empirical research refers to

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

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

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

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

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

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

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

LEARN ABOUT: Behavioral Research

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

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

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

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

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

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

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

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

LEARN ABOUT: Action Research

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

Learn More: Data Collection Methods: Types & Examples

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

LEARN MORE: Population vs Sample

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

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

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

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

LEARN ABOUT: Social Communication Questionnaire

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

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

LEARN ABOUT: 12 Best Tools for Researchers

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

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

LEARN MORE FREE TRIAL

MORE LIKE THIS

360 Degree Feedback Spider Chart is Back!

Aug 14, 2024

Jotform vs Wufoo: Comparison of Features and Prices

Aug 13, 2024

Product or Service: Which is More Important? — Tuesday CX Thoughts

Life@QuestionPro: Thomas Maiwald-Immer’s Experience

Aug 9, 2024

What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

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

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

What is Empirical Research?

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

Characteristics of Empirical Research

Empirical research is characterized by several key features:

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

Importance of Empirical Research

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

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

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

How to Conduct Empirical Research?

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

1. Select a Research Topic

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

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

2. Formulate Research Questions

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

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

3. Review Existing Literature

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

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

4. Define Variables

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

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

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

Empirical Research Design

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

Types of Empirical Research

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

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

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

Experimental Design

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

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

Observational Design

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

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

Survey Design

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

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

Case Study Design

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

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

Qualitative vs. Quantitative Research

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

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

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

Data Collection for Empirical Research

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

Sampling Methods

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

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

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

Data Collection Instruments

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

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

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

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

Book a Demo

Data Collection Procedures

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

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

Ethical Considerations

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

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

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

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

Empirical Research Data Analysis

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

Data Preparation

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

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

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

Descriptive Statistics

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

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

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

Inferential Statistics

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

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

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

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

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

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

How to Report Empirical Research Results?

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

1. Write the Research Paper

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

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

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

2. Create Visuals and Tables

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

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

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

3. Interpret Findings

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

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

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

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

Examples of Empirical Research

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

Social Sciences

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

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

Environmental Science

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

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

Business and Economics

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

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

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

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

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

Conclusion for Empirical Research

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

How to Collect Data for Empirical Research?

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

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

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

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

Get free access to the platform!

Join the loop 💌

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

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

Wait, there's more

13.08.2024 | 30min read

What is ANOVA Test? Definition, Types, Examples

08.08.2024 | 30min read

Environmental Analysis: Definition, Steps, Tools, Examples

Marketing Mix The 4 Ps of Marketing and How to Use Them

06.08.2024 | 32min read

Marketing Mix: The 4 Ps of Marketing & How to Use Them?

Penn State University Libraries

Empirical research in the social sciences and education.

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

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

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

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

Key characteristics to look for:

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

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

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

Reading and Evaluating Scholarly Materials

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

CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
Credo Video: How to Read Scholarly Materials (4 min.)
Credo Tutorial: How to Read Scholarly Materials
Credo Tutorial: Evaluating Information
Credo Video: Evaluating Statistics (4 min.)
Credo Tutorial: Evaluating for Diverse Points of View
Next: Finding Empirical Research in Library Databases >>
Last Updated: Aug 13, 2024 3:16 PM
URL: https://guides.libraries.psu.edu/emp

quicklinks Academic admin council Academic calendar Academic stds cte Admission Advising African studies Alumni engagement American studies Anthropology/sociology Arabic Arboretum Archives Arcus center Art Assessment committee Athletics Athletic training Biology Biology&chem center Black faculty&staff assoc Bookstore BrandK Business office Campus event calendar Campus safety Catalog Career & prof dev Health science Ctr for civic engagement Ctr for international pgrms Chemistry Chinese Classics College communication Community & global health Community council Complex systems studies Computer science Copyright Counseling Council of student reps Crisis response Critical ethnic studies Critical theory Development Dining services Directories Disability services Donor relations East Asian studies Economics and business Educational policies cte Educational quality assmt Engineering Environmental stewardship Environmental studies English Experiential education cte Facilities management Facilities reservations Faculty development cte Faculty executive cte Faculty grants Faculty personnel cte Fellowships & grants Festival playhouse Film & media studies Financial aid First year experience Fitness & wellness ctr French Gardens & growing spaces German Global crossroads Health center Jewish studies History Hornet hive Hornet HQ Hornet sports Human resources Inclusive excellence Index (student newspaper) Information services Institutional research Institutional review board Intercultural student life International & area studies International programs Intramural sports Japanese LandSea Learning commons Learning support Lgbtqai+ student resources Library Mail and copy center Math Math/physics center Microsoft Stream Microsoft Teams Moodle Movies (ch 22 online) Music OneDrive Outdoor programs Parents' resources Payroll Phi Beta Kappa Philharmonia Philosophy Physics Physical education Political science Pre-law advising Provost Psychology Public pol & urban affairs Recycling Registrar Religion Religious & spiritual life Research Guides (libguides) Residential life Safety (security) Sexual safety Shared passages program SharePoint online Sophomore experience Spanish Strategic plan Student accounts Student development Student activities Student organizations Study abroad Support staff Sustainability Teaching and learning cte Teaching commons Theatre arts Title IX Webmail Women, gender & sexuality Writing center

Psychology Research Guide

What is empirical research, finding empirical research, what is peer review.

Research Tips & Tricks
Statistics This link opens in a new window
Cite Sources
Library FAQ This link opens in a new window

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

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

Empirical research is published in books and in scholarly, peer-reviewed journals. Keep in mind that most library databases do not offer straightforward ways to identifying empirical research.

Finding Empirical Research in PsycINFO

PsycInfo 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 PubMed

PubMED 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 Click on the "Search" button

Finding Empirical Research in Library OneSearch & Google Scholar

These tools do 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 above.

Library OneSearch
Google Scholar

This refers to the process where authors who are doing research submit a paper they have written to a journal. The journal editor then sends the article to the author's peers (researchers and scholars) who are in the same discipline for review. The reviewers determine if the article should be published based on the quality of the research, including the validity of the data, the conclusions the authors' draw and the originality of the research. This process is important because it validates the research and gives it a sort of "seal of approval" from others in the research community.

Identifying a Journal is Peer-Reviewed

One of the best places to find out if a journal is peer-reviewed is to go to the journal website.

Most publishers have a website for a journal that tells you about the journal, how authors can submit an article, and what the process is for getting published.

If you find the journal website, look for the link that says information for authors, instructions for authors, submitting an article or something similar.

Finding Peer-Reviewed Articles

Start in a library database. Look for a peer-review or scholarly filter.

PsycInfo Most comprehensive database of psychology. Filters allow you to limit by methodology. Articles without full-text can be requested via Interlibrary loan.
Library OneSearch Search almost all the library resources. Look for a peer-review filter on the left.
<< Previous: Start Here
Next: Research Tips & Tricks >>
Last Updated: Jun 8, 2024 6:20 PM
URL: https://libguides.kzoo.edu/psyc

Ask a Librarian

Research: Overview & Approaches

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

Introduction to Empirical Research

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

Interpretive Research
Action-Based Research
Creative & Experimental Approaches

Your Librarian

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

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

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

<< Previous: Scholarly and Professional Relationships
Next: Interpretive Research >>
Last Updated: Aug 13, 2024 12:18 PM
URL: https://guides.lib.purdue.edu/research_approaches

Canvas | University | Ask a Librarian

Library Homepage
Arrendale Library

Empirical & Non-Empirical Research

Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

Quantitative vs. Qualitative
Reference Works for Social Sciences Research
What is Non-Empirical Research?
Contact Us!

Call us at 706-776-0111

Chat with a Librarian

Send Us Email

Library Hours

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

Key characteristics of empirical research include:

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

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

Empirical Research: Qualitative vs. Quantitative

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

Academic Writer

More about Academic Writer ...

Quantitative Research

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

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

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

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

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

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

Qualitative Research

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

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

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

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

Next: Quantitative vs. Qualitative >>
Last Updated: Jul 24, 2024 12:04 PM
URL: https://library.piedmont.edu/empirical-research
Ebooks & Online Video
New Materials
Renew Checkouts
Faculty Resources
Library Friends
Library Services
Our Mission
Library History
Ask a Librarian!
Making Citations
Working Online

Arrendale Library Piedmont University 706-776-0111

Scroll to top
Dark Light Dark Light

Defining Empirical Research— Types, Methods, and Examples

Author Survey Point Team
Published January 10, 2023

Empirical research is a research methodology that uses experiences and verifiable evidence to reach conclusions. Derived from the Greek word ‘ empeirikos ,’ which means experience, empirical research is based on believing only what can be seen, experienced, or verified. This makes empirical research stand out as scientific and trustworthy.

Empirical research can be qualitative or quantitative in nature to answer a variety of questions confidently. For example, one can use snowball sampling to gather contact details of homeless people in a city and then observe how they survive or behave over a period of time to form conclusions on the basis of those observations.

The observations and experiences upon which empirical research is based allow for the subject and the study conclusions to be independently validated. The results of empirical studies are helpful for testing theories and dispelling misconceptions.

Table of Contents

Types of Empirical Research

There are broadly two types of Empirical Research – Quantitative and Qualitative . In a generic sense, both these empirical research methodologies refer to a collective pool of data using calibrated scientific instruments. Let’s talk about these two below:

1. Quantitative Empirical Research

Information is gathered through numerical data in quantitative empirical research. Opinions, preferences, behaviors, tendencies, and other variables are quantified to collect information in the form of numbers. These numbers are further studied to reach conclusions.

For instance, you can gauge customer satisfaction by asking for ratings from 1 to 10, with 1 representing the least satisfied and 10 representing the most satisfied.

Numbers can be collected to summarize people’s preferences and allow them to be quantified.

2. Qualitative Empirical Research

For businesses to reach nuanced conclusions, more than just numerical data is needed to formulate informed opinions. To get in-depth information, the data collected has to be descriptive. Descriptive data helps the researcher do qualitative research on a subject and form hypotheses and theories accordingly. In qualitative empirical research, this process is called qualitative analysis.

Generally, these studies use a smaller sample size and are a little unorganized. There is a growing trend for qualitative research in focus groups, interviews, and experiments.

Research Methods Using Empirical Evidence

Data gathered through research needs to be analyzed. By analyzing empirical data with certain methods, questions that cannot be answered in a laboratory can be answered with conclusions that lab experiments cannot reach.

Quantitative Research Methods

We will take up and discuss the sub categories of quantitative method one by one:

1. Survey research

It uses surveys to gather numerical data for research. One of the most common survey research methods is sending a closed set of questions via email or other media to customers. These questions are easy as per their difficulty level and are efficient enough to yield higher responses.

2. Experimental research

Experimental research is done by gathering numerical data by conducting an experiment. An experiment to determine someone’s tendency to choose a specific response in a particular situation can help us better understand human behavior and choices.

3. Correlational research

Correlational research is done to find the correlation between attributes such as IQ levels and success. By establishing a correlation between one attribute and another, it can be used to predict outcomes.

Moreover, it can be quantified, so the degree of correlation can be determined.

