empirical studies in research

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Empirical Research: Definition, Methods, Types and Examples

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• 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 [emphasis added]: 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  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Introduction to Empirical Research

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

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PSYC 301: Intro to Research Methods

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Finding Empirical Research

Empirical research is published in books and in scholarly, peer-reviewed journals. PsycInfo  offers straightforward ways to identify empirical research, unlike most other databases.

Finding Empirical Research in PsycInfo

• PsycInfo Choose "Advanced Search" Scroll down the page to "Methodology," and choose "Empirical Study" Type your keywords into the search boxes Choose other limits, such as publication date, if needed Click on the "Search" button

Slideshow showing how to find empirical research in APA PsycInfo

Video of finding empirical articles in psycinfo.

• Searching for Peer-Reviewed Empirical Articles (YouTube Video) Created by the APA

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
• 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

Adapted from PennState University Libraries, Empirical Research in the Social Sciences and Education

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• Published: 01 June 2023

Data, measurement and empirical methods in the science of science

• Lu Liu 1 , 2 , 3 , 4 ,
• Benjamin F. Jones   ORCID: orcid.org/0000-0001-9697-9388 1 , 2 , 3 , 5 , 6 ,
• Brian Uzzi   ORCID: orcid.org/0000-0001-6855-2854 1 , 2 , 3 &
• Dashun Wang   ORCID: orcid.org/0000-0002-7054-2206 1 , 2 , 3 , 7  

Nature Human Behaviour volume  7 ,  pages 1046–1058 ( 2023 ) Cite this article

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The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding ‘science of science’. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field’s diverse methodologies and expand researchers’ toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

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Genome-wide association studies

Scientific advances are a key input to rising standards of living, health and the capacity of society to confront grand challenges, from climate change to the COVID-19 pandemic 1 , 2 , 3 . A deeper understanding of how science works and where innovation occurs can help us to more effectively design science policy and science institutions, better inform scientists’ own research choices, and create and capture enormous value for science and humanity. Building on these key premises, recent years have witnessed substantial development in the ‘science of science’ 4 , 5 , 6 , 7 , 8 , 9 , which uses large-scale datasets and diverse computational toolkits to unearth fundamental patterns behind scientific production and use.

The idea of turning scientific methods into science itself is long-standing. Since the mid-20th century, researchers from different disciplines have asked central questions about the nature of scientific progress and the practice, organization and impact of scientific research. Building on these rich historical roots, the field of the science of science draws upon many disciplines, ranging from information science to the social, physical and biological sciences to computer science, engineering and design. The science of science closely relates to several strands and communities of research, including metascience, scientometrics, the economics of science, research on research, science and technology studies, the sociology of science, metaknowledge and quantitative science studies 5 . There are noticeable differences between some of these communities, mostly around their historical origins and the initial disciplinary composition of researchers forming these communities. For example, metascience has its origins in the clinical sciences and psychology, and focuses on rigour, transparency, reproducibility and other open science-related practices and topics. The scientometrics community, born in library and information sciences, places a particular emphasis on developing robust and responsible measures and indicators for science. Science and technology studies engage the history of science and technology, the philosophy of science, and the interplay between science, technology and society. The science of science, which has its origins in physics, computer science and sociology, takes a data-driven approach and emphasizes questions on how science works. Each of these communities has made fundamental contributions to understanding science. While they differ in their origins, these differences pale in comparison to the overarching, common interest in understanding the practice of science and its societal impact.

Three major developments have encouraged rapid advances in the science of science. The first is in data 9 : modern databases include millions of research articles, grant proposals, patents and more. This windfall of data traces scientific activity in remarkable detail and at scale. The second development is in measurement: scholars have used data to develop many new measures of scientific activities and examine theories that have long been viewed as important but difficult to quantify. The third development is in empirical methods: thanks to parallel advances in data science, network science, artificial intelligence and econometrics, researchers can study relationships, make predictions and assess science policy in powerful new ways. Together, new data, measurements and methods have revealed fundamental new insights about the inner workings of science and scientific progress itself.

With multiple approaches, however, comes a key challenge. As researchers adhere to norms respected within their disciplines, their methods vary, with results often published in venues with non-overlapping readership, fragmenting research along disciplinary boundaries. This fragmentation challenges researchers’ ability to appreciate and understand the value of work outside of their own discipline, much less to build directly on it for further investigations.

Recognizing these challenges and the rapidly developing nature of the field, this paper reviews the empirical approaches that are prevalent in this literature. We aim to provide readers with an up-to-date understanding of the available datasets, measurement constructs and empirical methodologies, as well as the value and limitations of each. Owing to space constraints, this Review does not cover the full technical details of each method, referring readers to related guides to learn more. Instead, we will emphasize why a researcher might favour one method over another, depending on the research question.

Beyond a positive understanding of science, a key goal of the science of science is to inform science policy. While this Review mainly focuses on empirical approaches, with its core audience being researchers in the field, the studies reviewed are also germane to key policy questions. For example, what is the appropriate scale of scientific investment, in what directions and through what institutions 10 , 11 ? Are public investments in science aligned with public interests 12 ? What conditions produce novel or high-impact science 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ? How do the reward systems of science influence the rate and direction of progress 13 , 21 , 22 , 23 , 24 , and what governs scientific reproducibility 25 , 26 , 27 ? How do contributions evolve over a scientific career 28 , 29 , 30 , 31 , 32 , and how may diversity among scientists advance scientific progress 33 , 34 , 35 , among other questions relevant to science policy 36 , 37 .

Overall, this review aims to facilitate entry to science of science research, expand researcher toolkits and illustrate how diverse research approaches contribute to our collective understanding of science. Section 2 reviews datasets and data linkages. Section 3 reviews major measurement constructs in the science of science. Section 4 considers a range of empirical methods, focusing on one study to illustrate each method and briefly summarizing related examples and applications. Section 5 concludes with an outlook for the science of science.

Historically, data on scientific activities were difficult to collect and were available in limited quantities. Gathering data could involve manually tallying statistics from publications 38 , 39 , interviewing scientists 16 , 40 , or assembling historical anecdotes and biographies 13 , 41 . Analyses were typically limited to a specific domain or group of scientists. Today, massive datasets on scientific production and use are at researchers’ fingertips 42 , 43 , 44 . Armed with big data and advanced algorithms, researchers can now probe questions previously not amenable to quantification and with enormous increases in scope and scale, as detailed below.

Publication datasets cover papers from nearly all scientific disciplines, enabling analyses of both general and domain-specific patterns. Commonly used datasets include the Web of Science (WoS), PubMed, CrossRef, ORCID, OpenCitations, Dimensions and OpenAlex. Datasets incorporating papers’ text (CORE) 45 , 46 , 47 , data entities (DataCite) 48 , 49 and peer review reports (Publons) 33 , 50 , 51 have also become available. These datasets further enable novel measurement, for example, representations of a paper’s content 52 , 53 , novelty 15 , 54 and interdisciplinarity 55 .

