basis of empirical 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: Defining, Identifying, & Finding

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

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research 

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

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Empirical research in the social sciences and education.

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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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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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• What is empirical research: Methods, types & examples

Defne Çobanoğlu

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

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

• What is empirical research?

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

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

• When to use empirical research?

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

1. When quantitative or qualitative data is needed

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

2. When you need to test a hypothesis

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

3. When you want to establish causality

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

• Types of empirical research

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

Quantitative research

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

Qualitiative research

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

• 5 steps to conduct empirical research

Necessary steps for empirical research

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

1. Define the research question

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

2. Go through relevant literature

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

3. Decide on the methodology

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

4. Do data analysis

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

5. Conclusion

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

• Empirical research methodologies

Some essential methodologies to conduct empirical research

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

• Observation

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

• Experimentation

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

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

• Focus groups

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

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

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

• Advantages of empirical research

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

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

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

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

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

Is empirical research qualitative or quantitative?

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

Is empirical research the same as quantitative research?

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

What is the difference between theoretical and empirical research?

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

What is the difference between conceptual and empirical research?

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

What is the difference between empirical vs applied research?

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

• Final words

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

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

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

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Defining Empirical Research— Types, Methods, and Examples

• Author Survey Point Team
• Published January 10, 2023

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

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

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

Table of Contents

Types of Empirical Research

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

1. Quantitative Empirical Research

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

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

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

2. Qualitative Empirical Research

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

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

Research Methods Using Empirical Evidence

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

Quantitative Research Methods

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

1. Survey research

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

2. Experimental research

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

3. Correlational research

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

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

4. Longitudinal study

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

5. Cross-sectional

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

6. Casual comparison

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

Qualitative Research Methods

1. case study.

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

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

2. Observational method

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

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

3. One-on-one interview

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

4. Focus groups

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

5. Text analysis

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

Examples of Empirical Research

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

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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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What is Data Analysis? Definition, Tools, Examples

Empirical Research in the Social Sciences and Education

What is empirical research.

• Finding Empirical Research
• Designing Empirical Research
• Ethics & Anti-Racism in Research
• Citing, Writing, and Presenting Your Work

Academic Services Librarian | Research, Education, & Engagement

Gratitude to Penn State

Thank you to librarians at Penn State for serving as the inspiration for this library guide

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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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

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• Searching with the Thesaurus and Index Terms (YouTube Video) Created by the APA
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Empirical Research: What is Empirical Research?

• What is Empirical Research?
• Finding Empirical Research in Library Databases
• Designing Empirical Research

Introduction

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 (Introduction – Method – Results – and – Discussion), 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

Empirical research  is published in books and in  scholarly, peer-reviewed journals .

Make sure to select the  peer-review box  within each database!

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• URL: https://libguides.lahc.edu/empirical

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Empirical Research: Quantitative & Qualitative

• Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

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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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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 >>

© Copyright La Salle University. All rights reserved.

• 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. 

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1.2: Theory and Empirical Research

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This book is concerned with the connection between theoretical claims and empirical data. It is about using statistical modeling; in particular, the tool of regression analysis, which is used to develop and refine theories. We define theory broadly as a set of interrelated propositions that seek to explain and, in some cases, predict an observed phenomenon.

Theory: A set of interrelated propositions that seek to explain and predict an observed phenomenon.

Theories contain three important characteristics that we discuss in detail below.

Characteristics of Good Theories Coherent and internally consistent Causal in nature Generate testable hypotheses

1.2.1 Coherent and Internally Consistent

The set of interrelated propositions that constitute a well-structured theory are based on concepts . In well-developed theories, the expected relationships among these concepts are both coherent and internally consistent. Coherence means the identification of concepts and the specified relationships among them are logical, ordered, and integrated. An internally consistent theory will explain relationships with respect to a set of common underlying causes and conditions, providing for consistency in expected relationships (and avoidance of contradictions). For systematic quantitative research, the relevant theoretical concepts are defined such that they can be measured and quantified. Some concepts are relatively easy to quantify, such as the number of votes cast for the winning Presidential candidate in a specified year or the frequency of arrests for gang-related crimes in a particular region and time period. Others are more difficult, such as the concepts of democratization, political ideology or presidential approval. Concepts that are more difficult to measure must be carefully operationalized , which is a process of relating a concept to an observation that can be measured using a defined procedure. For example, political ideology is often operationalized through public opinion surveys that ask respondents to place themselves on a Likert-type scale of ideological categories.

Concepts and Variables

A concept is a commonality across observed individual events or cases. It is a regularity that we find in a complex world. Concepts are our building blocks to understanding the world and to developing theory that explains the world. Once we have identified concepts we seek to explain them by developing theories based on them. Once we have explained a concept we need to define it. We do so in two steps. First, we give it a dictionary-like definition, called a nominal definition. Then, we develop an operational definition that identifies how we can measure and quantify it.

Once a concept has been quantified, it is employed in modeling as a variable . In statistical modeling, variables are thought of as either dependent or independent variables. A dependent variable , Y, is the outcome variable; this is the concept we are trying to explain and/or predict. The independent variable(s) , X, is the variable(s) that is used to predict or explain the dependent variable. The expected relationships between (and among) the variables are specified by the theory.

Measurement

When measuring concepts, the indicators that are used in building and testing theories should be both valid and reliable . Validity refers to how well the measurement captures the concept. Face validity, for example, refers to the plausibility and general acceptance of the measure, while the domain validity of the measure concerns the degree to which it captures all relevant aspects of the concept. Reliability, by contrast, refers to how consistent the measure is with repeated applications. A measure is reliable if, when applied to the repeated observations in similar settings, the outcomes are consistent.

Assessing the Quality of a Measure

Measurement is the process of assigning numbers to the phenomenon or concept that you are interested in. Measurement is straight-forward when we can directly observe the phenomenon. One agrees on a metric, such as inches or pounds, and then figures out how many of those units are present for the case in question. Measurement becomes more challenging when you cannot directly observe the concept of interest. In political science and public policy, some of the things we want to measure are directly observable: how many dollars were spent on a project or how many votes the incumbent receives, but many of our concepts are not observable: is issue X on the public’s agenda, how successful is a program, or how much do citizens trust the president. When the concept is not directly observable the operational definition is especially important. The operational definition explains exactly what the researcher will do to assign a number for each subject/case.

In reality, there is always some possibility that the number assigned does not reflect the true value for that case, i.e., there may be some error involved. Error can come about for any number of reasons, including mistakes in coding, the need for subjective judgments, or a measuring instrument that lacks precision. These kinds of error will generally produce inconsistent results; that is, they reduce reliability. We can assess the reliability of an indicator using one of two general approaches. One approach is a test-retest method where the same subjects are measured at two different points in time. If the measure is reliable the correlation between the two observations should be high. We can also assess reliability by using multiple indicators of the same concept and determining if there is a strong inter-correlation among them using statistical formulas such as Cronbach’s alpha or Kuder-Richardson Formula 20 (KR-20).

We can also have error when our measure is not valid. Valid indicators measure the concept we think they are measuring. The indicator should both converge with the concept and discriminate between the concept and similar yet different concepts. Unfortunately, there is no failsafe way to determine whether an indicator is valid. There are, however, a few things you can do to gain confidence in the validity of the indicator. First, you can simply look at it from a logical perspective and ask if it seems like it is valid. Does it have face validity? Second, you can see if it correlates well with other indicators that are considered valid, and in ways that are consistent with theory. This is called construct validity. Third, you can determine if it works in the way expected, which is referred to as predictive validity. Finally, we have more confidence if other researchers using the same concept agree that the indicator is considered valid. This consensual validity at least ensures that different researchers are talking about the same thing.

