research or empirical

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

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

Home Market Research

Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

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

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

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

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

LEARN ABOUT: Behavioral Research

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

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

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

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

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

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

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

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

LEARN ABOUT: Action Research

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

LEARN MORE: Population vs Sample

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

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

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

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

LEARN ABOUT: Social Communication Questionnaire

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

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

LEARN ABOUT: 12 Best Tools for Researchers

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

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

LEARN MORE FREE TRIAL

MORE LIKE THIS

21 Best Customer Advocacy Software for Customers in 2024

Apr 19, 2024

10 Quantitative Data Analysis Software for Every Data Scientist

Apr 18, 2024

11 Best Enterprise Feedback Management Software in 2024

17 Best Online Reputation Management Software in 2024

Apr 17, 2024

University of Memphis Libraries
Research Guides

Empirical Research: Defining, Identifying, & Finding

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

Introduction
Database Tools
Search Terms
Image Descriptions

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

<< Previous: Home
Next: Identifying Empirical Research >>
Last Updated: Apr 2, 2024 11:25 AM
URL: https://libguides.memphis.edu/empirical-research

What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 35min read

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

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

What is Empirical Research?

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

Characteristics of Empirical Research

Empirical research is characterized by several key features:

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

Importance of Empirical Research

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

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

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

How to Conduct Empirical Research?

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

1. Select a Research Topic

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

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

2. Formulate Research Questions

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

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

3. Review Existing Literature

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

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

4. Define Variables

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

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

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

Empirical Research Design

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

Types of Empirical Research

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

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

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

Experimental Design

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

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

Observational Design

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

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

Survey Design

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

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

Case Study Design

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

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

Qualitative vs. Quantitative Research

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

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

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

Data Collection for Empirical Research

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

Sampling Methods

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

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

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

Data Collection Instruments

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

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

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

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

Book a Demo

Data Collection Procedures

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

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

Ethical Considerations

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

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

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

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

Empirical Research Data Analysis

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

Data Preparation

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

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

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

Descriptive Statistics

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

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

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

Inferential Statistics

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

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

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

Bar Charts and Histograms : Used to display the distribution of categorical 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

17.04.2024 | 25min read

Quota Sampling: Definition, Types, Methods, Examples

15.04.2024 | 34min read

What is Market Share? Definition, Formula, Examples

11.04.2024 | 34min read

What is Data Analysis? Definition, Tools, Examples

Penn State University Libraries

Empirical research in the social sciences and education.

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

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

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

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

Key characteristics to look for:

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

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

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

Reading and Evaluating Scholarly Materials

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

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

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: Apr 5, 2024 9:55 AM
URL: https://guides.lib.purdue.edu/research_approaches

Canvas | University | Ask a Librarian

Library Homepage
Arrendale Library

Empirical Research: Quantitative & Qualitative

Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

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

Call us at 706-776-0111

Chat with a Librarian

Send Us Email

Library Hours

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

Key characteristics of empirical research include:

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

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

Empirical Research: Qualitative vs. Quantitative

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

Academic Writer

More about Academic Writer ...

Quantitative Research

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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

Arrendale Library Piedmont University 706-776-0111

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

Empirical Research: A Comprehensive Guide for Academics

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

Why Empirical Research?

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

Qualitative and Quantitative Methods

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

Steps for Conducting Empirical Research

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

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

Advantages of Empirical Research

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

Disadvantages of Empirical Research

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

Tips for Empirical Writing

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

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

References:

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

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

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

Ethics in Science: Importance, Principles & Guidelines

Presenting research data effectively through tables and figures, you may also like, what is academic writing: tips for students, why traditional editorial process needs an upgrade, paperpal’s new ai research finder empowers authors to..., what is hedging in academic writing , how to use ai to enhance your college..., ai + human expertise – a paradigm shift..., how to use paperpal to generate emails &..., ai in education: it’s time to change the..., is it ethical to use ai-generated abstracts without..., do plagiarism checkers detect ai content.

Connelly Library

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 >>

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

PSYC 301: Intro to Research Methods

Advanced Search Strategies
Tracking the Research Process
Annotations
Article Cards
Organizing Sources
Writing an Outline
Citing Sources

Finding Empirical Research

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

Finding Empirical Research in PsycInfo

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

Slideshow showing how to find empirical research in APA PsycInfo

Video of finding empirical articles in psycinfo.

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

What is Empirical Research?

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

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

Using PsycInfo

Narrowing a Search (Canva Slideshow) Created by K Librarians
Searching with the Thesaurus and Index Terms (YouTube Video) Created by the APA
<< Previous: Home
Next: Advanced Search Strategies >>
Last Updated: Apr 12, 2024 1:36 PM
URL: https://libguides.kzoo.edu/psyc301

Ai, ethics & human agency, collaboration, information literacy, writing process, empirical research methods.

Alternative Article Title: Primary Research, Scientific Research , or Field Research .

Empirical Research may be called Primary Research, Scientific Research , or Field Research . People who conduct empirical research are typically called investigators , but they may also be called knowledge workers, scientists, empiricists, or researchers.

Empirical research is a research method that investigators use to test knowledge claims and develop new knowledge .

Empirical methods focus on observation and experimentation .

Investigators observe and conduct experiments in systematic ways

is largely determined by their rhetorical contexts. Different workplace contexts and academic disciplines have developed unique tools and techniques for gathering and interpreting information .

professions and business organizations—i.e., discourse communities , especially methodological communities.

Professions and workplaces develop unique tools and technique

Empirical research is informed by

empiricism , a philosophy that assumes knowledge is grounded in what you can see, hear, or experience
positivism , a philosophy that assumes the universe is an orderly place; a nonrandom order of the universe exists; events have causes and occur in regular patterns that can be determined through observation.

Investigators and discourse communities 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).

Textual 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).

Instead of relying on logical reasoning and Following Most contemporary empiricists would acknowledge that any act of observation and experimentation are somewhat subjective processes.

There are three major types of empirical research :

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

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).

Key Terms: positivism ; research methods ; research methodologies .

As humans, we learn about the world from experience, observation and experimentation. Even as babies we conduct informal research: what happens when we cry and complain? If we do x , does it cause y ? Over time, we invariably learn from our experience that our actions have consequences. We sharpen our abilities to identify commons patterns (e.g., whenwe write a lot, we are more creative). Invariably, as we evolve during our lives, we come to trust our experiences, our senses, and our procedural knowledge and declarative knowledge evolves.

In work and school settings, systematic engagement at efforts of observaion are called empirical or scientific research.

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.

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.

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

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

Student engrossed in reading on her laptop, surrounded by a stack of books

Academic Writing – How to Write for the Academic Community

Professional Writing – How to Write for the Professional World

Authority – How to Establish Credibility in Speech & Writing

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

More from M-W
To save this word, you'll need to log in. Log In

Definition of empirical

Did you know.

