws of nature and hence has an inevitable subjective element as people will have different interpretations of situations and events. The business world can only be understood through people's interpretation. This view is more likely to emphasise qualitative methods such as participant observation, focus groups and semi-structured interviewing.

While reality exists independently of human experience, people are not like objects in the natural world but are subject to social influences and processes. Like  empiricism  and  positivism , this emphasises the importance of explanation, but is also concerned with the social world and with its underlying structures.

Inductive and deductive approaches

At what point in your research you bring in a theoretical perspective will depend on whether you choose an:

• Inductive approach  – collect the data, then develop the theory.
• Deductive approach  – assume a theoretical position then test it against the data.

It should be emphasised that none of the above approaches are mutually exclusive and can be used in combination.

Sampling may be done either:

• On a  random  basis – a given number is selected completely at random.
• On a  systematic  basis – every  n th element  of the population is selected.
• On a  stratified random  basis – the population is divided into segments, for example, in a University, you could divide the population into academic, administrators, and academic related. A random number of each group is then selected.
• On a  cluster  basis – a particular subgroup is chosen at random.
• Convenience  – being present at a particular time e.g. at lunch in the canteen.
• Purposive  – people can be selected deliberately because their views are relevant to the issue concerned.
• Quota  – the assumption is made that there are subgroups in the population, and a quota of respondents is chosen to reflect this diversity.

Useful articles

Richard Laughlin in  Empirical research in accounting: alternative approaches and a case for "middle-range" thinking  provides an interesting general overview of the different perspectives on theory and methodology as applied to accounting. ( Accounting, Auditing & Accountability Journal,  Volume 8 Number 1).

D. Tranfield and K. Starkey in  The Nature, Social Organization and Promotion of Management Research: Towards Policy  look at the relationship between theory and practice in management research, and develop a number of analytical frameworks, including looking at Becher's conceptual schema for disciplines and Gibbons et al.'s taxonomy of knowledge production systems. ( British Journal of Management , vol. 9, no. 4 – abstract only).

Research design is about how you go about answering your question: what strategy you adopt, and what methods do you use to achieve your results. In particular you should ask yourself... 

There's a lot more to this article; just fill in the form below to instantly see the complete content.

Read the complete article

What's in the rest?

• Continuation of 'Design of the research'
• Books & websites for further information

Your data will be used, alongside feedback we may request, only to help inform and improve our 'How to' section – thank you.

Canvas | University | Ask a Librarian

• Library Homepage
• Arrendale Library

Empirical Research: Quantitative & Qualitative

• Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

• Quantitative vs. Qualitative
• Reference Works for Social Sciences Research
• Contact Us!

 Call us at 706-776-0111

  Chat with a Librarian

  Send Us Email

  Library Hours

Empirical research  is based on phenomena that can be observed and measured. Empirical research derives knowledge from actual experience rather than from theory or belief. 

Key characteristics of empirical research include:

• Specific research questions to be answered;
• Definitions of the population, behavior, or phenomena being studied;
• Description of the methodology or research design used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys);
• Two basic research processes or methods in empirical research: quantitative methods and qualitative methods (see the rest of the guide for more about these methods).

(based on the original from the Connelly LIbrary of LaSalle University)

Empirical Research: Qualitative vs. Quantitative

Learn about common types of journal articles that use APA Style, including empirical studies; meta-analyses; literature reviews; and replication, theoretical, and methodological articles.

Academic Writer

© 2024 American Psychological Association.

• More about Academic Writer ...

Quantitative Research

A quantitative research project is characterized by having a population about which the researcher wants to draw conclusions, but it is not possible to collect data on the entire population.

• For an observational study, it is necessary to select a proper, statistical random sample and to use methods of statistical inference to draw conclusions about the population. 
• For an experimental study, it is necessary to have a random assignment of subjects to experimental and control groups in order to use methods of statistical inference.

Statistical methods are used in all three stages of a quantitative research project.

For observational studies, the data are collected using statistical sampling theory. Then, the sample data are analyzed using descriptive statistical analysis. Finally, generalizations are made from the sample data to the entire population using statistical inference.

For experimental studies, the subjects are allocated to experimental and control group using randomizing methods. Then, the experimental data are analyzed using descriptive statistical analysis. Finally, just as for observational data, generalizations are made to a larger population.

Iversen, G. (2004). Quantitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 897-898). Thousand Oaks, CA: SAGE Publications, Inc.

Qualitative Research

What makes a work deserving of the label qualitative research is the demonstrable effort to produce richly and relevantly detailed descriptions and particularized interpretations of people and the social, linguistic, material, and other practices and events that shape and are shaped by them.

Qualitative research typically includes, but is not limited to, discerning the perspectives of these people, or what is often referred to as the actor’s point of view. Although both philosophically and methodologically a highly diverse entity, qualitative research is marked by certain defining imperatives that include its case (as opposed to its variable) orientation, sensitivity to cultural and historical context, and reflexivity. 

In its many guises, qualitative research is a form of empirical inquiry that typically entails some form of purposive sampling for information-rich cases; in-depth interviews and open-ended interviews, lengthy participant/field observations, and/or document or artifact study; and techniques for analysis and interpretation of data that move beyond the data generated and their surface appearances. 

Sandelowski, M. (2004).  Qualitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.),  Encyclopedia of social science research methods . (pp. 893-894). Thousand Oaks, CA: SAGE Publications, Inc.

• Next: Quantitative vs. Qualitative >>
• Last Updated: Mar 22, 2024 10:47 AM
• URL: https://library.piedmont.edu/empirical-research
• Ebooks & Online Video
• New Materials
• Renew Checkouts
• Faculty Resources
• Library Friends
• Library Services
• Our Mission
• Library History
• Ask a Librarian!
• Making Citations
• Working Online

Arrendale Library Piedmont University 706-776-0111

• Foundations
• Write Paper

Search form

• Experiments
• Anthropology
• Self-Esteem
• Social Anxiety

Empirical Research

Empirical research is the process of testing a hypothesis using experimentation, direct or indirect observation and experience.

This article is a part of the guide:

• Definition of Research
• Research Basics
• What is Research?
• Steps of the Scientific Method
• Purpose of Research

Browse Full Outline

• 1 Research Basics
• 2.1 What is Research?
• 2.2 What is the Scientific Method?
• 2.3 Empirical Research
• 3.1 Definition of Research
• 3.2 Definition of the Scientific Method
• 3.3 Definition of Science
• 4 Steps of the Scientific Method
• 5 Scientific Elements
• 6 Aims of Research
• 7 Purpose of Research
• 8 Science Misconceptions

The word empirical describes any information gained by experience, observation, or experiment . One of the central tenets of the scientific method is that evidence must be empirical, i.e. based on evidence observable to the senses.

Philosophically, empiricism defines a way of gathering knowledge by direct observation and experience rather than through logic or reason alone (in other words, by rationality). In the scientific paradigm the term refers to the use of hypotheses that can be tested using observation and experiment. In other words, it is the practical application of experience via formalized experiments.

Empirical data is produced by experiment and observation, and can be either quantitative or qualitative.

Objectives of Empirical Research

Empirical research is informed by observation, but goes far beyond it. Observations alone are merely observations. What constitutes empirical research is the scientist’s ability to formally operationalize those observations using testable research questions.

In well-conducted research, observations about the natural world are cemented in a specific research question or hypothesis. The observer can make sense of this information by recording results quantitatively or qualitatively.

Techniques will vary according to the field, the context and the aim of the study. For example, qualitative methods are more appropriate for many social science questions and quantitative methods more appropriate for medicine or physics.

However, underlying all empirical research is the attempt to make observations and then answer well-defined questions via the acceptance or rejection of a hypothesis, according to those observations.

Empirical research can be thought of as a more structured way of asking a question – and testing it. Conjecture, opinion, rational argument or anything belonging to the metaphysical or abstract realm are also valid ways of finding knowledge. Empiricism, however, is grounded in the “real world” of the observations given by our senses.

Reasons for Using Empirical Research Methods

Science in general and empiricism specifically attempts to establish a body of knowledge about the natural world. The standards of empiricism exist to reduce any threats to the validity of results obtained by empirical experiments. For example, scientists take great care to remove bias, expectation and opinion from the matter in question and focus only on what can be empirically supported.

By continually grounding all enquiry in what can be repeatedly backed up with evidence, science advances human knowledge one testable hypothesis at a time. The standards of empirical research – falsifiability, reproducibility – mean that over time empirical research is self-correcting and cumulative.

Eventually, empirical evidence forms over-arching theories, which themselves can undergo change and refinement according to our questioning. Several types of designs have been used by researchers, depending on the phenomena they are interested in.

The Scientific Cycle

Empirical research is not the only way to obtain knowledge about the world, however. While many students of science believe that “empirical scientific methods” and “science” are basically the same thing, the truth is that empiricism is just one of many tools in a scientist’s inventory.

In practice, empirical methods are commonly used together with non-empirical methods, and qualitative and quantitative methods produce richer data when combined. The scientific method can be thought of as a cycle, consisting of the following stages:

• Observation Observation  involves collecting and organizing empirical data. For example, a biologist may notice that individual birds of the same species will not migrate some years, but will during other years. The biologist also notices that on the years they migrate, the birds appear to be bigger in size. He also knows that migration is physiologically very demanding on a bird.
• Induction Induction  is then used to form a hypothesis . It is the process of reaching a conclusion by considering whether a collection of broader premises supports a specific claim. For example, taking the above observations and what is already known in the field of migratory bird research, the biologist may ask a question: “is sufficiently high body weight associated with the choice to migrate each year?”  He could assume that it is and stop there, but this is mere conjecture, and not science. Instead he finds a way to test his hypothesis. He devises an experiment where he tags and weighs a population of birds and watches to observe whether they migrate or not.
• Deduction Deduct ion relies on logic and rationality to come to specific conclusions given general premises. Deduction allows a scientist to craft the internal logic of his experimental design. For example, the argument in the biologist’s experiment is: if high bird weight predicts migration, then I would expect to see those birds who I measure at higher weights to migrate, and those who do not to opt out of migration. If I don’t see that birds with higher weight migrate more often than those who don’t, I can conclude that bird weight and migration are not connected after all.”
• Testing Test the hypothesis entails returning to empirical methods to put the hypothesis to the test. The biologist, after designing his experiment, conducting it and obtaining the results, now has to make sense of the data. Here, he can use statistical methods to determine the significance of any relationship he sees, and interpret his results. If he finds that almost every higher weight bird ends up migrating, he has found support (not proof) for his hypothesis that weight and migration are connected.
• Evaluation An often-forgotten step of the research process is to reflect and appraise the process. Here, interpretations are offered and the results set within a broader context. Scientists are also encouraged to consider the limitations of their research and suggest avenues for others to pick up where they left off.

http://en.wikipedia.org/wiki/Empirical http://en.wikipedia.org/wiki/Empirical_research

• Psychology 101
• Flags and Countries
• Capitals and Countries

Explorable.com , Lyndsay T Wilson (Sep 21, 2009). Empirical Research. Retrieved Jun 08, 2024 from Explorable.com: https://explorable.com/empirical-research

You Are Allowed To Copy The Text

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) .

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page.

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Related articles

What is the Scientific Method?

Empirical Evidence

Want to stay up to date? Follow us!

Save this course for later.

Don't have time for it all now? No problem, save it as a course and come back to it later.

Footer bottom

• Privacy Policy

• Subscribe to our RSS Feed
• Like us on Facebook
• Follow us on Twitter

• Survey Software The world’s leading omnichannel survey software
• Online Survey Tools Create sophisticated surveys with ease.
• Mobile Offline Conduct efficient field surveys.
• Text Analysis
• Close The Loop
• Automated Translations
• NPS Dashboard
• CATI Manage high volume phone surveys efficiently
• Cloud/On-premise Dialer TCPA compliant Cloud & on-premise dialer
• IVR Survey Software Boost productivity with automated call workflows.
• Analytics Analyze survey data with visual dashboards
• Panel Manager Nurture a loyal community of respondents.
• Survey Portal Best-in-class user friendly survey portal.
• Voxco Audience Conduct targeted sample research in hours.

Find the best survey software for you! (Along with a checklist to compare platforms)

Get Buyer’s Guide

• 40+ question types
• Drag-and-drop interface
• Skip logic and branching
• Multi-lingual survey
• Text piping
• Question library
• CSS customization
• White-label surveys
• Customizable ‘Thank You’ page
• Customizable survey theme
• Reminder send-outs
• Survey rewards
• Social media
• Website surveys
• Correlation analysis
• Cross-tabulation analysis
• Trend analysis
• Real-time dashboard
• Customizable report
• Email address validation
• Recaptcha validation
• SSL security

Take a peek at our powerful survey features to design surveys that scale discoveries.

Download feature sheet.

• Hospitality
• Financial Services
• Academic Research
• Customer Experience
• Employee Experience
• Product Experience
• Market Research
• Social Research
• Data Analysis

Explore Voxco 

Need to map Voxco’s features & offerings? We can help!

Watch a Demo 

Download Brochures 

Get a Quote

• NPS Calculator
• CES Calculator
• A/B Testing Calculator
• Margin of Error Calculator
• Sample Size Calculator
• CX Strategy & Management Hub
• Market Research Hub
• Patient Experience Hub
• Employee Experience Hub
• Market Research Guide
• Customer Experience Guide
• The Voxco Guide to Customer Experience
• NPS Knowledge Hub
• Survey Research Guides
• Survey Template Library
• Webinars and Events
• Feature Sheets
• Try a sample survey
• Professional services

Find the best customer experience platform

Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.

Get the Guide Now

We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.

VP Innovation & Strategic Partnerships, The Logit Group

• Client Stories
• Voxco Reviews
• Why Voxco Research?
• Careers at Voxco
• Vulnerabilities and Ethical Hacking

Explore Regional Offices

• Cloud/On-premise Dialer TCPA compliant Cloud on-premise dialer
• Predictive Analytics
• Customer 360
• Customer Loyalty
• Fraud & Risk Management
• AI/ML Enablement Services
• Credit Underwriting

Get Buyer’s Guide

• 100+ question types
• SMS surveys
• Banking & Financial Services
• Retail Solution
• Risk Management
• Customer Lifecycle Solutions
• Net Promoter Score
• Customer Behaviour Analytics
• Customer Segmentation
• Data Unification

Explore Voxco 

Watch a Demo 

Download Brochures 

• CX Strategy & Management Hub
• Blogs & White papers
• Case Studies

VP Innovation & Strategic Partnerships, The Logit Group

• Why Voxco Intelligence?
• Our clients
• Client stories
• Featuresheets

Dive into Empirical Research: An Insightful Overview

• April 1, 2021

Market Research Tool kit

Get started with Voxco’s Market Research Toolkit.

Market Research trends guide + Online Surveys guide + Agile MArket Research Guide + 5 Market Research Templates 

SHARE THE ARTICLE ON

What is Empirical research?

In empirical study, conclusions of the study are drawn from concrete empirical evidence. This evidence is also referred to as “verifiable” evidence. This evidence is gathered either through quantitative market research or qualitative market research methods.

An example of empirical analysis  would be if a researcher was interested in finding out whether listening to happy music promotes prosocial behaviour. An experiment could be conducted where one group of the audience is exposed to happy music and the other is not exposed to music at all. The participants could be given an opportunity to either help a stranger with something or not. The results are then evaluated to find whether happy music increases prosaically behavior or not.

See Voxco survey software in action with a Free demo.

What is an Empirical Study?

The origin of empirical methods starts from the quote “I will not believe it unless I see it myself.” Empirical observation emerged during the renaissance with medieval science. The word empirical is derived from the Greek word ‘empeirikos’ meaning ‘experienced’.