Measurement of Different Kinds of Concepts

Measurement can be applied to different kinds of concepts, which causes measures of different concepts to vary. There are three primary levels of measurement ; ordinal, interval, and nominal. Ordinal level measures indicate relative differences, such as more or less, but do not provide equal distances between intervals on the measurement scale. Therefore, ordinal measures cannot tell us how much more or less one observation is than another. Imagine a survey question asking respondents to identify their annual income. Respondents are given a choice of five different income levels: $0-20,000, $20,000-50,000, $50,000-$100,000, and $100,000+. This measure gives us an idea of the rank order of respondents’ income, but it is impossible for us to identify consistent differences between these responses. With an interval level measure, the variable is ordered and the differences between values are consistent. Sticking with the example of income, survey respondents are now asked to provide their annual income to the nearest ten thousand dollar mark (e.g., $10,000, $20,000, $30,000, etc.). This measurement technique produces an interval level variable because we have both a rank ordering and equal spacing between values. Ratio scales are interval measures with the special characteristic that the value of zero (0) indicates the absence of some property. A value of zero (0) income in our example may indicate a person does not have a job. Another example of a ratio scale is the Kelvin temperature scale because zero (0) degrees Kelvin indicates the complete absence of heat. Finally, a nominal level measure identifies categorical differences among observations. Numerical values assigned to nominal variables have no inherent meaning, but only differentiate one type" (e.g., gender, race, religion) from another.

1.2.2 Theories and Causality

Theories should be causal in nature, meaning that an independent variable is thought to have a causal influence on the dependent variable. In other words, a change in the independent variable causes a change in the dependent variable. Causality can be thought of as the motor" that drives the model and provides the basis for explanation and (possibly) prediction.

The Basis of Causality in Theories

• Time Ordering: The cause precedes the effect, X→Y
• Co-Variation: Changes in X are associated with changes in Y
• Non-Spuriousness: There is not a variable Z that causes both X and Y

To establish causality we want to demonstrate that a change in the independent variable is a necessary and sufficient condition for a change in the dependent variable (though more complex, interdependent relationships can also be quantitatively modeled). We can think of the independent variable as a treatment, τ, and we speculate that τ causes a change in our dependent variable, Y. The gold standard’’ for causal inference is an experiment where a) the level of ττ is controlled by the researcher and b) subjects are randomly assigned to a treatment or control group. The group that receives the treatment has outcome Y 1 and the control group has outcome Y 0 ; the treatment effect can be defined as τ=Y 1 -Y 0 . Causality is inferred because the treatment was only given to one group, and since these groups were randomly assigned other influences should wash out. Thus the difference τ=Y 1 -Y0 can be attributed to the treatment.

Given the nature of social science and public policy theorizing, we often can’t control the treatment of interest. For example, our case study in this text concerns the effect of political ideology on views about the environment. For this type of relationship, we cannot randomly assign ideology in an experimental sense. Instead, we employ statistical controls to account for the possible influences of confounding factors, such as age and gender. Using multiple regression we control for other factors that might influence the dependent variable. 1

1.2.3 Generation of Testable Hypothesis

Theory building is accomplished through the testing of hypotheses derived from theory. In simple form, a theory implies (sets of) relationships among concepts. These concepts are then operationalized. Finally, models are developed to examine how the measures are related. Properly specified hypotheses can be tested with empirical data, which are derived from the application of valid and reliable measures to relevant observations. The testing and re-testing of hypotheses develops levels of confidence that we can have for the core propositions that constitute the theory. In short, empirically grounded theories must be able to posit clear hypotheses that are testable. In this text, we discuss hypotheses and test them using relevant models and data.

As noted above, this text uses the concepts of political ideology and views about the environment as a case study in order to generate and test hypotheses about the relationships between these variables. For example, based on popular media accounts, it is plausible to expect that political conservatives are less likely to be concerned about the environment than political moderates or liberals. Therefore, we can pose the working hypothesis that measures of political ideology will be systematically related to measures of concern for the environment – with conservatives showing less concern for the environment. In classical hypothesis testing, the working hypothesis is tested against a null hypothesis . A null hypothesis is an implicit hypothesis that posits the independent variable has no effect (i.e., null effect) on the dependent variable. In our example, the null hypothesis states ideology has no effect on environmental concern.

Developing a Theoretical Framework and Rationale for a Research Proposal

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It is useful to recall that our work as scientists will be at its best when it simultaneously tackles real-world problems and enriches our understanding of basic biological, psychological, or social processes. A good theory can help us do both. All empirical research is based on assumptions. Even purely “descriptive” or “exploratory” studies necessarily involve choices about the phenomena and variables to observe and the level of detail at which to observe them. Researchers planning an empirical study confront the challenges of making these assumptions explicit, examining them critically, and designing the investigation to yield data that permit those assumptions to be evaluated and modified appropriately. This is the process of theory construction. Unfortunately, although all research is based on theory, many grant proposals lack a well-developed theoretical rationale. The theoretical framework often remains implicit in the proposal without being formally articulated. Consequently, even though the application may be based on a good idea, it is conceptually weak and receives a poor priority/impact score. This chapter will give you a useful strategy for developing a clearly articulated theoretical framework for your research project and using it to write your entire research plan.

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Acknowledgements

Preparation of this chapter was originally supported in part by a grant to the first author from the National Institute of Mental Health (K02 MH01455). The author gratefully acknowledges the assistance of Dr. William Woods, who gave insightful comments on an earlier draft.

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Herek, G.M. (2010). Developing a Theoretical Framework and Rationale for a Research Proposal. In: Pequegnat, W., Stover, E., Boyce, C. (eds) How to Write a Successful Research Grant Application. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1454-5_12

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Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

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• Published: 16 April 2024

Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study

• Luning Shao 1 ,
• Yuxin Miao 2 ,
• Shengce Ren 3 ,
• Sanfa Cai 4 &
• Fei Fan   ORCID: orcid.org/0000-0001-8756-5140 5 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  519 ( 2024 ) Cite this article

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• Business and management

Entrepreneurship education (EE) has rapidly evolved within higher education and has emerged as a pivotal mechanism for cultivating innovative and entrepreneurial talent. In China, while EE has made positive strides, it still faces a series of practical challenges. These issues cannot be effectively addressed solely through the efforts of universities. Based on the triple helix (TH) theory, this study delves into the unified objectives and practical content of EE in Chinese higher education. Through a comprehensive literature review on EE, coupled with educational objectives, planned behavior, and entrepreneurship process theories, this study introduces the 4H objective model of EE. 4H stands for Head (mindset), Hand (skill), Heart (attitude), and Help (support). Additionally, the research extends to a corresponding content model that encompasses entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate as tools for achieving the objectives. Based on a single-case approach, this study empirically explores the application of the content model at T-University. Furthermore, this paper elucidates how the university plays a role through the comprehensive development of entrepreneurial learning, practices, services, and climate in nurturing numerous entrepreneurs and facilitating the flourishing of the regional entrepreneurial ecosystem. This paper provides important contributions in its application of TH theory to develop EE within the Chinese context, and it provides clear guidance by elucidating the core objectives and practical content of EE. The proposed conceptual framework serves not only as a guiding tool but also as a crucial conduit for fostering the collaborative development of the EE ecosystem. To enhance the robustness of the framework, this study advocates strengthening empirical research on TH theory through multiple and comparative case studies.