Empirical Has Roots in Latin and Greek

When empirical first appeared as an adjective in English, it meant simply “in the manner of an empiric.” In the ancient world, empirics were members of a sect of doctors who practiced medicine using treatments observed to be clinically effective, rather than treatments based on theoretical principles. This sounds all fine and good to a modern reader, but empirics were in direct opposition to Galen, the 2nd century Greek physician whose theories and practices (including the theory of bodily humors ) dominated medicine in Europe from the Middle Ages until the mid-17th century. As the underdogs in this rivalry, empirics took some reputational hits, evidenced by the use of empiric to refer to someone who disregards or deviates from the rules of science or accepted practice; to be called an empiric was sometimes like being called a quack or charlatan. Empirical can still be used critically to describe ideas and practices that rely on experience or observation alone and without due regard for system or theory. But, perhaps in a bit of a case of “the Empirics strike back,” empirical more often keeps its narrower sense, and is used positively to describe evidence and information grounded in observation and experience, or capable of being verified or disproved by observation or experiment.

existential
experiential
experimental
observational

Examples of empirical in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'empirical.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

empiric "of physicians in ancient Greece and Rome holding that treatment should be based on observation rather than theory" (going back to Middle English emperic, borrowed from early Medieval Latin empīricus, borrowed from Greek empeirikós, "based on observation (of medical treatment), experienced") + -al entry 1 — more at empiric

1576, in the meaning defined at sense 1

Phrases Containing empirical

empirical formula

Get Word of the Day delivered to your inbox!

Dictionary Entries Near empirical

Cite this entry.

“Empirical.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/empirical. Accessed 23 Apr. 2024.

Kids Definition

Kids definition of empirical, medical definition, medical definition of empirical, more from merriam-webster on empirical.

Nglish: Translation of empirical for Spanish Speakers

Britannica English: Translation of empirical for Arabic Speakers

Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free!

Play Quordle: Guess all four words in a limited number of tries. Each of your guesses must be a real 5-letter word.

Can you solve 4 words at once?

Word of the day.

See Definitions and Examples »

Get Word of the Day daily email!

Popular in Grammar & Usage

Commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, how to use accents and diacritical marks, on 'biweekly' and 'bimonthly', popular in wordplay, the words of the week - apr. 19, 10 words from taylor swift songs (merriam's version), a great big list of bread words, 9 superb owl words, 10 words for lesser-known games and sports, games & quizzes.

Play Blossom: Solve today's spelling word game by finding as many words as you can using just 7 letters. Longer words score more points.

Conceptual Research vs. Empirical Research

What's the difference.

Conceptual research and empirical research are two distinct approaches to conducting research. Conceptual research focuses on exploring and developing theories, concepts, and ideas. It involves analyzing existing literature, theories, and concepts to gain a deeper understanding of a particular topic. Conceptual research is often used in the early stages of research to generate hypotheses and develop a theoretical framework. On the other hand, empirical research involves collecting and analyzing data to test hypotheses and answer research questions. It relies on observation, measurement, and experimentation to gather evidence and draw conclusions. Empirical research is more focused on obtaining concrete and measurable results, often through surveys, experiments, or observations. Both approaches are valuable in research, with conceptual research providing a foundation for empirical research and empirical research validating or refuting conceptual theories.

Further Detail

Introduction.

Research is a fundamental aspect of any field of study, providing a systematic approach to acquiring knowledge and understanding. In the realm of research, two primary methodologies are commonly employed: conceptual research and empirical research. While both approaches aim to contribute to the body of knowledge, they differ significantly in their attributes, methodologies, and outcomes. This article aims to explore and compare the attributes of conceptual research and empirical research, shedding light on their unique characteristics and applications.

Conceptual Research

Conceptual research, also known as theoretical research, focuses on the exploration and development of theories, concepts, and ideas. It is primarily concerned with abstract and hypothetical constructs, aiming to enhance understanding and generate new insights. Conceptual research often involves a comprehensive review of existing literature, analyzing and synthesizing various theories and concepts to propose new frameworks or models.

One of the key attributes of conceptual research is its reliance on deductive reasoning. Researchers start with a set of existing theories or concepts and use logical reasoning to derive new hypotheses or frameworks. This deductive approach allows researchers to build upon existing knowledge and propose innovative ideas. Conceptual research is often exploratory in nature, seeking to expand the boundaries of knowledge and provide a foundation for further empirical investigations.

Conceptual research is particularly valuable in fields where empirical data may be limited or difficult to obtain. It allows researchers to explore complex phenomena, develop theoretical frameworks, and generate hypotheses that can later be tested through empirical research. By focusing on abstract concepts and theories, conceptual research provides a theoretical foundation for empirical investigations, guiding researchers in their quest for empirical evidence.

Furthermore, conceptual research plays a crucial role in the development of new disciplines or interdisciplinary fields. It helps establish a common language and theoretical framework, facilitating communication and collaboration among researchers from different backgrounds. By synthesizing existing knowledge and proposing new concepts, conceptual research lays the groundwork for empirical studies and contributes to the overall advancement of knowledge.

Empirical Research

Empirical research, in contrast to conceptual research, is concerned with the collection and analysis of observable data. It aims to test hypotheses, validate theories, and provide evidence-based conclusions. Empirical research relies on the systematic collection of data through various methods, such as surveys, experiments, observations, or interviews. The data collected is then analyzed using statistical or qualitative techniques to draw meaningful conclusions.

One of the primary attributes of empirical research is its inductive reasoning approach. Researchers start with specific observations or data and use them to develop general theories or conclusions. This inductive approach allows researchers to derive broader implications from specific instances, providing a basis for generalization. Empirical research is often hypothesis-driven, seeking to test and validate theories or hypotheses through the collection and analysis of data.

Empirical research is highly valued for its ability to provide concrete evidence and support or refute existing theories. It allows researchers to investigate real-world phenomena, understand cause-and-effect relationships, and make informed decisions based on empirical evidence. By relying on observable data, empirical research enhances the credibility and reliability of research findings, contributing to the overall body of knowledge in a field.

Moreover, empirical research is particularly useful in applied fields, where practical implications and real-world applications are of utmost importance. It allows researchers to evaluate the effectiveness of interventions, assess the impact of policies, or measure the outcomes of specific actions. Empirical research provides valuable insights that can inform decision-making processes, guide policy development, and drive evidence-based practices.

Comparing Conceptual Research and Empirical Research

While conceptual research and empirical research differ in their methodologies and approaches, they are both essential components of the research process. Conceptual research focuses on the development of theories and concepts, providing a theoretical foundation for empirical investigations. Empirical research, on the other hand, relies on the collection and analysis of observable data to test and validate theories.

Conceptual research is often exploratory and aims to expand the boundaries of knowledge. It is valuable in fields where empirical data may be limited or difficult to obtain. By synthesizing existing theories and proposing new frameworks, conceptual research provides a theoretical basis for empirical studies. It helps researchers develop hypotheses and guides their quest for empirical evidence.

Empirical research, on the other hand, is hypothesis-driven and seeks to provide concrete evidence and support or refute existing theories. It allows researchers to investigate real-world phenomena, understand cause-and-effect relationships, and make informed decisions based on empirical evidence. Empirical research is particularly useful in applied fields, where practical implications and real-world applications are of utmost importance.

Despite their differences, conceptual research and empirical research are not mutually exclusive. In fact, they often complement each other in the research process. Conceptual research provides the theoretical foundation and guidance for empirical investigations, while empirical research validates and refines existing theories or concepts. The iterative nature of research often involves a continuous cycle of conceptual and empirical research, with each informing and influencing the other.