The word empirical, in today’s day and age, refers to collecting empirical data through methods of observation, experience, or by specific scientific instruments. All of these methods are dependent on observation and experiments which are used to collect data and test the same for arriving at conclusions. Online survey tools are an extremely effective technique which can be used for empirical methods.

Types and methodologies of empirical research

Empirical study uses qualitative or quantitative methods to conduct research and analyze results. 

• Quantitative research: Quantitative research is referred to as the process of collecting as well as analyzing numerical data. It is generally used to find patterns, averages, predictions, as well as cause-effect relationships between the variables being studied. It is also used to generalize the results of a particular study to the population in consideration.

• Qualitative research: Qualitative research can be defined as a method used for market research which aims at obtaining data through open-ended questions and conversations with the intended consumers. This method aims at establishing not only “what” people think but “how” they come to that opinion as well as “why” they think so.

Step by Step guide to Descriptive Research

Get ready to uncover the how, when, what, and where questions in a research problem

The empirical data that is collected from either of these methods has to be analyzed. Empirical evidence is analyzed using qualitative or quantitative methods. These methods are used to answer empirical questions that are clearly defined. The type of research design used by the researcher depends on the field and the nature of the problem. Some researchers use a combination of quantitative and qualitative methods to answer the questions set for the research.

Quantitative research methods

Quantitative research methods help in the analysis of the empirical evidence that has been gathered. By using these methods researchers can find support for their hypotheses.

• Survey research: Survey research is the most common and widely used tool for quantitative research. Surveys are used to gather data by asking relevant questions to the respondents who are thought to have the relevant information we are seeking to acquire. Generally, a formal list of questionnaires is prepared which is circulated to the respondents and they can self-report their thoughts. Researchers use a non-disguised approach so that the participants of the survey know exactly what they are answering. In general, respondents are asked questions regarding their demographic details, and the opinion that the researcher is interested in studying. Surveys can be conducted through online polls, paper-pencil questionnaires, web-intercept surveys, etc. 

For example: In market research, customers are deemed as the most important part of the organisation. It is a known fact that satisfied customers will help your organisation grow directly by remaining loyal to your company and also by becoming an advocate for your brand. Researchers can use customer satisfaction survey templates to assess their brand’s value and how likely their customers are to recommend their brand to others.

• Experimental research : This is one of the most recommended and reliant research methods in natural as well as social sciences. As the name suggests, experimental research (also known as experimentation) is usually based on one of more theories as its driving principle or rationale. In this method, the theory which is under study has not yet proven, it is merely a speculation. Thus, an experiment is performed in order to either prove or disprove the theory. If the results of the experiment are in line with the prediction made by the theory, then the theory is supported. If not, then the theory is refuted. 

For instance, if a researcher wants to study whether their dandruff protection product is successful in curing dandruff, and the only difference between the two groups under study is the product of interest (one group uses the product while group 2 uses a placebo), then dandruff could be considered as the dependent variable and the product curing it would be called an independent variable. Now, the independent variable, here, is “manipulated” in the sense that one group is exposed to it and one is not. All things being constant, if the product cures dandruff in group 1 as opposed to the group that is using a placebo, the experimental research findings are successful. This will help in establishing a cause and effect relationship, the product is “causing” the treatment (“effect”) of dandruff.

• Correlational research : A correlation refers to an association or a relationship between two entities. A correlational research studies how one entity impacts the other and what are the changes that are observed when either one of them changes.  correlation coefficient ranges from -1 to +1. A correlation coefficient of +1 indicates a perfect positive correlation whereas a correlation coefficient of -1 indicates a perfect negative correlation between two variables. A correlation coefficient of 0 indicates that there is no relationship between the variables under study.

Some examples of correlational research questions: 

• What is the relationship between gender and the purchase of a particular product under study?
• The relationship between stress and burnout in employees of an organisation.
• The relationship between choosing to work from home and the level of corona-phobia in employees.
• Longitudinal study : Longitudinal surveys, on the other hand, involve studying variables for a long period of time and observing the changes in them from time to time. Here, the data is collected from the respondents at the beginning of the study, and then the researcher collects data at different time intervals until the end of the study. Longitudinal surveys are more popularly used in medicinal science to understand and evaluate the effects of medicines, or vaccines, in the long-run on participants. Because longitudinal surveys take place for several years, researchers can establish the sequence of events that may affect the variable under study.

For example: If researchers want to understand how smoking affects the development of cancer in later stages of life, they would choose participants who are different from other observable variables but similar in one: smoking. In this case, researchers would observe the participants who started smoking from adolescence into later adulthood and examine the changes in their body that are caused due to smoking. They can see how smoking has influenced the immunity of participants, their reaction to stress, and other variables relevant to the researcher. Over time, researchers can also observe the effects of quitting smoking if some participants decide to quit smoking later in their life. This will help researchers understand the interaction between health and smoking in more detail.

• Cross sectional: In cross-sectional surveys, the study takes place at a single point in time. Hence, cross-sectional surveys do not entail the manipulation of the variables under study, and are limited in that way. Cross-sectional surveys allow researchers to study various characteristics, such as the demographic structure of the consumers, their interests, and attitudes, all at once. It aims to provide information about the population at the current moment in time. For example, cross-sectional surveys will tell us how the consumer is responding and feeling about the product at the present moment. It does not study the other variables that may affect the consumers’ reactions to the product in the future.

For example: Let us consider a researcher who is aiming to study developmental psychology. He/she may select groups of people who are of different ages but study them at one point in time. In this way, the difference between the groups will be attributed to their age differences instead of other variables that may happen over time.

Download Market Research Toolkit

Get market research trends guide, Online Surveys guide, Agile Market Research Guide & 5 Market research Template

Qualitative research methods

A qualitative approach is more appropriate when tackling some research questions. This is especially true if the researcher wishes to observe the behaviors of the target audience in-depth. The results here are in descriptive form. Qualitative research is not predictive in nature. It enables researchers to build and support their theories to advance future potential quantitative research. Qualitative research methods are used to come up with conclusions to support the theory or hypothesis under study.

• Case study: Case studies have evolved to become a valuable method for qualitative research. It is used for explaining a case of an organization or an entity. This is one of the simplest ways of conducting research because it involves an exhaustive understanding of the data collected and the interpretation of the same. 

For example: For example; let’s assume that a researcher is interested in understanding how to effectively solve the problems of turnover in organizations. While exploring, he came across an organization that had high rates of turnover and was able to solve the problem by the end of the year. The researcher can study this case in detail and come up with methods that increased the chances of success for this organization.

• Observational method :  When doing qualitative research, maintaining the existing records can be a valuable source of information in the future. This data can be used in new research and also provide insights for the same. Observation is one of the common aspects that is used in every method we described above. It can be systematic or naturalistic. Qualitative observation of respondents’ answers, or their behaviors in particular settings can yield enriching insights. Hence, observation in qualitative research is used to gather information about relevant characteristics that the researcher is interested in studying.

For instance, if a smartphone brand wants to see how customers react to its products in a showroom, observers may be hired to note the same. The observers can use the recorded observations to evaluate and draw inferences about the customers.

• One-on-one interview : Interviewing people of interest is one of the most common practices in qualitative research. Here, there is an in-depth personal interview carried out either face-to-face or through online mediums with one respondent at a time. This is a conversational method of gathering information and it invites the researcher with an opportunity to get a detailed response from the respondent.

For example: A one-on-one interview with an environmentalist will help to gather data on the current climate crisis in the world. 

• Focus groups :  Another most commonly used method in qualitative research apart from interviewing people is focus group. In this method, data is usually conducted once a researcher includes a limited number of consumers (usually ranging from 6 to 10) from the target market and forms a group. 

For example: Let’s assume a researcher wants to explore what are qualities consumers value when buying a laptop. This could be the display quality, battery life, brand value, or even the color. The researcher can make a focus group of people who buy laptops regularly and understand the dynamics a consumer considers when buying electronic devices.

• Text analysis : In text analysis, researchers analyze the social life of the respondents in the study and aim to decode the actions and the words of the respondents. Hence, text analysis is distinct from other qualitative research methods as it focuses on the social life of the respondents. In the last decade or so, text analysis has become increasingly popular due to the analysis of what consumers share on social media platforms in the form of blogs, images, and other texts. 

For example: Companies ask their customers to give detailed feedback on how satisfied they are with their customer support team. This data helps them make appropriate decisions to improve their team.

Sometimes researchers use a combination of methods to answer the questions. This is especially true when researchers tackle complex subject matters.

Exploratory Research Guide

Conducting exploratory research seems tricky but an effective guide can help.

Steps for conducting empirical research

Since empirical methods are based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyze it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

The very first step is for the researcher to identify the area of research and the problem can be addressed by finding out ways to solve it. The researcher should come up with various questions regarding what is the problem, who will benefit from the research, how should they go about the process, etc. The researchers should explore the purpose of the research in detail.

Step #2 : Supporting theories and relevant literature

After exploring and finding out the purpose of the research, the researcher must aim to find if there are existing theories that have addressed this before. The researcher has to figure out whether any previous studies can help them support their research. During this stage of empirical study, the researcher should aim at finding all relevant literature that will help them understand the problem at hand. The researcher should also come up with his/her own set of assumptions or problem statements that they wish to explore. 

Step #3: Creation of Hypothesis and measurement

If the researcher is aiming to solve a problem the problem has not been resolved efficiently in previous research, then the researcher creates his/her own problem statement. This problem statement, also called hypothesis, will be based on the questions that the researcher came up with while identifying the area of concern. The researcher can also form a hypothesis on the basis of prior research they found and studied during the literature review phase of the study.

Step #4: Methodology, research design and empirical data collection

Here the researcher has to define the strategies to be used for conducting the research. They can set up experiments in collecting data that can help them come up with probable hypotheses. On the basis of the hypotheses, researchers can decide whether they will require experimental or non-experimental methods for the conduction of the research. The research design will depend upon the field in which the research is to be conducted. The researchers will need to find parameters that can affect the validity of the research design. Researchers also need to choose appropriate methods of data collection, which in turn depends on the research question. There are many sampling methods that can be used by the researcher. Once, the data is collected, it has to be analysed.

Step #5: Data Analysis and result

Data can be analyzed either qualitatively and quantitatively. Researchers will need to decide which method they will employ depending upon the nature of the empirical data collected. Researchers can also use a combination of both for their study. On the basis of the analysis, the hypothesis will either be supported or rejected. Data analysis is the most important aspect of empirical observation.

Step #6: Conclusion

The researcher will have to collate the findings and make a report based on the empirical observations. The researcher can use previous theories and literature to support their hypothesis and lineage of findings. The researcher can also make recommendations for future research on similar issues.

Advantages of Empirical research

The advantages of empirical study are highlighted below:

• Used for authentication. Empirical study is used to authenticate previous findings of experiments and empirical observations. This research methodology makes the conducted study more authentic and accurate. 
• Empirical approach is useful for understanding dynamic changes. Due to the detailed process of literature review, empirical analysis is used in helping researchers understand dynamic changes in the field. It also enables them to strategies accordingly.
• Provides a level of control . Empirical approach empowers researchers to demonstrate a level of control by allowing them to control multiple variables under study.
• Empirical methods Increase internal validity . The high level of control in the research process makes an empirical method demonstrate high internal validity.

Disadvantages of Empirical research

Empirical approach is not without its limitations. Some of them include:

• Time consuming . Empirical studies are time consuming because it requires researchers to collect data through multiple sources. It also requires them to assess various parameters involved in the research. 
• Empirical approach is Expensive. The researcher may have to conduct the research at different locations or environments which may be expensive.
• Difficult to acquire consent/permission. Sometimes empirical studies may be difficult to conduct due to the rules that are to be followed when conducting it.
• Data collection in the empirical approach can be a problem. Since empirical data has to be collected from different methods and sources, it can pose a problem to the researchers.

Transform your insight generation process

Create an actionable feedback collection process.

Why is there a need for empirical research?

Because most people today only believe in their experiences, empirical observation is increasingly becoming important. It is used to validate various hypotheses or refute them in the face of evidence. It also increases human knowledge and advances scientific progression. 

For instance, empirical analysis is used by pharmaceutical companies to test specific drugs. This is done by administering the drug on an experimental group, while giving a placebo to the control group. This is done to prove theories about the proposed drug and check its efficacy. This is the most crucial way in which leading evidence for various drugs have been found for many years. 

Empirical methods are used not just in medical science, but also in history, social science, market research, etc.

In today’s world it has become critical to conduct empirical analysis in order to support hypotheses and gather knowledge in several fields. The methods under empirical studies mentioned above help researchers to carry out research.

Explore Voxco Survey Software

+ Omnichannel Survey Software 

+ Online Survey Software 

+ CATI Survey Software 

+ IVR Survey Software 

+ Market Research Tool

+ Customer Experience Tool 

+ Product Experience Software 

+ Enterprise Survey Software 

Representative Sample: Definition, method, and examples

Representative Sample: Definition, Method and Examples Market Research Toolkit Get started with Voxco’s Market Research Toolkit. Market Research trends guide + Online Surveys guide +

Employee benefits

Employee benefits Exclusive Step by Step guide to Descriptive Research Get ready to uncover the how, when, what, and where questions in a research problem

Healing Customer’s Pain with Closed-Feedback Loop

Healing Customer’s Pain with Closed-Feedback Loop SHARE THE ARTICLE ON Table of Contents Companies have acknowledged the importance of customer experience. They have embraced the

QUIRK’S LONDON: In Case You Missed It

Last week we were fortunate enough to spend some time exhibiting at the QUIRK’S conference held for the first time in London! For those who

What is Customer Service?

What is Customer Service? SHARE THE ARTICLE ON Table of Contents Customer service is the help you offer your customers- both when they purchase and

Canada Privacy Act-Compliance in Survey Hosting & Access

Market Researchers who have been following the news about data privacy and protection in the past few years know that there are major concerns with

We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Read More

A systematic literature review of empirical research on ChatGPT in education

• Open access
• Published: 26 May 2024
• Volume 3 , article number  60 , ( 2024 )

Cite this article

You have full access to this open access article

• Yazid Albadarin   ORCID: orcid.org/0009-0005-8068-8902 1 ,
• Mohammed Saqr 1 ,
• Nicolas Pope 1 &
• Markku Tukiainen 1  

699 Accesses

Explore all metrics

Over the last four decades, studies have investigated the incorporation of Artificial Intelligence (AI) into education. A recent prominent AI-powered technology that has impacted the education sector is ChatGPT. This article provides a systematic review of 14 empirical studies incorporating ChatGPT into various educational settings, published in 2022 and before the 10th of April 2023—the date of conducting the search process. It carefully followed the essential steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, as well as Okoli’s (Okoli in Commun Assoc Inf Syst, 2015) steps for conducting a rigorous and transparent systematic review. In this review, we aimed to explore how students and teachers have utilized ChatGPT in various educational settings, as well as the primary findings of those studies. By employing Creswell’s (Creswell in Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook], Pearson Education, London, 2015) coding techniques for data extraction and interpretation, we sought to gain insight into their initial attempts at ChatGPT incorporation into education. This approach also enabled us to extract insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of this review show that learners have utilized ChatGPT as a virtual intelligent assistant, where it offered instant feedback, on-demand answers, and explanations of complex topics. Additionally, learners have used it to enhance their writing and language skills by generating ideas, composing essays, summarizing, translating, paraphrasing texts, or checking grammar. Moreover, learners turned to it as an aiding tool to facilitate their directed and personalized learning by assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. However, the results of specific studies (n = 3, 21.4%) show that overuse of ChatGPT may negatively impact innovative capacities and collaborative learning competencies among learners. Educators, on the other hand, have utilized ChatGPT to create lesson plans, generate quizzes, and provide additional resources, which helped them enhance their productivity and efficiency and promote different teaching methodologies. Despite these benefits, the majority of the reviewed studies recommend the importance of conducting structured training, support, and clear guidelines for both learners and educators to mitigate the drawbacks. This includes developing critical evaluation skills to assess the accuracy and relevance of information provided by ChatGPT, as well as strategies for integrating human interaction and collaboration into learning activities that involve AI tools. Furthermore, they also recommend ongoing research and proactive dialogue with policymakers, stakeholders, and educational practitioners to refine and enhance the use of AI in learning environments. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Similar content being viewed by others

Empowering learners with ChatGPT: insights from a systematic literature exploration

Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners

Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT

Avoid common mistakes on your manuscript.