4. Longitudinal study

The longitudinal study is done by observing and repeatedly testing a subject over a long time. It aims to understand the long-term impact of various activities or choices on the subject.

5. Cross-sectional

Cross-sectional research studies a set of people with similarities in all variables, excluding the studied one. It helps the researcher establish a cause-and-effect relationship by using data from continuous observation of the subjects. Often followed by longitudinal research.

6. Casual comparison

By comparing two or more variables, casual comparison determines whether there is a cause-and-effect relationship between them.

Qualitative Research Methods

1. case study.

Case studies involve investigating and analyzing real-world examples, such as companies or other entities. It is put to use when an actual issue needs to be researched. It has extensive application in the commercial investigation.

Studying the experiences of other businesses and organizations that have dealt with similar issues in the past might shed light on the issues at hand for any given organization or group. Business schools also use case studies to make learning more interactive and fun for students.

2. Observational method

The observational method involves observing the subject and gathering qualitative data. A subject is observed for a considerable period of time, and qualitative observations are then studied to form conclusions.

Gathered data can also be quantitative, depending on the research topic. But since this type of research takes a long time, it is primarily qualitative data collected by observing subjects.

3. One-on-one interview

As the name suggests, one-on-one interviews involve making qualitative observations about the subject by directly interviewing them. It is conversational and helps get in-depth data about the subject’s personality, views, etc., which cannot be analyzed or estimated otherwise.

4. Focus groups

Focus groups are small groups of people contributing to open discussions on a particular topic. This method is used by product companies who want to know how well their products may perform in the market.

5. Text analysis

Almost any form of social media content, including textual and visual, can be analyzed to arrive at conclusions. This method is relatively new, but the qualitative research done using text analysis is very useful and has a far-reaching impact.

Examples of Empirical Research

Scientists looked at the long-term effects of video games on children by dividing a sample of kids into two groups, one of which played video games while the other did not. They then compared the two sets of kids’ development in various ways, including their eyesight, behavior, outlook, and personalities.
Consumers’ willingness to purchase a product at a given moment can be measured by having them rate their interest in doing so on a Likert scale from 1 to 10.
Wild animal populations were studied to understand seasonal habitat use patterns, activity, and reproduction patterns. You can do this through long-term observation or by studying previously collected data on animal behavior in a certain location.
The research analyzed people’s motivations based on their online presence and published content. Using the frequency of words used by the person on a particular platform throughout their online presence can provide this information.

What Can SurveyPoint Do For You?

Seeing a difference between two numbers is easy, but determining whether that difference is statistically significant takes a little more effort. Especially if your question has several possible answers or you’re comparing findings from different groups of respondents, the process can be tricky.

Invest in the right technologies to alleviate the burden of manual analysis. Streamline your workflow by letting SurveyPoint handle all the heavy lifting.

Learn to work smarter, not harder!

Explore our solutions that help researchers collect accurate insights, boost ROI, and retain respondents.

Free Trial•No Payment Details Required•Cancel Anytime

Survey Point Team

Everything You Need To Know About Python

All You Need To Know About Innovations Shaping India's Future

Everything You Need To Know About The Power of Gratitude

Encyclopedia

Writing with artificial intelligence, empirical research methods.

Related Concepts: Positivism ; Research Methods ; Research Methodologies

What are Empirical Research Methods?

Empirical research refers to methods that investigators use to test knowledge claims and develop new new knowledge through direct or indirect observation and experimentation. Empirical methods emphasize collecting data systematically and rigorously to ensure reliability and validity. Investigators observe phenomena, conduct experiments, and gather measurable evidence to support or refute hypotheses, allowing for the objective analysis and interpretation of results.

There are three major types of empirical research :

e.g., numbers, mathematical equations).
Mixed Methods (a mixture of Quantitative Methods and Qualitative Methods .

Investigators use empirical research methods

to create new knowledge (e.g., Basic Research )
to solve a problem at work, school, or personal life (e.g., Applied Research ).
to conduct replication studies–i.e., repeat a study with the same methods (or with slight variations, such as changes in subjects and experimenters).

Empirical research aims to be as objective as possible by being RAD —

(sufficient details about the research protocol is provided so the study can be repeated)
(the results and implications of the study can be extended in future research)
( quantitative evidence and/or Qualitative evidence are provided to substantiate claims, results, interpretations, implications).

Informally, as humans, we engage routinely in the intellectual strategies that inform empirical research:

we talk with others and listen to their stories to better understand their perceptions and experiences,
we make observations,
we survey friends, peers, coworkers
we cross cultures and learn about difference, and
we make predictions about future events based on our experiences and observations.

These same intellectual strategies we use to reason from our observations and experiences also undergird empirical research methods. For example,

a psychologist might develop a case study based on interviews
an anthropologist or sociologist might engage in participant observation to write an ethnographic study
a political science researcher might survey voter trends
a stock trader may project a stock bounce based on a 30-day moving average.

The main difference between informal and formal empirical research is intentionality : Formal empirical research presupposes a Research Plan , which is sometimes referred to as as Research Protocol . When investigators want their results to be taken seriously they have to employ the research methods a methodological community has for vetting knowledge claims .

Different academic communities (e.g., Natural Sciences, Social Science, Humanities, Arts) have unique ideas about how to conduct empirical research. Professionals in the workplace — e.g., geologists, anthropologists, biologists — use entirely different tools to gather and interpret data. Being credentialed in a particular discipline or profession is tied to mastery of unique methodological practices.

Across disciplines, however, empiricists share a number of operating assumptions: Empiricists

develop a research plan prior to engaging in research.
seek approval from Ethics Committees when human subjects or animal testing is involved
explain how subjects/research participants are chosen and given opportunities to opt in or opt out of studies.

Empiricists are meticulous about how they collect data because their research must be verifiable if they want other empiricists to take their work seriously. In other words, their research plan needs to be so explicit that subsequent researchers can conduct the same study.

Empirical Research is a Rhetorical Practice

Empiricists develop their research question and their research methods by considering their audience and purpose . Prior to initiating a study, researchers conduct secondary research–especially Searching as Strategic Exploration –to identify the current knowledge about a topic. As a consequence of their deep understanding of pertinent scholarly conversations on the topic, empiricists identify gaps in knowledge.

What is the role of textual research in empirical work?

Investigators conduct empirical research when the answers to research questions are not readily available from informal research or textual research , when the occasion is kairotic , when personal or financial gains are on the table. That said, most empirical research is informed by textual research: investigators review the conclusions and implications of previously published research past studies—they analyze scholarly conversations and research methods—prior to engaging in empirical studies.

T extual research plays an important role in empirical research . Empiricists engage in some textual research in order to understand scholarly conversations around the topics that interest them. Empiricists consult archives to learn methods for conducting empirical studies. However, there are important distinctions between how scholars weight claims in textual research and how scientists weigh claims in empirical studies.

Unlike investigators who use primarily textual methods , empiricists do not consider “claims of authority, intuition, imaginative conjecture, and abstract, theoretical, or systematic reasoning as sources of reliable belief” (Duignan, Fumerton, Quinton, Quinton 2020).

What epistemologies inform empirical work?

Empirical research is informed by two key philosophical foundations:

Empiricism: This philosophy assumes that knowledge is grounded in sensory experiences—what can be seen, heard, or otherwise experienced. Empiricism emphasizes the importance of evidence derived from observation and experimentation.
Positivism: This philosophy assumes that the universe is an orderly place where events have causes and occur in regular patterns. Positivism holds that these patterns can be discovered through systematic observation and that scientific inquiry can reveal objective truths about the natural world.

Duignan, B., Fumerton, R., Quinton, A. M., & Quinton, B. (2020). Empiricism. Encyclopedia Britannica. https://www.britannica.com/topic/empiricism

Haswell, R. (2005). NCTE/CCCC’s recent war on scholarship. Written Communication, 22 (2), 198-223.

Brevity - Say More with Less

Clarity (in Speech and Writing)

Coherence - How to Achieve Coherence in Writing

Flow - How to Create Flow in Writing

Inclusivity - Inclusive Language

The Elements of Style - The DNA of Powerful Writing

Academic Writing – How to Write for the Academic Community

You cannot climb a mountain without a plan / John Read

Structured Revision – How to Revise Your Work

Professional Writing – How to Write for the Professional World

Credibility & Authority – How to Be Credible & Authoritative in Research, Speech & Writing

Citation Guide – Learn How to Cite Sources in Academic and Professional Writing

Image of a colorful page with a big question in the center, "What is Page Design?"

Page Design – How to Design Messages for Maximum Impact

Suggested edits.

Please select the purpose of your message. * - Corrections, Typos, or Edits Technical Support/Problems using the site Advertising with Writing Commons Copyright Issues I am contacting you about something else
Your full name
Your email address *
Page URL needing edits *
Comments This field is for validation purposes and should be left unchanged.

Citation - Definition - Introduction to Citation in Academic & Professional Writing

Joseph M. Moxley

Explore the different ways to cite sources in academic and professional writing, including in-text (Parenthetical), numerical, and note citations.

Collaboration - What is the Role of Collaboration in Academic & Professional Writing?

Collaboration refers to the act of working with others or AI to solve problems, coauthor texts, and develop products and services. Collaboration is a highly prized workplace competency in academic...

Genre may reference a type of writing, art, or musical composition; socially-agreed upon expectations about how writers and speakers should respond to particular rhetorical situations; the cultural values; the epistemological assumptions...

Grammar refers to the rules that inform how people and discourse communities use language (e.g., written or spoken English, body language, or visual language) to communicate. Learn about the rhetorical...

Information Literacy - Discerning Quality Information from Noise

Information Literacy refers to the competencies associated with locating, evaluating, using, and archiving information. In order to thrive, much less survive in a global information economy — an economy where information functions as a...

Mindset refers to a person or community’s way of feeling, thinking, and acting about a topic. The mindsets you hold, consciously or subconsciously, shape how you feel, think, and act–and...

Rhetoric: Exploring Its Definition and Impact on Modern Communication

Learn about rhetoric and rhetorical practices (e.g., rhetorical analysis, rhetorical reasoning, rhetorical situation, and rhetorical stance) so that you can strategically manage how you compose and subsequently produce a text...

Style, most simply, refers to how you say something as opposed to what you say. The style of your writing matters because audiences are unlikely to read your work or...

The Writing Process - Research on Composing

The writing process refers to everything you do in order to complete a writing project. Over the last six decades, researchers have studied and theorized about how writers go about...

Writing Studies

Writing studies refers to an interdisciplinary community of scholars and researchers who study writing. Writing studies also refers to an academic, interdisciplinary discipline – a subject of study. Students in...

Featured Articles

La Salle University

Connelly library, library main menu.

Course Reserves
Interlibrary Loan (ILL)
Study Room Use & Reservations
Technology & Printing
Citation Guides
Reserve Library Space
Request Instruction
Copyright Information
Guides for Faculty

Special Collections

University Archives
Historical & Cultural Collections
Rare Bibles & Prayer Books
Historical Research Guides
Information & Guidelines
Staff Directory
Meet with a Librarian
Directions & Building Maps

Research Hub

Research Tools
Research Guides

Qualitative and Quantitative Research

What is "empirical research".

