empirical studies of research

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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

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

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

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

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

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

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

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Types and methodologies of empirical research

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

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Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

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

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

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

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

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

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

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

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Empirical research 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
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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.)
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Empirical Research: Quantitative & Qualitative

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Introduction: What is Empirical Research?

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

Key characteristics of empirical research include:

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

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

Empirical Research: Qualitative vs. Quantitative

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

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Quantitative Research

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research 

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

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Introduction to Empirical Research

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

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• 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.

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Empirical Research in the Social Sciences and Education

What is empirical research.

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

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

Key characteristics to look for:

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

Layout of Empirical Articles

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

• Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework 
• Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
• Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
• Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include  all  undergraduates. 
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• Last Updated: Nov 8, 2023 4:19 PM
• URL: https://libguides.stthomas.edu/empiricalresearcheducation

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

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

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

What is Empirical Research? 

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

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

Characteristics of Empirical Research

• Research Questions

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

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

• Definition of the Research Variables

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

• Description of the Research Methodology

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

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

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

Uses of Empirical Research

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

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

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

The Empirical Research Cycle

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

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

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

This information is useful for further research. 

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

Examples of Empirical Research 

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

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

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

Empirical Research Data Collection Methods

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

The following are common methods of gathering data in empirical research

• Survey/ Questionnaire

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

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

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

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

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

• Observation

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

How to collect Empirical Research Data with Questionnaire

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

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

Sign in to Formplus

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

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

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

Edit Form Title

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

Edit Form  

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

Customize Form

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

• Share your Form Link with Respondents

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

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

Empirical vs Non-Empirical Research

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

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

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

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

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

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

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

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

Advantages of Empirical Research 

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

Disadvantages of Empirical Research 

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

Conclusion 

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

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

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

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

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

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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?

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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?

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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.

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09.04.2024 | 29min read

What is a Confidence Interval and How to Calculate It?

05.04.2024 | 27min read

What is Field Research? Definition, Types, Methods, Examples

03.04.2024 | 29min read

What is Cluster Sampling? Definition, Methods, Examples

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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. 

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

Key characteristics to look for:

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

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

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

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

Using PsycInfo

• Narrowing a Search (Canva Slideshow) Created by K Librarians
• Searching with the Thesaurus and Index Terms (YouTube Video) Created by the APA
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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  

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Related Reads:

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

Ethics in Science: Importance, Principles & Guidelines 

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• 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. 

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

Key characteristics to look for:

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

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

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

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Scientism and Education pp 59–86 Cite as

Empirical Research in Education

Assumptions and Problems

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In the previous chapters we have reviewed the history of social science research and introduced some of the basic principles on which empirical research, or LPE, in education is based. In this chapter we turn our attention toward identifying how the principles of logical positivism, when applied to education research, are ineffective for strengthening the discipline.

In the following sections, we address some of the most problematic assumptions involved in carrying out empirical research in education and grapple with several related problems. Some of the major assumptions in social science research that promote positivistic or scientific principles in educational research include the following claims that we deconstruct within the course of our discussion:

Educational researchers, like physical scientists, are detached from their objects of study in that their personal preferences and biases are excluded from their subject matter, observations, and attending analyses.

Investigations of educational phenomena can be conducted in a value-neutral fashion, with the researcher eliminating all personal bias and preconceptions and employing language that expresses objectivity. In other words, there is objectivity and conceptual clarity in describing the studied phenomena within genuine scientific inquiry.

Educational research, like the physical sciences is nomothetic – that is, it is possible to extrapolate from educational research data laws that apply generally across numerous classroom and schooling contexts. In education, this assumption is particularly crucial since the search for the holy grail of some universal, but of course entirely illusive, instructional design drives much of the empirical investigation within the field. Two researchers working in different contexts who employ the same experimental method ought to arrive at the same conclusion. As we demonstrate in this chapter, within education this outcome is simply not the case.

We will demonstrate that each of these scientific principles, or assumptions, is fundamentally flawed when applied to educational research. Hence, education research is once again unable to meet the minimal standards of meaningful scientific inquiry. Later in this chapter we will also discuss the conceptual confusions that impact negatively on education. Finally, we examine how an implicit commitment to the direct reference theory of language, and the related search for conceptual certainty, leads to ontological errors about certain education concepts and how these errors affect student academic experience.

• Empirical Research
• Academic Achievement
• Educational Research
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• Elsevier - PMC COVID-19 Collection

Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19

Alba mendez-brito.

a Evidence-Based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, Berlin 13353, Germany

b Institute of Tropical Medicine and International Health, Charité University Berlin, Campus Virchow-Klinikum, Augustenburger Platz 1, Berlin 13353, Germany

Charbel El Bcheraoui

Francisco pozo-martin, associated data.

To evaluate which non-pharmaceutical interventions (NPIs) have been more and less effective in controlling the COVID-19 pandemic.

We performed a systematic review of published and unpublished empirical studies, either observational or interventional, analysing the comparative effectiveness of NPIs against the COVID-19 pandemic. We searched Embase/Medline and medRxiv to identify the relevant literature.