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Introduction

In the era of the knowledge economy, entrepreneurship has emerged as a fundamental driver of social and economic development. As early as 1911, Schumpeter proposed the well-known theory of economic development, wherein he first introduced the concepts of entrepreneurship and creative destruction as driving forces behind socioeconomic development. Numerous endogenous growth theories, such as the entrepreneurial ecosystem mechanism of Acs et al. ( 2018 ), which also underscores the pivotal role of entrepreneurship in economic development, are rooted in Schumpeter’s model. Recognized as a key means of cultivating entrepreneurs and enhancing their capabilities (Jin et al., 2023 ), entrepreneurship education (EE) has received widespread attention over the past few decades, especially in the context of higher education (Wong & Chan, 2022 ).

Driven by international trends and economic demands, China places significant emphasis on nurturing innovative talent and incorporating EE into the essential components of its national education system. The State Council’s “Implementation Opinions on Deepening the Reform of Innovation and Entrepreneurship Education in Higher Education” (hereafter referred to as the report) underscores the urgent necessity for advancing reforms in innovation and EE in higher education institutions. This initiative aligns with the national strategy of promoting innovation-driven development and enhancing economic quality and efficiency. Furthermore, institutions at various levels are actively and eagerly engaging in EE.

Despite the positive strides made in EE in China, its development still faces a series of formidable practical challenges. As elucidated in the report, higher education institutions face challenges such as a delay in the conceptualization of EE, inadequate integration with specialized education, and a disconnect from practical applications. Furthermore, educators exhibit a deficiency in awareness and capabilities, which manifests in a singular and less effective teaching methodology. The shortage of practical platforms, guidance, and support emphasizes the pressing need for comprehensive innovation and EE systems. These issues necessitate collaborative efforts from universities, industry, and policymakers.

Internationally established solutions for the current challenges have substantially matured, providing invaluable insights and guidance for the development of EE in the Chinese context. In the late 20th century, the concept of the entrepreneurial university gained prominence (Etzkowitz et al., 2000 ). Then, entrepreneurial universities expanded their role from traditional research and teaching to embrace a “third mission” centered on economic development. This transformation entailed fostering student engagement in entrepreneurial initiatives by offering resources and guidance to facilitate the transition of ideas into viable entrepreneurial ventures. Additionally, these entrepreneurial universities played a pivotal role in advancing the triple helix (TH) model (Henry, 2009 ). The TH model establishes innovation systems that facilitate knowledge conversion into economic endeavors by coordinating the functions of universities, government entities, and industry. The robustness of this perspective has been substantiated through comprehensive theoretical and empirical investigations (Mandrup & Jensen, 2017 ).

Therefore, this study aims to explore how EE in Chinese universities can adapt to new societal trends and demands through the guidance of TH theory. This research involves two major themes: educational objectives and content. Educational objectives play a pivotal role in regulating the entire process of educational activities, ensuring alignment with the principles and norms of education (Whitehead, 1967 ), while content provides a practical pathway to achieving these objectives. Specifically, the study has three pivotal research questions:

RQ1: What is the present landscape of EE research?

RQ2: What unified macroscopic goals should be formulated to guide EE in Chinese higher education?

RQ3: What specific EE system should be implemented to realize the identified goals in Chinese higher education?

The structure of this paper is as follows: First, we conduct a comprehensive literature review on EE to answer RQ1 , thereby establishing a robust theoretical foundation. Second, we outline our research methodology, encompassing both framework construction and case studies and providing a clear and explicit approach to our research process. Third, we derive the objectives and content model of EE guided by educational objectives, entrepreneurial motivations, and entrepreneurial process theories. Fourth, focusing on a typical university in China as our research subject, we conduct a case study to demonstrate the practical application of our research framework. Finally, we end the paper with the findings for RQ2 and RQ3 , discussions on the framework, and conclusions.

Literature review

The notion of TH first appeared in the early 1980s, coinciding with the global transition from an industrial to a knowledge-based economy (Cai & Etzkowitz, 2020 ). At that time, the dramatic increase in productivity led to overproduction, and knowledge became a valuable mechanism for driving innovation and economic growth (Mandrup & Jensen, 2017 ). Recognizing the potential of incorporating cutting-edge university technologies into industry and facilitating technology transfer and innovation, the US government took proactive steps to enhance the international competitiveness of American industries. This initiative culminated in the enactment of relevant legislation in 1980, which triggered a surge in technology transfer, patent licensing, and the establishment of new enterprises within the United States. Subsequently, European and Asian nations adopted similar measures, promoting the transformation of universities’ identity (Grimaldi et al., 2011 ). Universities assumed a central role in technology transfer, the formation of businesses, and regional revitalization within the knowledge society rather than occupying a secondary position within the industrial community. The conventional one-to-one relationships between universities, companies, and the government evolved into a dynamic TH model (Cai & Etzkowitz, 2020 ). Beyond their traditional roles in knowledge creation, wealth production, and policy coordination, these sectors began to engage in multifaceted interactions, effectively “playing the role of others” (Ranga & Etzkowitz, 2013 ).

The TH model encompasses three fundamental elements: 1) In a knowledge-based society, universities assume a more prominent role in innovation than in industry; 2) The three entities engage in collaborative relationships, with innovation policies emerging as a result of their mutual interactions rather than being solely dictated by the government; and 3) Each entity, while fulfilling its traditional functions, also takes on the roles of the other two parties (Henry, 2009 ). This model is closely aligned with EE.

On the one hand, EE can enhance the effectiveness of TH theory by strengthening the links between universities, industry, and government. The TH concept was developed based on entrepreneurial universities. The emerging entrepreneurial university model integrates economic development as an additional function. Etzkowitz’s research on the entrepreneurial university identified a TH model of academia-industry-government relations implemented by universities in an increasingly knowledge-based society (Galvao et al., 2019 ). Alexander and Evgeniy ( 2012 ) articulated that entrepreneurial universities are crucial to the implementation of triple-helix arrangements and that by integrating EE into their curricula, universities have the potential to strengthen triple-helix partnerships and boost the effectiveness of the triple-helix model.

On the other hand, TH theory also drives EE to achieve high-quality development. Previously, universities were primarily seen as sources of knowledge and human resources. However, they are now also regarded as reservoirs of technology. Within EE and incubation programs, universities are expanding their educational capabilities beyond individual education to shaping organizations (Henry, 2009 ). Surpassing their role as sources of new ideas for existing companies, universities blend their research and teaching processes in a novel way, emerging as pivotal sources for the formation of new companies, particularly in high-tech domains. Furthermore, innovation within one field of the TH influences others (Piqué et al., 2020 ). An empirical study by Alexander and Evgeniy ( 2012 ) outlined how the government introduced a series of initiatives to develop entrepreneurial universities, construct innovation infrastructure, and foster EE growth.

Overview of EE

EE occupies a crucial position in driving economic advancement, and this domain has been the focal point of extensive research. Fellnhofer ( 2019 ) examined 1773 publications from 1975 to 2014, introducing a more closely aligned taxonomy of EE research. This taxonomy encompasses eight major clusters: social and policy-driven EE, human capital studies related to self-employment, organizational EE and TH, (Re)design and evaluation of EE initiatives, entrepreneurial learning, EE impact studies, and the EE opportunity-related environment at the organizational level. Furthermore, Mohamed and Sheikh Ali ( 2021 ) conducted a systematic literature review of 90 EE articles published from 2009 to 2019. The majority of these studies focused on the development of EE (32%), followed by its benefits (18%) and contributions (12%). The selected research also addressed themes such as the relationship between EE and entrepreneurial intent, the effectiveness of EE, and its assessment (each comprising 9% of the sample).