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

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

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
<< Previous: Home
Next: Locating Articles in Cinahl and PsycInfo >>

Chat Assistance

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.

Next: Searching for Empirical Research Articles >>
Last Updated: Nov 16, 2023 8:24 AM

CityU Home - CityU Catalog

History & Society
Science & Tech
Biographies
Animals & Nature
Geography & Travel
Arts & Culture
Games & Quizzes
On This Day
One Good Fact
New Articles
Lifestyles & Social Issues
Philosophy & Religion
Politics, Law & Government
World History
Health & Medicine
Browse Biographies
Birds, Reptiles & Other Vertebrates
Bugs, Mollusks & Other Invertebrates
Environment
Fossils & Geologic Time
Entertainment & Pop Culture
Sports & Recreation
Visual Arts
Demystified
Image Galleries
Infographics
Top Questions
Britannica Kids
Saving Earth
Space Next 50
Student Center

When did science begin?
Where was science invented?

Blackboard inscribed with scientific formulas and calculations in physics and mathematics

empirical evidence

Our editors will review what you’ve submitted and determine whether to revise the article.

National Center for Biotechnology Information - PubMed Central - Expert opinion vs. empirical evidence
LiveScience - Empirical evidence: A definition
Academia - Empirical Evidence

empirical evidence , information gathered directly or indirectly through observation or experimentation that may be used to confirm or disconfirm a scientific theory or to help justify, or establish as reasonable, a person’s belief in a given proposition. A belief may be said to be justified if there is sufficient evidence to make holding the belief reasonable.

The concept of evidence is the basis of philosophical evidentialism, an epistemological thesis according to which a person is justified in believing a given proposition p if and only if the person’s evidence for p is proper or sufficient. In this context , the Scottish Enlightenment philosopher David Hume (1711–76) famously asserted that the “wise man…proportions his belief to the evidence.” In a similar vein, the American astronomer Carl Sagan popularized the statement, “Extraordinary claims require extraordinary evidence.”

Foundationalists , however, defend the view that certain basic, or foundational, beliefs are either inherently justified or justified by something other than another belief (e.g., a sensation or perception) and that all other beliefs may be justified only if they are directly or indirectly supported by at least one foundational belief (that is, only if they are either supported by at least one foundational belief or supported by other beliefs that are themselves supported by at least one foundational belief). The most influential foundationalist of the modern period was the French philosopher and mathematician René Descartes (1596–1650), who attempted to establish a foundation for justified beliefs regarding an external world in his intuition that, for as long as he is thinking, he exists (“I think, therefore I am”; see cogito, ergo sum ). A traditional argument in favour of foundationalism asserts that no other account of inferential justification—the act of justifying a given belief by inferring it from another belief that itself is justified—is possible. Thus, assume that one belief, Belief 1, is justified by another belief, Belief 2. How is Belief 2 justified? It cannot be justified by Belief 1, because the inference from Belief 2 to Belief 1 would then be circular and invalid. It cannot be justified by a third nonfoundational Belief 3, because the same question would then apply to that belief, leading to an infinite regress. And one cannot simply assume that Belief 2 is not justified, for then Belief 1 would not be justified through the inference from Belief 2. Accordingly, there must be some beliefs whose justification does not depend on other beliefs, and those justified beliefs must function as a foundation for the inferential justification of other beliefs.

Empirical evidence can be quantitative or qualitative. Typically, numerical quantitative evidence can be represented visually by means of diagrams, graphs, or charts, reflecting the use of statistical or mathematical data and the researcher’s neutral noninteractive role. It can be obtained by methods such as experiments, surveys, correlational research (to study the relationship between variables), cross-sectional research (to compare different groups), causal-comparative research (to explore cause-effect relationships), and longitudinal studies (to test a subject during a given time period).

Qualitative evidence, on the other hand, can foster a deeper understanding of behaviour and related factors and is not typically expressed by using numbers. Often subjective and resulting from interaction between the researcher and participants, it can stem from the use of methods such as interviews (based on verbal interaction), observation (informing ethnographic research design), textual analysis (involving the description and interpretation of texts), focus groups (planned group discussions), and case studies (in-depth analyses of individuals or groups).

Empirical evidence is subject to assessments of its validity. Validity can be internal, involving the soundness of an experiment’s design and execution and the accuracy of subsequent data analysis , or external, involving generalizability to other research contexts ( see ecological validity ).

Empirical Research

Living reference work entry
First Online: 22 May 2017
Cite this living reference work entry

Emeka Thaddues Njoku 2

544 Accesses

1 Citations

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...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Bibliography

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Textbooks Collection . Book 3.

Google Scholar

Comte, A., & Bridges, J. H. (Tr.) (1865). A general view of positivism . Trubner and Co. (reissued by Cambridge University Press , 2009).

Dilworth, C. B. (1982). Empirical research in the literature class. English Journal, 71 (3), 95–97.

Article Google Scholar

Heisenberg, W. (1971). Positivism, metaphysics and religion. In R. N. Nanshen (Ed.), Werner Heisenberg – Physics and beyond – Encounters and conversations , World Perspectives. 42. Translator: Arnold J. Pomerans. New York: Harper and Row.

Hossain, F. M. A. (2014). A critical analysis of empiricism. Open Journal of Philosophy, 2014 (4), 225–230.

Kant, I. (1783). Prolegomena to any future metaphysic (trans: Bennett, J.). Early Modern Texts. www.earlymoderntexts.com

Koch, S. (1992). Psychology’s Bridgman vs. Bridgman’s Bridgman: An essay in reconstruction. Theory and Psychology, 2 (3), 261–290.

Matin, A. (1968). An outline of philosophy . Dhaka: Mullick Brothers.

Mcleod, S. (2008). Psychology as science. http://www.simplypsychology.org/science-psychology.html

Popper, K. (1963). Conjectures and refutations: The growth of scientific knowledge . London: Routledge.

Simmel, G. (1908). The problem areas of sociology in Kurt H. Wolf: The sociology of Georg Simmel . London: The Free Press.

Weber, M. (1991). The nature of social action. In W. G. Runciman (Ed.), Weber: Selections in translation . Cambridge: Cambridge University Press.

Download references

Author information

Authors and affiliations.

Department of Political Science, University of Ibadan, Ibadan, Oyo, 200284, Nigeria

Emeka Thaddues Njoku

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emeka Thaddues Njoku .

Editor information

Editors and affiliations.

Rhinebeck, New York, USA

David A. Leeming

Rights and permissions

Reprints and permissions

Copyright information

About this entry

Cite this entry.

Njoku, E.T. (2017). Empirical Research. In: Leeming, D. (eds) Encyclopedia of Psychology and Religion. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27771-9_200051-1

Download citation

DOI : https://doi.org/10.1007/978-3-642-27771-9_200051-1

Received : 01 April 2017

Accepted : 08 May 2017

Published : 22 May 2017

Publisher Name : Springer, Berlin, Heidelberg

Print ISBN : 978-3-642-27771-9

Online ISBN : 978-3-642-27771-9

eBook Packages : Springer Reference Behavioral Science and Psychology Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Find a journal
Track your research

What is Empirical Research Study? [Examples & Method]

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

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

What is Empirical Research?

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

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

Characteristics of Empirical Research

Research Questions

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

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

Definition of the Research Variables

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

Description of the Research Methodology

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

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

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

Uses of Empirical Research

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

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

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

The Empirical Research Cycle

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

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

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

This information is useful for further research.

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

Examples of Empirical Research

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

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

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

Empirical Research Data Collection Methods

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

The following are common methods of gathering data in empirical research

Survey/ Questionnaire

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

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

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

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

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

Observation

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

How to collect Empirical Research Data with Questionnaire

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

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

Sign in to Formplus

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

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

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

Edit Form Title

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

Edit Form

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

Customize Form

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

Share your Form Link with Respondents

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

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

Empirical vs Non-Empirical Research

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

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

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

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

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

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

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

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

Advantages of Empirical Research

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

Disadvantages of Empirical Research

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

Conclusion

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

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

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

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

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

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

Research Questions: Definitions, Types + [Examples]

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

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

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

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

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

Recall Bias: Definition, Types, Examples & Mitigation

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

Formplus - For Seamless Data Collection

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

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Review Article
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

19k Accesses

18 Citations

116 Altmetric

Metrics details

Scientific community

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.

SciSciNet: A large-scale open data lake for the science of science research

A dataset for measuring the impact of research data and their curation

Envisioning a “science diplomacy 2.0”: on data, global challenges, and multi-layered networks

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.

Bush, V . S cience–the Endless Frontier: A Report to the President on a Program for Postwar Scientific Research (National Science Foundation, 1990).

Mokyr, J. The Gifts of Athena (Princeton Univ. Press, 2011).

Jones, B. F. in Rebuilding the Post-Pandemic Economy (eds Kearney, M. S. & Ganz, A.) 272–310 (Aspen Institute Press, 2021).

Wang, D. & Barabási, A.-L. The Science of Science (Cambridge Univ. Press, 2021).

Fortunato, S. et al. Science of science. Science 359 , eaao0185 (2018).

Article PubMed PubMed Central Google Scholar

Azoulay, P. et al. Toward a more scientific science. Science 361 , 1194–1197 (2018).

Article PubMed Google Scholar

Clauset, A., Larremore, D. B. & Sinatra, R. Data-driven predictions in the science of science. Science 355 , 477–480 (2017).

Article CAS PubMed Google Scholar

Zeng, A. et al. The science of science: from the perspective of complex systems. Phys. Rep. 714 , 1–73 (2017).

Article Google Scholar

Lin, Z., Yin. Y., Liu, L. & Wang, D. SciSciNet: a large-scale open data lake for the science of science research. Sci. Data, https://doi.org/10.1038/s41597-023-02198-9 (2023).

Ahmadpoor, M. & Jones, B. F. The dual frontier: patented inventions and prior scientific advance. Science 357 , 583–587 (2017).

Azoulay, P., Graff Zivin, J. S., Li, D. & Sampat, B. N. Public R&D investments and private-sector patenting: evidence from NIH funding rules. Rev. Econ. Stud. 86 , 117–152 (2019).

Yin, Y., Dong, Y., Wang, K., Wang, D. & Jones, B. F. Public use and public funding of science. Nat. Hum. Behav. 6 , 1344–1350 (2022).

Merton, R. K. The Sociology of Science: Theoretical and Empirical Investigations (Univ. Chicago Press, 1973).

Kuhn, T. The Structure of Scientific Revolutions (Princeton Univ. Press, 2021).

Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342 , 468–472 (2013).

Zuckerman, H. Scientific Elite: Nobel Laureates in the United States (Transaction Publishers, 1977).

Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566 , 378–382 (2019).

Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316 , 1036–1039 (2007).

Foster, J. G., Rzhetsky, A. & Evans, J. A. Tradition and innovation in scientists’ research strategies. Am. Sociol. Rev. 80 , 875–908 (2015).

Wang, D., Song, C. & Barabási, A.-L. Quantifying long-term scientific impact. Science 342 , 127–132 (2013).