We identified 34 studies. During the first wave of the COVID-19 pandemic, school closing was the most effective NPI, followed by workplace closing, business and venue closing and public event bans. Public information campaigns and mask wearing requirements were also effective in controlling the pandemic while being less disruptive for the population than other NPIs. There was no evidence on the effectiveness of public transport closure, testing and contact tracing strategies and quarantining or isolation of individuals. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, while general stringency of the NPIs was not.

Conclusions

In this systematic review, we found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Continuous monitoring of NPIs effectiveness is needed in order to adapt decision making.

Introduction

In December 2019, a pneumonia-like disease caused an outbreak in the city of Wuhan, China. 1 This disease, later named COVID-19, spread globally and was declared a pandemic in March 2020 by the World Health Organisation. By April 2021 it has already affected around 145 million people and resulted in more than three million deaths globally. 2 Until effective treatments are available and vaccines are extensively accessible and administered, governments rely on non-pharmaceutical interventions (NPIs) to control the epidemic. The positive effects of implementing NPIs in controlling the COVID-19 pandemic have been widely studied both at the national 3 , 4 , 5 and the international level. 6 However, due to the high social and economic costs of many of the interventions implemented, it is essential to understand their individual effectiveness to optimize implementation and lifting strategies. 7 , 8 A wide range of responses has been implemented worldwide, relying on previous knowledge of NPIs in controlling other epidemics. 9 Several intervention types have been implemented, including containment measures such as domestic or international travel bans, individual protection measures like mask wearing requirements, social distancing measures such as school closing and gathering bans and health system measures like testing and contact tracing strategies. Evidence on the effectiveness of NPIs is largely based on mathematical modeling, with a limited number of empirical studies, either observational or interventional, exploring this topic. The assessment of empirical studies provides real world effectiveness estimations that do not rely strongly on assumptions as do simulations in modeling. In this review, we summarize the current evidence from empirical studies on the comparative effectiveness of NPIs that have been implemented worldwide to control the current COVID-19 pandemic.

Methodology

In this review we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 10 statement. We searched Embase (including Medline resources) and MedRxiv for published literature and preprints, respectively. We restricted the search to articles available in English from January 1, 2020. The search was conducted on March 4 2021, for Embase and on March 9 for MedRxiv. The search strategies used for both databases are available in Annex 1.

Studies were included in the review if they:

• • Assessed NPI effectiveness only in the context of COVID-19.
• • Were either observational or interventional (i.e. quasi-experimental or experimental) studies of empirical data.
• • Included at least one of the following NPIs, as described and categorised in the Oxford COVID-19 Government Response Tracker (OxCGRT): 11 school closing, workplace closing, public event cancelation, social gathering restrictions, public transport closure, stay-at-home requirements, internal movement restrictions, international travel restrictions, public information campaigns, testing policies, contact tracing policies and facial covering policies.
• • Compared the effectiveness of at least two NPIs.
• • Analysed NPI effectiveness in the general population of any geographical area.
• • Measured any health outcome.

Studies were excluded from the review if they:

• • Were based on forecasts or simulations.
• • Did not assess the direct link between NPIs and the health outcome (for example, if the link was based on mobility).
• • Analysed the impact of adherence or compliance to NPIs.
• • Did not pre-specify the NPIs explored before the analysis (for example, breaking point analysis of epidemic curves were excluded).

To perform the quality assessment of the studies, we used a risk of bias tool based on a bibliometric review of ecological studies 12 and previously used in two published systematic reviews. 13 , 14 The tool assesses the study design, statistical methodology and quality of reporting. We added one question to the tool to expand the methodological assessment of the studies included. The risk of bias tool checklist and the final risk of bias rating of the studies are available in Annex 2.

One reviewer (AMB) screened the records, selected the studies for the review, extracted the data and assessed the risk of bias. A second reviewer (FPM) screened ten percent of the total records, all the records that were selected by abstract, and verified risk of bias judgments.

34 studies were included in the review, from which 28 have been published, one of them in a journal without peer review, and six were preprints. The PRISMA diagram flow is presented in Fig. 1 . An overview of the characteristics of all studies included in the review is provided in Annex 3.

PRISMA flow diagram for the selection of studies.

Methodological characteristics of the studies

Table 1 presents a summary of the setting, outcomes and NPIs assessed in the 34 studies.

Setting, outcomes assessed, and NPIs included in the studies.