Spanning from 1975 to 2019, these two reviews offer a comprehensive landscape of EE research. The perspective on EE has evolved, extending into multiple dimensions (Zaring et al., 2021 ). However, EE does not always achieve the expected outcomes, as challenges such as limited student interest and engagement as well as persistent negative attitudes are often faced (Mohamed & Sheikh Ali, 2021 ). In fact, the challenges faced by EE in most countries may be similar. However, the solutions may vary due to contextual differences (Fred Awaah et al., 2023 ). Furthermore, due to this evolution, there is a need for a more comprehensive grasp of pedagogical concepts and the foundational elements of modern EE (Hägg & Gabrielsson, 2020 ). Based on the objectives of this study, four specific themes were chosen for an in-depth literature review: the objectives, contents and methods, outcomes, and experiences of EE.

Objectives of EE

The objectives of EE may provide significant guidance for its implementation and the assessment of its effectiveness, and EE has evolved to form a diversified spectrum. Mwasalwiba ( 2010 ) presented a multifaceted phenomenon in which EE objectives are closely linked to entrepreneurial outcomes. These goals encompass nurturing entrepreneurial attitudes (34%), promoting new ventures (27%), contributing to local community development (24%), and imparting entrepreneurial skills (15%). Some current studies still emphasize particular dimensions of these goals, such as fostering new ventures or value creation (Jones et al., 2018 ; Ratten & Usmanij, 2021 ). These authors further stress the significance of incorporating practical considerations related to the business environment, which prompts learners to contemplate issues such as funding and resource procurement. This goal inherently underscores the importance of entrepreneurial thinking and encourages learners to transition from merely being students to developing entrepreneurial mindsets.

Additionally, Kuratko and Morris ( 2018 ) posit that the goal of EE should not be to produce entrepreneurs but to cultivate entrepreneurial mindsets in students, equipping them with methods for thinking and acting entrepreneurially and enabling them to perceive opportunities rapidly in uncertain conditions and harness resources as entrepreneurs would. While the objectives of EE may vary based on the context of the teaching institution, the fundamental goal is increasingly focused on conveying and nurturing an entrepreneurial mindset among diverse stakeholders. Hao’s ( 2017 ) research contends that EE forms a comprehensive system in which multidimensional educational objectives are established. These objectives primarily encompass cultivating students’ foundational qualities and innovative entrepreneurial personalities, equipping them with essential awareness of entrepreneurship, psychological qualities conducive to entrepreneurship, and a knowledge structure for entrepreneurship. Such a framework guides students towards independent entrepreneurship based on real entrepreneurial scenarios.

Various studies and practices also contain many statements about entrepreneurial goals. The Entrepreneurship Competence Framework, which was issued by the EU in 2016, delineates three competency domains: ideas and opportunities, resources and action. Additionally, the framework outlines 15 specific entrepreneurship competencies (Jun, 2017 ). Similarly, the National Content Standards for EE published by the US Consortium encompass three overarching strategies for articulating desired competencies for aspiring entrepreneurs: entrepreneurial skills, ready skills, and business functions (Canziani & Welsh, 2021 ). First, entrepreneurial skills are unique characteristics, behaviors, and experiences that distinguish entrepreneurs from ordinary employees or managers. Second, ready skills, which include business and entrepreneurial knowledge and skills, are prerequisites and auxiliary conditions for EE. Third, business functions help entrepreneurs create and operate business processes in business activities. These standards explain in the broadest terms what students need to be self-employed or to develop and grow a new venture. Although entrepreneurial skills may be addressed in particular courses offered by entrepreneurship faculties, it is evident that business readiness and functional skills significantly contribute to entrepreneurial success (Canziani & Welsh, 2021 ).

Contents and methods of EE

The content and methods employed in EE are pivotal factors for ensuring the delivery of high-quality entrepreneurial instruction, and they have significant practical implications for achieving educational objectives. The conventional model of EE, which is rooted in the classroom setting, typically features an instructor at the front of the room delivering concepts and theories through lectures and readings (Mwasalwiba, 2010 ). However, due to limited opportunities for student engagement in the learning process, lecture-based teaching methods prove less effective at capturing students’ attention and conveying new concepts (Rahman, 2020 ). In response, Okebukola ( 2020 ) introduced the Culturo-Techno-Contextual Approach (CTCA), which offers a hybrid teaching and learning method that integrates cultural, technological, and geographical contexts. Through a controlled experiment involving 400 entrepreneurship development students from Ghana, CTCA has been demonstrated to be a model for enhancing students’ comprehension of complex concepts (Awaah, 2023 ). Furthermore, learners heavily draw upon their cultural influences to shape their understanding of EE, emphasizing the need for educators to approach the curriculum from a cultural perspective to guide students in comprehending entrepreneurship effectively.

In addition to traditional classroom approaches, research has highlighted innovative methods for instilling entrepreneurial spirit among students. For instance, students may learn from specific university experiences or even engage in creating and running a company (Kolb & Kolb, 2011 ). Some scholars have developed an educational portfolio that encompasses various activities, such as simulations, games, and real company creation, to foster reflective practice (Neck & Greene, 2011 ). However, some studies have indicated that EE, when excessively focused on applied and practical content, yields less favorable outcomes for students aspiring to engage in successful entrepreneurship (Martin et al., 2013 ). In contrast, students involved in more academically oriented courses tend to demonstrate improved intellectual skills and often achieve greater success as entrepreneurs (Zaring et al., 2021 ). As previously discussed, due to the lack of a coherent theoretical framework in EE, there is a lack of uniformity and consistency in course content and methods (Ribeiro et al., 2018 ).

Outcomes of EE

Research on the outcomes of EE is a broad and continually evolving field, with most related research focusing on immediate or short-term impact factors. For example, Anosike ( 2019 ) demonstrated the positive effect of EE on human capital, and Chen et al. ( 2022 ) proposed that EE significantly moderates the impact of self-efficacy on entrepreneurial competencies in higher education students through an innovative learning environment. In particular, in the comprehensive review by Kim et al. ( 2020 ), six key EE outcomes were identified: entrepreneurial creation, entrepreneurial intent, opportunity recognition, entrepreneurial self-efficacy and orientation, need for achievement and locus of control, and other entrepreneurial knowledge. One of the more popular directions is the examination of the impact of EE on entrepreneurial intentions. Bae et al. ( 2014 ) conducted a meta-analysis of 73 studies to examine the relationship between EE and entrepreneurial intention and revealed little correlation. However, a meta-analysis of 389 studies from 2010 to 2020 by Zhang et al. ( 2022 ) revealed a positive association between the two variables.

Nabi et al. ( 2017 ) conducted a systematic review to determine the impact of EE in higher education. Their findings highlight that studies exploring the outcomes of EE have primarily concentrated on short-term and subjective assessments, with insufficient consideration of longer-term effects spanning five or even ten years. These longer-term impacts encompass factors such as the nature and quantity of startups, startup survival rates, and contributions to society and the economy. As noted in the Eurydice report, a significant impediment to advancing EE is the lack of comprehensive delineation concerning education outcomes (Bourgeois et al., 2016 ).

Experiences in the EE system

With the deepening exploration of EE, researchers have turned to studying university-centered entrepreneurship ecosystems (Allahar and Sookram, 2019 ). Such ecosystems are adopted to fill gaps in “educational and economic development resources”, such as entrepreneurship curricula. A growing number of universities have evolved an increasingly complex innovation system that extends from technology transfer offices, incubators, and technology parks to translational research and the promotion of EE across campuses (Cai & Etzkowitz, 2020 ). In the university context, the entrepreneurial ecosystem aligns with TH theory, in which academia, government, and industry create a trilateral network and hybrid organization (Ranga & Etzkowitz, 2013 ).