1 Introduction

Educational technology, a rapidly evolving field, plays a crucial role in reshaping the landscape of teaching and learning [ 82 ]. One of the most transformative technological innovations of our era that has influenced the field of education is Artificial Intelligence (AI) [ 50 ]. Over the last four decades, AI in education (AIEd) has gained remarkable attention for its potential to make significant advancements in learning, instructional methods, and administrative tasks within educational settings [ 11 ]. In particular, a large language model (LLM), a type of AI algorithm that applies artificial neural networks (ANNs) and uses massively large data sets to understand, summarize, generate, and predict new content that is almost difficult to differentiate from human creations [ 79 ], has opened up novel possibilities for enhancing various aspects of education, from content creation to personalized instruction [ 35 ]. Chatbots that leverage the capabilities of LLMs to understand and generate human-like responses have also presented the capacity to enhance student learning and educational outcomes by engaging students, offering timely support, and fostering interactive learning experiences [ 46 ].

The ongoing and remarkable technological advancements in chatbots have made their use more convenient, increasingly natural and effortless, and have expanded their potential for deployment across various domains [ 70 ]. One prominent example of chatbot applications is the Chat Generative Pre-Trained Transformer, known as ChatGPT, which was introduced by OpenAI, a leading AI research lab, on November 30th, 2022. ChatGPT employs a variety of deep learning techniques to generate human-like text, with a particular focus on recurrent neural networks (RNNs). Long short-term memory (LSTM) allows it to grasp the context of the text being processed and retain information from previous inputs. Also, the transformer architecture, a neural network architecture based on the self-attention mechanism, allows it to analyze specific parts of the input, thereby enabling it to produce more natural-sounding and coherent output. Additionally, the unsupervised generative pre-training and the fine-tuning methods allow ChatGPT to generate more relevant and accurate text for specific tasks [ 31 , 62 ]. Furthermore, reinforcement learning from human feedback (RLHF), a machine learning approach that combines reinforcement learning techniques with human-provided feedback, has helped improve ChatGPT’s model by accelerating the learning process and making it significantly more efficient.

This cutting-edge natural language processing (NLP) tool is widely recognized as one of today's most advanced LLMs-based chatbots [ 70 ], allowing users to ask questions and receive detailed, coherent, systematic, personalized, convincing, and informative human-like responses [ 55 ], even within complex and ambiguous contexts [ 63 , 77 ]. ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [ 16 ] with an estimated 13 million daily queries [ 49 ], surpassing all other applications [ 64 ]. This remarkable growth can be attributed to the unique features and user-friendly interface that ChatGPT offers. Its intuitive design allows users to interact seamlessly with the technology, making it accessible to a diverse range of individuals, regardless of their technical expertise [ 78 ]. Additionally, its exceptional performance results from a combination of advanced algorithms, continuous enhancements, and extensive training on a diverse dataset that includes various text sources such as books, articles, websites, and online forums [ 63 ], have contributed to a more engaging and satisfying user experience [ 62 ]. These factors collectively explain its remarkable global growth and set it apart from predecessors like Bard, Bing Chat, ERNIE, and others.

In this context, several studies have explored the technological advancements of chatbots. One noteworthy recent research effort, conducted by Schöbel et al. [ 70 ], stands out for its comprehensive analysis of more than 5,000 studies on communication agents. This study offered a comprehensive overview of the historical progression and future prospects of communication agents, including ChatGPT. Moreover, other studies have focused on making comparisons, particularly between ChatGPT and alternative chatbots like Bard, Bing Chat, ERNIE, LaMDA, BlenderBot, and various others. For example, O’Leary [ 53 ] compared two chatbots, LaMDA and BlenderBot, with ChatGPT and revealed that ChatGPT outperformed both. This superiority arises from ChatGPT’s capacity to handle a wider range of questions and generate slightly varied perspectives within specific contexts. Similarly, ChatGPT exhibited an impressive ability to formulate interpretable responses that were easily understood when compared with Google's feature snippet [ 34 ]. Additionally, ChatGPT was compared to other LLMs-based chatbots, including Bard and BERT, as well as ERNIE. The findings indicated that ChatGPT exhibited strong performance in the given tasks, often outperforming the other models [ 59 ].

Furthermore, in the education context, a comprehensive study systematically compared a range of the most promising chatbots, including Bard, Bing Chat, ChatGPT, and Ernie across a multidisciplinary test that required higher-order thinking. The study revealed that ChatGPT achieved the highest score, surpassing Bing Chat and Bard [ 64 ]. Similarly, a comparative analysis was conducted to compare ChatGPT with Bard in answering a set of 30 mathematical questions and logic problems, grouped into two question sets. Set (A) is unavailable online, while Set (B) is available online. The results revealed ChatGPT's superiority in Set (A) over Bard. Nevertheless, Bard's advantage emerged in Set (B) due to its capacity to access the internet directly and retrieve answers, a capability that ChatGPT does not possess [ 57 ]. However, through these varied assessments, ChatGPT consistently highlights its exceptional prowess compared to various alternatives in the ever-evolving chatbot technology.

The widespread adoption of chatbots, especially ChatGPT, by millions of students and educators, has sparked extensive discussions regarding its incorporation into the education sector [ 64 ]. Accordingly, many scholars have contributed to the discourse, expressing both optimism and pessimism regarding the incorporation of ChatGPT into education. For example, ChatGPT has been highlighted for its capabilities in enriching the learning and teaching experience through its ability to support different learning approaches, including adaptive learning, personalized learning, and self-directed learning [ 58 , 60 , 91 ]), deliver summative and formative feedback to students and provide real-time responses to questions, increase the accessibility of information [ 22 , 40 , 43 ], foster students’ performance, engagement and motivation [ 14 , 44 , 58 ], and enhance teaching practices [ 17 , 18 , 64 , 74 ].

On the other hand, concerns have been also raised regarding its potential negative effects on learning and teaching. These include the dissemination of false information and references [ 12 , 23 , 61 , 85 ], biased reinforcement [ 47 , 50 ], compromised academic integrity [ 18 , 40 , 66 , 74 ], and the potential decline in students' skills [ 43 , 61 , 64 , 74 ]. As a result, ChatGPT has been banned in multiple countries, including Russia, China, Venezuela, Belarus, and Iran, as well as in various educational institutions in India, Italy, Western Australia, France, and the United States [ 52 , 90 ].

Clearly, the advent of chatbots, especially ChatGPT, has provoked significant controversy due to their potential impact on learning and teaching. This indicates the necessity for further exploration to gain a deeper understanding of this technology and carefully evaluate its potential benefits, limitations, challenges, and threats to education [ 79 ]. Therefore, conducting a systematic literature review will provide valuable insights into the potential prospects and obstacles linked to its incorporation into education. This systematic literature review will primarily focus on ChatGPT, driven by the aforementioned key factors outlined above.

However, the existing literature lacks a systematic literature review of empirical studies. Thus, this systematic literature review aims to address this gap by synthesizing the existing empirical studies conducted on chatbots, particularly ChatGPT, in the field of education, highlighting how ChatGPT has been utilized in educational settings, and identifying any existing gaps. This review may be particularly useful for researchers in the field and educators who are contemplating the integration of ChatGPT or any chatbot into education. The following research questions will guide this study:

What are students' and teachers' initial attempts at utilizing ChatGPT in education?

What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?

2 Methodology

To conduct this study, the authors followed the essential steps of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) and Okoli’s [ 54 ] steps for conducting a systematic review. These included identifying the study’s purpose, drafting a protocol, applying a practical screening process, searching the literature, extracting relevant data, evaluating the quality of the included studies, synthesizing the studies, and ultimately writing the review. The subsequent section provides an extensive explanation of how these steps were carried out in this study.

2.1 Identify the purpose

Given the widespread adoption of ChatGPT by students and teachers for various educational purposes, often without a thorough understanding of responsible and effective use or a clear recognition of its potential impact on learning and teaching, the authors recognized the need for further exploration of ChatGPT's impact on education in this early stage. Therefore, they have chosen to conduct a systematic literature review of existing empirical studies that incorporate ChatGPT into educational settings. Despite the limited number of empirical studies due to the novelty of the topic, their goal is to gain a deeper understanding of this technology and proactively evaluate its potential benefits, limitations, challenges, and threats to education. This effort could help to understand initial reactions and attempts at incorporating ChatGPT into education and bring out insights and considerations that can inform the future development of education.

2.2 Draft the protocol

The next step is formulating the protocol. This protocol serves to outline the study process in a rigorous and transparent manner, mitigating researcher bias in study selection and data extraction [ 88 ]. The protocol will include the following steps: generating the research question, predefining a literature search strategy, identifying search locations, establishing selection criteria, assessing the studies, developing a data extraction strategy, and creating a timeline.

2.3 Apply practical screen

The screening step aims to accurately filter the articles resulting from the searching step and select the empirical studies that have incorporated ChatGPT into educational contexts, which will guide us in answering the research questions and achieving the objectives of this study. To ensure the rigorous execution of this step, our inclusion and exclusion criteria were determined based on the authors' experience and informed by previous successful systematic reviews [ 21 ]. Table 1 summarizes the inclusion and exclusion criteria for study selection.

2.4 Literature search

We conducted a thorough literature search to identify articles that explored, examined, and addressed the use of ChatGPT in Educational contexts. We utilized two research databases: Dimensions.ai, which provides access to a large number of research publications, and lens.org, which offers access to over 300 million articles, patents, and other research outputs from diverse sources. Additionally, we included three databases, Scopus, Web of Knowledge, and ERIC, which contain relevant research on the topic that addresses our research questions. To browse and identify relevant articles, we used the following search formula: ("ChatGPT" AND "Education"), which included the Boolean operator "AND" to get more specific results. The subject area in the Scopus and ERIC databases were narrowed to "ChatGPT" and "Education" keywords, and in the WoS database was limited to the "Education" category. The search was conducted between the 3rd and 10th of April 2023, which resulted in 276 articles from all selected databases (111 articles from Dimensions.ai, 65 from Scopus, 28 from Web of Science, 14 from ERIC, and 58 from Lens.org). These articles were imported into the Rayyan web-based system for analysis. The duplicates were identified automatically by the system. Subsequently, the first author manually reviewed the duplicated articles ensured that they had the same content, and then removed them, leaving us with 135 unique articles. Afterward, the titles, abstracts, and keywords of the first 40 manuscripts were scanned and reviewed by the first author and were discussed with the second and third authors to resolve any disagreements. Subsequently, the first author proceeded with the filtering process for all articles and carefully applied the inclusion and exclusion criteria as presented in Table  1 . Articles that met any one of the exclusion criteria were eliminated, resulting in 26 articles. Afterward, the authors met to carefully scan and discuss them. The authors agreed to eliminate any empirical studies solely focused on checking ChatGPT capabilities, as these studies do not guide us in addressing the research questions and achieving the study's objectives. This resulted in 14 articles eligible for analysis.

2.5 Quality appraisal

The examination and evaluation of the quality of the extracted articles is a vital step [ 9 ]. Therefore, the extracted articles were carefully evaluated for quality using Fink’s [ 24 ] standards, which emphasize the necessity for detailed descriptions of methodology, results, conclusions, strengths, and limitations. The process began with a thorough assessment of each study's design, data collection, and analysis methods to ensure their appropriateness and comprehensive execution. The clarity, consistency, and logical progression from data to results and conclusions were also critically examined. Potential biases and recognized limitations within the studies were also scrutinized. Ultimately, two articles were excluded for failing to meet Fink’s criteria, particularly in providing sufficient detail on methodology, results, conclusions, strengths, or limitations. The review process is illustrated in Fig.  1 .

The study selection process

2.6 Data extraction

The next step is data extraction, the process of capturing the key information and categories from the included studies. To improve efficiency, reduce variation among authors, and minimize errors in data analysis, the coding categories were constructed using Creswell's [ 15 ] coding techniques for data extraction and interpretation. The coding process involves three sequential steps. The initial stage encompasses open coding , where the researcher examines the data, generates codes to describe and categorize it, and gains a deeper understanding without preconceived ideas. Following open coding is axial coding , where the interrelationships between codes from open coding are analyzed to establish more comprehensive categories or themes. The process concludes with selective coding , refining and integrating categories or themes to identify core concepts emerging from the data. The first coder performed the coding process, then engaged in discussions with the second and third authors to finalize the coding categories for the first five articles. The first coder then proceeded to code all studies and engaged again in discussions with the other authors to ensure the finalization of the coding process. After a comprehensive analysis and capturing of the key information from the included studies, the data extraction and interpretation process yielded several themes. These themes have been categorized and are presented in Table  2 . It is important to note that open coding results were removed from Table  2 for aesthetic reasons, as it included many generic aspects, such as words, short phrases, or sentences mentioned in the studies.

2.7 Synthesize studies

In this stage, we will gather, discuss, and analyze the key findings that emerged from the selected studies. The synthesis stage is considered a transition from an author-centric to a concept-centric focus, enabling us to map all the provided information to achieve the most effective evaluation of the data [ 87 ]. Initially, the authors extracted data that included general information about the selected studies, including the author(s)' names, study titles, years of publication, educational levels, research methodologies, sample sizes, participants, main aims or objectives, raw data sources, and analysis methods. Following that, all key information and significant results from the selected studies were compiled using Creswell’s [ 15 ] coding techniques for data extraction and interpretation to identify core concepts and themes emerging from the data, focusing on those that directly contributed to our research questions and objectives, such as the initial utilization of ChatGPT in learning and teaching, learners' and educators' familiarity with ChatGPT, and the main findings of each study. Finally, the data related to each selected study were extracted into an Excel spreadsheet for data processing. The Excel spreadsheet was reviewed by the authors, including a series of discussions to ensure the finalization of this process and prepare it for further analysis. Afterward, the final result being analyzed and presented in various types of charts and graphs. Table 4 presents the extracted data from the selected studies, with each study labeled with a capital 'S' followed by a number.

This section consists of two main parts. The first part provides a descriptive analysis of the data compiled from the reviewed studies. The second part presents the answers to the research questions and the main findings of these studies.

3.1 Part 1: descriptive analysis

This section will provide a descriptive analysis of the reviewed studies, including educational levels and fields, participants distribution, country contribution, research methodologies, study sample size, study population, publication year, list of journals, familiarity with ChatGPT, source of data, and the main aims and objectives of the studies. Table 4 presents a comprehensive overview of the extracted data from the selected studies.

3.1.1 The number of the reviewed studies and publication years

The total number of the reviewed studies was 14. All studies were empirical studies and published in different journals focusing on Education and Technology. One study was published in 2022 [S1], while the remaining were published in 2023 [S2]-[S14]. Table 3 illustrates the year of publication, the names of the journals, and the number of reviewed studies published in each journal for the studies reviewed.