Clauset, A., Arbesman, S. & Larremore, D. B. Systematic inequality and hierarchy in faculty hiring networks. Sci. Adv. 1 , e1400005 (2015).

Ma, A., Mondragón, R. J. & Latora, V. Anatomy of funded research in science. Proc. Natl Acad. Sci. USA 112 , 14760–14765 (2015).

Article CAS PubMed PubMed Central Google Scholar

Ma, Y. & Uzzi, B. Scientific prize network predicts who pushes the boundaries of science. Proc. Natl Acad. Sci. USA 115 , 12608–12615 (2018).

Azoulay, P., Graff Zivin, J. S. & Manso, G. Incentives and creativity: evidence from the academic life sciences. RAND J. Econ. 42 , 527–554 (2011).

Schor, S. & Karten, I. Statistical evaluation of medical journal manuscripts. JAMA 195 , 1123–1128 (1966).

Platt, J. R. Strong inference: certain systematic methods of scientific thinking may produce much more rapid progress than others. Science 146 , 347–353 (1964).

Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2 , e124 (2005).

Simonton, D. K. Career landmarks in science: individual differences and interdisciplinary contrasts. Dev. Psychol. 27 , 119 (1991).

Way, S. F., Morgan, A. C., Clauset, A. & Larremore, D. B. The misleading narrative of the canonical faculty productivity trajectory. Proc. Natl Acad. Sci. USA 114 , E9216–E9223 (2017).

Sinatra, R., Wang, D., Deville, P., Song, C. & Barabási, A.-L. Quantifying the evolution of individual scientific impact. Science 354 , aaf5239 (2016).

Liu, L. et al. Hot streaks in artistic, cultural, and scientific careers. Nature 559 , 396–399 (2018).

Liu, L., Dehmamy, N., Chown, J., Giles, C. L. & Wang, D. Understanding the onset of hot streaks across artistic, cultural, and scientific careers. Nat. Commun. 12 , 5392 (2021).

Squazzoni, F. et al. Peer review and gender bias: a study on 145 scholarly journals. Sci. Adv. 7 , eabd0299 (2021).

Hofstra, B. et al. The diversity–innovation paradox in science. Proc. Natl Acad. Sci. USA 117 , 9284–9291 (2020).

Huang, J., Gates, A. J., Sinatra, R. & Barabási, A.-L. Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc. Natl Acad. Sci. USA 117 , 4609–4616 (2020).

Gläser, J. & Laudel, G. Governing science: how science policy shapes research content. Eur. J. Sociol. 57 , 117–168 (2016).

Stephan, P. E. How Economics Shapes Science (Harvard Univ. Press, 2012).

Garfield, E. & Sher, I. H. New factors in the evaluation of scientific literature through citation indexing. Am. Doc. 14 , 195–201 (1963).

Article CAS Google Scholar

de Solla Price, D. J. Networks of scientific papers. Science 149 , 510–515 (1965).

Etzkowitz, H., Kemelgor, C. & Uzzi, B. Athena Unbound: The Advancement of Women in Science and Technology (Cambridge Univ. Press, 2000).

Simonton, D. K. Scientific Genius: A Psychology of Science (Cambridge Univ. Press, 1988).

Khabsa, M. & Giles, C. L. The number of scholarly documents on the public web. PLoS ONE 9 , e93949 (2014).

Xia, F., Wang, W., Bekele, T. M. & Liu, H. Big scholarly data: a survey. IEEE Trans. Big Data 3 , 18–35 (2017).

Evans, J. A. & Foster, J. G. Metaknowledge. Science 331 , 721–725 (2011).

Milojević, S. Quantifying the cognitive extent of science. J. Informetr. 9 , 962–973 (2015).

Rzhetsky, A., Foster, J. G., Foster, I. T. & Evans, J. A. Choosing experiments to accelerate collective discovery. Proc. Natl Acad. Sci. USA 112 , 14569–14574 (2015).

Poncela-Casasnovas, J., Gerlach, M., Aguirre, N. & Amaral, L. A. Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria. Nat. Hum. Behav. 3 , 568–575 (2019).

Hardwicke, T. E. et al. Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition. R. Soc. Open Sci. 5 , 180448 (2018).

Nagaraj, A., Shears, E. & de Vaan, M. Improving data access democratizes and diversifies science. Proc. Natl Acad. Sci. USA 117 , 23490–23498 (2020).

Bravo, G., Grimaldo, F., López-Iñesta, E., Mehmani, B. & Squazzoni, F. The effect of publishing peer review reports on referee behavior in five scholarly journals. Nat. Commun. 10 , 322 (2019).

Tran, D. et al. An open review of open review: a critical analysis of the machine learning conference review process. Preprint at https://doi.org/10.48550/arXiv.2010.05137 (2020).

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Yang, Y., Wu, Y. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117 , 10762–10768 (2020).

Mukherjee, S., Uzzi, B., Jones, B. & Stringer, M. A new method for identifying recombinations of existing knowledge associated with high‐impact innovation. J. Prod. Innov. Manage. 33 , 224–236 (2016).

Leahey, E., Beckman, C. M. & Stanko, T. L. Prominent but less productive: the impact of interdisciplinarity on scientists’ research. Adm. Sci. Q. 62 , 105–139 (2017).

Sauermann, H. & Haeussler, C. Authorship and contribution disclosures. Sci. Adv. 3 , e1700404 (2017).

Oliveira, D. F. M., Ma, Y., Woodruff, T. K. & Uzzi, B. Comparison of National Institutes of Health grant amounts to first-time male and female principal investigators. JAMA 321 , 898–900 (2019).

Yang, Y., Chawla, N. V. & Uzzi, B. A network’s gender composition and communication pattern predict women’s leadership success. Proc. Natl Acad. Sci. USA 116 , 2033–2038 (2019).

Way, S. F., Larremore, D. B. & Clauset, A. Gender, productivity, and prestige in computer science faculty hiring networks. In Proc. 25th International Conference on World Wide Web 1169–1179. (ACM 2016)

Malmgren, R. D., Ottino, J. M. & Amaral, L. A. N. The role of mentorship in protege performance. Nature 465 , 622–626 (2010).

Ma, Y., Mukherjee, S. & Uzzi, B. Mentorship and protégé success in STEM fields. Proc. Natl Acad. Sci. USA 117 , 14077–14083 (2020).

Börner, K. et al. Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy. Proc. Natl Acad. Sci. USA 115 , 12630–12637 (2018).

Biasi, B. & Ma, S. The Education-Innovation Gap (National Bureau of Economic Research Working papers, 2020).

Bornmann, L. Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics. J. Informetr. 8 , 895–903 (2014).

Cleary, E. G., Beierlein, J. M., Khanuja, N. S., McNamee, L. M. & Ledley, F. D. Contribution of NIH funding to new drug approvals 2010–2016. Proc. Natl Acad. Sci. USA 115 , 2329–2334 (2018).

Spector, J. M., Harrison, R. S. & Fishman, M. C. Fundamental science behind today’s important medicines. Sci. Transl. Med. 10 , eaaq1787 (2018).

Haunschild, R. & Bornmann, L. How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data. Scientometrics 110 , 1209–1216 (2017).

Yin, Y., Gao, J., Jones, B. F. & Wang, D. Coevolution of policy and science during the pandemic. Science 371 , 128–130 (2021).

Sugimoto, C. R., Work, S., Larivière, V. & Haustein, S. Scholarly use of social media and altmetrics: a review of the literature. J. Assoc. Inf. Sci. Technol. 68 , 2037–2062 (2017).

Dunham, I. Human genes: time to follow the roads less traveled? PLoS Biol. 16 , e3000034 (2018).

Kustatscher, G. et al. Understudied proteins: opportunities and challenges for functional proteomics. Nat. Methods 19 , 774–779 (2022).

Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86 , 638 (1979).

Franco, A., Malhotra, N. & Simonovits, G. Publication bias in the social sciences: unlocking the file drawer. Science 345 , 1502–1505 (2014).

Vera-Baceta, M.-A., Thelwall, M. & Kousha, K. Web of Science and Scopus language coverage. Scientometrics 121 , 1803–1813 (2019).

Waltman, L. A review of the literature on citation impact indicators. J. Informetr. 10 , 365–391 (2016).

Garfield, E. & Merton, R. K. Citation Indexing: Its Theory and Application in Science, Technology, and Humanities (Wiley, 1979).

Kelly, B., Papanikolaou, D., Seru, A. & Taddy, M. Measuring Technological Innovation Over the Long Run Report No. 0898-2937 (National Bureau of Economic Research, 2018).

Kogan, L., Papanikolaou, D., Seru, A. & Stoffman, N. Technological innovation, resource allocation, and growth. Q. J. Econ. 132 , 665–712 (2017).

Hall, B. H., Jaffe, A. & Trajtenberg, M. Market value and patent citations. RAND J. Econ. 36 , 16–38 (2005).

Google Scholar

Yan, E. & Ding, Y. Applying centrality measures to impact analysis: a coauthorship network analysis. J. Am. Soc. Inf. Sci. Technol. 60 , 2107–2118 (2009).

Radicchi, F., Fortunato, S., Markines, B. & Vespignani, A. Diffusion of scientific credits and the ranking of scientists. Phys. Rev. E 80 , 056103 (2009).

Bollen, J., Rodriquez, M. A. & Van de Sompel, H. Journal status. Scientometrics 69 , 669–687 (2006).

Bergstrom, C. T., West, J. D. & Wiseman, M. A. The eigenfactor™ metrics. J. Neurosci. 28 , 11433–11434 (2008).

Cronin, B. & Sugimoto, C. R. Beyond Bibliometrics: Harnessing Multidimensional Indicators of Scholarly Impact (MIT Press, 2014).

Hicks, D., Wouters, P., Waltman, L., De Rijcke, S. & Rafols, I. Bibliometrics: the Leiden Manifesto for research metrics. Nature 520 , 429–431 (2015).

Catalini, C., Lacetera, N. & Oettl, A. The incidence and role of negative citations in science. Proc. Natl Acad. Sci. USA 112 , 13823–13826 (2015).

Alcacer, J. & Gittelman, M. Patent citations as a measure of knowledge flows: the influence of examiner citations. Rev. Econ. Stat. 88 , 774–779 (2006).

Ding, Y. et al. Content‐based citation analysis: the next generation of citation analysis. J. Assoc. Inf. Sci. Technol. 65 , 1820–1833 (2014).

Teufel, S., Siddharthan, A. & Tidhar, D. Automatic classification of citation function. In Proc. 2006 Conference on Empirical Methods in Natural Language Processing, 103–110 (Association for Computational Linguistics 2006)

Seeber, M., Cattaneo, M., Meoli, M. & Malighetti, P. Self-citations as strategic response to the use of metrics for career decisions. Res. Policy 48 , 478–491 (2019).

Pendlebury, D. A. The use and misuse of journal metrics and other citation indicators. Arch. Immunol. Ther. Exp. 57 , 1–11 (2009).

Biagioli, M. Watch out for cheats in citation game. Nature 535 , 201 (2016).