Study type, timeframe and geographical scope

All studies identified were ecological studies with data aggregated at population level. While most of the studies analysed country level data, some included more granular analysis at regional, 15 , 16 or city level. 17 Most of the studies were based on data from the first wave of the pandemic. Only Wibbens et al 18 assessed the impact of NPIs until November 2020 and Pozo-Martin et al. 19 performed a first analysis of the initial phase of the pandemic and a second from October until December 2020. Zhang et al. 20 analysed another relatively long study period until August, and several authors performed an analysis until July 2020. 21 , 22 , 23 Some authors standardised the start and/or end of the study period in order to be able to compare the effectiveness of NPIs across units of analysis at similar stages of the epidemic. 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31

With respect to the geographical scope, twelve studies analysed more than 65% of the world's territories. 16 , 17 , 21 , 23 , 32 , 33 , 34 , 35 , 36 , 37 , 38 49 Some focused on specific areas, with nine studies analysing data from the United States, performing mostly state-level, 20 , 26 , 31 , 39 , 40 , 41 but also county-level 22 , 42 , 43 analyses, three studies estimating the effects of NPIs in Europe 28 , 29 , 44 and two studies in all OECD countries. 19 , 45 Five studies selected specific countries that belong to different world regions 15 , 24 , 25 , 30 , 46 and three included both countries and US states. 18 , 27 , 47

Outcomes of interest

The authors have reported on different epidemiological parameters to assess the effectiveness of the NPIs studied. More than half of the studies reported on the reproduction number and the case growth rate, daily or weekly (detailed in Annex 3). For all of these studies, excluding Ebrahim et al. 22 , Li et al. (a) 23 and Liu et al. 36 the outcome was estimated by the authors through calculations assuming different epidemiological characteristics of SARS-CoV-2, like time until symptom onset and the distribution of the serial interval. 48 The number of confirmed cases, mortality or death rate, confirmed deaths and deaths growth rate come next as the most reported outcomes.

NPIs assessed

We analysed 16 NPIs that were consistently assessed in the studies included in the review. More than three quarters of the studies analysed the effectiveness of two NPIs: lockdowns (also called stay-at-home mandates or shelter-in-place orders) were analysed by 29 studies and school closing was analysed by 27. Half of the studies analysed in this review assessed the effectiveness of international travel or border restrictions and social gathering restrictions. Several NPIs were analysed in less than a quarter of the studies: business or venue closing, testing strategies, mask wearing requirements, social distancing, contact tracing strategies and isolation or quarantine (see Table 1 ).

Statistical methodology

Several authors used sophisticated and flexible methods, like Bayesian longitudinal models 18 , 19 , 24 , 25 , 29 , 30 , 31 or event studies, 15 , 20 , 36 , 42 while others used linear regression 16 , 21 , 23 , 28 , 34 , 35 , 38 , 49 and simple correlation coefficients. 37 , 43 The quality of the statistical methodology is evaluated in the risk of bias tool in Annex 2.

Risk of bias analysis

The maximum score from the tool was 18, the highest ranked study had a score of 17 and the lowest had a score of 11. The studies were grouped in three categories based on their rating: lower (with a rating of 11 or 12), intermediate (with a rating of 13–15) and higher (with a rating of 16 or 17) quality. Overall, thirty studies were considered to have to have intermediate and higher quality. The detailed quality assessment tool is provided in Annex 2.

Results of the studies

Results on the reproduction number, epidemic growth and incidence-related outcomes.

A heatmap of the findings of all studies (excluding Hsiang et al. 15 , who compared different NPIs from different countries) studying the reproduction number, epidemic growth and incidence-related outcomes is shown in Fig. 2 . Pozo-Martin et al. 19 performed independent analyses of two different timeframes, each analysis is represented separately in the heatmap.

Heatmap of the assessment of NPIs effectiveness in the studies analysing epidemic growth and incidence-related outcomes. The color grading is given according to the effectiveness ranking of each article. Darker green means higher effectiveness among the NPIs studied in the same article. Gray means no significant association with the outcome. White means the NPI was not studied. If no ranking was established, all the NPIs studied in the same article have the same shade of green. The rating provided is the result of the risk of bias analysis.

Rt = time varying Reproduction number; g  = growth rate; IRR1 = incident risk ratio of NPIs on the number of cases; IRR2 = incidence rate ratio; Growth* = epidemic growth expressed as ratios of rate ratios; CI = cumulative incidence. Pozo-Martin et al. 1 and 2: first and second waves.

Most effective interventions

Overall, school closing was found to be the most effective measure: 14 out of 24 studies (58%) that analysed this NPI found an association with reduced number of cases and its implementation. It was estimated to be the most effective policy in four studies 18 , 36 , 44 , 47 and the second most effective in four studies 23 , 30 , 31 , 33 . Brauner et al. 30 , estimated a mean reproduction number reduction of 39% after the closure of schools and universities. Haug et al. 47 found robust evidence of a mean reduction of 73% in the reproduction number associated with school closures.

Other NPIs that were consistently found among the most effective in reducing cases were: workplace closing, business or venue closing and public event bans. Workplace closing was associated with an improvement of the outcome in 12 out of 14 studies that analysed this NPI (86%). Among the studies that ranked the NPIs, four concluded that it was the most effective measure 18 , 34 , 36 , 19 and three found it was the second most effective. 23 , 26 , 38

Regarding business and venue closing, four out of seven studies assessing its effectiveness (57%) concluded that this measure had a significant impact on the outcome, from which two studies found it the most effective intervention 24 , 30 and another two the second most effective. 40 , 42

For public event bans, six out of 12 studies (50%) found that it was a predictor for the outcome. Li et al. (a) 23 found it was the most effective measure, reaching a peak effect of 25% reduction in the reproduction number 28 days after its implementation. Islam et al. 33 found that greater reduction of the incidence was always observed when public event bans were included in the combination of measures implemented. Two studies found it the second most effective control measure. 26 , 36

Intermediate effectiveness interventions

There are NPIs consistently found effective among the studies, which were not generally ranked as the most effective measures. These NPIs are lockdowns, movement limitations through national or international travel restrictions, social gathering bans ranging from 10 people to mass gathering bans, social distancing, public information campaigns and mask wearing requirements.