Notably, databases today capture more diverse aspects of science beyond publications, offering a richer and more encompassing view of research contexts and of researchers themselves (Fig. 1 ). For example, some datasets trace research funding to the specific publications these investments support 56 , 57 , allowing high-scale studies of the impact of funding on productivity and the return on public investment. Datasets incorporating job placements 58 , 59 , curriculum vitae 21 , 59 and scientific prizes 23 offer rich quantitative evidence on the social structure of science. Combining publication profiles with mentorship genealogies 60 , 61 , dissertations 34 and course syllabi 62 , 63 provides insights on mentoring and cultivating talent.

This figure presents commonly used data types in science of science research, information contained in each data type and examples of data sources. Datasets in the science of science research have not only grown in scale but have also expanded beyond publications to integrate upstream funding investments and downstream applications that extend beyond science itself.

Finally, today’s scope of data extends beyond science to broader aspects of society. Altmetrics 64 captures news media and social media mentions of scientific articles. Other databases incorporate marketplace uses of science, including through patents 10 , pharmaceutical clinical trials and drug approvals 65 , 66 . Policy documents 67 , 68 help us to understand the role of science in the halls of government 69 and policy making 12 , 68 .

While datasets of the modern scientific enterprise have grown exponentially, they are not without limitations. As is often the case for data-driven research, drawing conclusions from specific data sources requires scrutiny and care. Datasets are typically based on published work, which may favour easy-to-publish topics over important ones (the streetlight effect) 70 , 71 . The publication of negative results is also rare (the file drawer problem) 72 , 73 . Meanwhile, English language publications account for over 90% of articles in major data sources, with limited coverage of non-English journals 74 . Publication datasets may also reflect biases in data collection across research institutions or demographic groups. Despite the open science movement, many datasets require paid subscriptions, which can create inequality in data access. Creating more open datasets for the science of science, such as OpenAlex, may not only improve the robustness and replicability of empirical claims but also increase entry to the field.

As today’s datasets become larger in scale and continue to integrate new dimensions, they offer opportunities to unveil the inner workings and external impacts of science in new ways. They can enable researchers to reach beyond previous limitations while conducting original studies of new and long-standing questions about the sciences.

Measurement

Here we discuss prominent measurement approaches in the science of science, including their purposes and limitations.

Modern publication databases typically include data on which articles and authors cite other papers and scientists. These citation linkages have been used to engage core conceptual ideas in scientific research. Here we consider two common measures based on citation information: citation counts and knowledge flows.

First, citation counts are commonly used indicators of impact. The term ‘indicator’ implies that it only approximates the concept of interest. A citation count is defined as how many times a document is cited by subsequent documents and can proxy for the importance of research papers 75 , 76 as well as patented inventions 77 , 78 , 79 . Rather than treating each citation equally, measures may further weight the importance of each citation, for example by using the citation network structure to produce centrality 80 , PageRank 81 , 82 or Eigenfactor indicators 83 , 84 .

Citation-based indicators have also faced criticism 84 , 85 . Citation indicators necessarily oversimplify the construct of impact, often ignoring heterogeneity in the meaning and use of a particular reference, the variations in citation practices across fields and institutional contexts, and the potential for reputation and power structures in science to influence citation behaviour 86 , 87 . Researchers have started to understand more nuanced citation behaviours ranging from negative citations 86 to citation context 47 , 88 , 89 . Understanding what a citation actually measures matters in interpreting and applying many research findings in the science of science. Evaluations relying on citation-based indicators rather than expert judgements raise questions regarding misuse 90 , 91 , 92 . Given the importance of developing indicators that can reliably quantify and evaluate science, the scientometrics community has been working to provide guidance for responsible citation practices and assessment 85 .

Second, scientists use citations to trace knowledge flows. Each citation in a paper is a link to specific previous work from which we can proxy how new discoveries draw upon existing ideas 76 , 93 and how knowledge flows between fields of science 94 , 95 , research institutions 96 , regions and nations 97 , 98 , 99 , and individuals 81 . Combinations of citation linkages can also approximate novelty 15 , disruptiveness 17 , 100 and interdisciplinarity 55 , 95 , 101 , 102 . A rapidly expanding body of work further examines citations to scientific articles from other domains (for example, patents, clinical drug trials and policy documents) to understand the applied value of science 10 , 12 , 65 , 66 , 103 , 104 , 105 .

Individuals

Analysing individual careers allows researchers to answer questions such as: How do we quantify individual scientific productivity? What is a typical career lifecycle? How are resources and credits allocated across individuals and careers? A scholar’s career can be examined through the papers they publish 30 , 31 , 106 , 107 , 108 , with attention to career progression and mobility, publication counts and citation impact, as well as grant funding 24 , 109 , 110 and prizes 111 , 112 , 113 ,

Studies of individual impact focus on output, typically approximated by the number of papers a researcher publishes and citation indicators. A popular measure for individual impact is the h -index 114 , which takes both volume and per-paper impact into consideration. Specifically, a scientist is assigned the largest value h such that they have h papers that were each cited at least h times. Later studies build on the idea of the h -index and propose variants to address limitations 115 , these variants ranging from emphasizing highly cited papers in a career 116 , to field differences 117 and normalizations 118 , to the relative contribution of an individual in collaborative works 119 .

To study dynamics in output over the lifecycle, individuals can be studied according to age, career age or the sequence of publications. A long-standing literature has investigated the relationship between age and the likelihood of outstanding achievement 28 , 106 , 111 , 120 , 121 . Recent studies further decouple the relationship between age, publication volume and per-paper citation, and measure the likelihood of producing highly cited papers in the sequence of works one produces 30 , 31 .

As simple as it sounds, representing careers using publication records is difficult. Collecting the full publication list of a researcher is the foundation to study individuals yet remains a key challenge, requiring name disambiguation techniques to match specific works to specific researchers. Although algorithms are increasingly capable at identifying millions of career profiles 122 , they vary in accuracy and robustness. ORCID can help to alleviate the problem by offering researchers the opportunity to create, maintain and update individual profiles themselves, and it goes beyond publications to collect broader outputs and activities 123 . A second challenge is survivorship bias. Empirical studies tend to focus on careers that are long enough to afford statistical analyses, which limits the applicability of the findings to scientific careers as a whole. A third challenge is the breadth of scientists’ activities, where focusing on publications ignores other important contributions such as mentorship and teaching, service (for example, refereeing papers, reviewing grant proposals and editing journals) or leadership within their organizations. Although researchers have begun exploring these dimensions by linking individual publication profiles with genealogical databases 61 , 124 , dissertations 34 , grants 109 , curriculum vitae 21 and acknowledgements 125 , scientific careers beyond publication records remain under-studied 126 , 127 . Lastly, citation-based indicators only serve as an approximation of individual performance with similar limitations as discussed above. The scientific community has called for more appropriate practices 85 , 128 , ranging from incorporating expert assessment of research contributions to broadening the measures of impact beyond publications.