3.1.2 Educational levels and fields

The majority of the reviewed studies, 11 studies, were conducted in higher education institutions [S1]-[S10] and [S13]. Two studies did not specify the educational level of the population [S12] and [S14], while one study focused on elementary education [S11]. However, the reviewed studies covered various fields of education. Three studies focused on Arts and Humanities Education [S8], [S11], and [S14], specifically English Education. Two studies focused on Engineering Education, with one in Computer Engineering [S2] and the other in Construction Education [S3]. Two studies focused on Mathematics Education [S5] and [S12]. One study focused on Social Science Education [S13]. One study focused on Early Education [S4]. One study focused on Journalism Education [S9]. Finally, three studies did not specify the field of education [S1], [S6], and [S7]. Figure  2 represents the educational levels in the reviewed studies, while Fig.  3 represents the context of the reviewed studies.

Educational levels in the reviewed studies

Context of the reviewed studies

3.1.3 Participants distribution and countries contribution

The reviewed studies have been conducted across different geographic regions, providing a diverse representation of the studies. The majority of the studies, 10 in total, [S1]-[S3], [S5]-[S9], [S11], and [S14], primarily focused on participants from single countries such as Pakistan, the United Arab Emirates, China, Indonesia, Poland, Saudi Arabia, South Korea, Spain, Tajikistan, and the United States. In contrast, four studies, [S4], [S10], [S12], and [S13], involved participants from multiple countries, including China and the United States [S4], China, the United Kingdom, and the United States [S10], the United Arab Emirates, Oman, Saudi Arabia, and Jordan [S12], Turkey, Sweden, Canada, and Australia [ 13 ]. Figures  4 and 5 illustrate the distribution of participants, whether from single or multiple countries, and the contribution of each country in the reviewed studies, respectively.

The reviewed studies conducted in single or multiple countries

The Contribution of each country in the studies

3.1.4 Study population and sample size

Four study populations were included: university students, university teachers, university teachers and students, and elementary school teachers. Six studies involved university students [S2], [S3], [S5] and [S6]-[S8]. Three studies focused on university teachers [S1], [S4], and [S6], while one study specifically targeted elementary school teachers [S11]. Additionally, four studies included both university teachers and students [S10] and [ 12 , 13 , 14 ], and among them, study [S13] specifically included postgraduate students. In terms of the sample size of the reviewed studies, nine studies included a small sample size of less than 50 participants [S1], [S3], [S6], [S8], and [S10]-[S13]. Three studies had 50–100 participants [S2], [S9], and [S14]. Only one study had more than 100 participants [S7]. It is worth mentioning that study [S4] adopted a mixed methods approach, including 10 participants for qualitative analysis and 110 participants for quantitative analysis.

3.1.5 Participants’ familiarity with using ChatGPT

The reviewed studies recruited a diverse range of participants with varying levels of familiarity with ChatGPT. Five studies [S2], [S4], [S6], [S8], and [S12] involved participants already familiar with ChatGPT, while eight studies [S1], [S3], [S5], [S7], [S9], [S10], [S13] and [S14] included individuals with differing levels of familiarity. Notably, one study [S11] had participants who were entirely unfamiliar with ChatGPT. It is important to note that four studies [S3], [S5], [S9], and [S11] provided training or guidance to their participants before conducting their studies, while ten studies [S1], [S2], [S4], [S6]-[S8], [S10], and [S12]-[S14] did not provide training due to the participants' existing familiarity with ChatGPT.

3.1.6 Research methodology approaches and source(S) of data

The reviewed studies adopted various research methodology approaches. Seven studies adopted qualitative research methodology [S1], [S4], [S6], [S8], [S10], [S11], and [S12], while three studies adopted quantitative research methodology [S3], [S7], and [S14], and four studies employed mixed-methods, which involved a combination of both the strengths of qualitative and quantitative methods [S2], [S5], [S9], and [S13].

In terms of the source(s) of data, the reviewed studies obtained their data from various sources, such as interviews, questionnaires, and pre-and post-tests. Six studies relied on interviews as their primary source of data collection [S1], [S4], [S6], [S10], [S11], and [S12], four studies relied on questionnaires [S2], [S7], [S13], and [S14], two studies combined the use of pre-and post-tests and questionnaires for data collection [S3] and [S9], while two studies combined the use of questionnaires and interviews to obtain the data [S5] and [S8]. It is important to note that six of the reviewed studies were quasi-experimental [S3], [S5], [S8], [S9], [S12], and [S14], while the remaining ones were experimental studies [S1], [S2], [S4], [S6], [S7], [S10], [S11], and [S13]. Figures  6 and 7 illustrate the research methodologies and the source (s) of data used in the reviewed studies, respectively.

Research methodologies in the reviewed studies

Source of data in the reviewed studies

3.1.7 The aim and objectives of the studies

The reviewed studies encompassed a diverse set of aims, with several of them incorporating multiple primary objectives. Six studies [S3], [S6], [S7], [S8], [S11], and [S12] examined the integration of ChatGPT in educational contexts, and four studies [S4], [S5], [S13], and [S14] investigated the various implications of its use in education, while three studies [S2], [S9], and [S10] aimed to explore both its integration and implications in education. Additionally, seven studies explicitly explored attitudes and perceptions of students [S2] and [S3], educators [S1] and [S6], or both [S10], [S12], and [S13] regarding the utilization of ChatGPT in educational settings.

3.2 Part 2: research questions and main findings of the reviewed studies

This part will present the answers to the research questions and the main findings of the reviewed studies, classified into two main categories (learning and teaching) according to AI Education classification by [ 36 ]. Figure  8 summarizes the main findings of the reviewed studies in a visually informative diagram. Table 4 provides a detailed list of the key information extracted from the selected studies that led to generating these themes.

The main findings in the reviewed studies

4 Students' initial attempts at utilizing ChatGPT in learning and main findings from students' perspective

4.1 virtual intelligent assistant.

Nine studies demonstrated that ChatGPT has been utilized by students as an intelligent assistant to enhance and support their learning. Students employed it for various purposes, such as answering on-demand questions [S2]-[S5], [S8], [S10], and [S12], providing valuable information and learning resources [S2]-[S5], [S6], and [S8], as well as receiving immediate feedback [S2], [S4], [S9], [S10], and [S12]. In this regard, students generally were confident in the accuracy of ChatGPT's responses, considering them relevant, reliable, and detailed [S3], [S4], [S5], and [S8]. However, some students indicated the need for improvement, as they found that answers are not always accurate [S2], and that misleading information may have been provided or that it may not always align with their expectations [S6] and [S10]. It was also observed by the students that the accuracy of ChatGPT is dependent on several factors, including the quality and specificity of the user's input, the complexity of the question or topic, and the scope and relevance of its training data [S12]. Many students felt that ChatGPT's answers were not always accurate and most of them believed that it requires good background knowledge to work with.

4.2 Writing and language proficiency assistant

Six of the reviewed studies highlighted that ChatGPT has been utilized by students as a valuable assistant tool to improve their academic writing skills and language proficiency. Among these studies, three mainly focused on English education, demonstrating that students showed sufficient mastery in using ChatGPT for generating ideas, summarizing, paraphrasing texts, and completing writing essays [S8], [S11], and [S14]. Furthermore, ChatGPT helped them in writing by making students active investigators rather than passive knowledge recipients and facilitated the development of their writing skills [S11] and [S14]. Similarly, ChatGPT allowed students to generate unique ideas and perspectives, leading to deeper analysis and reflection on their journalism writing [S9]. In terms of language proficiency, ChatGPT allowed participants to translate content into their home languages, making it more accessible and relevant to their context [S4]. It also enabled them to request changes in linguistic tones or flavors [S8]. Moreover, participants used it to check grammar or as a dictionary [S11].

4.3 Valuable resource for learning approaches

Five studies demonstrated that students used ChatGPT as a valuable complementary resource for self-directed learning. It provided learning resources and guidance on diverse educational topics and created a supportive home learning environment [S2] and [S4]. Moreover, it offered step-by-step guidance to grasp concepts at their own pace and enhance their understanding [S5], streamlined task and project completion carried out independently [S7], provided comprehensive and easy-to-understand explanations on various subjects [S10], and assisted in studying geometry operations, thereby empowering them to explore geometry operations at their own pace [S12]. Three studies showed that students used ChatGPT as a valuable learning resource for personalized learning. It delivered age-appropriate conversations and tailored teaching based on a child's interests [S4], acted as a personalized learning assistant, adapted to their needs and pace, which assisted them in understanding mathematical concepts [S12], and enabled personalized learning experiences in social sciences by adapting to students' needs and learning styles [S13]. On the other hand, it is important to note that, according to one study [S5], students suggested that using ChatGPT may negatively affect collaborative learning competencies between students.

4.4 Enhancing students' competencies

Six of the reviewed studies have shown that ChatGPT is a valuable tool for improving a wide range of skills among students. Two studies have provided evidence that ChatGPT led to improvements in students' critical thinking, reasoning skills, and hazard recognition competencies through engaging them in interactive conversations or activities and providing responses related to their disciplines in journalism [S5] and construction education [S9]. Furthermore, two studies focused on mathematical education have shown the positive impact of ChatGPT on students' problem-solving abilities in unraveling problem-solving questions [S12] and enhancing the students' understanding of the problem-solving process [S5]. Lastly, one study indicated that ChatGPT effectively contributed to the enhancement of conversational social skills [S4].

4.5 Supporting students' academic success

Seven of the reviewed studies highlighted that students found ChatGPT to be beneficial for learning as it enhanced learning efficiency and improved the learning experience. It has been observed to improve students' efficiency in computer engineering studies by providing well-structured responses and good explanations [S2]. Additionally, students found it extremely useful for hazard reporting [S3], and it also enhanced their efficiency in solving mathematics problems and capabilities [S5] and [S12]. Furthermore, by finding information, generating ideas, translating texts, and providing alternative questions, ChatGPT aided students in deepening their understanding of various subjects [S6]. It contributed to an increase in students' overall productivity [S7] and improved efficiency in composing written tasks [S8]. Regarding learning experiences, ChatGPT was instrumental in assisting students in identifying hazards that they might have otherwise overlooked [S3]. It also improved students' learning experiences in solving mathematics problems and developing abilities [S5] and [S12]. Moreover, it increased students' successful completion of important tasks in their studies [S7], particularly those involving average difficulty writing tasks [S8]. Additionally, ChatGPT increased the chances of educational success by providing students with baseline knowledge on various topics [S10].

5 Teachers' initial attempts at utilizing ChatGPT in teaching and main findings from teachers' perspective

5.1 valuable resource for teaching.

The reviewed studies showed that teachers have employed ChatGPT to recommend, modify, and generate diverse, creative, organized, and engaging educational contents, teaching materials, and testing resources more rapidly [S4], [S6], [S10] and [S11]. Additionally, teachers experienced increased productivity as ChatGPT facilitated quick and accurate responses to questions, fact-checking, and information searches [S1]. It also proved valuable in constructing new knowledge [S6] and providing timely answers to students' questions in classrooms [S11]. Moreover, ChatGPT enhanced teachers' efficiency by generating new ideas for activities and preplanning activities for their students [S4] and [S6], including interactive language game partners [S11].

5.2 Improving productivity and efficiency

The reviewed studies showed that participants' productivity and work efficiency have been significantly enhanced by using ChatGPT as it enabled them to allocate more time to other tasks and reduce their overall workloads [S6], [S10], [S11], [S13], and [S14]. However, three studies [S1], [S4], and [S11], indicated a negative perception and attitude among teachers toward using ChatGPT. This negativity stemmed from a lack of necessary skills to use it effectively [S1], a limited familiarity with it [S4], and occasional inaccuracies in the content provided by it [S10].

5.3 Catalyzing new teaching methodologies

Five of the reviewed studies highlighted that educators found the necessity of redefining their teaching profession with the assistance of ChatGPT [S11], developing new effective learning strategies [S4], and adapting teaching strategies and methodologies to ensure the development of essential skills for future engineers [S5]. They also emphasized the importance of adopting new educational philosophies and approaches that can evolve with the introduction of ChatGPT into the classroom [S12]. Furthermore, updating curricula to focus on improving human-specific features, such as emotional intelligence, creativity, and philosophical perspectives [S13], was found to be essential.

5.4 Effective utilization of CHATGPT in teaching

According to the reviewed studies, effective utilization of ChatGPT in education requires providing teachers with well-structured training, support, and adequate background on how to use ChatGPT responsibly [S1], [S3], [S11], and [S12]. Establishing clear rules and regulations regarding its usage is essential to ensure it positively impacts the teaching and learning processes, including students' skills [S1], [S4], [S5], [S8], [S9], and [S11]-[S14]. Moreover, conducting further research and engaging in discussions with policymakers and stakeholders is indeed crucial for the successful integration of ChatGPT in education and to maximize the benefits for both educators and students [S1], [S6]-[S10], and [S12]-[S14].

6 Discussion

The purpose of this review is to conduct a systematic review of empirical studies that have explored the utilization of ChatGPT, one of today’s most advanced LLM-based chatbots, in education. The findings of the reviewed studies showed several ways of ChatGPT utilization in different learning and teaching practices as well as it provided insights and considerations that can facilitate its effective and responsible use in future educational contexts. The results of the reviewed studies came from diverse fields of education, which helped us avoid a biased review that is limited to a specific field. Similarly, the reviewed studies have been conducted across different geographic regions. This kind of variety in geographic representation enriched the findings of this review.

In response to RQ1 , "What are students' and teachers' initial attempts at utilizing ChatGPT in education?", the findings from this review provide comprehensive insights. Chatbots, including ChatGPT, play a crucial role in supporting student learning, enhancing their learning experiences, and facilitating diverse learning approaches [ 42 , 43 ]. This review found that this tool, ChatGPT, has been instrumental in enhancing students' learning experiences by serving as a virtual intelligent assistant, providing immediate feedback, on-demand answers, and engaging in educational conversations. Additionally, students have benefited from ChatGPT’s ability to generate ideas, compose essays, and perform tasks like summarizing, translating, paraphrasing texts, or checking grammar, thereby enhancing their writing and language competencies. Furthermore, students have turned to ChatGPT for assistance in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks, which fosters a supportive home learning environment, allowing them to take responsibility for their own learning and cultivate the skills and approaches essential for supportive home learning environment [ 26 , 27 , 28 ]. This finding aligns with the study of Saqr et al. [ 68 , 69 ] who highlighted that, when students actively engage in their own learning process, it yields additional advantages, such as heightened motivation, enhanced achievement, and the cultivation of enthusiasm, turning them into advocates for their own learning.

Moreover, students have utilized ChatGPT for tailored teaching and step-by-step guidance on diverse educational topics, streamlining task and project completion, and generating and recommending educational content. This personalization enhances the learning environment, leading to increased academic success. This finding aligns with other recent studies [ 26 , 27 , 28 , 60 , 66 ] which revealed that ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities. Ultimately, fostering students' performance, engagement, and motivation, leading to increase students' academic success [ 14 , 44 , 58 ]. This ultimate outcome is in line with the findings of Saqr et al. [ 68 , 69 ], which emphasized that learning strategies are important catalysts of students' learning, as students who utilize effective learning strategies are more likely to have better academic achievement.

Teachers, too, have capitalized on ChatGPT's capabilities to enhance productivity and efficiency, using it for creating lesson plans, generating quizzes, providing additional resources, generating and preplanning new ideas for activities, and aiding in answering students’ questions. This adoption of technology introduces new opportunities to support teaching and learning practices, enhancing teacher productivity. This finding aligns with those of Day [ 17 ], De Castro [ 18 ], and Su and Yang [ 74 ] as well as with those of Valtonen et al. [ 82 ], who revealed that emerging technological advancements have opened up novel opportunities and means to support teaching and learning practices, and enhance teachers’ productivity.