Both conceptual research and empirical research contribute to the advancement of knowledge in their respective fields. Conceptual research expands theoretical frameworks, proposes new concepts, and lays the groundwork for empirical investigations. Empirical research, on the other hand, provides concrete evidence, validates theories, and informs practical applications. Together, they form a symbiotic relationship, driving progress and innovation in various disciplines.

Conceptual research and empirical research are two distinct methodologies employed in the pursuit of knowledge and understanding. While conceptual research focuses on the development of theories and concepts, empirical research relies on the collection and analysis of observable data. Both approaches have their unique attributes, methodologies, and applications.

Conceptual research plays a crucial role in expanding theoretical frameworks, proposing new concepts, and providing a foundation for empirical investigations. It is particularly valuable in fields where empirical data may be limited or difficult to obtain. On the other hand, empirical research provides concrete evidence, validates theories, and informs practical applications. It is highly valued in applied fields, where evidence-based decision-making is essential.

Despite their differences, conceptual research and empirical research are not mutually exclusive. They often work in tandem, with conceptual research guiding the development of hypotheses and theoretical frameworks, and empirical research validating and refining these theories through the collection and analysis of data. Together, they contribute to the overall advancement of knowledge and understanding in various disciplines.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Library Research Guides

Peer Review - what is it, and how do I find peer reviewed articles?

What Is Peer Review?
Types of Articles
Finding Peer-reviewed Articles

Primary Study

Empirical study.

How Do I Know It's Primary?

Ask Me for Help!

Thank you to the librarians at the Mildred E. Sawyer Library at Suffolk University Boston for agreeing to share the content of their Research Guide on this topic. Your help was invaluable!

What is a Primary Study?

When an author conducts original research in a clinic or laboratory or out in the field, and then writes a detailed article about this research, this is a "primary study." Other related terms are "original research" or "research study." This means that new data or theories discussed in the article have not been previously published.

An original research article will be written by and for experts in their fields or disciplines. They are meant to be read by others in the field. As such, the article should use precise, technical language to ensure that other researchers have an exact understanding of what was done, how to do it, and why it matters.

By contrast, a secondary article is like most of your term papers. You base your arguments and conclusions on research materials that were written by other people. You did not personally go to Glacier Bay and collect data on the number of humpback whales present in the Bay each summer season, nor did you conduct research on the number and species of fish caught in local lakes, but you based your paper on the research of others, and your paper is considered secondary. Similarly, journalism is almost always considered secondary. In order to write an article for a journal or magazine, a writer must gather information and data from others and summarize those findings in the article. However, since the journalist did not do the research, the article would be a secondary secondary resource.

What is an Empirical Study?

A professor may also use the term "empirical" to describe a study or research article. In many cases, the term empirical is used to mean the exact same thing as a "primary study" or "original research" article. However, specifically, empirical research is based on direct observations or experiments. It seeks to answer a specific question or to test a hypothesis.

Finding an empirical study can be tricky. The word empirical might be used in describing the study. In a few cases, the subject term "EMPIRICAL research" might even be assigned to a record in a database. Other terms like quantitative data might also be used in the summary. You can try searching for these words --for example, family violence and empirical-- but otherwise you will need to read and evaluate the article to decide whether that particular research article could be termed an empirical study.

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

Introduction: sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies; a statement of the problem and plan for solving the problem.
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools, in other words, how the research was conducted.
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: Finding Peer-reviewed Articles
Next: How Do I Know It's Primary? >>
Last Updated: Mar 26, 2024 9:38 AM
URL: https://uas.alaska.libguides.com/peer-review

Textbooks on Reserve
Articles/Journals
Web sites and Statistics
Mental Health This link opens in a new window
Open Educational Resources (OER)
Empirical Research
New to Our Library
Identifying Participant Variables in Experimental Psychology

Contact the Reference & Outreach Librarian

Empirical Research OR Empirical study

Go to Database Finder https://libguides.hvcc.edu/az.php

Go to All Subjects , select Social Sciences and Psychology , select APA PsycArticles

Type in your keyword

In 'Select a Field' , select MR Methodology and type in empirical study or empirical research

NOTE: You can search for empirical research articles in many databases by including the phrases ("empirical research" OR "empirical study") and limiting to peer-reviewed articles. You can also include specific types of empirical research in your search, such as ("qualitative research" OR "quantitative research").

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. This simply means that the author(s) actually carried out the study. The results are based on real life experience and original research.

How do you know if a study is empirical?

Look for a description of the research "methodology."
Look at the journal title. Empirical articles are found in journals such as: Journal of Educational Research or the Journal of Educational Psychology. You will never find empirical articles in magazines like Newsweek or Time.
Check for the page numbers. An empirical article is normally over 10 pages long not one or two pages.
Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

Title: Describes the research and has the author’s name
Abstract: A short but comprehensive summary of the research
Introduction: sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies, and develops the hypotheses for the research.
Methodology: sometimes called "methods" or "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. Empirical articles will include charts, graphs, or statistical analysis.
Discussion: sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies.
References or Works Cited or Bibliography - this section contains information about the articles and books cited in the report and should be substantial.

Searching in OneSearch

This LibGuide is maintained by:

Kisha Sawyers, Reference and Outreach Librarian |

Room MRV 140 | Tel:7317 | [email protected]

<< Previous: Open Educational Resources (OER)
Next: New to Our Library >>
Last Updated: Feb 27, 2024 8:10 AM
URL: https://libguides.hvcc.edu/psychology
Guides by Expert Librarians
Database Finder
HVCC OneSearch
Library Catalog
How to Cite Sources
DOI | PMID Lookup

Library Services

Ask a Librarian
Reserve a Study Room
Media Services
Resource Sharing
Writing & Research Help

Quick Links

Open Educational Resources
HVCC Digital Collections
Library Account Sign in
Library Faculty & Staff
Liaison Program
Connect on Social Media

A State University of New York College

What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

Implement robust data management practices to maintain the integrity and security of research data.
Transparently document data analysis procedures, including software and statistical methods used.
Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

Be aware of potential biases in the research process and take steps to minimize them.
Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

Seek peer review from experts in your field before publishing or presenting your research findings.
Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

Strive to make your research findings replicable, allowing other researchers to validate your results independently.
Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

Enhance Your Research

Collect feedback and conduct research with IdeaScale’s award-winning software

Elevate Research And Feedback With Your IdeaScale Community!

IdeaScale is an innovation management solution that inspires people to take action on their ideas. Your community’s ideas can change lives, your business and the world. Connect to the ideas that matter and start co-creating the future.

Privacy Overview

Conceptual Vs. Empirical Research: Which Is Better?

Scientific research is often divided into two classes: conceptual research and empirical research. There used to be distinct ways of doing research and a researcher would proudly claim to be one or the other, praising his method and scorning the alternative. Today the distinction is not so clear.

What is Conceptual Research?

Conceptual research focuses on the concept or theory that explains or describes the phenomenon being studied. What causes disease? How can we describe the motions of the planets? What are the building blocks of matter? The conceptual researcher sits at his desk with pen in hand and tries to solve these problems by thinking about them. He does no experiments but may make use of observations by others, since this is the mass of data that he is trying to make sense of. Until fairly recently, conceptual research methodology was considered the most honorable form of research—it required using the brain, not the hands. Researchers such as the alchemists who did experiments were considered little better than blacksmiths—“filthy empiricists.”