Over many decades, science has exhibited a substantial and steady shift away from solo authorship towards coauthorship, especially among highly cited works 18 , 129 , 130 . In light of this shift, a research field, the science of team science 131 , 132 , has emerged to study the mechanisms that facilitate or hinder the effectiveness of teams. Team size can be proxied by the number of coauthors on a paper, which has been shown to predict distinctive types of advance: whereas larger teams tend to develop ideas, smaller teams tend to disrupt current ways of thinking 17 . Team characteristics can be inferred from coauthors’ backgrounds 133 , 134 , 135 , allowing quantification of a team’s diversity in terms of field, age, gender or ethnicity. Collaboration networks based on coauthorship 130 , 136 , 137 , 138 , 139 offer nuanced network-based indicators to understand individual and institutional collaborations.

However, there are limitations to using coauthorship alone to study teams 132 . First, coauthorship can obscure individual roles 140 , 141 , 142 , which has prompted institutional responses to help to allocate credit, including authorship order and individual contribution statements 56 , 143 . Second, coauthorship does not reflect the complex dynamics and interactions between team members that are often instrumental for team success 53 , 144 . Third, collaborative contributions can extend beyond coauthorship in publications to include members of a research laboratory 145 or co-principal investigators (co-PIs) on a grant 146 . Initiatives such as CRediT may help to address some of these issues by recording detailed roles for each contributor 147 .

Institutions

Research institutions, such as departments, universities, national laboratories and firms, encompass wider groups of researchers and their corresponding outputs. Institutional membership can be inferred from affiliations listed on publications or patents 148 , 149 , and the output of an institution can be aggregated over all its affiliated researchers 150 . Institutional research information systems (CRIS) contain more comprehensive research outputs and activities from employees.

Some research questions consider the institution as a whole, investigating the returns to research and development investment 104 , inequality of resource allocation 22 and the flow of scientists 21 , 148 , 149 . Other questions focus on institutional structures as sources of research productivity by looking into the role of peer effects 125 , 151 , 152 , 153 , how institutional policies impact research outcomes 154 , 155 and whether interdisciplinary efforts foster innovation 55 . Institution-oriented measurement faces similar limitations as with analyses of individuals and teams, including name disambiguation for a given institution and the limited capacity of formal publication records to characterize the full range of relevant institutional outcomes. It is also unclear how to allocate credit among multiple institutions associated with a paper. Moreover, relevant institutional employees extend beyond publishing researchers: interns, technicians and administrators all contribute to research endeavours 130 .

In sum, measurements allow researchers to quantify scientific production and use across numerous dimensions, but they also raise questions of construct validity: Does the proposed metric really reflect what we want to measure? Testing the construct’s validity is important, as is understanding a construct’s limits. Where possible, using alternative measurement approaches, or qualitative methods such as interviews and surveys, can improve measurement accuracy and the robustness of findings.

Empirical methods

In this section, we review two broad categories of empirical approaches (Table 1 ), each with distinctive goals: (1) to discover, estimate and predict empirical regularities; and (2) to identify causal mechanisms. For each method, we give a concrete example to help to explain how the method works, summarize related work for interested readers, and discuss contributions and limitations.

Descriptive and predictive approaches

Empirical regularities and generalizable facts.

The discovery of empirical regularities in science has had a key role in driving conceptual developments and the directions of future research. By observing empirical patterns at scale, researchers unveil central facts that shape science and present core features that theories of scientific progress and practice must explain. For example, consider citation distributions. de Solla Price first proposed that citation distributions are fat-tailed 39 , indicating that a few papers have extremely high citations while most papers have relatively few or even no citations at all. de Solla Price proposed that citation distribution was a power law, while researchers have since refined this view to show that the distribution appears log-normal, a nearly universal regularity across time and fields 156 , 157 . The fat-tailed nature of citation distributions and its universality across the sciences has in turn sparked substantial theoretical work that seeks to explain this key empirical regularity 20 , 156 , 158 , 159 .

Empirical regularities are often surprising and can contest previous beliefs of how science works. For example, it has been shown that the age distribution of great achievements peaks in middle age across a wide range of fields 107 , 121 , 160 , rejecting the common belief that young scientists typically drive breakthroughs in science. A closer look at the individual careers also indicates that productivity patterns vary widely across individuals 29 . Further, a scholar’s highest-impact papers come at a remarkably constant rate across the sequence of their work 30 , 31 .

The discovery of empirical regularities has had important roles in shaping beliefs about the nature of science 10 , 45 , 161 , 162 , sources of breakthrough ideas 15 , 163 , 164 , 165 , scientific careers 21 , 29 , 126 , 127 , the network structure of ideas and scientists 23 , 98 , 136 , 137 , 138 , 139 , 166 , gender inequality 57 , 108 , 126 , 135 , 143 , 167 , 168 , and many other areas of interest to scientists and science institutions 22 , 47 , 86 , 97 , 102 , 105 , 134 , 169 , 170 , 171 . At the same time, care must be taken to ensure that findings are not merely artefacts due to data selection or inherent bias. To differentiate meaningful patterns from spurious ones, it is important to stress test the findings through different selection criteria or across non-overlapping data sources.

Regression analysis

When investigating correlations among variables, a classic method is regression, which estimates how one set of variables explains variation in an outcome of interest. Regression can be used to test explicit hypotheses or predict outcomes. For example, researchers have investigated whether a paper’s novelty predicts its citation impact 172 . Adding additional control variables to the regression, one can further examine the robustness of the focal relationship.

Although regression analysis is useful for hypothesis testing, it bears substantial limitations. If the question one wishes to ask concerns a ‘causal’ rather than a correlational relationship, regression is poorly suited to the task as it is impossible to control for all the confounding factors. Failing to account for such ‘omitted variables’ can bias the regression coefficient estimates and lead to spurious interpretations. Further, regression models often have low goodness of fit (small R 2 ), indicating that the variables considered explain little of the outcome variation. As regressions typically focus on a specific relationship in simple functional forms, regressions tend to emphasize interpretability rather than overall predictability. The advent of predictive approaches powered by large-scale datasets and novel computational techniques offers new opportunities for modelling complex relationships with stronger predictive power.