Jo, W. S., Liu, L. & Wang, D. See further upon the giants: quantifying intellectual lineage in science. Quant. Sci. Stud. 3 , 319–330 (2022).

Boyack, K. W., Klavans, R. & Börner, K. Mapping the backbone of science. Scientometrics 64 , 351–374 (2005).

Gates, A. J., Ke, Q., Varol, O. & Barabási, A.-L. Nature’s reach: narrow work has broad impact. Nature 575 , 32–34 (2019).

Börner, K., Penumarthy, S., Meiss, M. & Ke, W. Mapping the diffusion of scholarly knowledge among major US research institutions. Scientometrics 68 , 415–426 (2006).

King, D. A. The scientific impact of nations. Nature 430 , 311–316 (2004).

Pan, R. K., Kaski, K. & Fortunato, S. World citation and collaboration networks: uncovering the role of geography in science. Sci. Rep. 2 , 902 (2012).

Jaffe, A. B., Trajtenberg, M. & Henderson, R. Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 108 , 577–598 (1993).

Funk, R. J. & Owen-Smith, J. A dynamic network measure of technological change. Manage. Sci. 63 , 791–817 (2017).

Yegros-Yegros, A., Rafols, I. & D’este, P. Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLoS ONE 10 , e0135095 (2015).

Larivière, V., Haustein, S. & Börner, K. Long-distance interdisciplinarity leads to higher scientific impact. PLoS ONE 10 , e0122565 (2015).

Fleming, L., Greene, H., Li, G., Marx, M. & Yao, D. Government-funded research increasingly fuels innovation. Science 364 , 1139–1141 (2019).

Bowen, A. & Casadevall, A. Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research. Proc. Natl Acad. Sci. USA 112 , 11335–11340 (2015).

Li, D., Azoulay, P. & Sampat, B. N. The applied value of public investments in biomedical research. Science 356 , 78–81 (2017).

Lehman, H. C. Age and Achievement (Princeton Univ. Press, 2017).

Simonton, D. K. Creative productivity: a predictive and explanatory model of career trajectories and landmarks. Psychol. Rev. 104 , 66 (1997).

Duch, J. et al. The possible role of resource requirements and academic career-choice risk on gender differences in publication rate and impact. PLoS ONE 7 , e51332 (2012).

Wang, Y., Jones, B. F. & Wang, D. Early-career setback and future career impact. Nat. Commun. 10 , 4331 (2019).

Bol, T., de Vaan, M. & van de Rijt, A. The Matthew effect in science funding. Proc. Natl Acad. Sci. USA 115 , 4887–4890 (2018).

Jones, B. F. Age and great invention. Rev. Econ. Stat. 92 , 1–14 (2010).

Newman, M. Networks (Oxford Univ. Press, 2018).

Mazloumian, A., Eom, Y.-H., Helbing, D., Lozano, S. & Fortunato, S. How citation boosts promote scientific paradigm shifts and nobel prizes. PLoS ONE 6 , e18975 (2011).

Hirsch, J. E. An index to quantify an individual’s scientific research output. Proc. Natl Acad. Sci. USA 102 , 16569–16572 (2005).

Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E. & Herrera, F. h-index: a review focused in its variants, computation and standardization for different scientific fields. J. Informetr. 3 , 273–289 (2009).

Egghe, L. An improvement of the h-index: the g-index. ISSI Newsl. 2 , 8–9 (2006).

Kaur, J., Radicchi, F. & Menczer, F. Universality of scholarly impact metrics. J. Informetr. 7 , 924–932 (2013).

Majeti, D. et al. Scholar plot: design and evaluation of an information interface for faculty research performance. Front. Res. Metr. Anal. 4 , 6 (2020).

Sidiropoulos, A., Katsaros, D. & Manolopoulos, Y. Generalized Hirsch h-index for disclosing latent facts in citation networks. Scientometrics 72 , 253–280 (2007).

Jones, B. F. & Weinberg, B. A. Age dynamics in scientific creativity. Proc. Natl Acad. Sci. USA 108 , 18910–18914 (2011).

Dennis, W. Age and productivity among scientists. Science 123 , 724–725 (1956).

Sanyal, D. K., Bhowmick, P. K. & Das, P. P. A review of author name disambiguation techniques for the PubMed bibliographic database. J. Inf. Sci. 47 , 227–254 (2021).

Haak, L. L., Fenner, M., Paglione, L., Pentz, E. & Ratner, H. ORCID: a system to uniquely identify researchers. Learn. Publ. 25 , 259–264 (2012).

Malmgren, R. D., Ottino, J. M. & Amaral, L. A. N. The role of mentorship in protégé performance. Nature 465 , 662–667 (2010).

Oettl, A. Reconceptualizing stars: scientist helpfulness and peer performance. Manage. Sci. 58 , 1122–1140 (2012).

Morgan, A. C. et al. The unequal impact of parenthood in academia. Sci. Adv. 7 , eabd1996 (2021).

Morgan, A. C. et al. Socioeconomic roots of academic faculty. Nat. Hum. Behav. 6 , 1625–1633 (2022).

San Francisco Declaration on Research Assessment (DORA) (American Society for Cell Biology, 2012).

Falk‐Krzesinski, H. J. et al. Advancing the science of team science. Clin. Transl. Sci. 3 , 263–266 (2010).

Cooke, N. J. et al. Enhancing the Effectiveness of Team Science (National Academies Press, 2015).

Börner, K. et al. A multi-level systems perspective for the science of team science. Sci. Transl. Med. 2 , 49cm24 (2010).

Leahey, E. From sole investigator to team scientist: trends in the practice and study of research collaboration. Annu. Rev. Sociol. 42 , 81–100 (2016).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 5163 (2018).

Hsiehchen, D., Espinoza, M. & Hsieh, A. Multinational teams and diseconomies of scale in collaborative research. Sci. Adv. 1 , e1500211 (2015).

Koning, R., Samila, S. & Ferguson, J.-P. Who do we invent for? Patents by women focus more on women’s health, but few women get to invent. Science 372 , 1345–1348 (2021).

Barabâsi, A.-L. et al. Evolution of the social network of scientific collaborations. Physica A 311 , 590–614 (2002).

Newman, M. E. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64 , 016131 (2001).

Newman, M. E. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64 , 016132 (2001).

Palla, G., Barabási, A.-L. & Vicsek, T. Quantifying social group evolution. Nature 446 , 664–667 (2007).

Ross, M. B. et al. Women are credited less in science than men. Nature 608 , 135–145 (2022).

Shen, H.-W. & Barabási, A.-L. Collective credit allocation in science. Proc. Natl Acad. Sci. USA 111 , 12325–12330 (2014).

Merton, R. K. Matthew effect in science. Science 159 , 56–63 (1968).

Ni, C., Smith, E., Yuan, H., Larivière, V. & Sugimoto, C. R. The gendered nature of authorship. Sci. Adv. 7 , eabe4639 (2021).

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N. & Malone, T. W. Evidence for a collective intelligence factor in the performance of human groups. Science 330 , 686–688 (2010).

Feldon, D. F. et al. Postdocs’ lab engagement predicts trajectories of PhD students’ skill development. Proc. Natl Acad. Sci. USA 116 , 20910–20916 (2019).

Boudreau, K. J. et al. A field experiment on search costs and the formation of scientific collaborations. Rev. Econ. Stat. 99 , 565–576 (2017).

Holcombe, A. O. Contributorship, not authorship: use CRediT to indicate who did what. Publications 7 , 48 (2019).

Murray, D. et al. Unsupervised embedding of trajectories captures the latent structure of mobility. Preprint at https://doi.org/10.48550/arXiv.2012.02785 (2020).

Deville, P. et al. Career on the move: geography, stratification, and scientific impact. Sci. Rep. 4 , 4770 (2014).

Edmunds, L. D. et al. Why do women choose or reject careers in academic medicine? A narrative review of empirical evidence. Lancet 388 , 2948–2958 (2016).

Waldinger, F. Peer effects in science: evidence from the dismissal of scientists in Nazi Germany. Rev. Econ. Stud. 79 , 838–861 (2012).

Agrawal, A., McHale, J. & Oettl, A. How stars matter: recruiting and peer effects in evolutionary biology. Res. Policy 46 , 853–867 (2017).

Fiore, S. M. Interdisciplinarity as teamwork: how the science of teams can inform team science. Small Group Res. 39 , 251–277 (2008).

Hvide, H. K. & Jones, B. F. University innovation and the professor’s privilege. Am. Econ. Rev. 108 , 1860–1898 (2018).

Murray, F., Aghion, P., Dewatripont, M., Kolev, J. & Stern, S. Of mice and academics: examining the effect of openness on innovation. Am. Econ. J. Econ. Policy 8 , 212–252 (2016).

Radicchi, F., Fortunato, S. & Castellano, C. Universality of citation distributions: toward an objective measure of scientific impact. Proc. Natl Acad. Sci. USA 105 , 17268–17272 (2008).

Waltman, L., van Eck, N. J. & van Raan, A. F. Universality of citation distributions revisited. J. Am. Soc. Inf. Sci. Technol. 63 , 72–77 (2012).

Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286 , 509–512 (1999).

de Solla Price, D. A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inf. Sci. 27 , 292–306 (1976).

Cole, S. Age and scientific performance. Am. J. Sociol. 84 , 958–977 (1979).

Ke, Q., Ferrara, E., Radicchi, F. & Flammini, A. Defining and identifying sleeping beauties in science. Proc. Natl Acad. Sci. USA 112 , 7426–7431 (2015).

Bornmann, L., de Moya Anegón, F. & Leydesdorff, L. Do scientific advancements lean on the shoulders of giants? A bibliometric investigation of the Ortega hypothesis. PLoS ONE 5 , e13327 (2010).

Mukherjee, S., Romero, D. M., Jones, B. & Uzzi, B. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot. Sci. Adv. 3 , e1601315 (2017).

Packalen, M. & Bhattacharya, J. NIH funding and the pursuit of edge science. Proc. Natl Acad. Sci. USA 117 , 12011–12016 (2020).

Zeng, A., Fan, Y., Di, Z., Wang, Y. & Havlin, S. Fresh teams are associated with original and multidisciplinary research. Nat. Hum. Behav. 5 , 1314–1322 (2021).

Newman, M. E. The structure of scientific collaboration networks. Proc. Natl Acad. Sci. USA 98 , 404–409 (2001).

Larivière, V., Ni, C., Gingras, Y., Cronin, B. & Sugimoto, C. R. Bibliometrics: global gender disparities in science. Nature 504 , 211–213 (2013).

West, J. D., Jacquet, J., King, M. M., Correll, S. J. & Bergstrom, C. T. The role of gender in scholarly authorship. PLoS ONE 8 , e66212 (2013).

Gao, J., Yin, Y., Myers, K. R., Lakhani, K. R. & Wang, D. Potentially long-lasting effects of the pandemic on scientists. Nat. Commun. 12 , 6188 (2021).

Jones, B. F., Wuchty, S. & Uzzi, B. Multi-university research teams: shifting impact, geography, and stratification in science. Science 322 , 1259–1262 (2008).