Twenty-seven studies analysed the relation between lockdown and the reproduction number, case growth rates and cases, and 18 out them (67%) found an association with their reduction. Five studies found lockdowns to be the most effective control measure. 18 , 27 , 31 , 40 , 42 Flaxman et al. 29 found it to be the only measure reducing the reproduction number below one. In contrast, five studies found it to be the least effective measure studied. 26 , 30 , 33 , 37 , 47 This disparity could be a result of the contrasting definitions of lockdown used by different authors. While most authors studied the additional impact of lockdown after the implementation of other NPIs, 24 , 25 , 30 others estimated the effect of lockdown including several other measures. 15 , 29 , 34 Brauner et al. 30 suggest, that in some countries the reproduction number may have decreased below one without enforcing lockdowns by issuing other NPIs. Li et al. (b) 26 studied lockdown for more than two months in the United States and concluded that it proved to be effective, but that its effect in reducing the growth rate of the number of cases decreased with time.

Both domestic and international travel restrictions have been associated with a reduction in the reproduction number, growth rate or incidence-related outcomes. Five out of 12 studies (42%) assessing domestic travel restrictions and nine out of 15 (60%) studying international travel restrictions found an association. International travel restrictions were shown to be more effective than domestic or national travel restrictions in the studies analysing both policies. Haug et al. 47 estimated that international border restrictions reduced the reproduction number by 56%, while individual movement restrictions reduced it by 42%. In contrast, Wibbens et al. 18 concluded that even recommendations of avoiding movement across regions and cities were more effective than bans on all international regions and total border closures in reducing weekly growth rates.

The definition of social gathering restrictions ranged from mass gathering bans to banning gathering of less than ten people. While mass gathering bans were associated with a reduction of incidence-related outcomes in 7 out of 14 studies (50%), social gathering bans were associated in 11 out of 15 (73%). Consistently in higher and intermediate quality studies, restrictions of smaller social gatherings have been found more effective than mass gathering restrictions, Haug et al. 47 , and Banholzer et al. (a) 24 reached the same conclusion.

While some authors consider social distancing to be a combination of certain other restrictive measures 16 , 17 , 41 sometimes it is considered as the official encouragement by the government to keep social distance. 29 Flaxman et al. 29 evaluated the effectiveness of officially encouraging social distance and did not observe an impact on the reproduction number. However, several studies that defined social distancing as a mix of several policies found an association between these measures and a reduction in the reproduction number, 17 the growth rate 41 and the epidemic growth. 16

Two health system measures found to be effective in reducing COVID-19 cases are public information campaigns and mask wearing requirements. Six out of eight studies (75%) analysing public information campaigns and six out of seven (86%) analysing mask wearing requirements found an association with the outcomes of interest. Several studies found public information campaigns highly effective. 18 , 26 , 37 , 38 Wibbens et al. 18 found it the most impactful measure when there was a coordinated public information campaign. Li et al. (b) 26 found that the growth rate reduction induced by public health information campaigns increased during the study period, reaching its peak two months after implementation. However, some high rated studies found public information campaigns to be among the least effective policies. 36 , 47 With respect to mask wearing requirements, three intermediate and high-quality studies 17 , 19 , 27 found it to be among the most effective measures. Chernozhukov et al. 39 , found that the only significant measure reducing the case growth was implementing mask wearing requirements for employees in public facing businesses.

Least effective interventions

There was no consistent evidence on the effectiveness of public transport closure or of the following three health system NPIs: testing strategies, contact tracing strategies and isolation or quarantine strategies.

Only one of the 12 studies that analysed its effect found an association between public transport closures and the reproduction number, growth rate or case related outcomes. 32 Neither of the six studies analysing the effect of testing policies found testing policies to improve the outcome. Pozo-Martin et al. 19 found in an analysis of the measures between October and December that both testing symptomatic and asymptomatic individuals was a predictor for a higher growth rate. None of the three studies analysing contact tracing strategies found a significant association with COVID-19 cases. Two out of four studies analysing isolation or quarantine strategies found an association with the outcome. However, in these studies these interventions were among the least effective. The findings regarding testing strategies, contact tracing strategies and isolation or quarantine strategies were consistent across intermediate and high-quality studies as rated in our review.