Mechanistic models

Mechanistic modelling is an important approach to explaining empirical regularities, drawing from methods primarily used in physics. Such models predict macro-level regularities of a system by modelling micro-level interactions among basic elements with interpretable and modifiable formulars. While theoretical by nature, mechanistic models in the science of science are often empirically grounded, and this approach has developed together with the advent of large-scale, high-resolution data.

Simplicity is the core value of a mechanistic model. Consider for example, why citations follow a fat-tailed distribution. de Solla Price modelled the citing behaviour as a cumulative advantage process on a growing citation network 159 and found that if the probability a paper is cited grows linearly with its existing citations, the resulting distribution would follow a power law, broadly aligned with empirical observations. The model is intentionally simplified, ignoring myriad factors. Yet the simple cumulative advantage process is by itself sufficient in explaining a power law distribution of citations. In this way, mechanistic models can help to reveal key mechanisms that can explain observed patterns.

Moreover, mechanistic models can be refined as empirical evidence evolves. For example, later investigations showed that citation distributions are better characterized as log-normal 156 , 173 , prompting researchers to introduce a fitness parameter to encapsulate the inherent differences in papers’ ability to attract citations 174 , 175 . Further, older papers are less likely to be cited than expected 176 , 177 , 178 , motivating more recent models 20 to introduce an additional aging effect 179 . By combining the cumulative advantage, fitness and aging effects, one can already achieve substantial predictive power not just for the overall properties of the system but also the citation dynamics of individual papers 20 .

In addition to citations, mechanistic models have been developed to understand the formation of collaborations 136 , 180 , 181 , 182 , 183 , knowledge discovery and diffusion 184 , 185 , topic selection 186 , 187 , career dynamics 30 , 31 , 188 , 189 , the growth of scientific fields 190 and the dynamics of failure in science and other domains 178 .

At the same time, some observers have argued that mechanistic models are too simplistic to capture the essence of complex real-world problems 191 . While it has been a cornerstone for the natural sciences, representing social phenomena in a limited set of mathematical equations may miss complexities and heterogeneities that make social phenomena interesting in the first place. Such concerns are not unique to the science of science, as they represent a broader theme in computational social sciences 192 , 193 , ranging from social networks 194 , 195 to human mobility 196 , 197 to epidemics 198 , 199 . Other observers have questioned the practical utility of mechanistic models and whether they can be used to guide decisions and devise actionable policies. Nevertheless, despite these limitations, several complex phenomena in the science of science are well captured by simple mechanistic models, showing a high degree of regularity beneath complex interacting systems and providing powerful insights about the nature of science. Mixing such modelling with other methods could be particularly fruitful in future investigations.

Machine learning

The science of science seeks in part to forecast promising directions for scientific research 7 , 44 . In recent years, machine learning methods have substantially advanced predictive capabilities 200 , 201 and are playing increasingly important parts in the science of science. In contrast to the previous methods, machine learning does not emphasize hypotheses or theories. Rather, it leverages complex relationships in data and optimizes goodness of fit to make predictions and categorizations.

Traditional machine learning models include supervised, semi-supervised and unsupervised learning. The model choice depends on data availability and the research question, ranging from supervised models for citation prediction 202 , 203 to unsupervised models for community detection 204 . Take for example mappings of scientific knowledge 94 , 205 , 206 . The unsupervised method applies network clustering algorithms to map the structures of science. Related visualization tools make sense of clusters from the underlying network, allowing observers to see the organization, interactions and evolution of scientific knowledge. More recently, supervised learning, and deep neural networks in particular, have witnessed especially rapid developments 207 . Neural networks can generate high-dimensional representations of unstructured data such as images and texts, which encode complex properties difficult for human experts to perceive.

Take text analysis as an example. A recent study 52 utilizes 3.3 million paper abstracts in materials science to predict the thermoelectric properties of materials. The intuition is that the words currently used to describe a material may predict its hitherto undiscovered properties (Fig. 2 ). Compared with a random material, the materials predicted by the model are eight times more likely to be reported as thermoelectric in the next 5 years, suggesting that machine learning has the potential to substantially speed up knowledge discovery, especially as data continue to grow in scale and scope. Indeed, predicting the direction of new discoveries represents one of the most promising avenues for machine learning models, with neural networks being applied widely to biology 208 , physics 209 , 210 , mathematics 211 , chemistry 212 , medicine 213 and clinical applications 214 . Neural networks also offer a quantitative framework to probe the characteristics of creative products ranging from scientific papers 53 , journals 215 , organizations 148 , to paintings and movies 32 . Neural networks can also help to predict the reproducibility of papers from a variety of disciplines at scale 53 , 216 .

This figure illustrates the word2vec skip-gram methods 52 , where the goal is to predict useful properties of materials using previous scientific literature. a , The architecture and training process of the word2vec skip-gram model, where the 3-layer, fully connected neural network learns the 200-dimensional representation (hidden layer) from the sparse vector for each word and its context in the literature (input layer). b , The top two principal components of the word embedding. Materials with similar features are close in the 2D space, allowing prediction of a material’s properties. Different targeted words are shown in different colours. Reproduced with permission from ref. 52 , Springer Nature Ltd.

While machine learning can offer high predictive accuracy, successful applications to the science of science face challenges, particularly regarding interpretability. Researchers may value transparent and interpretable findings for how a given feature influences an outcome, rather than a black-box model. The lack of interpretability also raises concerns about bias and fairness. In predicting reproducible patterns from data, machine learning models inevitably include and reproduce biases embedded in these data, often in non-transparent ways. The fairness of machine learning 217 is heavily debated in applications ranging from the criminal justice system to hiring processes. Effective and responsible use of machine learning in the science of science therefore requires thoughtful partnership between humans and machines 53 to build a reliable system accessible to scrutiny and modification.

Causal approaches

The preceding methods can reveal core facts about the workings of science and develop predictive capacity. Yet, they fail to capture causal relationships, which are particularly useful in assessing policy interventions. For example, how can we test whether a science policy boosts or hinders the performance of individuals, teams or institutions? The overarching idea of causal approaches is to construct some counterfactual world where two groups are identical to each other except that one group experiences a treatment that the other group does not.

Towards causation

Before engaging in causal approaches, it is useful to first consider the interpretative challenges of observational data. As observational data emerge from mechanisms that are not fully known or measured, an observed correlation may be driven by underlying forces that were not accounted for in the analysis. This challenge makes causal inference fundamentally difficult in observational data. An awareness of this issue is the first step in confronting it. It further motivates intermediate empirical approaches, including the use of matching strategies and fixed effects, that can help to confront (although not fully eliminate) the inference challenge. We first consider these approaches before turning to more fully causal methods.