Chu, J. S. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118 , e2021636118 (2021).

Wang, J., Veugelers, R. & Stephan, P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators. Res. Policy 46 , 1416–1436 (2017).

Stringer, M. J., Sales-Pardo, M. & Amaral, L. A. Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal. J. Assoc. Inf. Sci. Technol. 61 , 1377–1385 (2010).

Bianconi, G. & Barabási, A.-L. Bose-Einstein condensation in complex networks. Phys. Rev. Lett. 86 , 5632 (2001).

Bianconi, G. & Barabási, A.-L. Competition and multiscaling in evolving networks. Europhys. Lett. 54 , 436 (2001).

Yin, Y. & Wang, D. The time dimension of science: connecting the past to the future. J. Informetr. 11 , 608–621 (2017).

Pan, R. K., Petersen, A. M., Pammolli, F. & Fortunato, S. The memory of science: Inflation, myopia, and the knowledge network. J. Informetr. 12 , 656–678 (2018).

Yin, Y., Wang, Y., Evans, J. A. & Wang, D. Quantifying the dynamics of failure across science, startups and security. Nature 575 , 190–194 (2019).

Candia, C. & Uzzi, B. Quantifying the selective forgetting and integration of ideas in science and technology. Am. Psychol. 76 , 1067 (2021).

Milojević, S. Principles of scientific research team formation and evolution. Proc. Natl Acad. Sci. USA 111 , 3984–3989 (2014).

Guimera, R., Uzzi, B., Spiro, J. & Amaral, L. A. N. Team assembly mechanisms determine collaboration network structure and team performance. Science 308 , 697–702 (2005).

Newman, M. E. Coauthorship networks and patterns of scientific collaboration. Proc. Natl Acad. Sci. USA 101 , 5200–5205 (2004).

Newman, M. E. Clustering and preferential attachment in growing networks. Phys. Rev. E 64 , 025102 (2001).

Iacopini, I., Milojević, S. & Latora, V. Network dynamics of innovation processes. Phys. Rev. Lett. 120 , 048301 (2018).

Kuhn, T., Perc, M. & Helbing, D. Inheritance patterns in citation networks reveal scientific memes. Phys. Rev. 4 , 041036 (2014).

Jia, T., Wang, D. & Szymanski, B. K. Quantifying patterns of research-interest evolution. Nat. Hum. Behav. 1 , 0078 (2017).

Zeng, A. et al. Increasing trend of scientists to switch between topics. Nat. Commun. https://doi.org/10.1038/s41467-019-11401-8 (2019).

Siudem, G., Żogała-Siudem, B., Cena, A. & Gagolewski, M. Three dimensions of scientific impact. Proc. Natl Acad. Sci. USA 117 , 13896–13900 (2020).

Petersen, A. M. et al. Reputation and impact in academic careers. Proc. Natl Acad. Sci. USA 111 , 15316–15321 (2014).

Jin, C., Song, C., Bjelland, J., Canright, G. & Wang, D. Emergence of scaling in complex substitutive systems. Nat. Hum. Behav. 3 , 837–846 (2019).

Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature 595 , 181–188 (2021).

Lazer, D. et al. Computational social science. Science 323 , 721–723 (2009).

Lazer, D. M. et al. Computational social science: obstacles and opportunities. Science 369 , 1060–1062 (2020).

Albert, R. & Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74 , 47 (2002).

Newman, M. E. The structure and function of complex networks. SIAM Rev. 45 , 167–256 (2003).

Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327 , 1018–1021 (2010).

Alessandretti, L., Aslak, U. & Lehmann, S. The scales of human mobility. Nature 587 , 402–407 (2020).

Pastor-Satorras, R. & Vespignani, A. Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86 , 3200 (2001).

Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87 , 925 (2015).

Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).

Dong, Y., Johnson, R. A. & Chawla, N. V. Will this paper increase your h-index? Scientific impact prediction. In Proc. 8th ACM International Conference on Web Search and Data Mining, 149–158 (ACM 2015)

Xiao, S. et al. On modeling and predicting individual paper citation count over time. In IJCAI, 2676–2682 (IJCAI, 2016)

Fortunato, S. Community detection in graphs. Phys. Rep. 486 , 75–174 (2010).

Chen, C. Science mapping: a systematic review of the literature. J. Data Inf. Sci. 2 , 1–40 (2017).

CAS Google Scholar

Van Eck, N. J. & Waltman, L. Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics 111 , 1053–1070 (2017).

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015).

Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577 , 706–710 (2020).

Krenn, M. & Zeilinger, A. Predicting research trends with semantic and neural networks with an application in quantum physics. Proc. Natl Acad. Sci. USA 117 , 1910–1916 (2020).

Iten, R., Metger, T., Wilming, H., Del Rio, L. & Renner, R. Discovering physical concepts with neural networks. Phys. Rev. Lett. 124 , 010508 (2020).

Guimerà, R. et al. A Bayesian machine scientist to aid in the solution of challenging scientific problems. Sci. Adv. 6 , eaav6971 (2020).

Segler, M. H., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018).

Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep learning improves prediction of drug–drug and drug–food interactions. Proc. Natl Acad. Sci. USA 115 , E4304–E4311 (2018).

Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172 , 1122–1131.e9 (2018).

Peng, H., Ke, Q., Budak, C., Romero, D. M. & Ahn, Y.-Y. Neural embeddings of scholarly periodicals reveal complex disciplinary organizations. Sci. Adv. 7 , eabb9004 (2021).

Youyou, W., Yang, Y. & Uzzi, B. A discipline-wide investigation of the replicability of psychology papers over the past two decades. Proc. Natl Acad. Sci. USA 120 , e2208863120 (2023).

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR) 54 , 1–35 (2021).

Way, S. F., Morgan, A. C., Larremore, D. B. & Clauset, A. Productivity, prominence, and the effects of academic environment. Proc. Natl Acad. Sci. USA 116 , 10729–10733 (2019).

Li, W., Aste, T., Caccioli, F. & Livan, G. Early coauthorship with top scientists predicts success in academic careers. Nat. Commun. 10 , 5170 (2019).

Hendry, D. F., Pagan, A. R. & Sargan, J. D. Dynamic specification. Handb. Econ. 2 , 1023–1100 (1984).

Jin, C., Ma, Y. & Uzzi, B. Scientific prizes and the extraordinary growth of scientific topics. Nat. Commun. 12 , 5619 (2021).

Azoulay, P., Ganguli, I. & Zivin, J. G. The mobility of elite life scientists: professional and personal determinants. Res. Policy 46 , 573–590 (2017).

Slavova, K., Fosfuri, A. & De Castro, J. O. Learning by hiring: the effects of scientists’ inbound mobility on research performance in academia. Organ. Sci. 27 , 72–89 (2016).

Sarsons, H. Recognition for group work: gender differences in academia. Am. Econ. Rev. 107 , 141–145 (2017).

Campbell, L. G., Mehtani, S., Dozier, M. E. & Rinehart, J. Gender-heterogeneous working groups produce higher quality science. PLoS ONE 8 , e79147 (2013).

Azoulay, P., Graff Zivin, J. S. & Wang, J. Superstar extinction. Q. J. Econ. 125 , 549–589 (2010).

Furman, J. L. & Stern, S. Climbing atop the shoulders of giants: the impact of institutions on cumulative research. Am. Econ. Rev. 101 , 1933–1963 (2011).

Williams, H. L. Intellectual property rights and innovation: evidence from the human genome. J. Polit. Econ. 121 , 1–27 (2013).

Rubin, A. & Rubin, E. Systematic Bias in the Progress of Research. J. Polit. Econ. 129 , 2666–2719 (2021).

Lu, S. F., Jin, G. Z., Uzzi, B. & Jones, B. The retraction penalty: evidence from the Web of Science. Sci. Rep. 3 , 3146 (2013).

Jin, G. Z., Jones, B., Lu, S. F. & Uzzi, B. The reverse Matthew effect: consequences of retraction in scientific teams. Rev. Econ. Stat. 101 , 492–506 (2019).

Azoulay, P., Bonatti, A. & Krieger, J. L. The career effects of scandal: evidence from scientific retractions. Res. Policy 46 , 1552–1569 (2017).

Goodman-Bacon, A. Difference-in-differences with variation in treatment timing. J. Econ. 225 , 254–277 (2021).

Callaway, B. & Sant’Anna, P. H. Difference-in-differences with multiple time periods. J. Econ. 225 , 200–230 (2021).

Hill, R. Searching for Superstars: Research Risk and Talent Discovery in Astronomy Working Paper (Massachusetts Institute of Technology, 2019).

Bagues, M., Sylos-Labini, M. & Zinovyeva, N. Does the gender composition of scientific committees matter? Am. Econ. Rev. 107 , 1207–1238 (2017).

Sampat, B. & Williams, H. L. How do patents affect follow-on innovation? Evidence from the human genome. Am. Econ. Rev. 109 , 203–236 (2019).

Moretti, E. & Wilson, D. J. The effect of state taxes on the geographical location of top earners: evidence from star scientists. Am. Econ. Rev. 107 , 1858–1903 (2017).

Jacob, B. A. & Lefgren, L. The impact of research grant funding on scientific productivity. J. Public Econ. 95 , 1168–1177 (2011).

Li, D. Expertise versus bias in evaluation: evidence from the NIH. Am. Econ. J. Appl. Econ. 9 , 60–92 (2017).

Pearl, J. Causal diagrams for empirical research. Biometrika 82 , 669–688 (1995).

Pearl, J. & Mackenzie, D. The Book of Why: The New Science of Cause and Effect (Basic Books, 2018).

Traag, V. A. Inferring the causal effect of journals on citations. Quant. Sci. Stud. 2 , 496–504 (2021).

Traag, V. & Waltman, L. Causal foundations of bias, disparity and fairness. Preprint at https://doi.org/10.48550/arXiv.2207.13665 (2022).

Imbens, G. W. Potential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. J. Econ. Lit. 58 , 1129–1179 (2020).

Heckman, J. J. & Pinto, R. Causality and Econometrics (National Bureau of Economic Research, 2022).

Aggarwal, I., Woolley, A. W., Chabris, C. F. & Malone, T. W. The impact of cognitive style diversity on implicit learning in teams. Front. Psychol. 10 , 112 (2019).

Balietti, S., Goldstone, R. L. & Helbing, D. Peer review and competition in the Art Exhibition Game. Proc. Natl Acad. Sci. USA 113 , 8414–8419 (2016).

Paulus, F. M., Rademacher, L., Schäfer, T. A. J., Müller-Pinzler, L. & Krach, S. Journal impact factor shapes scientists’ reward signal in the prospect of publication. PLoS ONE 10 , e0142537 (2015).

Williams, W. M. & Ceci, S. J. National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track. Proc. Natl Acad. Sci. USA 112 , 5360–5365 (2015).

Collaboration, O. S. Estimating the reproducibility of psychological science. Science 349 , aac4716 (2015).

Camerer, C. F. et al. Evaluating replicability of laboratory experiments in economics. Science 351 , 1433–1436 (2016).

Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2 , 637–644 (2018).