What is Empirical Research?

For all of their lofty status, conceptual researchers regularly produced theories that were wrong. Aristotle taught that large cannonballs fell to earth faster than small ones, and many generations of professors repeated his teachings until Galileo proved them wrong. Galileo was an empiricist of the best sort, one who performed original experiments not merely to destroy old theories but to provide the basis for new theories. A reaction against the ivory tower theoreticians culminated in those who claimed to have no use for theory, arguing that empirical acquisition of knowledge was the only way to the truth. A pure empiricist would simply graph data and see if he got a straight line relation between variables. If so, he had a good “empirical” relationship that would make useful predictions. The theory behind the correlation was irrelevant.

The Scientific Method: A Bit of Both

The modern scientific method is really a combination of empirical and conceptual research. Using known experimental data a scientist formulates a working hypothesis to explain some aspect of nature. He then performs new experiments designed to test predictions of the theory, to support it or disprove it. Einstein is often cited as an example of a conceptual researcher, but he based his theories on experimental observations and proposed experiments, real and thought, which would test his theories. On the other hand, Edison is often considered an empiricist, the “Edisonian method” being a by-word for trial and error. But Edison appreciated the work of theorists and hired some of the best. Random screening of myriad possibilities is still valuable: pharmaceutical companies looking for new drugs do this, sometimes with great success. Personally, I tend to be a semi-empiricist. In graduate school I used the Hammett linear free-energy relation (a semi-empirical equation) to gain insight into chemical transition states. So I don’t debate on “conceptual vs. empirical research.” There is a range of possibilities between both the forms, all of which have their uses.

Excellent explanations in a simple language.

Greeting from Enago Academy! Thank you for your positive comment. We are glad to know that you found our resources useful. Your feedback is very valuable to us. Happy reading!

Thanks for this article,really helpful university of zambia

Albert Einstein did theoretical work–he had no laboratory, Put simply, through new conceptual models, he re-interpreted the findings of others and expressed them mathematically.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Beyond spellcheck- How Copyediting guarantees an error-free submission

Reporting Research

Beyond Spellcheck: How copyediting guarantees error-free submission

Submitting a manuscript is a complex and often an emotional experience for researchers. Whether it’s…

Old Webinars
Webinar Mobile App

How to Find the Right Journal and Fix Your Manuscript Before Submission

Selection of right journal Meets journal standards Plagiarism free manuscripts Rated from reviewer's POV

Manuscripts & Grants

Research Aims and Objectives: The dynamic duo for successful research

Picture yourself on a road trip without a destination in mind — driving aimlessly, not…

How Academic Editors Can Enhance the Quality of Your Manuscript

Avoiding desk rejection Detecting language errors Conveying your ideas clearly Following technical requirements

Effective Data Presentation for Submission in Top-tier Journals

Importance of presenting research data effectively How to create tables and figures How to avoid…

Top 4 Guidelines for Health and Clinical Research Report

Top 10 Questions for a Complete Literature Review

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

2000+ blog articles
50+ Webinars
10+ Expert podcasts
50+ Infographics
10+ Checklists
Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What should universities' stance be on AI tools in research and academic writing?

CSUSM Home Page
Cougar Courses
Student Technology Help Desk

CSUSM University Library

Library FAQ

Research help.

Scholarworks
Special Collections
University Library
User Services
23 Articles & Journals
21 Borrowing & Circulation
4 Community, Alumni, & Retirees
15 CSUSM Library Award
2 Food & Drink
18 For Faculty
23 For Students
2 Group Study Rooms
12 Interlibrary Loan
1 Laptop Loan
1 Library Class
15 Library Databases
29 Library Reserves
47 Library Services Help
1 Lost & Found
2 Off Campus Access
52 Research Help
15 Scholarworks
4 Technical Help
7 Technology
77 User Services
2 Writing Center

FAQ Search Box

How do i know if a research article is empirical.

Empirical research draws from observed or measured phenomena and derives knowledge from actual experimentation or observation. Empirical research articles are considered original, primary research. In these types of articles, readers will generally find the following sections organized by IMRaD format (Introduction, Method, Results, and Discussion).

(I)ntroduction : Includes the research hypotheses and the literature review (current research on or related to the topic).

Introduction section of an empirical article with a callout that reads, "The introduction of the article includes the literature review and the research hypotheses for the current study."

(M)ethods : How the research was conducted, including participants, research design, and measures used.

Methods section of an empirical article with a callout box that reads, "The method section of the empirical article outlines the experimental/observational procedures employed in the study."

(R)esults : What did researchers learn from their study/experiment--this section may include statistical data and research participant quotes.

Results section of an empirical article with a callout box that reads, "The results section includes research findings depicted in the form of charts, graphs, tables or research participant quotes."

(D)iscussion : Research conclusions that state why the study was important and its impact on future research and/or practices.

Discussion section of an empirical article with a callout box that reads, "The discussion section includes research conclusions and potential impacts on future research."

Also check out:

How to find an empirical research article in PsycInfo (video).

What are empirical research articles tutorial

If you need additional assistance, please ask a librarian .

Library Services Help

Library Hours

During Library hours, library staff offer help with any general library questions, including questions about your account, how to access textbooks and films, and more.

Send an Email

Call : (760) 750-4348

Share on Facebook

Was this helpful? Yes 1 No 0

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
My Account Login
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 17 April 2024

The economic commitment of climate change

Maximilian Kotz ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
Anders Levermann ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
Leonie Wenz ORCID: orcid.org/0000-0002-8500-1568 1 , 3

Nature volume 628 , pages 551–557 ( 2024 ) Cite this article

57k Accesses

3393 Altmetric

Metrics details

Environmental economics
Environmental health
Interdisciplinary studies
Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Climate damage projections beyond annual temperature

Paul Waidelich, Fulden Batibeniz, … Sonia I. Seneviratne

Investment incentive reduced by climate damages can be restored by optimal policy

Sven N. Willner, Nicole Glanemann & Anders Levermann

Climate economics support for the UN climate targets

Martin C. Hänsel, Moritz A. Drupp, … Thomas Sterner

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References 7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs. 2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs. 2 , 18 , we use climate variables in their first-differenced form following ref. 3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs. 2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref. 19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section 1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section 2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section 2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section 3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref. 5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref. 2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs. 3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref. 2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref. 2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref. 35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section 5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs. 7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref. 15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs. 57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs. 7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section 2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, ${\bar{T}}_{x,y}$ , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, ${\widetilde{T}}_{x,y}$ :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , ${\bar{T}}_{x,m,{y}}$ is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref. 8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref. 18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs. 2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, ${\hat{T}}_{r}$ , or annual mean temperature, ${\bar{T}}_{r}$ , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref. 61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N = M = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs. 2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section 1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

Glanemann, N., Willner, S. N. & Levermann, A. Paris Climate Agreement passes the cost-benefit test. Nat. Commun. 11 , 110 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527 , 235–239 (2015).

Article ADS CAS PubMed Google Scholar

Kalkuhl, M. & Wenz, L. The impact of climate conditions on economic production. Evidence from a global panel of regions. J. Environ. Econ. Manag. 103 , 102360 (2020).

Article Google Scholar

Moore, F. C. & Diaz, D. B. Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Change 5 , 127–131 (2015).