Results on mortality and death-related outcomes

A heatmap of the findings of all studies assessing mortality and death-related outcomes is shown in Fig. 3 . Note that some studies can be found in both Figs. 2 and ​ and3, 3 , since several authors assessed more than one outcome. Twelve studies considered mortality or death-related outcomes in their analysis: four studied mortality, three death growth rate, three studied confirmed deaths, one studied the incident risk ratio on the number of deaths and one the case fatality rate. The evidence of the comparative effectiveness of NPIs with respect to mortality-related outcomes is not clear. School closing seemed to be the most effective NPI, six out of ten studies (60%) analysing this measure found it was significantly associated with the outcome. International travel restrictions are associated with a decrease in mortality in 4 out of 7 studies (57%). Mask wearing requirements, public event bans and mass gathering bans show consistent association with the outcome in all of the studies assessing these interventions. However, mask wearing requirements and social distancing were only studied in two and one article respectively. In line with studies analysing epidemic growth and incidence-related outcomes, testing strategies and contact tracing strategies show no evidence of being associated with COVID-19 deaths.

Heatmap of the assessment of NPIs effectiveness in the studies analysing mortality and death-related outcomes. The color grading is given according to the effectiveness ranking of each article. Darker green means higher effectiveness among the NPIs studied in the same article. Gray means no significant association with the outcome. White means the NPI was not studied. If no ranking was established all the NPIs studied in the same article have the same shade of green. The rating provided is the result of the risk of bias analysis.

g  = growth rate; IRR = incident risk ratio of NPIs on the number of deaths; CFR = case fatality rate.

Eight studies assessed both a death-related outcome and an incidence-related outcome, while four studies focused only in mortality related outcomes. 28 , 35 , 45 , 49 Several studies that explored the effectiveness of NPIs in both types of outcomes found associations between them and the number of cases, reproduction number or growth rate that were not relevant for mortality. 26 , 40 , 44 , 46 Hunter et al. 44 found that mass gathering bans were relevant for reducing cases and deaths, but business closures only for deaths. Piovani et al. 45 reached a similar conclusion regarding mass gathering bans, and also due to school closing. Both Papadopoulos et al. 37 and Chernozhukov et al. 39 found school closing to be the most effective NPI in reducing the number of deaths. In contrast, two authors found that the closure of schools decreased the number of deaths, but not the number of cases. 37 , 39 Leffler et al. 35 concluded that in countries with cultural norms or government policies supporting public mask-wearing, per-capita coronavirus mortality increased on average 16.2% each week, as compared with 61.9% each week in remaining countries.

Dose-response effect of NPIs

The OxCGRT stringency index is a score for each country that provides information about the overall intensity of mostly social distancing policies implemented by the Governments in a certain moment of time. 11 There are contradictory results about stringency being a predictor of improved outcomes. While Leffler et al. 35 could not find any association between the stringency index and mortality, Deb et al. 32 estimated that countries that have put in place stringent measures have reduced the number of confirmed cases and deaths by more than 200 percent relative to the absence of measures.

Different levels of business closing were studied by several authors. Hunter et al. 44 observed no additional value to closing all non-essential services in comparison to only initial business closure. In contrast, Brauner et al. 30 estimated that closing some high-risk businesses reduced the reproduction number by 31% while closing most nonessential businesses reduced it by 40%. Brauner et al. also found a dose-response effect for gathering bans: they estimated a 36% and 21% reduction in the reproduction number when limiting gatherings to 10 people or less, and to 100 people or less, respectively. Liu et al. 36 reached similar conclusions, adding that restrictions on gatherings of more than 1000 people were not effective.

Pozo-Martin et al. 19 in an analysis of the first COVID-19 wave and Koh et al. 34 reached a similar conclusion regarding workplace closure. Recommended workplace closure or staying at home had been effective, implying that voluntary physical distancing has played an important role. However, Pozo-Martin et al. 19 also observed improved outcomes, when all‑but-essential workplaces were closed. Regarding mask wearing requirements, Pozo-Martin et al. 19 found that the effectiveness increased when they were mandated for all public places in all geographical areas within a country.

With respect to lockdown measures, Koh et al. 34 suggest that early on in the outbreak complete lockdowns may be unnecessary to control viral transmission, because partial lockdowns show to be equally effective. This finding is supported by the analysis of Papadopoulos et al. 37 , which concluded that the maximum stringency of individual lockdown policies was not associated with reduced case numbers or mortality.

Wibbens et al. 18 performed a detailed analysis of the individual OxCGRT intensity levels of NPIs in the United States and concluded, that, in general, the higher the policy intensity, the higher the relative impact on reducing the growth of infections. However, the difference in some cases might not be sufficiently relevant to upscale the level of the measure, taken the socioeconomic burden associated. They found that school closure and travel restrictions needed to be implemented at maximum stringency to reach a high impact and public information campaigns are most impactful at the lowest recorded level. Stokes et al. 49 associated stricter measures with reduced mortality.

Timeliness of implementation

Regarding the effect of time delays in NPI implementation in the incidence and incidence-related outcomes, most studies found an association between the time delay and worse outcomes. Koh et al. 34 concluded that all NPIs have to be implemented early to be effective. Chaudhry et al. 46 found that days to travel restrictions was positively associated with the number of cases. Both Islam et al. 33 and Jalali et al. 43 concluded that earlier implementation of lockdowns was associated with a greater reduction in incidence of COVID-19 and in the latter case they also found an association with early introduction of face mask requirements. Papadopoulos et al. 37 concluded that early timing of lockdown introduction is of greater importance than its stringency. In contrast, Pozo-Martin et al. 19 did not find that a delay in the response was a predictor of epidemic growth in the OECD countries.