Matching. Matching utilizes rich information to construct a control group that is similar to the treatment group on as many observable characteristics as possible before the treatment group is exposed to the treatment. Inferences can then be made by comparing the treatment and the matched control groups. Exact matching applies to categorical values, such as country, gender, discipline or affiliation 35 , 218 . Coarsened exact matching considers percentile bins of continuous variables and matches observations in the same bin 133 . Propensity score matching estimates the probability of receiving the ‘treatment’ on the basis of the controlled variables and uses the estimates to match treatment and control groups, which reduces the matching task from comparing the values of multiple covariates to comparing a single value 24 , 219 . Dynamic matching is useful for longitudinally matching variables that change over time 220 , 221 .

Fixed effects. Fixed effects are a powerful and now standard tool in controlling for confounders. A key requirement for using fixed effects is that there are multiple observations on the same subject or entity (person, field, institution and so on) 222 , 223 , 224 . The fixed effect works as a dummy variable that accounts for the role of any fixed characteristic of that entity. Consider the finding where gender-diverse teams produce higher-impact papers than same-gender teams do 225 . A confounder may be that individuals who tend to write high-impact papers may also be more likely to work in gender-diverse teams. By including individual fixed effects, one accounts for any fixed characteristics of individuals (such as IQ, cultural background or previous education) that might drive the relationship of interest.

In sum, matching and fixed effects methods reduce potential sources of bias in interpreting relationships between variables. Yet, confounders may persist in these studies. For instance, fixed effects do not control for unobserved factors that change with time within the given entity (for example, access to funding or new skills). Identifying casual effects convincingly will then typically require distinct research methods that we turn to next.

Quasi-experiments

Researchers in economics and other fields have developed a range of quasi-experimental methods to construct treatment and control groups. The key idea here is exploiting randomness from external events that differentially expose subjects to a particular treatment. Here we review three quasi-experimental methods: difference-in-differences, instrumental variables and regression discontinuity (Fig. 3 ).

a – c , This figure presents illustrations of ( a ) differences-in-differences, ( b ) instrumental variables and ( c ) regression discontinuity methods. The solid line in b represents causal links and the dashed line represents the relationships that are not allowed, if the IV method is to produce causal inference.

Difference-in-differences. Difference-in-difference regression (DiD) investigates the effect of an unexpected event, comparing the affected group (the treated group) with an unaffected group (the control group). The control group is intended to provide the counterfactual path—what would have happened were it not for the unexpected event. Ideally, the treated and control groups are on virtually identical paths before the treatment event, but DiD can also work if the groups are on parallel paths (Fig. 3a ). For example, one study 226 examines how the premature death of superstar scientists affects the productivity of their previous collaborators. The control group are collaborators of superstars who did not die in the time frame. The two groups do not show significant differences in publications before a death event, yet upon the death of a star scientist, the treated collaborators on average experience a 5–8% decline in their quality-adjusted publication rates compared with the control group. DiD has wide applicability in the science of science, having been used to analyse the causal effects of grant design 24 , access costs to previous research 155 , 227 , university technology transfer policies 154 , intellectual property 228 , citation practices 229 , evolution of fields 221 and the impacts of paper retractions 230 , 231 , 232 . The DiD literature has grown especially rapidly in the field of economics, with substantial recent refinements 233 , 234 .

Instrumental variables. Another quasi-experimental approach utilizes ‘instrumental variables’ (IV). The goal is to determine the causal influence of some feature X on some outcome Y by using a third, instrumental variable. This instrumental variable is a quasi-random event that induces variation in X and, except for its impact through X , has no other effect on the outcome Y (Fig. 3b ). For example, consider a study of astronomy that seeks to understand how telescope time affects career advancement 235 . Here, one cannot simply look at the correlation between telescope time and career outcomes because many confounds (such as talent or grit) may influence both telescope time and career opportunities. Now consider the weather as an instrumental variable. Cloudy weather will, at random, reduce an astronomer’s observational time. Yet, the weather on particular nights is unlikely to correlate with a scientist’s innate qualities. The weather can then provide an instrumental variable to reveal a causal relationship between telescope time and career outcomes. Instrumental variables have been used to study local peer effects in research 151 , the impact of gender composition in scientific committees 236 , patents on future innovation 237 and taxes on inventor mobility 238 .

Regression discontinuity. In regression discontinuity, policies with an arbitrary threshold for receiving some benefit can be used to construct treatment and control groups (Fig. 3c ). Take the funding paylines for grant proposals as an example. Proposals with scores increasingly close to the payline are increasingly similar in their both observable and unobservable characteristics, yet only those projects with scores above the payline receive the funding. For example, a study 110 examines the effect of winning an early-career grant on the probability of winning a later, mid-career grant. The probability has a discontinuous jump across the initial grant’s payline, providing the treatment and control groups needed to estimate the causal effect of receiving a grant. This example utilizes the ‘sharp’ regression discontinuity that assumes treatment status to be fully determined by the cut-off. If we assume treatment status is only partly determined by the cut-off, we can use ‘fuzzy’ regression discontinuity designs. Here the probability of receiving a grant is used to estimate the future outcome 11 , 110 , 239 , 240 , 241 .

Although quasi-experiments are powerful tools, they face their own limitations. First, these approaches identify causal effects within a specific context and often engage small numbers of observations. How representative the samples are for broader populations or contexts is typically left as an open question. Second, the validity of the causal design is typically not ironclad. Researchers usually conduct different robustness checks to verify whether observable confounders have significant differences between the treated and control groups, before treatment. However, unobservable features may still differ between treatment and control groups. The quality of instrumental variables and the specific claim that they have no effect on the outcome except through the variable of interest, is also difficult to assess. Ultimately, researchers must rely partly on judgement to tell whether appropriate conditions are met for causal inference.

This section emphasized popular econometric approaches to causal inference. Other empirical approaches, such as graphical causal modelling 242 , 243 , also represent an important stream of work on assessing causal relationships. Such approaches usually represent causation as a directed acyclic graph, with nodes as variables and arrows between them as suspected causal relationships. In the science of science, the directed acyclic graph approach has been applied to quantify the causal effect of journal impact factor 244 and gender or racial bias 245 on citations. Graphical causal modelling has also triggered discussions on strengths and weaknesses compared to the econometrics methods 246 , 247 .

Experiments

In contrast to quasi-experimental approaches, laboratory and field experiments conduct direct randomization in assigning treatment and control groups. These methods engage explicitly in the data generation process, manipulating interventions to observe counterfactuals. These experiments are crafted to study mechanisms of specific interest and, by designing the experiment and formally randomizing, can produce especially rigorous causal inference.