Article ADS Google Scholar

Drouet, L., Bosetti, V. & Tavoni, M. Net economic benefits of well-below 2°C scenarios and associated uncertainties. Oxf. Open Clim. Change 2 , kgac003 (2022).

Ueckerdt, F. et al. The economically optimal warming limit of the planet. Earth Syst. Dyn. 10 , 741–763 (2019).

Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M. & Levermann, A. Day-to-day temperature variability reduces economic growth. Nat. Clim. Change 11 , 319–325 (2021).

Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601 , 223–227 (2022).

Kousky, C. Informing climate adaptation: a review of the economic costs of natural disasters. Energy Econ. 46 , 576–592 (2014).

Harlan, S. L. et al. in Climate Change and Society: Sociological Perspectives (eds Dunlap, R. E. & Brulle, R. J.) 127–163 (Oxford Univ. Press, 2015).

Bolton, P. et al. The Green Swan (BIS Books, 2020).

Alogoskoufis, S. et al. ECB Economy-wide Climate Stress Test: Methodology and Results European Central Bank, 2021).

Weber, E. U. What shapes perceptions of climate change? Wiley Interdiscip. Rev. Clim. Change 1 , 332–342 (2010).

Markowitz, E. M. & Shariff, A. F. Climate change and moral judgement. Nat. Clim. Change 2 , 243–247 (2012).

Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42 , 153–168 (2017).

Auffhammer, M., Hsiang, S. M., Schlenker, W. & Sobel, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7 , 181–198 (2013).

Kolstad, C. D. & Moore, F. C. Estimating the economic impacts of climate change using weather observations. Rev. Environ. Econ. Policy 14 , 1–24 (2020).

Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. 4 , 66–95 (2012).

Newell, R. G., Prest, B. C. & Sexton, S. E. The GDP-temperature relationship: implications for climate change damages. J. Environ. Econ. Manag. 108 , 102445 (2021).

Kikstra, J. S. et al. The social cost of carbon dioxide under climate-economy feedbacks and temperature variability. Environ. Res. Lett. 16 , 094037 (2021).

Article ADS CAS Google Scholar

Bastien-Olvera, B. & Moore, F. Persistent effect of temperature on GDP identified from lower frequency temperature variability. Environ. Res. Lett. 17 , 084038 (2022).

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Byers, E. et al. AR6 scenarios database. Zenodo https://zenodo.org/records/7197970 (2022).

Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557 , 549–553 (2018).

Kotz, M., Wenz, L. & Levermann, A. Footprint of greenhouse forcing in daily temperature variability. Proc. Natl Acad. Sci. 118 , e2103294118 (2021).

Article CAS PubMed PubMed Central Google Scholar

Myhre, G. et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9 , 16063 (2019).

Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470 , 378–381 (2011).

England, M. R., Eisenman, I., Lutsko, N. J. & Wagner, T. J. The recent emergence of Arctic Amplification. Geophys. Res. Lett. 48 , e2021GL094086 (2021).

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5 , 560–564 (2015).

Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7 , 423–427 (2017).

Callahan, C. W. & Mankin, J. S. Globally unequal effect of extreme heat on economic growth. Sci. Adv. 8 , eadd3726 (2022).

Diffenbaugh, N. S. & Burke, M. Global warming has increased global economic inequality. Proc. Natl Acad. Sci. 116 , 9808–9813 (2019).

Callahan, C. W. & Mankin, J. S. National attribution of historical climate damages. Clim. Change 172 , 40 (2022).

Burke, M. & Tanutama, V. Climatic constraints on aggregate economic output. National Bureau of Economic Research, Working Paper 25779. https://doi.org/10.3386/w25779 (2019).

Kahn, M. E. et al. Long-term macroeconomic effects of climate change: a cross-country analysis. Energy Econ. 104 , 105624 (2021).

Desmet, K. et al. Evaluating the economic cost of coastal flooding. National Bureau of Economic Research, Working Paper 24918. https://doi.org/10.3386/w24918 (2018).

Hsiang, S. M. & Jina, A. S. The causal effect of environmental catastrophe on long-run economic growth: evidence from 6,700 cyclones. National Bureau of Economic Research, Working Paper 20352. https://doi.org/10.3386/w2035 (2014).

Ritchie, P. D. et al. Shifts in national land use and food production in Great Britain after a climate tipping point. Nat. Food 1 , 76–83 (2020).

Dietz, S., Rising, J., Stoerk, T. & Wagner, G. Economic impacts of tipping points in the climate system. Proc. Natl Acad. Sci. 118 , e2103081118 (2021).

Bastien-Olvera, B. A. & Moore, F. C. Use and non-use value of nature and the social cost of carbon. Nat. Sustain. 4 , 101–108 (2021).

Carleton, T. et al. Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. Q. J. Econ. 137 , 2037–2105 (2022).

Bastien-Olvera, B. A. et al. Unequal climate impacts on global values of natural capital. Nature 625 , 722–727 (2024).

Malik, A. et al. Impacts of climate change and extreme weather on food supply chains cascade across sectors and regions in Australia. Nat. Food 3 , 631–643 (2022).

Article ADS PubMed Google Scholar

Kuhla, K., Willner, S. N., Otto, C., Geiger, T. & Levermann, A. Ripple resonance amplifies economic welfare loss from weather extremes. Environ. Res. Lett. 16 , 114010 (2021).

Schleypen, J. R., Mistry, M. N., Saeed, F. & Dasgupta, S. Sharing the burden: quantifying climate change spillovers in the European Union under the Paris Agreement. Spat. Econ. Anal. 17 , 67–82 (2022).

Dasgupta, S., Bosello, F., De Cian, E. & Mistry, M. Global temperature effects on economic activity and equity: a spatial analysis. European Institute on Economics and the Environment, Working Paper 22-1 (2022).

Neal, T. The importance of external weather effects in projecting the macroeconomic impacts of climate change. UNSW Economics Working Paper 2023-09 (2023).

Deryugina, T. & Hsiang, S. M. Does the environment still matter? Daily temperature and income in the United States. National Bureau of Economic Research, Working Paper 20750. https://doi.org/10.3386/w20750 (2014).

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020).

Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12 , 2097–2120 (2020).

Adler, R. et al. The New Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly Analysis Product 1072–1084 (University of Maryland, 2016).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12 , 3055–3070 (2019).

Wenz, L., Carr, R. D., Kögel, N., Kotz, M. & Kalkuhl, M. DOSE – global data set of reported sub-national economic output. Sci. Data 10 , 425 (2023).

Article PubMed PubMed Central Google Scholar

Gennaioli, N., La Porta, R., Lopez De Silanes, F. & Shleifer, A. Growth in regions. J. Econ. Growth 19 , 259–309 (2014).

Board of Governors of the Federal Reserve System (US). U.S. dollars to euro spot exchange rate. https://fred.stlouisfed.org/series/AEXUSEU (2022).

World Bank. GDP deflator. https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS (2022).

Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11 , 084003 (2016).

Murakami, D. & Yamagata, Y. Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling. Sustainability 11 , 2106 (2019).

Koch, J. & Leimbach, M. Update of SSP GDP projections: capturing recent changes in national accounting, PPP conversion and Covid 19 impacts. Ecol. Econ. 206 (2023).

Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353 , aad9837 (2016).

Article PubMed Google Scholar

Bergé, L. Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm. DEM Discussion Paper Series 18-13 (2018).

Kalkuhl, M., Kotz, M. & Wenz, L. DOSE - The MCC-PIK Database Of Subnational Economic output. Zenodo https://zenodo.org/doi/10.5281/zenodo.4681305 (2021).

Kotz, M., Wenz, L. & Levermann, A. Data and code for “The economic commitment of climate change”. Zenodo https://zenodo.org/doi/10.5281/zenodo.10562951 (2024).

Dasgupta, S. et al. Effects of climate change on combined labour productivity and supply: an empirical, multi-model study. Lancet Planet. Health 5 , e455–e465 (2021).

Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3 , 497–501 (2013).

Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. 114 , 9326–9331 (2017).

Wheeler, T. R., Craufurd, P. Q., Ellis, R. H., Porter, J. R. & Prasad, P. V. Temperature variability and the yield of annual crops. Agric. Ecosyst. Environ. 82 , 159–167 (2000).

Rowhani, P., Lobell, D. B., Linderman, M. & Ramankutty, N. Climate variability and crop production in Tanzania. Agric. For. Meteorol. 151 , 449–460 (2011).

Ceglar, A., Toreti, A., Lecerf, R., Van der Velde, M. & Dentener, F. Impact of meteorological drivers on regional inter-annual crop yield variability in France. Agric. For. Meteorol. 216 , 58–67 (2016).

Shi, L., Kloog, I., Zanobetti, A., Liu, P. & Schwartz, J. D. Impacts of temperature and its variability on mortality in New England. Nat. Clim. Change 5 , 988–991 (2015).

Xue, T., Zhu, T., Zheng, Y. & Zhang, Q. Declines in mental health associated with air pollution and temperature variability in China. Nat. Commun. 10 , 2165 (2019).

Article ADS PubMed PubMed Central Google Scholar

Liang, X.-Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. 114 , E2285–E2292 (2017).

Desbureaux, S. & Rodella, A.-S. Drought in the city: the economic impact of water scarcity in Latin American metropolitan areas. World Dev. 114 , 13–27 (2019).

Damania, R. The economics of water scarcity and variability. Oxf. Rev. Econ. Policy 36 , 24–44 (2020).

Davenport, F. V., Burke, M. & Diffenbaugh, N. S. Contribution of historical precipitation change to US flood damages. Proc. Natl Acad. Sci. 118 , e2017524118 (2021).

Dave, R., Subramanian, S. S. & Bhatia, U. Extreme precipitation induced concurrent events trigger prolonged disruptions in regional road networks. Environ. Res. Lett. 16 , 104050 (2021).

Download references

Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

Author information

Authors and affiliations.

Research Domain IV, Research Domain IV, Potsdam Institute for Climate Impact Research, Potsdam, Germany

Maximilian Kotz, Anders Levermann & Leonie Wenz

Institute of Physics, Potsdam University, Potsdam, Germany

Maximilian Kotz & Anders Levermann

Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

Leonie Wenz

You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

Corresponding author

Correspondence to Leonie Wenz .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Xin-Zhong Liang, Chad Thackeray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref. 2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

Supplementary information

Supplementary information, peer review file, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

Download citation

Received : 25 January 2023

Accepted : 21 February 2024

Published : 17 April 2024

Issue Date : 18 April 2024

DOI : https://doi.org/10.1038/s41586-024-07219-0

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods

Affiliations.

1 Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia. Electronic address: [email protected].
2 Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia. Electronic address: [email protected].
3 Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia. Electronic address: [email protected].
4 Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia. Electronic address: [email protected].
5 Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia. Electronic address: [email protected].
PMID: 38564959
DOI: 10.1016/j.ijmedinf.2024.105417

Objective: With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance.

Materials and methods: We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations.

Results: Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI.

Discussion: Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation.

Conclusion: This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.

Keywords: Artificial intelligence; Healthcare; Public and patient involvement; Scoping review.

Publication types

Artificial Intelligence*
Delivery of Health Care*
Health Facilities

Entrepreneurship and High-Quality Development of Enterprises—Empirical Research Based on Chinese-Listed Companies

Published: 20 April 2024

Cite this article

Tannek Marcel 1 ,
Zhang Zenglian 2 ,
Obina Ayele Reve Yanick 1 &
Bezafy Paulin 3

Explore all metrics

This paper deeply discusses the far-reaching influence, function, and internal mechanisms of entrepreneurship in promoting the development of high-quality economies by taking a-share listed companies in Chongqing and Chengdu from 2015 to 2022. In order to quantify the impact of entrepreneurship, this paper adopts a comprehensive index, which is constructed by the principal component analysis method and supplemented by total factor productivity as a strict robustness test. These paper’s findings emphasize the key role of entrepreneurship in promoting China’s rapid economic growth and show that entrepreneurs can realize the extraordinary development of enterprises by using internal control and directing a large amount of investments to R&D. This could lay the foundation for high-quality growth, which is becoming more and more important in the face of economic uncertainties. Our research is an indispensable resource for policymakers and entrepreneurs in China, providing them with valuable insights and strategies to meet the complex challenges in the process of pursuing high-quality economic development. In the changing economic environment, a subtle understanding the role of entrepreneurs could promote future economic growth and innovation of China.

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

Access this article

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data Availability

Not applicable.

Arteaga, R., & Hyland, J. (2013). Pivot: How top entrepreneurs adapt and change course to find ultimate success. John Wiley & Sons.

Ban, Q. (2022). The quality of corporate social responsibility information disclosure and enterprise innovation: Evidence from Chinese listed companies. Sustainability, 15 (1), 238. https://doi.org/10.3390/su15010238

Article Google Scholar

Baumgartner, R. J., & Rauter, R. (2017). Strategic perspectives of corporate sustainability management to develop a sustainable organization. Journal of Cleaner Production, 140 , 81–92. https://doi.org/10.1016/j.jclepro.2016.04.146

Bilal, A. R., & Fatima, T. (2022). Deliberate practice and individual entrepreneurial orientation training retention: A multi-wave field experiment. European Journal of Work and Organizational Psychology, 31 (3), 352–366. https://doi.org/10.1080/1359432X.2021.1989675

Brixy, U., Sternberg, R., & Stüber, H. (2012). The selectiveness of the entrepreneurial process. Journal of Small Business Management, 50 (1), 105–131.

Brown, R., & Mason, C. (2017). Looking inside the spiky bits: A critical review and conceptualisation of entrepreneurial ecosystems. Small Business Economics, 49 , 11–30.

Chen, Y., Zhu, M., Lu, J., Zhou, Q., & Ma, W. (2020). Evaluation of ecological city and analysis of obstacle factors under the background of high-quality development: Taking cities in the Yellow River Basin as examples. Ecological Indicators, 118 , 106771. https://doi.org/10.1016/j.ecolind.2020.106771

Cui, T., & Zhang, J. (2018). Bibliometric and review of the research on circular economy through the evolution of Chinese public policy. Scientometrics, 116 (2), 1013–1037. https://doi.org/10.1007/s11192-018-2782-y

Dicke, T., Parker, P. D., Marsh, H. W., Kunter, M., Schmeck, A., & Leutner, D. (2014). Self-efficacy in classroom management, classroom disturbances, and emotional exhaustion: A moderated mediation analysis of teacher candidates. Journal of Educational Psychology, 106 (2), 569.