In response to RQ2 , "What are the main findings derived from empirical studies that have incorporated ChatGPT into learning and teaching?", the findings from this review provide profound insights and raise significant concerns. Starting with the insights, chatbots, including ChatGPT, have demonstrated the potential to reshape and revolutionize education, creating new, novel opportunities for enhancing the learning process and outcomes [ 83 ], facilitating different learning approaches, and offering a range of pedagogical benefits [ 19 , 43 , 72 ]. In this context, this review found that ChatGPT could open avenues for educators to adopt or develop new effective learning and teaching strategies that can evolve with the introduction of ChatGPT into the classroom. Nonetheless, there is an evident lack of research understanding regarding the potential impact of generative machine learning models within diverse educational settings [ 83 ]. This necessitates teachers to attain a high level of proficiency in incorporating chatbots, such as ChatGPT, into their classrooms to create inventive, well-structured, and captivating learning strategies. In the same vein, the review also found that teachers without the requisite skills to utilize ChatGPT realized that it did not contribute positively to their work and could potentially have adverse effects [ 37 ]. This concern could lead to inequity of access to the benefits of chatbots, including ChatGPT, as individuals who lack the necessary expertise may not be able to harness their full potential, resulting in disparities in educational outcomes and opportunities. Therefore, immediate action is needed to address these potential issues. A potential solution is offering training, support, and competency development for teachers to ensure that all of them can leverage chatbots, including ChatGPT, effectively and equitably in their educational practices [ 5 , 28 , 80 ], which could enhance accessibility and inclusivity, and potentially result in innovative outcomes [ 82 , 83 ].

Additionally, chatbots, including ChatGPT, have the potential to significantly impact students' thinking abilities, including retention, reasoning, analysis skills [ 19 , 45 ], and foster innovation and creativity capabilities [ 83 ]. This review found that ChatGPT could contribute to improving a wide range of skills among students. However, it found that frequent use of ChatGPT may result in a decrease in innovative capacities, collaborative skills and cognitive capacities, and students' motivation to attend classes, as well as could lead to reduced higher-order thinking skills among students [ 22 , 29 ]. Therefore, immediate action is needed to carefully examine the long-term impact of chatbots such as ChatGPT, on learning outcomes as well as to explore its incorporation into educational settings as a supportive tool without compromising students' cognitive development and critical thinking abilities. In the same vein, the review also found that it is challenging to draw a consistent conclusion regarding the potential of ChatGPT to aid self-directed learning approach. This finding aligns with the recent study of Baskara [ 8 ]. Therefore, further research is needed to explore the potential of ChatGPT for self-directed learning. One potential solution involves utilizing learning analytics as a novel approach to examine various aspects of students' learning and support them in their individual endeavors [ 32 ]. This approach can bridge this gap by facilitating an in-depth analysis of how learners engage with ChatGPT, identifying trends in self-directed learning behavior, and assessing its influence on their outcomes.

Turning to the significant concerns, on the other hand, a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as "hallucination" [ 3 , 49 ]. In this context, this review found that the provided information was not entirely satisfactory. Consequently, the utilization of chatbots presents potential concerns, such as generating and providing inaccurate or misleading information, especially for students who utilize it to support their learning. This finding aligns with other findings [ 6 , 30 , 35 , 40 ] which revealed that incorporating chatbots such as ChatGPT, into education presents challenges related to its accuracy and reliability due to its training on a large corpus of data, which may contain inaccuracies and the way users formulate or ask ChatGPT. Therefore, immediate action is needed to address these potential issues. One possible solution is to equip students with the necessary skills and competencies, which include a background understanding of how to use it effectively and the ability to assess and evaluate the information it generates, as the accuracy and the quality of the provided information depend on the input, its complexity, the topic, and the relevance of its training data [ 28 , 49 , 86 ]. However, it's also essential to examine how learners can be educated about how these models operate, the data used in their training, and how to recognize their limitations, challenges, and issues [ 79 ].

Furthermore, chatbots present a substantial challenge concerning maintaining academic integrity [ 20 , 56 ] and copyright violations [ 83 ], which are significant concerns in education. The review found that the potential misuse of ChatGPT might foster cheating, facilitate plagiarism, and threaten academic integrity. This issue is also affirmed by the research conducted by Basic et al. [ 7 ], who presented evidence that students who utilized ChatGPT in their writing assignments had more plagiarism cases than those who did not. These findings align with the conclusions drawn by Cotton et al. [ 13 ], Hisan and Amri [ 33 ] and Sullivan et al. [ 75 ], who revealed that the integration of chatbots such as ChatGPT into education poses a significant challenge to the preservation of academic integrity. Moreover, chatbots, including ChatGPT, have increased the difficulty in identifying plagiarism [ 47 , 67 , 76 ]. The findings from previous studies [ 1 , 84 ] indicate that AI-generated text often went undetected by plagiarism software, such as Turnitin. However, Turnitin and other similar plagiarism detection tools, such as ZeroGPT, GPTZero, and Copyleaks, have since evolved, incorporating enhanced techniques to detect AI-generated text, despite the possibility of false positives, as noted in different studies that have found these tools still not yet fully ready to accurately and reliably identify AI-generated text [ 10 , 51 ], and new novel detection methods may need to be created and implemented for AI-generated text detection [ 4 ]. This potential issue could lead to another concern, which is the difficulty of accurately evaluating student performance when they utilize chatbots such as ChatGPT assistance in their assignments. Consequently, the most LLM-driven chatbots present a substantial challenge to traditional assessments [ 64 ]. The findings from previous studies indicate the importance of rethinking, improving, and redesigning innovative assessment methods in the era of chatbots [ 14 , 20 , 64 , 75 ]. These methods should prioritize the process of evaluating students' ability to apply knowledge to complex cases and demonstrate comprehension, rather than solely focusing on the final product for assessment. Therefore, immediate action is needed to address these potential issues. One possible solution would be the development of clear guidelines, regulatory policies, and pedagogical guidance. These measures would help regulate the proper and ethical utilization of chatbots, such as ChatGPT, and must be established before their introduction to students [ 35 , 38 , 39 , 41 , 89 ].

In summary, our review has delved into the utilization of ChatGPT, a prominent example of chatbots, in education, addressing the question of how ChatGPT has been utilized in education. However, there remain significant gaps, which necessitate further research to shed light on this area.

7 Conclusions

This systematic review has shed light on the varied initial attempts at incorporating ChatGPT into education by both learners and educators, while also offering insights and considerations that can facilitate its effective and responsible use in future educational contexts. From the analysis of 14 selected studies, the review revealed the dual-edged impact of ChatGPT in educational settings. On the positive side, ChatGPT significantly aided the learning process in various ways. Learners have used it as a virtual intelligent assistant, benefiting from its ability to provide immediate feedback, on-demand answers, and easy access to educational resources. Additionally, it was clear that learners have used it to enhance their writing and language skills, engaging in practices such as generating ideas, composing essays, and performing tasks like summarizing, translating, paraphrasing texts, or checking grammar. Importantly, other learners have utilized it in supporting and facilitating their directed and personalized learning on a broad range of educational topics, assisting in understanding concepts and homework, providing structured learning plans, and clarifying assignments and tasks. Educators, on the other hand, found ChatGPT beneficial for enhancing productivity and efficiency. They used it for creating lesson plans, generating quizzes, providing additional resources, and answers learners' questions, which saved time and allowed for more dynamic and engaging teaching strategies and methodologies.

However, the review also pointed out negative impacts. The results revealed that overuse of ChatGPT could decrease innovative capacities and collaborative learning among learners. Specifically, relying too much on ChatGPT for quick answers can inhibit learners' critical thinking and problem-solving skills. Learners might not engage deeply with the material or consider multiple solutions to a problem. This tendency was particularly evident in group projects, where learners preferred consulting ChatGPT individually for solutions over brainstorming and collaborating with peers, which negatively affected their teamwork abilities. On a broader level, integrating ChatGPT into education has also raised several concerns, including the potential for providing inaccurate or misleading information, issues of inequity in access, challenges related to academic integrity, and the possibility of misusing the technology.

Accordingly, this review emphasizes the urgency of developing clear rules, policies, and regulations to ensure ChatGPT's effective and responsible use in educational settings, alongside other chatbots, by both learners and educators. This requires providing well-structured training to educate them on responsible usage and understanding its limitations, along with offering sufficient background information. Moreover, it highlights the importance of rethinking, improving, and redesigning innovative teaching and assessment methods in the era of ChatGPT. Furthermore, conducting further research and engaging in discussions with policymakers and stakeholders are essential steps to maximize the benefits for both educators and learners and ensure academic integrity.

It is important to acknowledge that this review has certain limitations. Firstly, the limited inclusion of reviewed studies can be attributed to several reasons, including the novelty of the technology, as new technologies often face initial skepticism and cautious adoption; the lack of clear guidelines or best practices for leveraging this technology for educational purposes; and institutional or governmental policies affecting the utilization of this technology in educational contexts. These factors, in turn, have affected the number of studies available for review. Secondly, the utilization of the original version of ChatGPT, based on GPT-3 or GPT-3.5, implies that new studies utilizing the updated version, GPT-4 may lead to different findings. Therefore, conducting follow-up systematic reviews is essential once more empirical studies on ChatGPT are published. Additionally, long-term studies are necessary to thoroughly examine and assess the impact of ChatGPT on various educational practices.

Despite these limitations, this systematic review has highlighted the transformative potential of ChatGPT in education, revealing its diverse utilization by learners and educators alike and summarized the benefits of incorporating it into education, as well as the forefront critical concerns and challenges that must be addressed to facilitate its effective and responsible use in future educational contexts. This review could serve as an insightful resource for practitioners who seek to integrate ChatGPT into education and stimulate further research in the field.

Data availability

The data supporting our findings are available upon request.

Abbreviations

• Artificial intelligence

AI in education

Large language model

Artificial neural networks

Chat Generative Pre-Trained Transformer

Recurrent neural networks

Long short-term memory

Reinforcement learning from human feedback

Natural language processing

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

AlAfnan MA, Dishari S, Jovic M, Lomidze K. ChatGPT as an educational tool: opportunities, challenges, and recommendations for communication, business writing, and composition courses. J Artif Intell Technol. 2023. https://doi.org/10.37965/jait.2023.0184 .

Article   Google Scholar  

Ali JKM, Shamsan MAA, Hezam TA, Mohammed AAQ. Impact of ChatGPT on learning motivation. J Engl Stud Arabia Felix. 2023;2(1):41–9. https://doi.org/10.56540/jesaf.v2i1.51 .

Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus. 2023. https://doi.org/10.7759/cureus.35179 .

Anderson N, Belavý DL, Perle SM, Hendricks S, Hespanhol L, Verhagen E, Memon AR. AI did not write this manuscript, or did it? Can we trick the AI text detector into generated texts? The potential future of ChatGPT and AI in sports & exercise medicine manuscript generation. BMJ Open Sport Exerc Med. 2023;9(1): e001568. https://doi.org/10.1136/bmjsem-2023-001568 .

Ausat AMA, Massang B, Efendi M, Nofirman N, Riady Y. Can chat GPT replace the role of the teacher in the classroom: a fundamental analysis. J Educ. 2023;5(4):16100–6.

Google Scholar  

Baidoo-Anu D, Ansah L. Education in the Era of generative artificial intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4337484 .

Basic Z, Banovac A, Kruzic I, Jerkovic I. Better by you, better than me, chatgpt3 as writing assistance in students essays. 2023. arXiv preprint arXiv:2302.04536 .‏

Baskara FR. The promises and pitfalls of using chat GPT for self-determined learning in higher education: an argumentative review. Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai. 2023;2:95–101. https://doi.org/10.47435/sentikjar.v2i0.1825 .

Behera RK, Bala PK, Dhir A. The emerging role of cognitive computing in healthcare: a systematic literature review. Int J Med Inform. 2019;129:154–66. https://doi.org/10.1016/j.ijmedinf.2019.04.024 .

Chaka C. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: the case of five AI content detection tools. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.2.12 .

Chiu TKF, Xia Q, Zhou X, Chai CS, Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Comput Educ Artif Intell. 2023;4:100118. https://doi.org/10.1016/j.caeai.2022.100118 .

Choi EPH, Lee JJ, Ho M, Kwok JYY, Lok KYW. Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Educ Today. 2023;125:105796. https://doi.org/10.1016/j.nedt.2023.105796 .

Cotton D, Cotton PA, Shipway JR. Chatting and cheating: ensuring academic integrity in the era of ChatGPT. Innov Educ Teach Int. 2023. https://doi.org/10.1080/14703297.2023.2190148 .

Crawford J, Cowling M, Allen K. Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). J Univ Teach Learn Pract. 2023. https://doi.org/10.53761/1.20.3.02 .

Creswell JW. Educational research: planning, conducting, and evaluating quantitative and qualitative research [Ebook]. 4th ed. London: Pearson Education; 2015.

Curry D. ChatGPT Revenue and Usage Statistics (2023)—Business of Apps. 2023. https://www.businessofapps.com/data/chatgpt-statistics/

Day T. A preliminary investigation of fake peer-reviewed citations and references generated by ChatGPT. Prof Geogr. 2023. https://doi.org/10.1080/00330124.2023.2190373 .

De Castro CA. A Discussion about the Impact of ChatGPT in education: benefits and concerns. J Bus Theor Pract. 2023;11(2):p28. https://doi.org/10.22158/jbtp.v11n2p28 .

Deng X, Yu Z. A meta-analysis and systematic review of the effect of Chatbot technology use in sustainable education. Sustainability. 2023;15(4):2940. https://doi.org/10.3390/su15042940 .

Eke DO. ChatGPT and the rise of generative AI: threat to academic integrity? J Responsib Technol. 2023;13:100060. https://doi.org/10.1016/j.jrt.2023.100060 .

Elmoazen R, Saqr M, Tedre M, Hirsto L. A systematic literature review of empirical research on epistemic network analysis in education. IEEE Access. 2022;10:17330–48. https://doi.org/10.1109/access.2022.3149812 .

Farrokhnia M, Banihashem SK, Noroozi O, Wals AEJ. A SWOT analysis of ChatGPT: implications for educational practice and research. Innov Educ Teach Int. 2023. https://doi.org/10.1080/14703297.2023.2195846 .

Fergus S, Botha M, Ostovar M. Evaluating academic answers generated using ChatGPT. J Chem Educ. 2023;100(4):1672–5. https://doi.org/10.1021/acs.jchemed.3c00087 .

Fink A. Conducting research literature reviews: from the Internet to Paper. Incorporated: SAGE Publications; 2010.

Firaina R, Sulisworo D. Exploring the usage of ChatGPT in higher education: frequency and impact on productivity. Buletin Edukasi Indonesia (BEI). 2023;2(01):39–46. https://doi.org/10.56741/bei.v2i01.310 .

Firat, M. (2023). How chat GPT can transform autodidactic experiences and open education.  Department of Distance Education, Open Education Faculty, Anadolu Unive .‏ https://orcid.org/0000-0001-8707-5918

Firat M. What ChatGPT means for universities: perceptions of scholars and students. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.22 .

Fuchs K. Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse? Front Educ. 2023. https://doi.org/10.3389/feduc.2023.1166682 .

García-Peñalvo FJ. La percepción de la inteligencia artificial en contextos educativos tras el lanzamiento de ChatGPT: disrupción o pánico. Educ Knowl Soc. 2023;24: e31279. https://doi.org/10.14201/eks.31279 .

Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor A, Chartash D. How does ChatGPT perform on the United States medical Licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9: e45312. https://doi.org/10.2196/45312 .