All the studies that analysed the effect of timeliness in mortality or mortality-related outcomes found an association between early implementation and improved outcomes. 28 , 35 , 37 , 45 , 49 Papadopoulos et al. 37 , concluded that early generalised school closure, early generalised workplace closure, early restriction of international travel and early public information campaigns were independently associated with reduced national COVID-19 death rate. Similarly, Leffler et al. 35 found an association between early international travel restrictions and a reduction of COVID-19 per-capita mortality. Piovani et al. 45 found that the early application of mass gatherings and school closures was associated with an important reduction in COVID-19 mortality. Fountoulakis  et al. 28 found that early implementation of public events bans was a crucial factor for reducing deaths.

Effect of number of NPIs

Some authors reported on the effects on the outcome depending of the number of NPIs implemented. Islam et al. 33 concluded that the implementation of any physical distancing intervention was associated with an overall reduction in COVID-19 incidence of 13%. Bo et al. 17 and Jüni et al. 16 determined that the implementation of two or more types of NPIs was more effective for containing the spread of COVID-19 than implementing only one type. Bo et al. 17 also found that all NPI implementations involving social distancing were associated with a greater decrease in the reproduction number than those not involving distancing and concluded that combinations with more types of NPIs seemed to be associated with slower epidemic growth.

Based on 34 ecological studies identified, this systematic review found that, among the 16 NPIs studied, school closure has been the most effective in reducing COVID-19 cases during the first wave of the pandemic. Workplace closures, business or venue closures and public event bans were also consistently considered among the most effective measures in reducing the number of cases. Public information campaigns and mask wearing requirements also proved to be effective in controlling the pandemic, while having less disruptive effects on the population than other NPIs. In contrast, public transport closure, testing strategies, contact tracing strategies and isolation or quarantine strategies showed no evidence of being effective in the studies assessed. Most of the studies assessing mortality were not able to estimate a comparative effectiveness of the interventions. While early implementation was consistently associated with a higher effectiveness in reducing COVID-19 cases and deaths, the stringency of the interventions was not. NPIs are effective in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths.

We found that the most effective NPI was school closing. This NPI has been widely used since the beginning of the pandemic due to its effectiveness against influenza outbreaks. 50 , 51 , 52 However, school closing carries a heavy socioeconomical burden, hindering education and social interactions for children and causing additional child-care obligations for parents, linked to work absenteeism. 53 There are conflicting results in the literature regarding the effectiveness of this NPI in mitigating the COVID-19 pandemic since it is still not clear if SARS-CoV-2 transmission occurs differently among children. 54 Some authors have concluded that school closing is not an effective NPI and that COVID-19 control can be reached without this measure. 55 , 56 , 57 Viner et al. 58 performed a systematic review on the effects of school closing on the spread of respiratory diseases and included four modeling studies at the beginning of the COVID-19 pandemic. They found that the effect of school closures was comparatively lower than other measures. In a recent systematic review of observational studies analysing school closing and reopening Walsh et al. 59 found that half of the studies with lower risk of bias concluded that school closing reduced community transmission while half of the studies found no effect. Other analyses 60 , 61 , 62 , 63 , 64 , 65 suggest that school closing may have been a more important factor during the first wave of the COVID-19 epidemic than initially thought. As the epidemiological situation has improved, reopening schools has become imperative. Walsh et al. 59 found that there is no increase in community transmission of COVID-19 after reopening schools in a low transmission context with appropriate mitigation measures, as it was observed in Norway and Denmark. 66 However, in countries like Germany, that reopened schools when community transmission was still high, it can lead to an increase in the growth rate. 66 In the United Kingdom schools have been closed twice, first during the first round of restrictions starting March 2020, reopening between June and August 2020. Second, schools closed in December 2020 or January 2021 and reopened in March and April 2021. 67 Mitigation measures have been established for schools to reopen across the United Kingdom, including mask wearing and testing, 68 and after the reopening in both instances cases still went down. 69 Indeed, several studies worldwide have found that mitigation measures allow to reopen schools safely. 70 , 71 , 72 , 73 Lessler et al. 70 found that even implementing low levels of in-school mitigation measures COVID-19 outcomes were reduced. On average, each measure implemented was associated with a 9% decrease in the odds of COVID-19-like illness. 70 In conclusion, a cautious approach for reopening should be adapted to each context, with specific mitigation measures, stepwise opening and monitoring the effects of reopening for in-school and community transmission.