Dong, Z., & Zhang, Z. (2022). Does the business environment improve the sustainable development of enterprises? Sustainability, 14 (20), 13499. https://doi.org/10.3390/su142013499

Fan, F., Lian, H., & Wang, S. (2020). Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities. Growth and Change, 51 (1), 440–463. https://doi.org/10.1111/grow.12346

Fang, Z., Yang, C., & Song, X. (2022). Construction of influencing factor model for high-quality green development of Chinese industrial enterprises. Frontiers in Environmental Science, 10 , 1006224. https://doi.org/10.3389/fenvs.2022.1006224

Flynn, B. B., Schroeder, R. G., & Sakakibara, S. (1994). A framework for quality management research and an associated measurement instrument. Journal of Operations Management, 11 (4), 339–366. https://doi.org/10.1016/S0272-6963(97)90004-8

Fuertes-Callen, Y., & Cuellar-Fernandez, B. (2019). Inter-Relationship between Firm Growth and Profitability in a Context of Economic Crisis. https://doi.org/10.3846/jbem.2019.6928

Gelard, P., & Saleh, K. E. (2011). Impact of some contextual factors on entrepreneurial intention of university students. African Journal of Business Management, 5 (26), 10707. https://doi.org/10.1016/S0272-6963(97)90004-8

Gu, W., Wang, J., Hua, X., & Liu, Z. (2021). Entrepreneurship and high-quality economic development: Based on the triple bottom line of sustainable development. International Entrepreneurship and Management Journal, 17 , 1–27. https://doi.org/10.1007/s11365-020-00684-9

He, C., Lu, J., & Qian, H. (2019). Entrepreneurship in China. Small Business Economics, 52 , 563–572.

Hechavarría, D. M., Terjesen, S. A., Ingram, A. E., Renko, M., Justo, R., & Elam, A. (2017). Taking care of business: The impact of culture and gender on entrepreneurs’ blended value creation goals. Small Business Economics, 48 , 225–257. https://doi.org/10.1007/s11187-016-9747-4

Hu, S., & Yang, H. (2022). The correlation between enterprise internal control quality and research and development investment intensity. Computational Intelligence and Neuroscience . https://doi.org/10.1155/2022/1788142

Huang, Y., An, L., Wang, J., Chen, Y., Wang, S., & Wang, P. (2021). The role of entrepreneurship policy in college students’ entrepreneurial intention: The intermediary role of entrepreneurial practice and entrepreneurial spirit. Frontiers in Psychology, 12 , 585698. https://doi.org/10.3389/fpsyg.2021.585698

Kandade, K., Samara, G., Parada, M. J., & Dawson, A. (2021). From family successors to successful business leaders: A qualitative study of how high-quality relationships develop in family businesses. Journal of Family Business Strategy, 12 (2), 100334. https://doi.org/10.1016/j.jfbs.2019.100334

Kasseeah, H. (2016). Investigating the impact of entrepreneurship on economic development: A regional analysis. Journal of Small Business and Enterprise Development, 23 (3), 896–916. https://doi.org/10.1108/JSBED-09-2015-0130

Luo, G., Guo, J., Yang, F., & Wang, C. (2023). Environmental regulation, green innovation and high-quality development of enterprise: Evidence from China. Journal of Cleaner Production, 418 , 138112. https://doi.org/10.1016/j.jclepro.2023.138112

Malecki, E. J. (2018). Entrepreneurship and entrepreneurial ecosystems. Geography Compass, 12 (3), e12359. https://doi.org/10.1111/gec3.12359

Manisera, M., van der Kooij, A. J., & Dusseldorp, E. (2010). Identifying the component structure of satisfaction scales by nonlinear principal components analysis. Quality Technology and Quantitative Management, 7 (2), 97–115. https://doi.org/10.1080/16843703.2010.11673222

Mays, N., Pope, C., & Popay, J. (2005). Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field. Journal of Health Services Research & Policy, 10 (1_suppl), 6–20. https://doi.org/10.1258/1355819054308576

McMullen, J. S., & Dimov, D. (2013). Time and the entrepreneurial journey: The problems and promise of studying entrepreneurship as a process. Journal of Management Studies, 50 (8), 1481–1512.

Mei, H., Ma, Z., Jiao, S., Chen, X., Lv, X., & Zhan, Z. (2017). The sustainable personality in entrepreneurship: The relationship between big six personality, entrepreneurial self-efficacy, and entrepreneurial intention in the Chinese context. Sustainability, 9 (9), 1649. https://doi.org/10.3390/su9091649

Padilla-Angulo, L. (2019). Student associations and entrepreneurial intentions. Studies in Higher Education, 44 (1), 45–58. https://doi.org/10.1080/03075079.2017.1336215

Pan, W., Wang, J., Lu, Z., Liu, Y., & Li, Y. (2021). High-quality development in China: Measurement system, spatial pattern, and improvement paths. Habitat International, 118 , 102458. https://doi.org/10.1016/j.habitatint.2021.102458

Peng, B., Sheng, X., & Wei, G. (2020). Does environmental protection promote economic development? From the perspective of coupling coordination between environmental protection and economic development. Environmental Science and Pollution Research, 27 , 39135–39148.

Phillips, W., Lee, H., Ghobadian, A., O’regan, N., & James, P. (2015). Social innovation and social entrepreneurship: A systematic review. Group & Organization Management, 40 (3), 428–461.

Reeves, M., & Deimler, M. (2012). Adaptability: The new competitive advantage. Own the Future: 50 Ways to Win from the Boston Consulting Group, 19–26. https://doi.org/10.1002/9781119204084.ch2 .

Rusu, V. D., & Roman, A. (2021). The role of entrepreneurial performance in supporting economic development of countries: An empirical approach. Entrepreneurship Research Journal, 0 , 20200445. https://doi.org/10.1515/erj-2020-0445

Salimath, M. S., & Cullen, J. B. (2010). International Journal of Organizational Analysis, 18 (3), 358–385. https://doi.org/10.1108/19348831011062175

Schmitz, A., Urbano, D., Guerrero, M., & Dandolini, G. A. (2017). Activities related to innovation and entrepreneurship in the academic setting: A literature review. Entrepreneurial Universities: Exploring the Academic and Innovative Dimensions of Entrepreneurship in Higher Education , 1–17.

Schot, J., & Steinmueller, W. E. (2018). Three frames for innovation policy: R&D, systems of innovation and transformative change. Research Policy, 47 (9), 1554–1567. https://doi.org/10.1016/j.respol.2018.08.011

Sun, X., Fang, S., & Zhang, S. (2021). High-quality economic development in Huaihe economic zone level measurement and evaluation. Journal of Mathematics, 2021 , 1–16. https://doi.org/10.1155/2021/6615884

Wang, K.-L., Sun, T.-T., & Xu, R.-Y. (2023). The impact of artificial intelligence on total factor productivity: Empirical evidence from China’s manufacturing enterprises. Economic Change and Restructuring, 56 (2), 1113–1146. https://doi.org/10.1007/s10644-022-09467-4

Xue, Y., Jiang, C., Guo, Y., Liu, J., Wu, H., & Hao, Y. (2022). Corporate social responsibility and high-quality development: Do green innovation, environmental investment and corporate governance matter? Emerging Markets Finance and Trade, 58 (11), 3191–3214. https://doi.org/10.1080/1540496X.2022.2034616

Yuan, B., & Cao, X. (2022). Do corporate social responsibility practices contribute to green innovation? The mediating role of green dynamic capability. Technology in Society, 68 , 101868. https://doi.org/10.1016/j.techsoc.2022.101868

Zheng, W., & Fen, Y. (2023). The digital economy and the green and high-quality development of the industry—A study on the mechanism of action and regional heterogeneity. Environmental Science and Pollution Research, 30 (19), 55846–55863.