Laboratory experiments. Laboratory experiments build counterfactual worlds in well-controlled laboratory environments. Researchers randomly assign participants to the treatment or control group and then manipulate the laboratory conditions to observe different outcomes in the two groups. For example, consider laboratory experiments on team performance and gender composition 144 , 248 . The researchers randomly assign participants into groups to perform tasks such as solving puzzles or brainstorming. Teams with a higher proportion of women are found to perform better on average, offering evidence that gender diversity is causally linked to team performance. Laboratory experiments can allow researchers to test forces that are otherwise hard to observe, such as how competition influences creativity 249 . Laboratory experiments have also been used to evaluate how journal impact factors shape scientists’ perceptions of rewards 250 and gender bias in hiring 251 .

Laboratory experiments allow for precise control of settings and procedures to isolate causal effects of interest. However, participants may behave differently in synthetic environments than in real-world settings, raising questions about the generalizability and replicability of the results 252 , 253 , 254 . To assess causal effects in real-world settings, researcher use randomized controlled trials.

Randomized controlled trials. A randomized controlled trial (RCT), or field experiment, is a staple for causal inference across a wide range of disciplines. RCTs randomly assign participants into the treatment and control conditions 255 and can be used not only to assess mechanisms but also to test real-world interventions such as policy change. The science of science has witnessed growing use of RCTs. For instance, a field experiment 146 investigated whether lower search costs for collaborators increased collaboration in grant applications. The authors randomly allocated principal investigators to face-to-face sessions in a medical school, and then measured participants’ chance of writing a grant proposal together. RCTs have also offered rich causal insights on peer review 256 , 257 , 258 , 259 , 260 and gender bias in science 261 , 262 , 263 .

While powerful, RCTs are difficult to conduct in the science of science, mainly for two reasons. The first concerns potential risks in a policy intervention. For instance, while randomizing funding across individuals could generate crucial causal insights for funders, it may also inadvertently harm participants’ careers 264 . Second, key questions in the science of science often require a long-time horizon to trace outcomes, which makes RCTs costly. It also raises the difficulty of replicating findings. A relative advantage of the quasi-experimental methods discussed earlier is that one can identify causal effects over potentially long periods of time in the historical record. On the other hand, quasi-experiments must be found as opposed to designed, and they often are not available for many questions of interest. While the best approaches are context dependent, a growing community of researchers is building platforms to facilitate RCTs for the science of science, aiming to lower their costs and increase their scale. Performing RCTs in partnership with science institutions can also contribute to timely, policy-relevant research that may substantially improve science decision-making and investments.

Research in the science of science has been empowered by the growth of high-scale data, new measurement approaches and an expanding range of empirical methods. These tools provide enormous capacity to test conceptual frameworks about science, discover factors impacting scientific productivity, predict key scientific outcomes and design policies that better facilitate future scientific progress. A careful appreciation of empirical techniques can help researchers to choose effective tools for questions of interest and propel the field. A better and broader understanding of these methodologies may also build bridges across diverse research communities, facilitating communication and collaboration, and better leveraging the value of diverse perspectives. The science of science is about turning scientific methods on the nature of science itself. The fruits of this work, with time, can guide researchers and research institutions to greater progress in discovery and understanding across the landscape of scientific inquiry.

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Acknowledgements

The authors thank all members of the Center for Science of Science and Innovation (CSSI) for invaluable comments. This work was supported by the Air Force Office of Scientific Research under award number FA9550-19-1-0354, National Science Foundation grant SBE 1829344, and the Alfred P. Sloan Foundation G-2019-12485.

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Empirical Research

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The term “empirical” entails gathered data based on experience, observations, or experimentation. In empirical research, knowledge is developed from factual experience as opposed to theoretical assumption and usually involved the use of data sources like datasets or fieldwork, but can also be based on observations within a laboratory setting. Testing hypothesis or answering definite questions is a primary feature of empirical research. Empirical research, in other words, involves the process of employing working hypothesis that are tested through experimentation or observation. Hence, empirical research is a method of uncovering empirical evidence.

Through the process of gathering valid empirical data, scientists from a variety of fields, ranging from the social to the natural sciences, have to carefully design their methods. This helps to ensure quality and accuracy of data collection and treatment. However, any error in empirical data collection process could inevitably render such...

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Empirical Research in the Social Sciences and Education

What is empirical research.

• Finding Empirical Research
• Designing Empirical Research
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An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief. 

How do you know if you are reading an empirical article? Ask yourself: "What did the authors actually do?" or "How could this study be re-created?"

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or phenomena  being studied
• Description of the  process or methodology  used to study this population or phenomena, including selection criteria, controls, and testing instruments (example: surveys, questionnaires, etc)
• You can readily describe what the  authors actually did 

Layout of Empirical Articles

Scholarly journals sometimes use a specific layout for empirical articles, called the "IMRaD" format, to communicate empirical research findings. There are four main components:

• Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework 
• Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
• Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
• Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include  all  undergraduates. 
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Identifying Empirical Research Articles

Identifying empirical articles.

• Searching for Empirical Research Articles

What is Empirical Research?

An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research. To learn more about the differences between primary and secondary research, see our related guide:

• Primary and Secondary Sources

By the end of this guide, you will be able to:

• Identify common elements of an empirical article
• Use a variety of search strategies to search for empirical articles within the library collection

Look for the  IMRaD  layout in the article to help identify empirical research. Sometimes the sections will be labeled differently, but the content will be similar. 

• I ntroduction: why the article was written, research question or questions, hypothesis, literature review
• M ethods: the overall research design and implementation, description of sample, instruments used, how the authors measured their experiment
• R esults: output of the author's measurements, usually includes statistics of the author's findings
• D iscussion: the author's interpretation and conclusions about the results, limitations of study, suggestions for further research

Parts of an Empirical Research Article

Parts of an empirical article.

The screenshots below identify the basic IMRaD structure of an empirical research article. 

Introduction

The introduction contains a literature review and the study's research hypothesis.

The method section outlines the research design, participants, and measures used.

Results 

The results section contains statistical data (charts, graphs, tables, etc.) and research participant quotes.

The discussion section includes impacts, limitations, future considerations, and research.

Learn the IMRaD Layout: How to Identify an Empirical Article

This short video overviews the IMRaD method for identifying empirical research.

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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.

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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.

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• 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
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• empirical research cycle
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EDU 610: Educational Research: Empirical Research

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Empirical Research Studies

• Defining Empirical Articles
• Searching for Empirical Articles
• Examples of Empirical Articles

What is an empirical article?

Empirical research articles are scholarly, peer-reviewed journal articles that are based on data collected during the authors' real-life experiments or observations. They are primary research documents that contain either qualitative or quantitative research methods:

• Qualitative research focuses on collecting in-depth information from small sample sizes in order to describe a trend. Data is typically collected through one-on-one interviews with participants.
• Quantitative research uses large, representative sample sizes to collect a variety of statistics that can then be generalized. Data is typically collected through a questionnaire, attitude scale, or achievement test.

How can I tell if an article is empirical?