Hashana AJ, Brundha P, Ayoobkhan MUA, Fazila S. Deep Learning in ChatGPT—A Survey. In   2023 7th international conference on trends in electronics and informatics (ICOEI) . 2023. (pp. 1001–1005). IEEE. https://doi.org/10.1109/icoei56765.2023.10125852

Hirsto L, Saqr M, López-Pernas S, Valtonen T. (2022). A systematic narrative review of learning analytics research in K-12 and schools.  Proceedings . https://ceur-ws.org/Vol-3383/FLAIEC22_paper_9536.pdf

Hisan UK, Amri MM. ChatGPT and medical education: a double-edged sword. J Pedag Educ Sci. 2023;2(01):71–89. https://doi.org/10.13140/RG.2.2.31280.23043/1 .

Hopkins AM, Logan JM, Kichenadasse G, Sorich MJ. Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift. JNCI Cancer Spectr. 2023. https://doi.org/10.1093/jncics/pkad010 .

Househ M, AlSaad R, Alhuwail D, Ahmed A, Healy MG, Latifi S, Sheikh J. Large Language models in medical education: opportunities, challenges, and future directions. JMIR Med Educ. 2023;9: e48291. https://doi.org/10.2196/48291 .

Ilkka T. The impact of artificial intelligence on learning, teaching, and education. Minist de Educ. 2018. https://doi.org/10.2760/12297 .

Iqbal N, Ahmed H, Azhar KA. Exploring teachers’ attitudes towards using CHATGPT. Globa J Manag Adm Sci. 2022;3(4):97–111. https://doi.org/10.46568/gjmas.v3i4.163 .

Irfan M, Murray L, Ali S. Integration of Artificial intelligence in academia: a case study of critical teaching and learning in Higher education. Globa Soc Sci Rev. 2023;8(1):352–64. https://doi.org/10.31703/gssr.2023(viii-i).32 .

Jeon JH, Lee S. Large language models in education: a focus on the complementary relationship between human teachers and ChatGPT. Educ Inf Technol. 2023. https://doi.org/10.1007/s10639-023-11834-1 .

Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT—Reshaping medical education and clinical management. Pak J Med Sci. 2023. https://doi.org/10.12669/pjms.39.2.7653 .

King MR. A conversation on artificial intelligence, Chatbots, and plagiarism in higher education. Cell Mol Bioeng. 2023;16(1):1–2. https://doi.org/10.1007/s12195-022-00754-8 .

Kooli C. Chatbots in education and research: a critical examination of ethical implications and solutions. Sustainability. 2023;15(7):5614. https://doi.org/10.3390/su15075614 .

Kuhail MA, Alturki N, Alramlawi S, Alhejori K. Interacting with educational chatbots: a systematic review. Educ Inf Technol. 2022;28(1):973–1018. https://doi.org/10.1007/s10639-022-11177-3 .

Lee H. The rise of ChatGPT: exploring its potential in medical education. Anat Sci Educ. 2023. https://doi.org/10.1002/ase.2270 .

Li L, Subbareddy R, Raghavendra CG. AI intelligence Chatbot to improve students learning in the higher education platform. J Interconnect Netw. 2022. https://doi.org/10.1142/s0219265921430325 .

Limna P. A Review of Artificial Intelligence (AI) in Education during the Digital Era. 2022. https://ssrn.com/abstract=4160798

Lo CK. What is the impact of ChatGPT on education? A rapid review of the literature. Educ Sci. 2023;13(4):410. https://doi.org/10.3390/educsci13040410 .

Luo W, He H, Liu J, Berson IR, Berson MJ, Zhou Y, Li H. Aladdin’s genie or pandora’s box For early childhood education? Experts chat on the roles, challenges, and developments of ChatGPT. Early Educ Dev. 2023. https://doi.org/10.1080/10409289.2023.2214181 .

Meyer JG, Urbanowicz RJ, Martin P, O’Connor K, Li R, Peng P, Moore JH. ChatGPT and large language models in academia: opportunities and challenges. Biodata Min. 2023. https://doi.org/10.1186/s13040-023-00339-9 .

Mhlanga D. Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4354422 .

Neumann, M., Rauschenberger, M., & Schön, E. M. (2023). “We Need To Talk About ChatGPT”: The Future of AI and Higher Education.‏ https://doi.org/10.1109/seeng59157.2023.00010

Nolan B. Here are the schools and colleges that have banned the use of ChatGPT over plagiarism and misinformation fears. Business Insider . 2023. https://www.businessinsider.com

O’Leary DE. An analysis of three chatbots: BlenderBot, ChatGPT and LaMDA. Int J Intell Syst Account, Financ Manag. 2023;30(1):41–54. https://doi.org/10.1002/isaf.1531 .

Okoli C. A guide to conducting a standalone systematic literature review. Commun Assoc Inf Syst. 2015. https://doi.org/10.17705/1cais.03743 .

OpenAI. (2023). https://openai.com/blog/chatgpt

Perkins M. Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. J Univ Teach Learn Pract. 2023. https://doi.org/10.53761/1.20.02.07 .

Plevris V, Papazafeiropoulos G, Rios AJ. Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard. arXiv (Cornell University) . 2023. https://doi.org/10.48550/arxiv.2305.18618

Rahman MM, Watanobe Y (2023) ChatGPT for education and research: opportunities, threats, and strategies. Appl Sci 13(9):5783. https://doi.org/10.3390/app13095783

Ram B, Verma P. Artificial intelligence AI-based Chatbot study of ChatGPT, google AI bard and baidu AI. World J Adv Eng Technol Sci. 2023;8(1):258–61. https://doi.org/10.30574/wjaets.2023.8.1.0045 .

Rasul T, Nair S, Kalendra D, Robin M, de Oliveira Santini F, Ladeira WJ, Heathcote L. The role of ChatGPT in higher education: benefits, challenges, and future research directions. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.29 .

Ratnam M, Sharm B, Tomer A. ChatGPT: educational artificial intelligence. Int J Adv Trends Comput Sci Eng. 2023;12(2):84–91. https://doi.org/10.30534/ijatcse/2023/091222023 .

Ray PP. ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys Syst. 2023;3:121–54. https://doi.org/10.1016/j.iotcps.2023.04.003 .

Roumeliotis KI, Tselikas ND. ChatGPT and Open-AI models: a preliminary review. Future Internet. 2023;15(6):192. https://doi.org/10.3390/fi15060192 .

Rudolph J, Tan S, Tan S. War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. J Appl Learn Teach. 2023. https://doi.org/10.37074/jalt.2023.6.1.23 .

Ruiz LMS, Moll-López S, Nuñez-Pérez A, Moraño J, Vega-Fleitas E. ChatGPT challenges blended learning methodologies in engineering education: a case study in mathematics. Appl Sci. 2023;13(10):6039. https://doi.org/10.3390/app13106039 .

Sallam M, Salim NA, Barakat M, Al-Tammemi AB. ChatGPT applications in medical, dental, pharmacy, and public health education: a descriptive study highlighting the advantages and limitations. Narra J. 2023;3(1): e103. https://doi.org/10.52225/narra.v3i1.103 .

Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023. https://doi.org/10.1186/s13054-023-04380-2 .

Saqr M, López-Pernas S, Helske S, Hrastinski S. The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: a full program study. Comput Educ. 2023;199:104787. https://doi.org/10.1016/j.compedu.2023.104787 .

Saqr M, Matcha W, Uzir N, Jovanović J, Gašević D, López-Pernas S. Transferring effective learning strategies across learning contexts matters: a study in problem-based learning. Australas J Educ Technol. 2023;39(3):9.

Schöbel S, Schmitt A, Benner D, Saqr M, Janson A, Leimeister JM. Charting the evolution and future of conversational agents: a research agenda along five waves and new frontiers. Inf Syst Front. 2023. https://doi.org/10.1007/s10796-023-10375-9 .

Shoufan A. Exploring students’ perceptions of CHATGPT: thematic analysis and follow-up survey. IEEE Access. 2023. https://doi.org/10.1109/access.2023.3268224 .

Sonderegger S, Seufert S. Chatbot-mediated learning: conceptual framework for the design of Chatbot use cases in education. Gallen: Institute for Educational Management and Technologies, University of St; 2022. https://doi.org/10.5220/0010999200003182 .

Book   Google Scholar  

Strzelecki A. To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interact Learn Environ. 2023. https://doi.org/10.1080/10494820.2023.2209881 .

Su J, Yang W. Unlocking the power of ChatGPT: a framework for applying generative AI in education. ECNU Rev Educ. 2023. https://doi.org/10.1177/20965311231168423 .

Sullivan M, Kelly A, McLaughlan P. ChatGPT in higher education: Considerations for academic integrity and student learning. J ApplLearn Teach. 2023;6(1):1–10. https://doi.org/10.37074/jalt.2023.6.1.17 .

Szabo A. ChatGPT is a breakthrough in science and education but fails a test in sports and exercise psychology. Balt J Sport Health Sci. 2023;1(128):25–40. https://doi.org/10.33607/bjshs.v127i4.1233 .

Taecharungroj V. “What can ChatGPT do?” analyzing early reactions to the innovative AI chatbot on Twitter. Big Data Cognit Comput. 2023;7(1):35. https://doi.org/10.3390/bdcc7010035 .

Tam S, Said RB. User preferences for ChatGPT-powered conversational interfaces versus traditional methods. Biomed Eng Soc. 2023. https://doi.org/10.58496/mjcsc/2023/004 .

Tedre M, Kahila J, Vartiainen H. (2023). Exploration on how co-designing with AI facilitates critical evaluation of ethics of AI in craft education. In: Langran E, Christensen P, Sanson J (Eds).  Proceedings of Society for Information Technology and Teacher Education International Conference . 2023. pp. 2289–2296.

Tlili A, Shehata B, Adarkwah MA, Bozkurt A, Hickey DT, Huang R, Agyemang B. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 2023. https://doi.org/10.1186/s40561-023-00237-x .

Uddin SMJ, Albert A, Ovid A, Alsharef A. Leveraging CHATGPT to aid construction hazard recognition and support safety education and training. Sustainability. 2023;15(9):7121. https://doi.org/10.3390/su15097121 .

Valtonen T, López-Pernas S, Saqr M, Vartiainen H, Sointu E, Tedre M. The nature and building blocks of educational technology research. Comput Hum Behav. 2022;128:107123. https://doi.org/10.1016/j.chb.2021.107123 .

Vartiainen H, Tedre M. Using artificial intelligence in craft education: crafting with text-to-image generative models. Digit Creat. 2023;34(1):1–21. https://doi.org/10.1080/14626268.2023.2174557 .

Ventayen RJM. OpenAI ChatGPT generated results: similarity index of artificial intelligence-based contents. Soc Sci Res Netw. 2023. https://doi.org/10.2139/ssrn.4332664 .

Wagner MW, Ertl-Wagner BB. Accuracy of information and references using ChatGPT-3 for retrieval of clinical radiological information. Can Assoc Radiol J. 2023. https://doi.org/10.1177/08465371231171125 .

Wardat Y, Tashtoush MA, AlAli R, Jarrah AM. ChatGPT: a revolutionary tool for teaching and learning mathematics. Eurasia J Math, Sci Technol Educ. 2023;19(7):em2286. https://doi.org/10.29333/ejmste/13272 .

Webster J, Watson RT. Analyzing the past to prepare for the future: writing a literature review. Manag Inf Syst Quart. 2002;26(2):3.

Xiao Y, Watson ME. Guidance on conducting a systematic literature review. J Plan Educ Res. 2017;39(1):93–112. https://doi.org/10.1177/0739456x17723971 .

Yan D. Impact of ChatGPT on learners in a L2 writing practicum: an exploratory investigation. Educ Inf Technol. 2023. https://doi.org/10.1007/s10639-023-11742-4 .

Yu H. Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching. Front Psychol. 2023;14:1181712. https://doi.org/10.3389/fpsyg.2023.1181712 .

Zhu C, Sun M, Luo J, Li T, Wang M. How to harness the potential of ChatGPT in education? Knowl Manag ELearn. 2023;15(2):133–52. https://doi.org/10.34105/j.kmel.2023.15.008 .

Download references

The paper is co-funded by the Academy of Finland (Suomen Akatemia) Research Council for Natural Sciences and Engineering for the project Towards precision education: Idiographic learning analytics (TOPEILA), Decision Number 350560.

Author information

Authors and affiliations.

School of Computing, University of Eastern Finland, 80100, Joensuu, Finland

Yazid Albadarin, Mohammed Saqr, Nicolas Pope & Markku Tukiainen

You can also search for this author in PubMed   Google Scholar

Contributions

YA contributed to the literature search, data analysis, discussion, and conclusion. Additionally, YA contributed to the manuscript’s writing, editing, and finalization. MS contributed to the study’s design, conceptualization, acquisition of funding, project administration, allocation of resources, supervision, validation, literature search, and analysis of results. Furthermore, MS contributed to the manuscript's writing, revising, and approving it in its finalized state. NP contributed to the results, and discussions, and provided supervision. NP also contributed to the writing process, revisions, and the final approval of the manuscript in its finalized state. MT contributed to the study's conceptualization, resource management, supervision, writing, revising the manuscript, and approving it.

Corresponding author

Correspondence to Yazid Albadarin .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  4

The process of synthesizing the data presented in Table  4 involved identifying the relevant studies through a search process of databases (ERIC, Scopus, Web of Knowledge, Dimensions.ai, and lens.org) using specific keywords "ChatGPT" and "education". Following this, inclusion/exclusion criteria were applied, and data extraction was performed using Creswell's [ 15 ] coding techniques to capture key information and identify common themes across the included studies.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Albadarin, Y., Saqr, M., Pope, N. et al. A systematic literature review of empirical research on ChatGPT in education. Discov Educ 3 , 60 (2024). https://doi.org/10.1007/s44217-024-00138-2

Download citation

Received : 22 October 2023

Accepted : 10 May 2024

Published : 26 May 2024

DOI : https://doi.org/10.1007/s44217-024-00138-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Large language models
• Educational technology
• Systematic review

Advertisement

• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 05 June 2024

Misunderstanding the harms of online misinformation

• Ceren Budak   ORCID: orcid.org/0000-0002-7767-3217 1 ,
• Brendan Nyhan   ORCID: orcid.org/0000-0001-7497-1799 2 ,
• David M. Rothschild   ORCID: orcid.org/0000-0002-7792-1989 3 ,
• Emily Thorson   ORCID: orcid.org/0000-0002-6514-801X 4 &
• Duncan J. Watts   ORCID: orcid.org/0000-0001-5005-4961 5  

Nature volume  630 ,  pages 45–53 ( 2024 ) Cite this article

1766 Accesses

374 Altmetric

Metrics details

• Communication

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Toolbox of individual-level interventions against online misinformation

Exposure to untrustworthy websites in the 2016 US election

Psychological inoculation protects against the social media infodemic

Myers, S. L. How social media amplifies misinformation more than information. The New York Times , https://www.nytimes.com/2022/10/13/technology/misinformation-integrity-institute-report.html (13 October 2022).

Haidt, J. Why the past 10 years of American life have been uniquely stupid. The Atlantic , https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/ (11 April 2022).

Haidt, J. Yes, social media really is undermining democracy. The Atlantic , https://www.theatlantic.com/ideas/archive/2022/07/social-media-harm-facebook-meta-response/670975/ (28 July 2022).

Tufekci, Z. YouTube, the great radicalizer. The New York Times , https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html (10 March 2018).

Romer, P. A tax that could fix big tech. The New York Times , https://www.nytimes.com/2019/05/06/opinion/tax-facebook-google.html (6 May 2019).

Schnell, M. Clyburn blames polarization on the advent of social media. The Hill , https://thehill.com/homenews/sunday-talk-shows/580440-clyburn-says-polarization-is-at-its-worst-because-the-advent-of/ (7 November 2021).

Robert F. Kennedy Human Rights/AP-NORC Poll (AP/NORC, 2023).