Duflo, E. & Banerjee, A. Handbook of Field Experiments (Elsevier, 2017).

Tomkins, A., Zhang, M. & Heavlin, W. D. Reviewer bias in single versus double-blind peer review. Proc. Natl Acad. Sci. USA 114 , 12708–12713 (2017).

Blank, R. M. The effects of double-blind versus single-blind reviewing: experimental evidence from the American Economic Review. Am. Econ. Rev. 81 , 1041–1067 (1991).

Boudreau, K. J., Guinan, E. C., Lakhani, K. R. & Riedl, C. Looking across and looking beyond the knowledge frontier: intellectual distance, novelty, and resource allocation in science. Manage. Sci. 62 , 2765–2783 (2016).

Lane, J. et al. When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects Working Paper #21-007 (Harvard Business School, 2020).

Teplitskiy, M. et al. Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review (Harvard Business School, 2019).

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Science faculty’s subtle gender biases favor male students. Proc. Natl Acad. Sci. USA 109 , 16474–16479 (2012).

Forscher, P. S., Cox, W. T., Brauer, M. & Devine, P. G. Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. Nat. Hum. Behav. 3 , 257–264 (2019).

Dennehy, T. C. & Dasgupta, N. Female peer mentors early in college increase women’s positive academic experiences and retention in engineering. Proc. Natl Acad. Sci. USA 114 , 5964–5969 (2017).

Azoulay, P. Turn the scientific method on ourselves. Nature 484 , 31–32 (2012).

Download references

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.

Author information

Authors and affiliations.

Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA

Lu Liu, Benjamin F. Jones, Brian Uzzi & Dashun Wang

Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA

Kellogg School of Management, Northwestern University, Evanston, IL, USA

College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA

National Bureau of Economic Research, Cambridge, MA, USA

Benjamin F. Jones

Brookings Institution, Washington, DC, USA

McCormick School of Engineering, Northwestern University, Evanston, IL, USA

Dashun Wang

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dashun Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Human Behaviour thanks Ludo Waltman, Erin Leahey and Sarah Bratt for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Liu, L., Jones, B.F., Uzzi, B. et al. Data, measurement and empirical methods in the science of science. Nat Hum Behav 7 , 1046–1058 (2023). https://doi.org/10.1038/s41562-023-01562-4

Download citation

Received : 30 June 2022

Accepted : 17 February 2023

Published : 01 June 2023

Issue Date : July 2023

DOI : https://doi.org/10.1038/s41562-023-01562-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Publication, funding, and experimental data in support of human reference atlas construction and usage.

Yongxin Kong
Katy Börner

Scientific Data (2024)

Rescaling the disruption index reveals the universality of disruption distributions in science

Alex J. Yang
Hongcun Gong
Sanhong Deng

Scientometrics (2024)

Signs of Dysconscious Racism and Xenophobiaism in Knowledge Production and the Formation of Academic Researchers: A National Study

Dina Zoe Belluigi

Journal of Academic Ethics (2024)

Scientific Data (2023)

Quick links

Explore articles by subject
Guide to authors
Editorial policies

How to... Conduct empirical research

Share this content

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

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

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

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

Let's look at some examples:

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

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

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

RQ1: What objectives underpinned the corporate brand?

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

RQ3: How was the desired rearticulation promoted to shareholders?

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

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

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

Or sometimes the RQ is stated as a general objective:

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

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

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

or as a proposition:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Interpretivism

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


typically use	typically use
are	are
involve the researcher as ideally an	require more and on the part of the researcher.
may focus on cause and effect.	focuses on understanding of phenomena in their social, institutional, political and economic context.
require a hypothesis.	require a
have the that they may force people into categories, also it cannot go into much depth about subjects and issues.	have the that they focus on a few individuals, and may therefore be difficult to generalise.

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

Inductive and deductive approaches

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

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


is more usually linked with an approach.	is more usually linked with the approach.
is more likely to use qualitative methods, such as interviewing, observation etc., with a more flexible structure.	is more likely to use quantitative methods, such as experiments, questionnaires etc., and a highly structured methodology with controls.
does not simply look at cause and effect, but at people's perceptions of events, and at the context of the research.	is the more scientific method, concerned with cause and effect, and the relationship between variables.
builds theory after collection of the data.	starts from a theoretical perspective, and develops a hypothesis which is tested against the data.
is more likely to use an in-depth study of a smaller sample.	is more likely to use a larger sample.
is less likely to be concerned with generalisation (a danger is that no patterns emerge).	is concerned with generalisation.
tresses the researcher involvement.	stresses the independence of the researcher.

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

Sampling may be done either:

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

Useful articles

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

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

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

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

Read the complete article

What's in the rest?

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

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

Skip to primary navigation
Skip to main content
Skip to primary sidebar
Skip to footer

FLASH SALE! - "FINANCIAL MODELING COURSE BUNDLE AT 60% OFF" Enroll Now

Empirical Evidence

Priya, an MBA in Finance with 5 years of content writing expertise, crafts insightful articles on investment, finance, banking, accounting, economics, and business management. Former Management Trainee skilled in credit analysis.

Collins Enosh, Content Editor at WallStreetMojo, blends four years of writing experience with a passion for simplicity. A Master’s in English Literature graduate from Bishop Heber College, Trichy, India.

Dheeraj Vaidya, CFA, FRM is Co-Founder of Wallstreetmojo.com and possesses 18+ years of expertise in Financial Modeling, Valuations, and Excel. With a background as a former Equity Research Analyst at JPMorgan and CLSA, he brings unparalleled proficiency to these key financial domains.

Empirical Evidence Definition

Empirical evidence refers to factual data that is collected by conducting observations and experiments. It is a systematic process; every step is documented. Further, raw data is used to derive meaningful conclusions and findings.

An Investigator first states a hypothesis and then accepts or rejects the theory based on analysis and tests. The analysis relies on facts and figures. Empirical evidence is an indispensable component of marketing, finance, research & development, psychology, engineering, medicine, and sociology.

Empirical evidence explained, frequently asked questions (faqs), recommended articles, key takeaways.

Empirical evidence can be verified and validated. An investigator collects facts and figures to conduct scientific research in various disciplines.
Empirical data can be collected through primary sources—surveys, observation, documentation, and experimentation. Secondary sources refer to published reports, articles, and newspapers.
Empirical data is classified into quantitative, qualitative, or a combination of both.
Empirical data are considered reliable. But, at the same time, this reliability is tarnished by errors. To track errors, researchers document every step of data collection.

Financial Modeling & Valuation Courses Bundle (25+ Hours Video Series)

–>> If you want to learn Financial Modeling & Valuation professionally , then do check this Financial Modeling & Valuation Course Bundle ( 25+ hours of video tutorials with step by step McDonald’s Financial Model ). Unlock the art of financial modeling and valuation with a comprehensive course covering McDonald’s forecast methodologies, advanced valuation techniques, and financial statements.

Empirical evidence refers to factual data. Most factual data are represented in terms of numerical figures that can be verified and validated. Raw data is collected by conducting experiments and observations—performed in a controlled environment.

You are free to use this image on your website, templates, etc, Please provide us with an attribution link How to Provide Attribution? Article Link to be Hyperlinked For eg: Source: Empirical Evidence (wallstreetmojo.com)

Empirical evidence is scientific and standardized—even after repetitive experiments, it remains intact. Based on empirical data, researchers try to prove a hypothesis. They conduct various statistical tests to accept or reject a hypothesis— hypothesis testing Hypothesis Testing Hypothesis Testing is the statistical tool that helps measure the probability of the correctness of the hypothesis result derived after performing the hypothesis on the sample data. It confirms whether the primary hypothesis results derived were correct. read more .

Along with finance Finance Finance is a broad term that essentially refers to money management or channeling money for various purposes. read more and accounting Accounting Accounting is the process of processing and recording financial information on behalf of a business, and it serves as the foundation for all subsequent financial statements. read more , empirical data is indispensable in psychology, medicine, sociology, engineering, and marketing. Data collection sometimes requires using human senses like sight, touch, hearing, taste, or smell; contemporarily, though, machine sensors and laboratory testing are more common. Newspapers, journals, published reports, and articles, serve as secondary sources of empirical data.

On the one hand, fact-based data are considered credible; at the same time, errors can easily tarnish the reliability of a data set. Investigators must ensure an error-free collection of data. A small mistake can undo the entire data set, which was collected in a painstaking process. Incorrect information results in irrelevant results.

In order to rectify mistakes, researchers should be able to track the process. Therefore, researchers document data sources, categorized groups, processes, techniques, methods, and controlled variables. To resolve errors and issues, they retest a particular group of data.

Empirical evidence can be classified into the following three types:

Quantitative : Such information is quantifiable, i.e., it can be measured in units. It can be counted or represented in numbers or figures. Moreover, it is drawn from the implementation of mathematical and statistical methods. For clarity, quantitative data is often represented graphically—charts and diagrams. Quantitative data is considered very reliable—it is collected by conducting surveys and experiments.
Qualitative : It is available in a non-numerical format. Qualitative data is collected by conducting observations, interviews, case studies, text analysis, etc. Also, it cannot be expressed in figures or numbers. It is more common in fields where human behavior comes into play—psychology, social science, and marketing. Qualitative information is not considered all that reliable—it is prone to personal bias.
Mixed : Sometimes, data is collected both in terms of numbers and experience. In marketing, for example, along with product sales volume, customer satisfaction also plays a vital role in the product’s success.

Let us see some examples to understand the application of empirical evidence in finance and marketing:

The finance manager of ABC Ltd. compares the company’s book profit Book Profit Book Profit is the profit amount that a business earns from its operations & activities but has not been realized yet. It is not tracked by analysts or stakeholders & its calculation is relevant only to evaluate a Company’s tax liability. read more with that of its competitors; LMN Enterprises, PQR Ltd., ST Corp., and XYZ Ltd. The purpose of the analysis is to determine the company’s position in terms of its profitability in the market.

In this example, empirical evidence refers to secondary information gathered from the published financial reports of competitor firms.

DLC farms supply fresh fruits and vegetables to some famous restaurants in the US. Now, the company’s marketing manager wants to determine customer satisfaction.

The manager prepares a list of clients and selects the top 50 clients who provide more than 60% of the business—out of 200 clients. For the chosen 50 clients, he collects data on customer experience, expectations, and feedback.

In this example, primary information is the empirical evidence. Based on raw data, the manager makes further conclusions.

Empirical evidence is the quantitative or qualitative information collected by an investigator in order to support scientific research. The data is used to prove or reject a particular hypothesis theory.

The empirical data can be determined through the following sources: 1. Primary Source: It is raw data—collected by conducting surveys, observation, documentation, and experimentation. 2. Secondary Source: It is readily available information. It usually comes from a verified source—published reports, articles, magazines, newspapers, and white papers.

Anecdotal and empirical are two different ways of collecting data. The former is based on the experience and opinion of an individual or a few people. It provides partial or personal data which may not stand true for the whole population. Empirical evidence, on the other hand, represents facts. It is collected through scientific methods like observation and experimentation. Empirical data can be used to derive a generalized conclusion.