We found other social distancing NPIs to be consistently effective in controlling the COVID-19 pandemic: workplace closing, business and venue closing and public event bans. As reflected in the results of this review, there are differences in the effectiveness of closing all businesses or targeted ones. Further, business and venue closures affect the economy disproportionally 74 and therefore, careful consideration for implementing these measures needs to be taken. A tailored approach is necessary for each context as workplace closures can pose different social or psychological problems to workers. 75 Nonetheless, strategies can be adopted to reduce their potential negative effects. 75 Another widely used measure is the ban of public events. At the beginning of the pandemic several public events were considered “super spreader events” in China 76 and in other countries. 77 , 78 A super spreader event describes a situation when only one or some positive cases infect many people. Sun et al. 79 estimated that 15% of the people accounted for 80% of the infections in the Hunan province in China in early 2020. Estimations for local transmission in Hong Kong 80 and other locations 81 back up these findings. The existence of super spreaders is considered to be a common characteristic of coronaviruses, and it is related with several factors, like prolonged indoor gatherings with poor ventilation. 82 However, the exact reasons why some individuals are able to infect many people and other individuals only a few remain unclear. Note that super-spreader events are not avoidable only via public event bans, but also through venue closures and gathering restrictions, which were found to be effective NPIs in our analysis. In a mathematical model developed by Chang et al. 83 using mobility data, they observed that restaurants, cafes and gyms could account for most COVID-19 infections in US cities, with 80% of the predicted transmission being linked to 10% of the locations.

Our results regarding several health system interventions need to be cautiously interpreted. For instance, testing and contact tracing policies and isolation or quarantining, which are standard public health activities, were not found to be effective in controlling the COVID-19 pandemic. This can be attributed to several factors. First, if the outcome of the study is related to the number of cases, when case detection improves through more efficient testing and tracing the case number reported will rise, without representing a real rise of cases. Second, countries that have implemented successful control strategies strongly relying on these interventions, like China, 84 , 85 South Korea, 86 Singapore 87 and New Zealand 88 have not been specifically addressed among the studies included in this review. In our analysis, testing and contact tracing policies and isolation or quarantining were only assessed in few studies among those that met the inclusion criteria. However, the findings were consistent across intermediate and high-quality studies. In the existing literature, testing and contact tracing strategies, followed by quarantining or isolation, have been considered essential in controlling COVID-19 spread. It has been observed that higher testing volume or testing coverage are correlated with improved control of the pandemic. 19 , 89 , 90 , 91 However, in this review we assessed the impact of different testing strategies, meaning which individuals get tested and how, not the number of tests performed. Several studies have highlighted the importance of a comprehensive test, trace and quarantine approach in different contexts. 92 , 93 Hellewell et al. 94 found through a modeling study that the pandemic could be controlled under certain testing and tracing strategies and concluded that the most important factor in determining whether an outbreak was controllable or not was the delay between symptom onset and isolation. A recent systematic review comparing mass testing and contact tracing with conventional test and trace strategies concluded that mass testing could be more effective in controlling the pandemic. 95

Interestingly, another effective health system measure is the adoption of public information campaigns by the governments. Although this intervention is not among the most effective measures in our analysis, it is consistently associated with a reduction of COVID-19 cases. Mask wearing requirements were also consistently effective in reducing COVID-19 cases in our review. The use of masks can be associated to individual discomfort, but it does not present such an important disruption for daily activities as most of the other measures analysed in this review. Much research has been produced around the use of face masks and its effectiveness, mostly confirming its positive impact in controlling the virus spread. 96 , 97 Through mask wearing requirements workplaces, schools and businesses have been allowed to open. Considering the disruptive effects of most NPIs and their high societal and economical cost, the implementation of effective public information campaigns and mask requirements can present large benefits with less efforts and socioeconomical consequences than other NPIs.

Similarly, due to the socio-economic burden associated with scaling up some NPIs, the specific context needs to be considered. As reflected in the results of this review, upscaling the level of some measures does not always imply improved outcomes. Besides, the difference in the reduction of cases or deaths might not be sufficiently relevant to implement more stringent measures, considering its socioeconomical impact.

Our findings rely mostly on the analysis of studies published based on data from the first wave of the COVID-19 pandemic. However, several studies analysing the effectiveness of NPIs during the second wave have been released as preprints recently. Sharma et al. 98 studied the implementation effects of 17 NPIs in 114 subnational areas from 7 European countries. They found that the combined effect of general NPI implementation was smaller during the second wave, which can be attributed, among other factors, to the influence of maintained individual protective behaviours after the first wave. In line with the results of our review, they concluded that in the second wave closing specific businesses was highly effective, together with strict small gathering restrictions. In contrast, they estimate that school closing was not as effective during the second wave in comparison to the first, which could be linked to the control measures adopted in schools after reopening. In another preprint, Ge et al. 99 analysed the effect of NPIs from the first wave until March 2021 in 133 countries, assessing vaccine rollouts as well. Consistent with Sharma et al. 98 , Ge et al. 99 observed differences in the effectiveness of NPIs during the different waves; they found that school closing was the most effective measure during the first wave, but not among the most relevant in the second. Gathering restrictions and facial covering requirements were consistently considered effective among waves, whereas international travel restrictions played a more important role in the control of the second wave. Regarding vaccine rollout, they considered that vaccination was increasingly contributing to the pandemic control, despite its effect having a significantly lower impact than the NPIs by the time of the study.