Several factors can help you decide whether or not an article is empirical:

• Academic journals such as Review of Educational Research , Journal of Educational Psychology , and Child Development often publish empirical articles.
• Popular magazines such as Time or Newsweek don't publish empirical articles.
• Professional journals such as the Journal of Technology and Teacher Education or Educational Leadership will publish empirical articles, while professional magazines (e.g., TEACH , Education Today , and Education Matters ) won't publish empirical articles.
• Did the author(s):
• Administer a survey or questionnaire ?
• Conduct an interview ?
• Collect data ?
• Use an assessment to measure results?
• Empirical articles include many of the above characteristics.
• Introduction/Literature Review
• This section will include information on how the study was conducted: how it was designed, who the participants were and how they participated, and how the results were measured.
• This information allows other researchers to replicate the study with their own participants.
• Results/Findings
• Discussion/Conclusion/Implications
• Although authors might combine some sections, label them differently, or not use any headings at all, empirical articles will contain all of the above information.
• Because empirical articles contain so many details about their studies, they tend to be longer.
• They also contain charts, tables, and other graphics to help display the data that was collected.

College of Southern Maryland. CSM Library. (2018, Jan. 19). Empirical research article. Retrieved July 15, 2019, from https://libguides.csmd.edu/empirical_research

Pan, M. L. (2016). Preparing literature reviews: Qualitative and quantitative approaches (5th ed.). New York, NY: Routledge.

Penn State University Libraries. (2019, May 9). Empirical research in the social sciences and education. Retrieved July 15, 2019, from https://guides.libraries.psu.edu/emp

University of La Verne. Wilson Library. (2018, June 26). Identify empirical research articles. Retrieved July 15, 2019, from https://laverne.libguides.com/empirical-articles

GALILEO (and EBSCOhost databases)

Consider adding one of the following key terms to your search:

• Data Analysis
• Field Study
• Investigation
• Observation
• Questionnaire
• Statistical Analysis

For best results, use GALILEO's Advanced Search option. This will let you put your chosen key term in its own search box using the AND operator.

Another option is to limit your results by type:

• Look for the Refine Results column on the left-hand side of your results list.
• Underneath the Limit by Type section, click on Reports .
• Underneath the Limit To section, click on both the Full Text and Scholarly (Peer Reviewed) Journals options.

Look for similar options when searching subject-specific databases.

Education Database (ProQuest)

• Click on the Document type section on the left-hand side of your results list.
• In the drop-down menu that appears, click on the More option.
• Statistics/Data Report

Education Database (ProQuest) http://www.galileo.usg.edu/express?link=zued&inst=thco

ERIC (https://eric.ed.gov/)

• Look for the Descriptor section on the left-hand side of your results list.
• Questionnaires
• Qualitative Research
• Click on the More option to view more descriptors.

• Look for the Publication Type section on the left-hand side of your results list.
• Reports - Research
• Reports - Evaluative
• Tests/Questionnaires
• Numerical/Qualitative Data
• Click on the More option to view more publication types.

Lawrence, A. C., Al-Bataineh, A. T., & Hatch, D. (2018). Educator perspectives on the instructional effects of one-to-one computing implementation. Contemporary Educational Technology, 9 (2), 206-224. https://doi.org/10.30935/cet.414950

Semerci, A. (2018). Students' views on the use of tablet computers in education. World Journal on Educational Technology: Current Issues, 10 (2), 104-114. https://doi.org/10.18844/wjet.v10i2.3420

Turner, K. (2019). One-to-one learning and self-determination theory. International Journal of Instruction, 12 (2), 1-16. https://doi.org/10.29333/iji.2019.1221a

USU Libraries. (2019, April 22). What's empirical research? [Video file]. Retrieved from https://www.youtube.com/watch?v=fZ-LGZdqWLU

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Qualitative and Quantitative Research

What is "empirical research".

• empirical research
• Locating Articles in Cinahl and PsycInfo
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• Getting the Articles

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
• 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
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How to Recognize Empirical Journal Articles

Definition of an empirical study:  An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research.

Parts of a standard empirical research article:  (articles will not necessary use the exact terms listed below.)

• Abstract  ... A paragraph length description of what the study includes.
• Introduction ...Includes a statement of the hypotheses for the research and a review of other research on the topic.
• Who are participants
• Design of the study
• What the participants did
• What measures were used
• Results ...Describes the outcomes of the measures of the study.
• Discussion ...Contains the interpretations and implications of the study.
• References ...Contains citation information on the material cited in the report. (also called bibliography or works cited)

Characteristics of an Empirical Article:

• Empirical articles will include charts, graphs, or statistical analysis.
• Empirical research articles are usually substantial, maybe from 8-30 pages long.
• There is always a bibliography found at the end of the article.

Type of publications that publish empirical studies:

• Empirical research articles are published in scholarly or academic journals
• These journals are also called “peer-reviewed,” or “refereed” publications.

Examples of such publications include:

• American Educational Research Journal
• Computers & Education
• Journal of Educational Psychology

Databases that contain empirical research:  (selected list only)

• List of other useful databases by subject area

This page is adapted from Eric Karkhoff's  Sociology Research Guide: Identify Empirical Articles page (Cal State Fullerton Pollak Library).

Sample Empirical Articles

Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online Mathematics Homework Increases Student Achievement. AERA Open .  ( L INK TO ARTICLE )

Lester, J., Yamanaka, A., & Struthers, B. (2016). Gender microaggressions and learning environments: The role of physical space in teaching pedagogy and communication.  Community College Journal of Research and Practice , 40(11), 909-926. ( LINK TO ARTICLE )

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Empirical Research

Introduction, what is empirical research, attribution.

• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Case Sudies

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
• 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

Portions of this guide were built using suggestions from other libraries, including Penn State and Utah State University libraries.

• Next: Finding Empirical Research in Library Databases >>
• Last Updated: Jan 10, 2023 8:31 AM
• URL: https://enmu.libguides.com/EmpiricalResearch

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

Empirical research is published in books and in scholarly, peer-reviewed journals. However, most library databases do not offer straightforward ways to locate empirical research. Below are tips for some of Penn State's most popular Education and Behavioral/Social Sciences databases. If you need further help, contact a Librarian at your location . 

Finding Empirical Research in LionSearch

• LionSearch This link opens in a new window

LionSearch does not have a method for locating empirical research. Using "empirical" as a keyword will find some studies, but miss many others. Consider using one of the more specialized databases below. 

Finding Empirical Research in PsycINFO (ProQuest version, for Psychology topics)

• PsycINFO (via ProQuest) This link opens in a new window more... less... PsycINFO provides access to international literature in psychology and related disciplines. Unrivaled in its depth of psychological coverage and respected worldwide for its high quality, the database is enriched with literature from an array of disciplines related to psychology such as psychiatry, education, business, medicine, nursing, pharmacology, law, linguistics, and social work. Nearly all records contain nonevaluative summaries, and all records from 1967 to the present are indexed using the Thesaurus of Psychological Index Terms.