The EE system is also a popular topic in China. Several researchers have summarized the Chinese experience in EE, including case studies and overall experience, such as the summary of the progress and system development of EE in Chinese universities over the last decade by Weiming et al. ( 2013 ) and the summary of the Chinese experience in innovation and EE by Maoxin ( 2017 ). Other researchers take an in-depth look at the international knowledge of EE, such as discussions on the EE system of Denmark by Yuanyuan ( 2015 ), analyzes of the ecological system of EE at the Technical University of Munich by Yubing and Ziyan ( 2015 ), and comparisons of international innovation and EE by Ke ( 2017 ).

In general, although there has been considerable discussion on EE, the existing body of work has not properly addressed the practical challenges faced by EE in China. On the one hand, the literature is fragmented and has not yet formed a unified and mature theoretical framework. Regarding what should be taught and how it can be taught and assessed, the answers in related research are ambiguous (Hoppe, 2016 ; Wong & Chan, 2022 ). On the other hand, current research lacks empirical evidence in the context of China, and guidance on how to put the concept of EE into practice is relatively limited. These dual deficiencies impede the effective and in-depth development of EE in China. Consequently, it is imperative to comprehensively redefine the objectives and contents of EE to provide clear developmental guidance for Chinese higher education institutions.

Research methodology

To answer the research questions, this study employed a comprehensive approach by integrating both literature-based and empirical research methods. The initial phase focused on systematically reviewing the literature related to entrepreneurial education, aiming to construct a clear set of frameworks for the objectives and content of EE in higher education institutions. The second phase involved conducting a case study at T-University, in which the theoretical frameworks were applied to a real-world context. This case not only contributed to validating the theoretical constructs established through the literature review but also provided valuable insights into the practical operational dynamics of entrepreneurial education within the specific university setting.

Conceptual framework stage

This paper aims to conceptualize the objective and content frameworks for EE. The methodology sequence is as follows: First, we examine the relevant EE literature to gain insights into existing research themes. Subsequently, we identify specific research articles based on these themes, such as “entrepreneurial intention”, “entrepreneurial self-efficacy”, and “entrepreneurial approach”, among others. Third, we synthesize the shared objectives of EE across diverse research perspectives through an analysis of the selected literature. Fourth, we construct an objective model for EE within higher education by integrating Bloom’s educational objectives ( 1956 ) and Gagne’s five learning outcomes ( 1984 ), complemented by entrepreneurship motivation and process considerations. Finally, we discuss the corresponding content framework.

Case study stage

To further elucidate the conceptual framework, this paper delves into the methods for the optimization of EE in China through a case analysis. Specifically, this paper employs a single-case approach. While a single case study may have limited external validity (Onjewu et al., 2021 ), if a case study informs current theory and conceptualizes the explored issues, it can still provide valuable insights from its internal findings (Buchanan, 1999 ).

T-University, which is a comprehensive university in China, is chosen as the subject of the case study for the following reasons. First, T-University is located in Shanghai, which is a Chinese international technological innovation center approved by the State Council. Shanghai’s “14th Five-Year Plan” proposes the establishment of a multichannel international innovation collaboration platform and a global innovation cooperation network. Second, T-University has initiated curriculum reforms and established a regional knowledge economy ecosystem by utilizing EE as a guiding principle, which aligns with the characteristics of its geographical location, history, culture, and disciplinary settings. This case study will showcase T-University’s experiences in entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate, elucidating the positive outcomes of this triangular interaction and offering practical insights for EE in other contexts.

The data collection process of this study was divided into two main stages: field research and archival research. The obtained data included interview transcripts, field notes, photos, internal documents, websites, reports, promotional materials, and published articles. In the initial stage, we conducted a 7-day field trip, including visits to the Innovation and Entrepreneurship Institute, the Career Development Centre, the Academic Affairs Office, and the Graduate School. Moreover, we conducted semistructured interviews with several faculty members and students involved in entrepreneurship education at the university to understand the overall state of implementation of entrepreneurship education at the university. In the second stage, we contacted the Academic Affairs Office and the Student Affairs Office at the university and obtained internal materials related to entrepreneurship education. Additionally, we conducted a comprehensive collection and created a summary of publicly available documents, official school websites, public accounts, and other electronic files. To verify the validity of the multisource data, we conducted triangulation and ultimately used consistent information as the basis for the data analysis.

For the purpose of our study, thematic analysis was employed to delve deeply into the TH factors, the objective and content frameworks, and their interrelationships. Thematic analysis is a method for identifying, analyzing, and reporting patterns within data. This approach emphasizes a comprehensive interpretation of the data, as it extracts information from multiple perspectives and derives valuable conclusions through summary and induction (Onjewu et al., 2021 ). Therefore, thematic analysis likely serves as the foundation for most other qualitative data analysis methods (Willig, 2013 ). In this study, three researchers individually conducted rigorous analyses and comprehensive reviews to ensure the accuracy and reliability of the data. Subsequently, they engaged in collaborative discussions to explore their differences and ultimately reach a consensus.

Framework construction

Theoretical basis of ee in universities.

The study is grounded in the theories of educational objectives, planned behavior, and the entrepreneurial process. Planned behavior theory can serve to elucidate the emergence of entrepreneurial activity, while entrepreneurial process theory can be used to delineate the essential elements of successful entrepreneurship.

Theory of educational objectives. The primary goal of education is to assist students in shaping their future. Furthermore, education should directly influence students and facilitate their future development. Education can significantly enhance students’ prospects by imparting specific skills and fundamental principles and cultivating the correct attitudes and mindsets (Bruner, 2009 ). According to “The Aims of Education” by Whitehead, the objective of education is to stimulate creativity and vitality. Gagne identifies five learning outcomes that enable teachers to design optimal learning conditions based on the presentation of these outcomes, encompassing “attitude,” “motor skills,” “verbal information,” “intellectual skills,” and “cognitive strategies”. Bloom et al. ( 1956 ) argue that education has three aims, which concern the cognitive, affective, and psychomotor domains. Gedeon ( 2017 ) posits that EE involves critical input and output elements. The key objectives encompass mindset (Head), skill (hand), attitude (heart), and support (help). The input objectives include EE teachers, resources, facilities, courses, and teaching methods. The output objectives encompass the impacts of the input factors, such as the number of students, the number of awards, and the establishment of new companies. The primary aims of Gedeon ( 2017 ) correspond to those of Bloom et al. ( 1956 ).

Theory of planned behavior. The theory of planned behavior argues that human behavior is the outcome of well-thought-out planning (Ajzen, 1991 ). Human behavior depends on behavioral intentions, which are affected by three main factors. The first is derived from the individual’s “attitude” towards taking a particular action; the second is derived from the influence of “subjective norms” from society; and the third is derived from “perceived behavioral control” (Ajzen, 1991 ). Researchers have adopted this theory to study entrepreneurial behavior and EE.

Theory of the entrepreneurship process. Researchers have proposed several entrepreneurial models, most of which are processes (Baoshan & Baobao, 2008 ). The theory of the entrepreneurship process focuses on the critical determinants of entrepreneurial success. The essential variables of the entrepreneurial process model significantly impact entrepreneurial performance. Timmons et al. ( 2004 ) argue that successful entrepreneurial activities require an appropriate match among opportunities, entrepreneurial teams, resources, and a dynamic balance as the business develops. Their model emphasizes flexibility and equilibrium, and it is believed that entrepreneurial activities change with time and space. As a result, opportunities, teams, and resources will be unbalanced and need timely adjustment.

4H objective model of EE

Guided by TH theory, the objectives of EE should consider universities’ transformational identity in the knowledge era and promote collaboration among students, faculty, researchers, and external players (Mandrup & Jensen, 2017 ). Furthermore, through a comprehensive analysis of the literature and pertinent theoretical underpinnings, the article introduces the 4H model for the EE objectives, as depicted in Fig. 1 .