As in every evidence review, the comparability of the studies analysed depends on their design and methodological heterogeneity. First, different outcomes were studied to assess the effectiveness of NPIs among the studies. These were mainly the reproduction number, the growth rate, the number of cases and the number of deaths. Mortality data gives only information about severe cases, but is less influenced by testing strategies and testing capacity than case counts. 100 , 101 Testing and contact tracing capacity has been an essential constraint during the pandemic, especially at the beginning. Growth rate is more easily calculated than the reproduction number and avoids many inferential difficulties in estimating the latter. 101 For calculating the reproduction number several epidemiological assumptions, like time until symptom onset, time until death or serial interval, need to be estimated. 100 , 101 However, the reproduction number provides more information than the growth rate on the impact of control measures given the non-linear epidemic curve of COVID-19. 101 If the proportion of cases that are unreported remains constant throughout the study time, estimates of the reproduction number are unaffected by underreporting. 48

Second, the number of NPIs analysed and their definitions differ among the studies included in our review. Some homogeneity is expected for studies with the same NPIs data source, like the 14 studies using the OxCGRT dataset (details in Annex 3). However, there are some differing definitions of specific NPIs among the studies. For instance, school closure can include or exclude secondary schools and/or universities, the amount of people assessed for social gathering restrictions varies and there are differences in the definition of lockdown, business closure or social distancing requirements. Furthermore, some authors consider the onset of a policy when it is officially recommended and others when it is enforced. Several studies consider both options by analysing different stringency levels instead of applying a binary approach.

There are some limitations in the body of evidence of this review. First, all the studies use retrospective and observational data to draw inferences about the effectiveness of NPIs. Conclusions from these studies are limited to the specific time and places studied and may be affected by confounding effects from unobserved factors. However, the broad geographical scope of the studies and the very different methodological approaches used to answer the study questions increase the robustness of our findings. Second, only studies that established a direct link between the implementation of NPIs and an outcome were included in the review. Therefore, it is assumed by all studies that the effect on the outcome depends only on the implementation of the measures. Nonetheless, individual behavior, even before the implementation of measures, 39 , 102 and the compliance with the NPIs 103 , 104 have played an essential role in controlling the pandemic. Third, all studies included in this review assess the effectiveness of NPIs during the first wave of the pandemic. This improves comparability among the studies: during the first months of the pandemic people were still adapting to protective behaviours, almost all the population was susceptible to the virus and no vaccine immunization had started, so that the effect of NPIs implementation could be more directly linked to the outcomes. However, these studies are less relevant to understand the effect of “controlled openings”, for instance businesses opening with improved hygiene conditions, adapted workplaces and schools with social distancing protocols and openings relying on testing. The worldwide availably of personal protective equipment, hospital equipment and testing material has also remarkably improved since the beginning of the pandemic. Finally, there is an overrepresentation of high-income countries in the region-targeted studies, mainly the USA and Europe, but also most OECD countries. There are neither studies focused on low- or middle-income countries nor on Asia or Oceania, even though some of these countries are included in the worldwide studies. Comparing measures within Europe and the USA leads to analysing territories with more similarities in pandemic control approaches. Many Asian countries have had recent experience with pandemics before COVID-19 105 , 106 and, as stated before, some of them relied strongly on health system interventions. Regarding low- and middle-income countries, fewer economic resources translate to less surveillance and testing capacity, which increases underreporting and hinders the assessment of effectiveness of NPIs. However, in the first pandemic wave many Sub-Saharan African countries implemented social distancing measures before the first detected case, 36 which may have played a role in the low burden of disease encountered in the region during that time. 107 Nonetheless, in Africa, the second wave has been more severe, and despite this situation the stringency of the measures implemented in the continent is decreasing. 107 The knowledge of NPIs effectiveness for specific regions should not only be limited to high-income counties in order to allow policy makers worldwide to make tailored decisions.

This systematic review has certain limitations. First, we did not include in our review the evidence coming from mathematical studies simulating the impact of NPIs on epidemic control. While they generate high quality evidence, these studies rely on assumptions about the type and intensity of the NPIs being implemented rather than on the actual policy implementation. In contrast, this review provides information from data-driven studies that have explored the real-life impact of NPI implementation. Second, due to the novelty of the topic and the urgency to answer the study question we included preprints in the review. Even though these studies have not yet undergone peer review, a risk of bias tool was used to assess their quality. All of the preprints included were considered to have sufficient quality and add relevant evidence to the review. Regarding the quality assessment, there is no consolidated risk of bias tool for evaluating ecological studies. Nonetheless, we used a published tool that has been validated in several systematic reviews of ecological studies before. 13 , 14

To the best of our knowledge, at the time of writing there is no other review of empirical studies assessing the comparative effectiveness of more than two NPIs against COVID-19. We found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. Public information campaigns and mask wearing requirements, less disruptive to the population than other NPIs, were also effective measures. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Since scientific knowledge, individual behavior and resources keep on changing and adapting throughout the pandemic, more research needs to be targeted to understand changes in the effectiveness of NPIs and whether controlled openings and lifting of restrictions are compatible with epidemic control.

Declaration of Competing Interest

Acknowledgments.

Funding sources: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.jinf.2021.06.018 .

Appendix. Supplementary materials