This has been a guide to what is Empirical Evidence and its Meaning. We explain empirical evidence in research, psychology, its definition & examples. You can learn more about it from the following articles –

Empirical Rule
Normal Distribution

Reader Interactions

Infact I enjoyed the write up

Thank you for making the term empirical evidence, along with its “side kicks” (e.g., quantitative & qualitative) into understandable language.

Very good article with illustrations

Empirical Research: Defining, Identifying, & Finding

Searching for empirical research.

Defining Empirical Research
Introduction

Where Do I Find Empirical Research?

How do i find more empirical research in my search.

Database Tools
Search Terms
Image Descriptions

Because empirical research refers to the method of investigation rather than a method of publication, it can be published in a number of places. In many disciplines empirical research is most commonly published in scholarly, peer-reviewed journals . Putting empirical research through the peer review process helps ensure that the research is high quality.

Finding Peer-Reviewed Articles

You can find peer-reviewed articles in a general web search along with a lot of other types of sources. However, these specialized tools are more likely to find peer-reviewed articles:

Library databases
Academic search engines such as Google Scholar

Common Types of Articles That Are Not Empirical

However, just finding an article in a peer-reviewed journal is not enough to say it is empirical, since not all the articles in a peer-reviewed journal will be empirical research or even peer reviewed. Knowing how to quickly identify some types non-empirical research articles in peer-reviewed journals can help speed up your search.

Peer-reviewed articles that systematically discuss and propose abstract concepts and methods for a field without primary data collection.
Example: Grosser, K. & Moon, J. (2019). CSR and feminist organization studies: Towards an integrated theorization for the analysis of gender issues .
Peer-reviewed articles that systematically describe, summarize, and often categorize and evaluate previous research on a topic without collecting new data.
Example: Heuer, S. & Willer, R. (2020). How is quality of life assessed in people with dementia? A systematic literature review and a primer for speech-language pathologists .
Note: empirical research articles will have a literature review section as part of the Introduction , but in an empirical research article the literature review exists to give context to the empirical research, which is the primary focus of the article. In a literature review article, the literature review is the focus.
While these articles are not empirical, they are often a great source of information on previous empirical research on a topic with citations to find that research.
Non-peer-reviewed articles where the authors discuss their thoughts on a particular topic without data collection and a systematic method. There are a few differences between these types of articles.
Written by the editors or guest editors of the journal.
Example: Naples, N. A., Mauldin, L., & Dillaway, H. (2018). From the guest editors: Gender, disability, and intersectionality .
Written by guest authors. The journal may have a non-peer-reviewed process for authors to submit these articles, and the editors of the journal may invite authors to write opinion articles.
Example: García, J. J.-L., & Sharif, M. Z. (2015). Black lives matter: A commentary on racism and public health .
Written by the readers of a journal, often in response to an article previously-published in the journal.
Example: Nathan, M. (2013). Letters: Perceived discrimination and racial/ethnic disparities in youth problem behaviors .
Non-peer-reviewed articles that describe and evaluate books, products, services, and other things the audience of the journal would be interested in.
Example: Robinson, R. & Green, J. M. (2020). Book review: Microaggressions and traumatic stress: Theory, research, and clinical treatment .

Even once you know how to recognize empirical research and where it is published, it would be nice to improve your search results so that more empirical research shows up for your topic.

There are two major ways to find the empirical research in a database search:

Use built-in database tools to limit results to empirical research.
Include search terms that help identify empirical research.
<< Previous: Discussion
Next: Database Tools >>
Last Updated: Apr 2, 2024 11:25 AM
URL: https://libguides.memphis.edu/empirical-research

IMAGES

Empirical Research: Definition, Methods, Types and Examples
Empirical Research: Definition, Methods, Types and Examples
What Is Empirical Research? Definition, Types & Samples in 2024
What Is Empirical Research? Definition, Types & Samples
15 Empirical Evidence Examples (2024)
The structure of the empirical research.

VIDEO

Orgonite pendant
Empirical, molecular, and structural formulas
Finding and Calculating an Empirical Formula of a Compound
Empirical Formula & Molecular Formula Determination From Percent Composition
Conceptual Versus Empirical Research
Empirical Formula and Molecular Formula Introduction

COMMENTS

Empirical research
Empirical research is research using empirical evidence. ... In scientific use, the term empirical refers to the gathering of data using only evidence that is observable by the senses or in some cases using calibrated scientific instruments. What early philosophers described as empiricist and empirical research have in common is the dependence ...
What Is Empirical Research? Definition, Types & Samples in 2024
Empirical research is defined as any study whose conclusions are exclusively derived from concrete, verifiable evidence. The term empirical basically means that it is guided by scientific experimentation and/or evidence. Likewise, a study is empirical when it uses real-world evidence in investigating its assertions.
Empirical Research: Definition, Methods, Types and Examples
Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore "verifiable" evidence. ... 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 ...
What is Empirical Research? Definition, Methods, Examples
Empirical research is characterized by several key features: Observation and Measurement: It involves the systematic observation or measurement of variables, events, or behaviors. Data Collection: Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
Empirical Research: Defining, Identifying, & Finding
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 ...
Empirical Research in the Social Sciences and Education
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
Empirical Research
Mcleod noted that empirical research, as a tool for investigation within the field of psychology, began in the 1800s with behaviorists who assert that psychology is a scientific discipline, which requires scientific principles in investigating human behavior, stressed its use.They further claimed that there are unseen factors that influence human behavior.
Empirical Research
Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. ... This refers to the process where authors who are doing research submit a paper they have written to a journal. The journal editor then sends the article to the author's peers (researchers and ...
Empirical Research
Strategies for Empirical Research in Writing is a particularly accessible approach to both qualitative and quantitative empirical research methods, helping novices appreciate the value of empirical research in writing while easing their fears about the research process. This comprehensive book covers research methods ranging from traditional ...
Empirical Research
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;
Defining Empirical Research— Types, Methods, and Examples
Empirical research is a research methodology that uses experiences and verifiable evidence to reach conclusions. Derived from the Greek word ' empeirikos ,' which means experience, empirical research is based on believing only what can be seen, experienced, or verified. This makes empirical research stand out as scientific and trustworthy.
Empirical Research Methods
Empirical research refers to methods that investigators use to test knowledge claims and develop new new knowledge through direct or indirect observation and experimentation. Empirical methods emphasize collecting data systematically and rigorously to ensure reliability and validity. Investigators observe phenomena, conduct experiments, and.
What is "Empirical Research"?
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 ...
Identifying Empirical Articles
Identifying Empirical Research Articles. 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. Introduction: why the article was written, research question or questions, hypothesis, literature review; Methods: the overall research design and implementation, description of sample ...
Empirical evidence
Empirical evidence is subject to assessments of its validity. Validity can be internal, involving the soundness of an experiment's design and execution and the accuracy of subsequent data analysis , or external, involving generalizability to other research contexts ( see ecological validity ).
Empirical Research
'Empirical research' refers to a type of study based on actual observations and experiences rather than theories or beliefs. ... Empirical research of medical ethics is realized from outside the field, as an opportunity to answer sociological questions and to enhance our understanding of structures, roles, values, and rituals. Whereas the first ...
Empirical Research
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.
What is Empirical Research Study? [Examples & Method]
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 ...
Data, measurement and empirical methods in the science of science
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 ...
Conduct empirical research
Share this content. Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data ...
Empirical Evidence
Empirical evidence refers to factual data that is collected by conducting observations and experiments. It is a systematic process; every step is documented. ... An investigator collects facts and figures to conduct scientific research in various disciplines. Empirical data can be collected through primary sources—surveys, observation ...
Searching for Empirical Research
Because empirical research refers to the method of investigation rather than a method of publication, it can be published in a number of places. In many disciplines empirical research is most commonly published in scholarly, peer-reviewed journals. Putting empirical research through the peer review process helps ensure that the research is high ...
Section 1: The Empirical Approach to Knowledge
organized and summarized, which is a primary function of statistical analysis. The term participants is used when. individuals have consented. The term subjects is appropriate when. there is no consent. The term participation is. predominant. The term empiricism refers to what? The empirical approach to knowledge.
Full article: Determinants of effective university-industry
Impact, as a word, refers to the positive and negative effects of research, but most used definitions implicitly concentrate on benefits. In relation to Oman's performance, the best performance was recorded in human capital and research, while the worst was recorded in knowledge and technology outputs (World Intellectual Property Organization ...
Why your help is unhelpful: A multistage mediation model exploring
Recent occupational health research has begun exploring unhelpful workplace social support (UWSS). UWSS refers to actions taken by a colleague that the recipient believes are intended to be helpful but are perceived as ineffective. For example, a colleague may provide help that is not wanted or do something incorrectly while providing aid. Despite the perceived good intentions of UWSS ...

Empirical Research: Definition, Methods, Types and Examples

Empirical research: Definition

Types and methodologies of empirical research

MORE LIKE THIS

360 Degree Feedback Spider Chart is Back!

Jotform vs Wufoo: Comparison of Features and Prices

Product or Service: Which is More Important? — Tuesday CX Thoughts

Life@QuestionPro: Thomas Maiwald-Immer’s Experience

Other categories

What is Empirical Research? Definition, Methods, Examples

What is Empirical Research?

Characteristics of Empirical Research

Importance of Empirical Research

How to Conduct Empirical Research?

1. Select a Research Topic

2. Formulate Research Questions

3. Review Existing Literature

4. Define Variables

Empirical Research Design

Types of Empirical Research

Experimental Design

Observational Design

Survey Design

Case Study Design

Qualitative vs. Quantitative Research

Data Collection for Empirical Research

Sampling Methods

Data Collection Instruments

Data Collection Procedures

Ethical Considerations

Empirical Research Data Analysis

Data Preparation

Descriptive Statistics

Inferential Statistics

Qualitative Data Analysis

Data Visualization

How to Report Empirical Research Results?

1. Write the Research Paper

2. Create Visuals and Tables

3. Interpret Findings

Examples of Empirical Research

Social Sciences

Environmental Science

Business and Economics

Conclusion for Empirical Research

How to Collect Data for Empirical Research?

Wait, there's more

Penn State University Libraries

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Reading and Evaluating Scholarly Materials

Psychology Research Guide

Finding Empirical Research in PsycINFO

Finding Empirical Research in PubMed

Finding Empirical Research in Library OneSearch & Google Scholar

Identifying a Journal is Peer-Reviewed

Finding Peer-Reviewed Articles

Research: Overview & Approaches

Introduction to Empirical Research

Your Librarian

Empirical & Non-Empirical Research

Introduction: What is Empirical Research?

Quantitative Research

Qualitative Research

Defining Empirical Research— Types, Methods, and Examples

Types of Empirical Research

1. Quantitative Empirical Research

2. Qualitative Empirical Research

Research Methods Using Empirical Evidence

Quantitative Research Methods

1. Survey research

2. Experimental research

3. Correlational research

4. Longitudinal study

5. Cross-sectional

6. Casual comparison

Qualitative Research Methods

2. Observational method

3. One-on-one interview

4. Focus groups