The 4H objective model of entrepreneurship education.

The model comprises two levels. The first level pertains to outcomes at the entrepreneurial behavior level, encompassing entrepreneurial intention and entrepreneurial performance. These two factors support universities’ endeavors to nurture individuals with an entrepreneurial mindset and potential and contribute to the region’s growth of innovation and entrepreneurship. The second level pertains to fundamentals, which form the foundation of the first level. The article defines these as the 4H model, representing mindset (Head), skill (Hand), attitude (Heart), and support (Help). This model integrates key theories, including educational objectives, the entrepreneurship process, and planned behavior.

First, according to the theory of educational objectives, the cognitive, emotional, and skill objectives proposed by Bloom et al. ( 1956 ) correspond to the key goals of education offered by Gedeon ( 2017 ), namely, Head, Hand, and Heart; thus, going forward, in this study, these three objectives are adopted. Second, according to the theory of planned behavior, for the promotion of entrepreneurial intention, reflection on the control of beliefs, social norms, and perceptual behaviors must be included. EE’s impact on the Head, Hand, and Heart will promote the power of entrepreneurs’ thoughts and perceptual actions. Therefore, this approach is beneficial for enhancing entrepreneurial intentions. Third, according to entrepreneurship process theory, entrepreneurial performance is affected by various factors, including entrepreneurial opportunities, teams, and resources. Consideration of the concepts of Head, Hand, and Heart can enhance entrepreneurial opportunity recognition and entrepreneurial team capabilities. However, as the primary means of obtaining external resources, social networks play an essential role in improving the performance of innovation and entrepreneurship companies (Gao et al., 2023 ). Therefore, an effective EE program should tell students how to take action, connect them with those who can help them succeed (Ronstadt, 1985 ), and help them access the necessary resources. If EE institutions can provide relevant help, they will consolidate entrepreneurial intentions and improve entrepreneurial performance, enabling the EE’s objective to better support the Head, Hand, and Heart.

Content model of EE

EE necessitates establishing a systematic implementation framework to achieve the 4H objectives. Current research on EE predominantly focuses on two facets: one focuses on EE methods to improve students’ skills, and the other focuses on EE outcome measurements, which consider the impact of EE on different stakeholders. Based on this, to foster innovation in EE approaches and enable long-term sustainable EE outcomes, the 4H Model of EE objectives mandates that pertinent institutions provide entrepreneurial learning, entrepreneurial practice, startup services, and a suitable entrepreneurial climate. These components constitute the four integral facets of the content model for EE, as depicted in Fig. 2 .

The content model of entrepreneurship education.

Entrepreneurial learning

Entrepreneurial learning mainly refers to the learning of innovative entrepreneurial knowledge and theory. This factor represents the core of EE and can contribute significantly to the Head component. It can also improve the entrepreneurial thinking ability of academic subjects through classroom teaching, lectures, information reading and analysis, discussion, debates, etc. Additionally, it can positively affect the Hand and Heart elements of EE.

Entrepreneurial practice

Entrepreneurial practice mainly refers to academic subjects comprehensively enhancing their cognition and ability by participating in entrepreneurial activities. This element is also a key component of EE and plays a significant role in the cultivation of the Hand element. Entrepreneurial practice is characterized by participation in planning and implementing entrepreneurial programs, competitions, and simulation activities. Furthermore, it positively impacts EE’s Head, Heart, and Help factors.

Startup services

Startup services mainly refer to entrepreneurial-related support services provided by EE institutions, which include investment and financing, project declaration, financial and legal support, human resources, marketing, and intermediary services. These services can improve the success of entrepreneurship projects. Therefore, they can reinforce the expectations of entrepreneurs’ success and positively impact the Heart, Hand, and Head objectives of EE.

Entrepreneurial climate

The entrepreneurial climate refers to the entrepreneurial environment created by EE institutions and their community and is embodied mainly in the educational institutions’ external and internal entrepreneurial culture and ecology. The environment can impact the entrepreneurial attitude of educated individuals and the Heart objective of EE. Additionally, it is beneficial for realizing EE’s Head, Hand, and Help goals.

Case study: EE practice of T-University

Overview of ee at t-university.

T-University is one of the first in China to promote innovation and EE. Since the 1990s, a series of policies have been introduced, and different platforms have been set up. After more than 20 years of teaching, research, and practice, an innovation and entrepreneurship education system with unique characteristics has gradually evolved. The overall goal of this system is to ensure that 100% of students receive such education, with 10% of students completing the program and 1% achieving entrepreneurship with a high-quality standard. The overall employment rate of 2020 graduates reached 97.49%. In recent years, the proportion of those pursuing entrepreneurship has been more than 1% almost every year. The T-Rim Knowledge-Based Economic Circle, an industrial cluster formed around knowledge spillover from T-University’s dominant disciplines, employs more than 400 T-University graduates annually.

In 2016, T-University established the School of Innovation & Entrepreneurship, with the president serving as its dean. This school focuses on talent development and is pivotal in advancing innovation-driven development strategies. It coordinates efforts across various departments and colleges to ensure comprehensive coverage of innovation and EE, the integration of diverse academic disciplines, and the transformation of interdisciplinary scientific and technological advancements (see Fig. 3 ).

T-University innovation and entrepreneurship education map.

T-University is dedicated to integrating innovation and EE into every stage of talent development. As the guiding framework for EE, the university has established the Innovation and EE sequence featuring “three-dimensional, linked, and cross-university cooperation” with seven educational elements. These elements include the core curriculum system of innovation and entrepreneurship, the “one top-notch and three excellences” and experimental zones of innovation and entrepreneurship talent cultivation model, the four-level “China-Shanghai-University-School” training programs for innovation and entrepreneurship, four-level “International-National-Municipal-University” science and technology competitions, four-level “National-Municipal-University-School” innovation and entrepreneurship practice bases, three-level “Venture Valley-Entrepreneurship Fund-Industry Incubation” startup services and a high-level teaching team with both full-time and part-time personnel.

T-University has implemented several initiatives. First, the university has implemented 100% student innovation and EE through reforming the credit setting and curriculum system. Through the Venture Valley class, mobile class, and “joint summer school”, more than 10% of the students completed the Innovation and EE program. Moreover, through the professional reform pilot and eight professional incubation platforms in the National Science and Technology Park of T-University and other measures, 1% of the students established high-quality entrepreneurial enterprises. Second, the university is committed to promoting the integration of innovation and entrepreneurship and training programs, exploring and practising a variety of innovative talent cultivation models, and adding undergraduate innovation ability development as a mandatory component of the training program. In addition, pilot reforms have been conducted in engineering, medicine, and law majors, focusing on integrating research and education.

T-University has constructed a high-level integrated innovation and entrepreneurship practice platform by combining internal and external resources. This platform serves as the central component in Fig. 3 , forming a sequence of innovation and entrepreneurship practice opportunities, including 1) the On-and-off Campus Basic Practice Platform, 2) the Entrepreneurship Practice Platform with the Integration of Production, Learning, and Research, 3) the Transformation Platform of Major Scientific Research Facilities and Achievements, and 4) the Strategic Platform of the T-Rim Knowledge-Based Economic Circle. All these platforms are accessible to students based on their specific tasks and objectives.