To find empirical articles in PsycINFO (ProQuest version):

• Use the "Advanced Search"
• Type your keywords into the search boxes
• Scroll down the page to "Methodology," and choose "Empirical Study"
• Choose other limits, such as publication date, if needed
• Click on the "Search" button

Finding Empirical Research in ERIC (ProQuest version, for Education topics)

• ERIC (ProQuest) This link opens in a new window more... less... ERIC (Educational Resources Information Center) is the major database for education literature, sponsored by the U.S. Department. of Education. The same database content is available on many platforms.
• Scroll down the page to "Document Type," and choose "143 Reports: Research"
• Action Research
• Case Studies
• Content Analysis
• Data Analysis
• Ethnography
• Evaluation Methods
• Evaluation Research
• Experiments
• Focus Groups
• Field Studies
• Longitudinal Studies
• Mail Surveys
• Mixed Methods Research
• Naturalistic Observation
• Online Surveys
• Participant Observation
• Participatory Research
• Qualitative Research
• Questionnaires
• Statistical Analysis
• Statistical Studies
• Statistical Surveys
• Telephone Surveys
• Use Studies

Finding Empirical Research in Sociological Abstracts (ProQuest version)

• Sociological Abstracts This link opens in a new window more... less... CSA Sociological Abstracts abstracts and indexes the international literature in sociology and related disciplines in the social and behavioral sciences. The database provides abstracts of journal articles and citations to book reviews drawn from over 1,700 serials publications, and also provides abstracts of books, book chapters, dissertations, and conference papers. Records added after 1974 contain in-depth and nonevaluative abstracts of journal articles.
• Archival Research
• Discourse Analysis
• Grounded Theory
• Observation
• Oral History
• Quantitative Analysis

Finding Empirical Research in Criminal Justice Abstracts (EBSCO version)

• Criminal Justice Abstracts This link opens in a new window more... less... Provides abstracts of articles from the major journals in criminology and related disciplines, as well as books and reports from government and nongovernmental agencies. For each document, an informative summary of the findings, methodology, and conclusions is provided. Topics include crime trends, prevention projects, corrections, juvenile delinquency, police, courts, offenders, victims, and sentencing.

Criminal Justice Abstracts (EBSCO version) does not have a simple method to locate empirical research. Using "empirical" as a keyword will find some studies, but miss others. Consider using terminology recommended by the Criminal Justice Abstracts subject index. Some useful keywords are:

• Empirical Research
• Quantitative Research

Finding Empirical Research in Worldwide Political Science Abstracts (ProQuest version)

• Worldwide Political Science Abstracts This link opens in a new window more... less... Worldwide Political Science Abstracts is building on the merged backfiles of Political Science Abstracts, published by IFI / Plenum, 1975-2000, and ABC POL SCI, published by ABC-CLIO, 1984-2000. The database provides citations, abstracts, and indexing of the international serials literature in political science and its complementary fields, including international relations, law, and public administration / policy. The serials list of the new database is actively under construction, with a focus on expanding international coverage. As of February 2004 approximately 1,432 titles are being monitored for coverage; this list will continue to grow.

Worldwide Political Science Abstracts (ProQuest version) does not have a simple method to locate empirical research. Using "empirical" as a keyword will find some studies, but miss others. Consider using terminology recommended by the Worldwide Political Science Abstracts thesaurus. Some useful keywords are:

• Public Opinion Research

Finding Empirical Research in Linguistics and Language Behavior Abstracts (ProQuest version)

• Linguistics and Language Behavior Abstracts (LLBA) This link opens in a new window more... less... The definitive database on the nature and use of language, Linguistics and Language Behavior Abstracts covers three fundamental areas: research in linguistics (the nature and structure of human speech); research in language.
• Computer Modeling and Simulation

Finding Empirical Research in CINAHL (EBSCO version, for Nursing and Allied Health topics)

• CINAHL (Cumulative Index for Nursing and Allied Health) This link opens in a new window more... less... One of two major databases for nursing, providing references to over 1,800 nursing and allied health journal articles in addition to citations for book chapters, nursing dissertations, association publications, educational software, conference proceedings and selected full-text for state nursing journal articles, legal cases, patient education material, research instruments, standards of practice, critical paths, nurse practice acts, drugs, clinical innovations and government publications. References for alternative/complementary medicine, consumer health and health sciences librarianship are also included. Coverage: 1982 - Present. Updates: Monthly.
• Clinical Trials
• Ethnographic Research
• Experimental Studies
• Naturalistic Inquiry
• Nonexperimental Studies
• One-Shot Case Study
• Phenomenological Research
• Qualitative Studies
• Quantitative Studies
• Randomized Controlled Trials
• Time and Motion Studies
• Under "Limit your results," check off "Evidence-Based Practice"
• Choose other limits, such as published date, if needed

Finding Empirical Research in PubMed (NIH version, for health topics)

• PubMed (Medline) This link opens in a new window more... less... PubMed is a web interface that allows you to search MEDLINE, the National Library of Medicine's premier database of citations and abstracts for biomedical research articles. The core subject is medicine, but subject coverage also includes bioethics, biology, chemistry, dentistry, environmental health, genetics, gerontology, health care planning and administration, history of medicine, hospital administration, microbiology, nutrition, nursing (International Nursing Index), physiology, pre-clinical sciences, public health, sports medicine, veterinary medicine and zoology. MEDLINE covers over 4,800 journals published in the United States and 70 other countries. The database contains over 15 million citations dating back to 1950. Coverage is worldwide and updated weekly. Learn more about PubMed at: https://pubmed.ncbi.nlm.nih.gov/about/. or Try the Tutorial at: http://www.nlm.nih.gov/bsd/pubmed_tutorial/m1001.html

There are 2 ways to find empirical articles in PubMed (NIH version):

One technique is to limit your search results after you perform a search:

• Type in your keywords and click on the "Search" button
• To the left of your results, under "Article Types," check off the types of studies that interest you

Another alternative is to construct a more sophisticated search:

• From PubMed's main screen, click on "Advanced" link underneath the search box
• On the Advanced Search Builder screen type your keywords into the search boxes
• Change one of the empty boxes from "All Fields" to "Publication Type"
• To the right of Publication Type, click on "Show Index List" and choose a methodology that interests you. You can choose more than one by holding down the "Ctrl" or "⌘" on your keyboard as you click on each methodology
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• Last Updated: Feb 18, 2024 8:33 PM
• URL: https://guides.libraries.psu.edu/emp