Goeas, E. & Nienaber, B. Battleground Poll 65: Civility in Politics: Frustration Driven by Perception (Tarrance Group, 2019).

Murray, M. Poll: Nearly two-thirds of Americans say social media platforms are tearing us apart. NBC News , https://www.nbcnews.com/politics/meet-the-press/poll-nearly-two-thirds-americans-say-social-media-platforms-are-n1266773 (2021).

Auxier, B. 64% of Americans say social media have a mostly negative effect on the way things are going in the U.S. today. Pew Research Center (2020).

Koomey, J. G. et al. Sorry, wrong number: the use and misuse of numerical facts in analysis and media reporting of energy issues. Annu. Rev. Energy Env. 27 , 119–158 (2002).

Article   Google Scholar  

Gonon, F., Bezard, E. & Boraud, T. Misrepresentation of neuroscience data might give rise to misleading conclusions in the media: the case of attention deficit hyperactivity disorder. PLoS ONE 6 , e14618 (2011).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Copenhaver, A., Mitrofan, O. & Ferguson, C. J. For video games, bad news is good news: news reporting of violent video game studies. Cyberpsychol. Behav. Soc. Netw. 20 , 735–739 (2017).

Article   PubMed   Google Scholar  

Bratton, L. et al. The association between exaggeration in health-related science news and academic press releases: a replication study. Wellcome Open Res. 4 , 148 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Allcott, H., Braghieri, L., Eichmeyer, S. & Gentzkow, M. The welfare effects of social media. Am. Econ. Rev. 110 , 629–676 (2020).

Braghieri, L., Levy, R. & Makarin, A. Social media and mental health. Am. Econ. Rev. 112 , 3660–3693 (2022).

Guess, A. M., Barberá, P., Munzert, S. & Yang, J. The consequences of online partisan media. Proc. Natl Acad. Sci. USA 118 , e2013464118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sabatini, F. & Sarracino, F. Online social networks and trust. Soc. Indic. Res. 142 , 229–260 (2019).

Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R. & Hertwig, R. How behavioural sciences can promote truth, autonomy and democratic discourse online. Nat. Hum. Behav. 4 , 1102–1109 (2020). This paper provides a review of possible harms from social media .

Lapowsky, I. The mainstream media melted down as fake news festered. Wired , https://www.wired.com/2016/12/2016-mainstream-media-melted-fake-news-festered/ (26 December 2016).

Lalani, F. & Li, C. Why So Much Harmful Content Has Proliferated Online—and What We Can Do about It Technical Report (World Economic Forum, 2020).

Stewart, E. America’s growing fake news problem, in one chart. Vox , https://www.vox.com/policy-and-politics/2020/12/22/22195488/fake-news-social-media-2020 (22 December 2020).

Sanchez, G. R., Middlemass, K. & Rodriguez, A. Misinformation Is Eroding the Public’s Confidence in Democracy (Brookings Institution, 2022).

Bond, S. False Information Is Everywhere. ‘Pre-bunking’ Tries to Head It off Early. NPR , https://www.npr.org/2022/10/28/1132021770/false-information-is-everywhere-pre-bunking-tries-to-head-it-off-ear (National Public Radio, 2022).

Tufekci, Z. Algorithmic harms beyond Facebook and google: emergent challenges of computational agency. Colo. Tech. Law J. 13 , 203 (2015).

Google Scholar  

Cohen, J. N. Exploring echo-systems: how algorithms shape immersive media environments. J. Media Lit. Educ. 10 , 139–151 (2018).

Shin, J. & Valente, T. Algorithms and health misinformation: a case study of vaccine books on Amazon. J. Health Commun. 25 , 394–401 (2020).

Ceylan, G., Anderson, I. A. & Wood, W. Sharing of misinformation is habitual, not just lazy or biased. Proc. Natl Acad. Sci. USA 120 , e2216614120 (2023).

Pauwels, L., Brion, F. & De Ruyver, B. Explaining and Understanding the Role of Exposure to New Social Media on Violent Extremism. an Integrative Quantitative and Qualitative Approach (Belgian Science Policy, 2014).

McHugh, B. C., Wisniewski, P., Rosson, M. B. & Carroll, J. M. When social media traumatizes teens: the roles of online risk exposure, coping, and post-traumatic stress. Internet Res. 28 , 1169–1188 (2018).

Soral, W., Liu, J. & Bilewicz, M. Media of contempt: social media consumption predicts normative acceptance of anti-Muslim hate speech and Islamo-prejudice. Int. J. Conf. Violence 14 , 1–13 (2020).

Many believe misinformation is increasing extreme political views and behaviors. AP-NORC https://apnorc.org/projects/many-believe-misinformation-is-increasing-extreme-political-views-an (2022).

Fandos, N., Kang, C. & Isaac, M. Tech executives are contrite about election meddling, but make few promises on Capitol Hill. The New York Times , https://www.nytimes.com/2017/10/31/us/politics/facebook-twitter-google-hearings-congress.html (31 October 2017).

Eady, G., Paskhalis, T., Zilinsky, J., Bonneau, R., Nagler, J. & Tucker, J. A. Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior. Nat. Commun. 14 , 62 (2023). This paper shows that exposure to Russian misinformation on social media in 2016 was a small portion of people’s news diets and not associated with shifting attitudes.

Badawy, A., Addawood, A., Lerman, K. & Ferrara, E. Characterizing the 2016 Russian IRA influence campaign. Soc. Netw. Anal. Min. 9 , 31 (2019). This paper shows that exposure to and amplification of Russian misinformation on social media in 2016 was concentrated among Republicans (who would have been predisposed to support Donald Trump regardless) .

Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D. M. & Watts, D. J. Examining the consumption of radical content on YouTube. Proc. Natl Acad. Sci. USA 118 , e2101967118 (2021). This paper shows that extreme content is consumed on YouTube by a small portion of the population who tend to consume similar content elsewhere online and that consumption is largely driven by demand, not algorithms .

Chen, A. Y., Nyhan, B., Reifler, J., Robertson, R. E. & Wilson, C. Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels. Sci. Adv. 9 , eadd8080 (2023). This paper shows that people who consume extremist content on YouTube have highly resentful attitudes and typically find the content through subscriptions and external links, not algorithmic recommendations to non-subscribers .

Munger, K. & Phillips, J. Right-wing YouTube: a supply and demand perspective. Int. J. Press Polit. 27 , 186–219 (2022).

Lasser, J., Aroyehun, S. T., Simchon, A., Carrella, F., Garcia, D. & Lewandowsky, S. Social media sharing of low-quality news sources by political elites. PNAS Nexus 1 , pgac186 (2022).

Muddiman, A., Budak, C., Murray, C., Kim, Y. & Stroud, N. J. Indexing theory during an emerging health crisis: how U.S. TV news indexed elite perspectives and amplified COVID-19 misinformation. Ann. Inte. Commun. Assoc. 46 , 174–204 (2022). This paper shows how mainstream media also spreads misinformation through amplification of misleading statements from elites .

Pereira, F. B. et al. Detecting misinformation: identifying false news spread by political leaders in the Global South. Preprint at OSF , https://doi.org/10.31235/osf.io/hu4qr (2022).

Horwitz, J. & Seetharaman, D. Facebook executives shut down efforts to make the site less divisive. Wall Street Journal , https://www.wsj.com/articles/facebook-knows-it-encourages-division-top-executives-nixed-solutions-11590507499 (26 May 2020).

Hosseinmardi, H., Ghasemian, A., Rivera-Lanas, M., Horta Ribeiro, M., West, R. & Watts, D. J. Causally estimating the effect of YouTube’s recommender system using counterfactual bots. Proc. Natl Acad. Sci. USA 121 , e2313377121 (2024).

Article   CAS   PubMed   Google Scholar  

Nyhan, B. et al. Like-minded sources on facebook are prevalent but not polarizing. Nature 620 , 137–144 (2023).

Guess, A. M. et al. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381 , 398–404 (2023). This paper shows that algorithms supply less untrustworthy content than reverse chronological feeds .

Article   ADS   CAS   PubMed   Google Scholar  

Asimovic, N., Nagler, J., Bonneau, R. & Tucker, J. A. Testing the effects of Facebook usage in an ethnically polarized setting. Proc. Natl Acad. Sci. USA 118 , e2022819118 (2021).

Allen, J., Mobius, M., Rothschild, D. M. & Watts, D. J. Research note: Examining potential bias in large-scale censored data. Harv. Kennedy Sch. Misinformation Rev. 2 , https://doi.org/10.37016/mr-2020-74 (2021). This paper shows that engagement metrics such as clicks and shares that are regularly used in popular and academic research do not take into account the fact that fake news is clicked and shared at a higher rate relative to exposure and viewing than non-fake news .

Scheuerman, M. K., Jiang, J. A., Fiesler, C. & Brubaker, J. R. A framework of severity for harmful content online. Proc. ACM Hum. Comput. Interact. 5 , 1–33 (2021).

Vosoughi, S., Roy, D. & Aral, S. The spread of true and false news online. Science 359 , 1146–1151 (2018).

Roy, D. Happy to see the extensive coverage of our science paper on spread of true and false news online, but over-interpretations of the scope of our study prompted me to diagram actual scope (caution, not to scale!). Twitter , https://twitter.com/dkroy/status/974251282071474177 (15 March 2018).

Greenemeier, L. You can’t handle the truth—at least on Twitter. Scientific American , https://www.scientificamerican.com/article/you-cant-handle-the-truth-at-least-on-twitter/ (8 March 2018).

Frankel, S. Deceptively edited video of Biden proliferates on social media. The New York Times , https://www.nytimes.com/2020/11/02/technology/biden-video-edited.html (2 November 2020).

Jiameng P. et al. Deepfake videos in the wild: analysis and detection. In Proc. Web Conference 2021 981–992 (International World Wide Web Conference Committee, 2021).

Widely Viewed Content Report: What People See on Facebook: Q1 2023 Report (Facebook, 2023).

Mayer, J. How Russia helped swing the election for Trump. The New Yorker , https://www.newyorker.com/magazine/2018/10/01/how-russia-helped-to-swing-the-election-for-trump (24 September 2018).

Jamieson, K. H. Cyberwar: How Russian Hackers and Trolls Helped Elect A President: What We Don’t, Can’t, and Do Know (Oxford Univ. Press, 2020).

Solon, O. & Siddiqui, S. Russia-backed Facebook posts ‘reached 126m Americans’ during US election. The Guardian , https://www.theguardian.com/technology/2017/oct/30/facebook-russia-fake-accounts-126-million (30 October 2017).

Watts, D. J. & Rothschild, D. M. Don’t blame the election on fake news. Blame it on the media. Columbia J. Rev. 5 , https://www.cjr.org/analysis/fake-news-media-election-trump.php (2017). This paper explores how seemingly large exposure levels to problematic content actually represent a small proportion of total news exposure .

Jie, Y. Frequency or total number? A comparison of different presentation formats on risk perception during COVID-19. Judgm. Decis. Mak. 17 , 215–236 (2022).

Reyna, V. F. & Brainerd, C. J. Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learn. Individ. Differ. 18 , 89–107 (2008). This paper details research into how salient numbers can lead to confusion in judgements of risk and probability, such as denominator neglect in which people fixate on a large numerator and do not consider the appropriate denominator .

Jones, J. Americans: much misinformation, bias, inaccuracy in news. Gallup , https://news.gallup.com/opinion/gallup/235796/americans-misinformation-bias-inaccuracy-news.aspx (2018).

Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B. & Lazer, D. Fake news on Twitter during the 2016 US presidential election. Science 363 , 374–378 (2019).

Guess, A. M., Nyhan, B. & Reifler, J. Exposure to untrustworthy websites in the 2016 US election. Nat. Hum. Behav. 4 , 472–480 (2020). This paper shows untrustworthy news exposure was relatively rare in US citizens’ web browsing in 2016 .

Altay, S., Nielsen, R. K. & Fletcher, R. Quantifying the “infodemic”: people turned to trustworthy news outlets during the 2020 coronavirus pandemic. J. Quant. Descr. Digit. Media 2 , 1–30 (2022).

Allen, J., Howland, B., Mobius, M., Rothschild, D. & Watts, D. J. Evaluating the fake news problem at the scale of the information ecosystem. Sci. Adv. 6 , eaay3539 (2020). This paper shows that exposure to fake news is a vanishingly small part of people’s overall news diets when you take television into account .

Article   ADS   PubMed   PubMed Central   Google Scholar  

Guess, A. M., Nyhan, B., O’Keeffe, Z. & Reifler, J. The sources and correlates of exposure to vaccine-related (mis)information online. Vaccine 38 , 7799–7805 (2020). This paper shows hows how a small portion of the population accounts for the vast majority of exposure to vaccine-sceptical content .

Chong, D. & Druckman, J. N. Framing public opinion in competitive democracies. Am. Polit. Sci. Rev. 101 , 637–655 (2007).

Arendt, F. Toward a dose-response account of media priming. Commun. Res. 42 , 1089–1115 (2015). This paper shows that people may need repeated exposure to information for it to affect their attitudes .

Arceneaux, K., Johnson, M. & Murphy, C. Polarized political communication, oppositional media hostility, and selective exposure. J. Polit. 74 , 174–186 (2012).

Feldman, L. & Hart, P. Broadening exposure to climate change news? How framing and political orientation interact to influence selective exposure. J. Commun. 68 , 503–524 (2018).

Druckman, J. N. Political preference formation: competition, deliberation, and the (ir)relevance of framing effects. Am. Polit. Sci. Rev. 98 , 671–686 (2004).

Bakshy, E., Messing, S. & Adamic, L. A. Exposure to ideologically diverse news and opinion on facebook. Science 348 , 1130–1132 (2015).

Article   ADS   MathSciNet   CAS   PubMed   Google Scholar  

Bozarth, L., Saraf, A. & Budak, C. Higher ground? How groundtruth labeling impacts our understanding of fake news about the 2016 U.S. presidential nominees. In Proc. International AAAI Conference on Web and Social Media Vol. 14, 48–59 (Association for the Advancement of Artificial Intelligence, 2020).

Gerber, A. S., Gimpel, J. G., Green, D. P. & Shaw, D. R. How large and long-lasting are the persuasive effects of televised campaign ads? Results from a randomized field experiment. Am. Polit. Sci. Rev. 105 , 135–150 (2011). This paper shows that the effect of news decays rapidly; news needs repeated exposure for long-term impact .

Hill, S. J., Lo, J., Vavreck, L. & Zaller, J. How quickly we forget: the duration of persuasion effects from mass communication. Polit. Commun. 30 , 521–547 (2013). This paper shows that the effect of persuasive advertising decays rapidly, necessitating repeated exposure for lasting effect .

Larsen, M. V. & Olsen, A. L. Reducing bias in citizens’ perception of crime rates: evidence from a field experiment on burglary prevalence. J. Polit. 82 , 747–752 (2020).

Roose, K. What if Facebook is the real ‘silent majority’? The New York Times , https://www.nytimes.com/2020/08/28/us/elections/what-if-facebook-is-the-real-silent-majority.html (27 August 2020).

Breland, A. A new report shows how Trump keeps buying Facebook ads. Mother Jones , https://www.motherjones.com/politics/2021/07/real-facebook-oversight-board/ (28 July 2021).

Marchal, N., Kollanyi, B., Neudert, L.-M. & Howard, P. N. Junk News during the EU Parliamentary Elections: Lessons from A Seven-language Study of Twitter and Facebook (Univ. Oxford, 2019).

Ellison, N. B., Trieu, P., Schoenebeck, S., Brewer, R. & Israni, A. Why we don’t click: interrogating the relationship between viewing and clicking in social media contexts by exploring the “non-click”. J. Comput. Mediat. Commun. 25 , 402–426 (2020).

Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles,and, D. & Rand, D. G. Shifting attention to accuracy can reduce misinformation online. Nature 592 , 590–595 (2021).

Ghezae, I. et al. Partisans neither expect nor receive reputational rewards for sharing falsehoods over truth online. Open Science Framework https://osf.io/5jwgd/ (2023).

Guess, A. M. et al. Reshares on social media amplify political news but do not detectably affect beliefs or opinions. Science 381 , 404–408 (2023).

Godel, W. et al. Moderating with the mob: evaluating the efficacy of real-time crowdsourced fact-checking. J. Online Trust Saf. 1 , https://doi.org/10.54501/jots.v1i1.15 (2021).

Rogers, K. Facebook’s algorithm is broken. We collected some suggestion on how to fix it. FiveThirtyEight , https://fivethirtyeight.com/features/facebooks-algorithm-is-broken-we-collected-some-spicy-suggestions-on-how-to-fix-it/ (16 November 2021).

Roose, K. The making of a YouTube radical. The New York Times , https://www.nytimes.com/interactive/2019/06/08/technology/youtube-radical.html (8 June 2019).

Eslami, M. et al. First I “like” it, then I hide it: folk theories of social feeds. In Proc. 2016 CHI Conference on Human Factors in Computing Systems 2371–2382 (Association for Computing Machinery, 2016).

Silva, D. E., Chen, C. & Zhu, Y. Facets of algorithmic literacy: information, experience, and individual factors predict attitudes toward algorithmic systems. New Media Soc. https://doi.org/10.1177/14614448221098042 (2022).

Eckles, D. Algorithmic Transparency and Assessing Effects of Algorithmic Ranking. Testimony before the Senate Subcommittee on Communications, Media, and Broadband , https://www.commerce.senate.gov/services/files/62102355-DC26-4909-BF90-8FB068145F18 (U.S. Senate Committee on Commerce, Science, and Transportation, 2021).

Kantrowitz, A. Facebook removed the news feed algorithm in an experiment. Then it gave up. OneZero , https://onezero.medium.com/facebook-removed-the-news-feed-algorithm-in-an-experiment-then-it-gave-up-25c8cb0a35a3 (25 October 2021).

Ribeiro, M. H., Hosseinmardi, H., West, R. & Watts, D. J. Deplatforming did not decrease parler users’ activity on fringe social media. PNAS Nexus 2 , pgad035 (2023). This paper shows that shutting down Parler just displaced user activity to other fringe social media websites .

Alfano, M., Fard, A. E., Carter, J. A., Clutton, P. & Klein, C. Technologically scaffolded atypical cognition: the case of YouTube’s recommender system. Synthese 199 , 835–858 (2021).

Huszár, F. et al. Algorithmic amplification of politics on Twitter. Proc. Natl Acad. Sci. USA 119 , e2025334119 (2022).

Levy, R. Social media, news consumption, and polarization: evidence from a field experiment. Am. Econ. Rev. 111 , 831–870 (2021).

Cho, J., Ahmed, S., Hilbert, M., Liu, B. & Luu, J. Do search algorithms endanger democracy? An experimental investigation of algorithm effects on political polarization. J. Broadcast. Electron. Media 64 , 150–172 (2020).

Lewandowsky, S., Robertson, R. E. & DiResta, R. Challenges in understanding human-algorithm entanglement during online information consumption. Perspect. Psychol. Sci. https://doi.org/10.1177/17456916231180809 (2023).

Narayanan, A. Understanding Social Media Recommendation Algorithms (Knight First Amendment Institute at Columbia University, 2023).

Finkel, E. J. et al. Political sectarianism in America. Science 370 , 533–536 (2020).

Auxier, B. & Anderson, M. Social Media Use in 2021 (Pew Research Center, 2021).

Frimer, J. A. et al. Incivility is rising among American politicians on Twitter. Soc. Psychol. Personal. Sci. 14 , 259–269 (2023).

Broderick, R. & Darmanin, J. The “yellow vest” riots in France are what happens when Facebook gets involved with local news. Buzzfeed News , https://www.buzzfeednews.com/article/ryanhatesthis/france-paris-yellow-jackets-facebook (2018).

Salzberg, S. De-platform the disinformation dozen. Forbes , https://www.forbes.com/sites/stevensalzberg/2021/07/19/de-platform-the-disinformation-dozen/ (2021).

Karell, D., Linke, A., Holland, E. & Hendrickson, E. “Born for a storm”: hard-right social media and civil unrest. Am. Soc. Rev. 88 , 322–349 (2023).

Smith, N. & Graham, T. Mapping the anti-vaccination movement on Facebook. Inf. Commun. Soc. 22 , 1310–1327 (2019).

Brady, W. J., McLoughlin, K., Doan, T. N. & Crockett, M. J. How social learning amplifies moral outrage expression in online social networks. Sci. Adv. 7 , eabe5641 (2021).

Suhay, E., Bello-Pardo, E. & Maurer, B. The polarizing effects of online partisan criticism: evidence from two experiments. Int. J. Press Polit. 23 , 95–115 (2018).

Arugute, N., Calvo, E. & Ventura, T. Network activated frames: content sharing and perceived polarization in social media. J. Commun. 73 , 14–24 (2023).

Nordbrandt, M. Affective polarization in the digital age: testing the direction of the relationship between social media and users’ feelings for out-group parties. New Media Soc. 25 , 3392–3411 (2023). This paper shows that affective polarization predicts media use, not the other way around .

AFP. Street protests, a French tradition par excellence. The Local https://www.thelocal.fr/20181205/revolutionary-tradition-the-story-behind-frances-street-protests (2018).

Spier, R. E. Perception of risk of vaccine adverse events: a historical perspective. Vaccine 20 , S78–S84 (2001). This article documents the history of untrustworthy information about vaccines, which long predates social media .

Bryant, L. V. The YouTube algorithm and the alt-right filter bubble. Open Inf. Sci. 4 , 85–90 (2020).

Sismeiro, C. & Mahmood, A. Competitive vs. complementary effects in online social networks and news consumption: a natural experiment. Manage. Sci. 64 , 5014–5037 (2018).

Fergusson, L. & Molina, C. Facebook Causes Protests Documento CEDE No. 41 , https://doi.org/10.2139/ssrn.3553514 (2019).

Lu, Y., Wu, J., Tan, Y. & Chen, J. Microblogging replies and opinion polarization: a natural experiment. MIS Q. 46 , 1901–1936 (2022).

Porter, E. & Wood, T. J. The global effectiveness of fact-checking: evidence from simultaneous experiments in Argentina, Nigeria, South Africa, and the United Kingdom. Proc. Natl Acad. Sci. USA 118 , e2104235118 (2021).

Arechar, A. A. et al. Understanding and combatting misinformation across 16 countries on six continents. Nat. Hum. Behav. 7 , 1502–1513 (2023).

Blair, R. A. et al. Interventions to Counter Misinformation: Lessons from the Global North and Applications to the Global South (USAID Development Experience Clearinghouse, 2023).

Haque, M. M. et al. Combating misinformation in Bangladesh: roles and responsibilities as perceived by journalists, fact-checkers, and users. Proc. ACM Hum. Comput. Interact. 4 , 1–32 (2020).

Humprecht, E., Esser, F. & Van Aelst, P. Resilience to online disinformation: a framework for cross-national comparative research. Int. J. Press Polit. 25 , 493–516 (2020).

Gillum, J. & Elliott, J. Sheryl Sandberg and top Facebook execs silenced an enemy of Turkey to prevent a hit to the company’s business. ProPublica , https://www.propublica.org/article/sheryl-sandberg-and-top-facebook-execs-silenced-an-enemy-of-turkey-to-prevent-a-hit-to-their-business (24 February 2021).

Nord M. et al. Democracy Report 2024: Democracy Winning and Losing at the Ballot V-Dem Report (Univ. Gothenburg V-Dem Institute, 2024).

Alba, D. How Duterte used Facebook to fuel the Philippine drug war. Buzzfeed , https://www.buzzfeednews.com/article/daveyalba/facebook-philippines-dutertes-drug-war (4 September 2018).

Zakrzewski, C., De Vynck, G., Masih, N. a& Mahtani, S. How Facebook neglected the rest of the world, fueling hate speech and violence in India. Washington Post , https://www.washingtonpost.com/technology/2021/10/24/india-facebook-misinformation-hate-speech/ (24 October 2021).

Simonite, T. Facebook is everywhere; its moderation is nowhere close. Wired , https://www.wired.com/story/facebooks-global-reach-exceeds-linguistic-grasp/ (21 October 2021).

Cruz, J. C. B. & Cheng, C. Establishing baselines for text classification in low-resource languages. Preprint at https://arxiv.org/abs/2005.02068 (2020). This paper shows one of the challenges that makes content moderation costlier in less resourced countries .

Müller, K. & Schwarz, C. Fanning the flames of hate: social media and hate crime. J. Eur. Econ. Assoc. 19 , 2131–2167 (2021).

Bursztyn, L., Egorov, G., Enikolopov, R. & Petrova, M. Social Media and Xenophobia: Evidence from Russia (National Bureau of Economic Research, 2019).

Lewandowsky, S., Jetter, M. & Ecker, U. K. H. Using the President’s tweets to understand political diversion in the age of social media. Nat. Commun. 11 , 5764 (2020).

Bursztyn, L., Rao, A., Roth, C. P. & Yanagizawa-Drott, D. H. Misinformation During a Pandemic (National Bureau of Economic Research, 2020).

Motta, M. & Stecula, D. Quantifying the effect of Wakefield et al. (1998) on skepticism about MMR vaccine safety in the US. PLoS ONE 16 , e0256395 (2021).

Sanderson, Z., Brown, M. A., Bonneau, R., Nagler, J. & Tucker, J. A. Twitter flagged Donald Trump’s tweets with election misinformation: they continued to spread both on and off the platform. Harv. Kennedy Sch. Misinformation Rev. 2 , https://doi.org/10.37016/mr-2020-77 (2021).

Anhalt-Depies, C., Stenglein, J. L., Zuckerberg, B., Townsend, P. A. & Rissman, A. R. Tradeoffs and tools for data quality, privacy, transparency, and trust in citizen science. Biol. Conserv. 238 , 108195 (2019).

Gerber, N., Gerber, P. & Volkamer, M. Explaining the privacy paradox: a systematic review of literature investigating privacy attitude and behavior. Comput. Secur. 77 , 226–261 (2018). This paper explores the trade-offs between privacy and research .

Isaak, J. & Hanna, M. J. User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer 51 , 56–59 (2018).

Vogus, C. Independent Researcher Access to Social Media Data: Comparing Legislative Proposals (Center for Democracy and Technology, 2022).

Xie, Y. “Undemocracy”: inequalities in science. Science 344 , 809–810 (2014).

Nielsen, M. W. & Andersen, J. P. Global citation inequality is on the rise. Proc. Natl Acad. Sci. USA 118 , e2012208118 (2021).

King, D. A. The scientific impact of nations. Nature 430 , 311–316 (2004).

Zaugg, I. A., Hossain, A. & Molloy, B. Digitally-disadvantaged languages. Internet Policy Rev. 11 , 1–11 (2022).

Zaugg, I. A. in Digital Inequalities in the Global South (eds Ragnedda, M. & Gladkova, A.) 247–267 (Springer, 2020).

Sablosky, J. Dangerous organizations: Facebook’s content moderation decisions and ethnic visibility in Myanmar. Media Cult. Soc. 43 , 1017–1042 (2021). This paper highlights the challenges of content moderation in the Global South .

Warofka, A. An independent assessment of the human rights impact of Facebook in Myanmar. Facebook Newsroom , https://about.fb.com/news/2018/11/myanmar-hria/ (2018).

Fick, M. & Dave, P. Facebook’s flood of languages leave it struggling to monitor content. Reuters , https://www.reuters.com/article/idUSKCN1RZ0DL/ (23 April 2019).

Newman, N. Executive Summary and Key Findings of the 2020 Report (Reuters Institute for the Study of Journalism, 2020).

Hilbert, M. The bad news is that the digital access divide is here to stay: domestically installed bandwidths among 172 countries for 1986–2014. Telecommun. Policy 40 , 567–581 (2016).

Traynor, I. Internet governance too US-centric, says European commission. The Guardian , https://www.theguardian.com/technology/2014/feb/12/internet-governance-us-european-commission (12 February 2014).

Pennycook, G., Cannon, T. D. & Rand, D. G. Prior exposure increases perceived accuracy of fake news. J. Exp. Psychol. Gen. 147 , 1865–1880 (2018).

Guess, A. M. et al. “Fake news” may have limited effects beyond increasing beliefs in false claims. Kennedy Sch. Misinformation Rev. 1 , https://doi.org/10.37016/mr-2020-004 (2020).

Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K. & Larson, H. J. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 5 , 337–348 (2021).

Lorenz-Spreen, P., Oswald, L., Lewandowsky, S. & Hertwig, R. Digital media and democracy: a systematic review of causal and correlational evidence worldwide. Nat. Hum. Behav. 7 , 74–101 (2023). This paper provides a review of evidence on social media effects .

Donato, K. M., Singh, L., Arab, A., Jacobs, E. & Post, D. Misinformation about COVID-19 and Venezuelan migration: trends in Twitter conversation during a pandemic. Harvard Data Sci. Rev. 4 , https://doi.org/10.1162/99608f92.a4d9a7c7 (2022).

Wieczner, J. Big lies vs. big lawsuits: why Dominion Voting is suing Fox News and a host of Trump allies. Fortune , https://fortune.com/longform/dominion-voting-lawsuits-fox-news-trump-allies-2020-election-libel-conspiracy-theories/ (2 April 2021).

Calma, J. Twitter just closed the book on academic research. The Verge https://www.theverge.com/2023/5/31/23739084/twitter-elon-musk-api-policy-chilling-academic-research (2023).

Edelson, L., Graef, I. & Lancieri, F. Access to Data and Algorithms: for an Effective DMA and DSA Implementation (Centre on Regulation in Europe, 2023).

Download references

Author information

Authors and affiliations.

University of Michigan School of Information, Ann Arbor, MI, USA

Ceren Budak

Department of Government, Dartmouth College, Hanover, NH, USA

Brendan Nyhan

Microsoft Research, New York, NY, USA

David M. Rothschild

Maxwell School of Citizenship and Public Affairs, Syracuse University, Syracuse, NY, USA

Emily Thorson

Department of Computer and Information Science, Annenberg School of Communication, and Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA, USA

Duncan J. Watts

You can also search for this author in PubMed   Google Scholar

Contributions

C.B., B.N., D.M.R., E.T. and D.J.W. wrote and revised the paper. D.M.R. collected the data and prepared Fig. 1 .

Corresponding author

Correspondence to David M. Rothschild .

Ethics declarations

Competing interests.

The authors declare no competing interests, but provide the following information in the interests of transparency and full disclosure. C.B. and D.J.W. previously worked for Microsoft Research and D.M.R. currently works for Microsoft Research. B.N. has received grant funding from Meta. B.N. and E.T. are participants in the US 2020 Facebook and Instagram Election Study as independent academic researchers. D.J.W. has received funding from Google Research. D.M.R. and D.J.W. both previously worked at Yahoo!.

Peer review

Peer review information.

Nature thanks Stephan Lewandowsky, David Rand, Emma Spiro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Budak, C., Nyhan, B., Rothschild, D.M. et al. Misunderstanding the harms of online misinformation. Nature 630 , 45–53 (2024). https://doi.org/10.1038/s41586-024-07417-w

Download citation

Received : 13 October 2021

Accepted : 11 April 2024

Published : 05 June 2024

Issue Date : 06 June 2024

DOI : https://doi.org/10.1038/s41586-024-07417-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.