Moreover, the university has reinforced its support for entrepreneurship and collaborated with local governments in Sichuan, Dalian, and Shenzhen to establish off-campus bases jointly. In 2016, in partnership with other top universities in China, the university launched the Innovation and Entrepreneurship Alliance of Universities in the Yangtze River Delta. This alliance effectively brings together government bodies, businesses, social communities, universities, and funding resources in the Yangtze River Delta, harnessing the synergistic advantages of these institutions. In 2018, the university assumed the director role for the Ministry of Education’s Steering Committee for Innovation and Entrepreneurship. Through collaborations with relevant government agencies and enterprises, T-University has continued its efforts to reform and advance innovation and EE, establishing multiple joint laboratories to put theory into practice.

Startup service

In terms of entrepreneurial services, T-University has focused on the employment guidance center and the science and technology Park, working closely with the local industrial and commercial bureaus in the campus area to provide centralized entrepreneurial services. Through entities such as the Shanghai Municipal College Entrepreneurship Guidance Station, entrepreneurship seedling gardens, the science and technology park, and off-campus bases such as the entrepreneurship valley, the university has established a full-cycle service system that is tailored to students’ innovative and entrepreneurial activities, providing continuous professional guidance and support from the early startup stage to maturity.

Notably, the T-University Science and Technology Park has set up nine professional incubation service platforms that cover investment and financing, human resources, entrepreneurship training, project declaration, financial services, professional intermediaries, market promotion, advanced assessment, and the labor union. Moreover, the Technology Park has established a corporate service mechanism for liaison officers, counselors, and entrepreneurship mentors to ensure that enterprises receive comprehensive support and guidance. Through these services, T-University has successfully cultivated numerous high-tech backbone enterprises, such as New Vision Healthcare, Zhong Hui Ecology, Tongjie Technology, Tonglei Civil Engineering, and Tongchen Environmental Protection, which indicates the positive effect of these entrepreneurial services.

T-University places significant emphasis on fostering the entrepreneurial climate, which is effectively nurtured through the T-Rim Knowledge-Based Economic Circle and on-campus entrepreneurship activities. Moreover, T-University is dedicated to establishing and cultivating a dynamic T-Rim Knowledge-Based Economic Circle in strategic alignment with the district government and key agencies. This innovative ecosystem strategically centers around three prominent industrial clusters: the creative and design industry, the international engineering consulting services industry, and the new energy/materials and environmental technology industry. These industrial clusters provide fertile ground for graduates’ employment and entrepreneurial pursuits and have yielded remarkable economic outputs. In 2020, the combined value of these clusters surged to a staggering RMB 50 billion, with 80% of entrepreneurs being teachers, students, or alumni from T-University.

This commitment has led to the establishment of an intricate design industry chain featuring architectural design and urban planning design; it also supports services in automobile design, landscape design, software design, environmental engineering design, art media design, and associated services such as graphic production, architectural modeling, and engineering consulting.

The EE system at T-University

T-University has undertaken a comprehensive series of initiatives to promote EE, focusing on four key aspects: entrepreneurial learning, entrepreneurial practice, startup service, and the entrepreneurial climate. As of the end of 2021, the National Technology Park at T-University has cumulatively supported more than 3000 enterprises. Notably, the park has played a pivotal role in assisting more than 300 enterprises established by college students.

In its commitment to EE, the university maintains an open approach to engaging with society. Simultaneously, it integrates innovative elements such as technology, information, and talent to facilitate students’ entrepreneurial endeavors. Through the synergy between the university, government entities, and the market, EE cultivates a cadre of entrepreneurial talent. The convergence of these talents culminates in the formation of an innovative and creative industry cluster within the region, representing the tangible outcome of the university’s “disciplinary chain—technology chain—industry chain” approach to EE. This approach has gradually evolved into the innovative ecosystem of the T-Rim Knowledge-Based Economic Circle.

Findings and discussion

Unified macroscopic objectives of ee.

To date, a widespread consensus on defining EE in practical terms has yet to be achieved (Mwasalwiba, 2010 ; Nabi et al., 2017 ). Entrepreneurial education should strive towards a common direction, which is reflected in the agreement on educational objectives and recommended teaching methods(Aparicio et al., 2019 ). Mason and Arshed ( 2013 ) criticized that entrepreneurial education should teach about entrepreneurship rather than for entrepreneurship. Therefore, EE should not only focus on singular outcome-oriented aspects but also emphasize the cultivation of fundamental aspects such as cognition, abilities, attitudes, and skills.

This study embarks on a synthesis of the EE-related literature, integrating educational objective theory, planned behavior theory, and entrepreneurial process theory. The 4H model of EE objectives, which consists of basic and outcome levels, is proposed. This model aims to comprehensively capture the core elements of EE, addressing both students’ performance in entrepreneurial outcomes and their development of various aspects of foundational cognitive attributes and skills.

The basic level of the EE objective model includes the 4Hs, namely Head (mindset), Hand (skill), Heart (attitude), and Help (support). First, Head has stood out as a prominent learning outcome within EE over the past decade (Fretschner & Lampe, 2019 ). Attention given to the “Head” aspect not only highlights the development of individuals recognized as “entrepreneurs” (Mitra, 2017 ) but also underscores its role in complementing the acquisition of skills and practical knowledge necessary for initiating new ventures and leading more productive lives (Neck & Corbett, 2018 ).

Second, the Hand aspect also constitutes a significant developmental goal and learning outcome of EE. The trajectory of EE is evolving towards a focus on entrepreneurial aspects, and the learning outcomes equip students with skills relevant to entrepreneurship (Wong & Chan, 2022 ). Higher education institutions should go beyond fundamental principles associated with knowledge and actively cultivate students’ entrepreneurial skills and spirit.

Third, Heart represents EE objectives that are related to students’ psychological aspects, as students’ emotions, attitudes, and other affective factors impact their perception of entrepreneurship (Cao, 2021 ). Moreover, the ultimate goal of EE is to instill an entrepreneurial attitude and pave the way for future success as entrepreneurs in establishing new businesses and fostering job creation (Kusumojanto et al., 2021 ). Thus, the cultivation of this mindset is not only linked to the understanding of entrepreneurship but also intricately tied to the aspiration for personal fulfillment (Yang, 2013 ).

Fourth, entrepreneurship support (Help) embodies the goal of providing essential resource support to students to establish a robust foundation for their entrepreneurial endeavors. The establishment of a comprehensive support system is paramount for EE in universities. This establishment encompasses the meticulous design of the curriculum, the development of training bases, and the cultivation of teacher resources (Xu, 2017 ). A well-structured support system is crucial for equipping students with the necessary knowledge and skills to successfully navigate the complexities of entrepreneurship (Greene & Saridakis, 2008 ).

The outcome level of the EE objective model encompasses entrepreneurial intention and entrepreneurial performance, topics that have been extensively discussed in the previous literature. Entrepreneurial intention refers to individuals’ subjective willingness and plans for entrepreneurial behavior (Wong & Chan, 2022 ) and represents the starting point of the entrepreneurial process. Entrepreneurial performance refers to individuals’ actual behaviors and achievements in entrepreneurial activities (Wang et al., 2021 ) and represents the ultimate manifestation of entrepreneurial goals. In summary, the proposed 4H model of the EE objectives covers fundamental attitudes, cognition, skills, support, and ultimate outcomes, thus answering the question of what EE should teach.

Specific implementable system of EE

To facilitate the realization of EE goals, this study developed a corresponding content model as an implementable system and conducted empirical research through a case university. Guided by the 4H objectives, the content model also encompasses four dimensions: entrepreneurial learning, entrepreneurial practice, startup service, and entrepreneurial climate. Through a detailed exposition of the practical methods at T-university, this study provides support for addressing the question of how to teach EE.