Download references

Author information

Authors and affiliations.

Department of Economics and Management, University of Science and Technology Beijing, Beijing, China

Tannek Marcel & Obina Ayele Reve Yanick

Department of Economics and Management, Center for Appearance Research, University of Science and Technology Beijing, Beijing, China

Zhang Zenglian

Department of Economics and Management, Universite d’Antsiranana, Antsiranana, Madagascar

Bezafy Paulin

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tannek Marcel .

Ethics declarations

Competing interest.

The authors declare no competing interests.

Additional information

Publisher's note.

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

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

Marcel, T., Zenglian, Z., Yanick, O.A.R. et al. Entrepreneurship and High-Quality Development of Enterprises—Empirical Research Based on Chinese-Listed Companies. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01922-z

Download citation

Received : 28 September 2023

Accepted : 18 March 2024

Published : 20 April 2024

DOI : https://doi.org/10.1007/s13132-024-01922-z

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

Entrepreneurship
High-quality development
Find a journal
Publish with us
Track your research

COMMENTS

Empirical research
A scientist gathering data for her research. Empirical research is research using empirical evidence. It is also a way of gaining knowledge by means of direct and indirect observation or experience. Empiricism values some research more than other kinds. Empirical evidence (the record of one's direct observations or experiences) can be analyzed ...
Empirical Research: Definition, Methods, Types and Examples
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.
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: 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 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).
What is Empirical Research? Definition, Methods, Examples
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.
Empirical Research in the Social Sciences and Education
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."
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: Quantitative & Qualitative
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;
Empirical Research
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: A Comprehensive Guide for Academics
In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7. Define Your Objectives: When you write about your research, start by making your goals clear.
Experimental vs Empirical: Differences And Uses For Each One
Empirical research can take many forms, including surveys, case studies, field research, and experiments. It is commonly used in the social sciences, such as psychology, sociology, and anthropology, as well as in business and economics. Empirical research is often contrasted with theoretical research, which seeks to develop new theories and ...
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."
Finding 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 ...
Empirical Research Methods
People who conduct empirical research are typically called investigators, but they may also be called knowledge workers, scientists, empiricists, or researchers. —. Empirical research is a research method that investigators use to test knowledge claims and develop new knowledge. Empirical methods focus on observation and experimentation.
City University of Seattle Library: Identifying Empirical Research
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 Definition & Meaning
empirical: [adjective] originating in or based on observation or experience.
Conceptual Research vs. Empirical Research
Conceptual research focuses on the development of theories and concepts, providing a theoretical foundation for empirical investigations. Empirical research, on the other hand, relies on the collection and analysis of observable data to test and validate theories. Conceptual research is often exploratory and aims to expand the boundaries of ...
What is a Primary or Empirical Study?
However, specifically, empirical research is based on direct observations or experiments. It seeks to answer a specific question or to test a hypothesis. Finding an empirical study can be tricky. The word empirical might be used in describing the study. In a few cases, the subject term "EMPIRICAL research" might even be assigned to a record in ...
Empirical Research
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. This simply means that the author (s) actually carried out the study. The results are based on real life experience and original research.
What is Research? Definition, Types, Methods and Process
Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. Learn more about types of research, processes, and methods with best practices. ... Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other ...
Conceptual Vs. Empirical Research: Which Is Better?
The modern scientific method is really a combination of empirical and conceptual research. Using known experimental data a scientist formulates a working hypothesis to explain some aspect of nature. He then performs new experiments designed to test predictions of the theory, to support it or disprove it. Einstein is often cited as an example of ...
How do I know if a research article is empirical?
Empirical research draws from observed or measured phenomena and derives knowledge from actual experimentation or observation. Empirical research articles are considered original, primary research. In these types of articles, readers will generally find the following sections organized by IMRaD format (Introduction, Method, Results, and ...
The economic commitment of climate change
Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons1-6. Here we use recent empirical ...
Facilitating public involvement in research about healthcare AI: A
Objective: With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance.
Entrepreneurship and High-Quality Development of Enterprises—Empirical
To validate this hypothesis, the paper will likely employ empirical research methods, such as surveys, case studies, or statistical analyses (Sun et al., 2021). It may collect data from a sample of Chinese list company led by entrepreneurs and assess the relationship between the quality of internal control practices and the corresponding high ...
Sustainability
In terms of research samples and methods, this paper selects Chinese listed companies as research objects, and in the future, it can focus on specific industries (e.g., chemical industry, iron and steel industry, etc.) to carry out empirical evidence or case studies, to obtain more typical and targeted research results.
Forecast of the number of nursing beds per 1000 older people from 2023
This study is designed as empirical quantitative research. Methods. Data from the National Bureau of Statistics and the Ministry of Civil Affairs regarding older adults (aged≥60) from 2000 to 2022 and nursing beds from 1978 to 2022 were analysed. The differential autoregressive integrated moving averages model and Monte Carlo simulation were ...
Turning Customers into Citizens: The Impact of Online Experiential
This research aims to understand customer motivations to display customer citizenship behaviour in the emerging market setting of Egypt. It develops and empirically tests the multidimensional perspective of online experiential value (grounded in the theory of consumption value) and customer citizenship behaviour to determine customer motivations to become company citizens on social media brand ...

Empirical Research: Definition, Methods, Types and Examples

Empirical research: Definition

Types and methodologies of empirical research

MORE LIKE THIS

21 Best Customer Advocacy Software for Customers in 2024

10 Quantitative Data Analysis Software for Every Data Scientist

11 Best Enterprise Feedback Management Software in 2024

17 Best Online Reputation Management Software in 2024

Other categories

Empirical Research: Defining, Identifying, & Finding

Characteristics of Empirical Research

Characteristics of Quantitative Research

Characteristics of Qualitative Research

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

Research: Overview & Approaches

Introduction to Empirical Research

Your Librarian

Empirical Research: Quantitative & Qualitative

Introduction: What is Empirical Research?

Quantitative Research

Qualitative Research

Empirical Research: A Comprehensive Guide for Academics

Why Empirical Research?

Qualitative and Quantitative Methods

Steps for Conducting Empirical Research

Advantages of Empirical Research

Disadvantages of Empirical Research

Tips for Empirical Writing

Related Reads:

Ethics in Science: Importance, Principles & Guidelines

Qualitative and Quantitative Research

PSYC 301: Intro to Research Methods

Finding Empirical Research

Finding Empirical Research in PsycInfo

Slideshow showing how to find empirical research in APA PsycInfo

What is Empirical Research?

Using PsycInfo

Table of Contents

Empirical Research is a Rhetorical Practice