In the traditional EE paradigm, there is often an overreliance on the transmission of theoretical knowledge, which leads to a deficiency in students’ practical experience and capabilities (Kremel and Wetter-Edman, 2019 ). Moreover, due to the rapidly changing and dynamic nature of the environment, traditional educational methods frequently become disconnected from real-world demands. In response to these issues, the approach of “learning by doing” has emerged as a complementary and improved alternative to traditional methods (Colombelli et al., 2022 ).

The proposed content model applies the “learning by doing” approach to the construction of the EE system. For entrepreneurial learning, the university has constructed a comprehensive innovation and EE chain that encompasses courses, experimental areas, projects, competitions, practice bases, and teaching teams. For entrepreneurial practice, the university has built a high-level, integrated innovation and entrepreneurship practice platform that provides students with the opportunity to turn their ideas into actual projects. For startup services, the university has established close collaborative relationships with local governments and enterprises and has set up nine professional incubation service platforms. For the entrepreneurial climate, the university cultivated a symbiotic innovation and EE ecosystem by promoting the construction of the T-Rim Knowledge-Based Economic Circle. Through the joint efforts of multiple parties, the entrepreneurial activities of teachers, students, and alumni have become vibrant and have formed a complete design industry chain and an enterprise ecosystem that coexists with numerous SMEs.

Development of a framework based on the TH theory

Through the exploration of the interactive relationships among universities, governments, and industries, TH theory points out a development direction for solving the dilemma of EE. Through the lens of TH theory, this study developed a comprehensive framework delineating the macroscopic objectives and practical methods of EE, as depicted in Fig. 4 . In this context, EE has become a common undertaking for multiple participants. Therefore, universities can effectively leverage the featured external and internal resources, facilitating the organic integration of entrepreneurial learning, practice, services, and climate. This, in turn, will lead to better achievement of the unified goals of EE.

Practical contents and objectives based on the triple helix theory.

Numerous scholars have explored the correlation between EE and the TH theory. Zhou and Peng ( 2008 ) articulated the concept of an entrepreneurial university as “the university that strongly influences the regional development of industries as well as economic growth through high-tech entrepreneurship based on strong research, technology transfer, and entrepreneurship capability.” Moreover, Tianhao et al. ( 2020 ) emphasized the significance of fostering collaboration among industry, academia, and research as the optimal approach to enhancing the efficacy of EE. Additionally, Ribeiro et al. ( 2018 ) underscored the pivotal role of MIT’s entrepreneurial ecosystem in facilitating startup launches. They called upon educators, university administrators, and policymakers to allocate increased attention to how university ecosystems can cultivate students’ knowledge, skills, and entrepreneurial mindsets. Rather than viewing EE within the confines of universities in isolation, we advocate for establishing an integrated system that encompasses universities, government bodies, and businesses. Such a system would streamline their respective roles and ultimately bolster regional innovation and entrepreneurship efforts.

Jones et al. ( 2021 ) reported that with the widespread embrace of EE by numerous countries, the boundaries between universities and external ecosystems are becoming increasingly blurred. This convergence not only fosters a stronger entrepreneurial culture within universities but also encourages students to actively establish startups. However, these startups often face challenges related to limited value and long-term sustainability. From the perspective of TH theory, each university can cultivate an ecosystem conducive to specialized entrepreneurial activities based on its unique resources and advantages. To do so, universities should actively collaborate with local governments and industries, leveraging shared resources and support to create a more open, inclusive, and innovation-supporting ecosystem that promotes lasting reform and sustainability.

There are two main ways in which this paper contributes to the literature. First, this study applies TH theory to both theoretical and empirical research on EE in China, presenting a novel framework for the operation of EE. Previous research has applied TH theory in contexts such as India, Finland, and Russia, showcasing the unique contributions of TH in driving social innovation. This paper introduces the TH model to the Chinese context, illustrating collaborative efforts and support for EE from universities, industries, and governments through the construction of EE objectives and content models. Therefore, this paper not only extends the applicability of the TH theory globally but also provides valuable insights for EE in the Chinese context.

Second, the proposed conceptual framework clarifies the core goals and practical content of EE. By emphasizing the comprehensive cultivation of knowledge, skills, attitudes, and resources, this framework provides a concrete reference for designing EE courses, activities, and support services. Moreover, the framework underscores the importance of collaborative efforts among stakeholders, facilitating resource integration to enhance the quality and impact of EE. Overall, the conceptual framework presented in this paper serves not only as a guiding tool but also as a crucial bridge for fostering the collaborative development of the EE ecosystem.

While EE has widespread global recognition, many regions still face similar developmental challenges, such as a lack of organized objectives and content delivery methods. This article, grounded in the context of EE in Chinese higher education institutions, seeks to address the current challenges guided by TH theory. By aligning EE with socioeconomic demands and leveraging TH theory, this study offers insights into the overall goals and practical content of EE.

This study presents a 4H objective model of EE comprising two levels. The first level focuses on outcomes related to entrepreneurial behavior, including entrepreneurial intentions and performance, which highlight the practical effects of EE. The second level is built as the foundation of the outcomes and encompasses the four elements of mindset, skill, attitude, and support. This multilayered structure provides a more systematic and multidimensional consideration for the cultivation of entrepreneurial talent. The framework offers robust support for practical instructional design and goal setting. Additionally, the research extends to the corresponding content model, incorporating four elements: entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate. This content model serves as a practical instructional means to achieve EE goals, enhancing the feasibility of implementing these objectives in practice.

Moreover, this study focused on a representative Chinese university, T-University, to showcase the successful implementation of the 4H and content models. Through this case, we may observe how the university, through comprehensive development in entrepreneurial learning, practice, services, and climate, nurtured many entrepreneurs and facilitated the formation of the innovation and entrepreneurship industry cluster. This approach not only contributes to the university’s reputation and regional economic growth but also offers valuable insights for other regions seeking to advance EE.

This study has several limitations that need to be acknowledged. First, the framework proposed is still preliminary. While its application has been validated through a case study, further exploration is required to determine the detailed classification and elaboration of its constituent elements to deepen the understanding of the EE system. Second, the context of this study is specific to China, and the findings may not be directly generalizable to other regions. Future research should investigate the adaptability of the framework in various cultural and educational contexts from a broader international perspective. Finally, the use of a single-case approach limits the generalizability of the research conclusions. Subsequent studies can enhance comprehensiveness by employing a comparative or multiple-case approach to assess the framework’s reliability and robustness.

In conclusion, this study emphasizes the need to strengthen the application of TH theory in EE and advocates for the enhancement of framework robustness through multiple and comparative case studies. An increase in the quantity of evidence will not only generate greater public interest but also deepen the dynamic interactions among universities, industries, and the nation. This, in turn, may expedite the development of EE in China and foster the optimization of the national economy and the overall employment environment.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, in particular for the individual informants who come from a small, specific population, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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Acknowledgements

We express our sincere gratitude to all individuals who contributed to the data collection process. Furthermore, we extend our appreciation to Linlin Yang and Jinxiao Chen from Tongji University for their invaluable suggestions on the initial draft. Special thanks are also due to Prof. Yuzhuo Cai from Tampere University for his insightful contributions to this paper. Funding for this study was provided by the Chinese National Social Science Funds [BIA190205] and the Shanghai Educational Science Research General Project [C2023033].

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All the authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Luning Shao, Yuxin Miao, Sanfa Cai and Fei Fan. The first Chinese outline and draft were written by Luning Shao, Yuxin Miao, and Shengce Ren. The English draft of the manuscript was prepared by Fei Fan. All the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Shao, L., Miao, Y., Ren, S. et al. Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study. Humanit Soc Sci Commun 11 , 519 (2024). https://doi.org/10.1057/s41599-024-03024-2

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Traditional Knowledge in Food Activism and Governance

Indigenous values and perspectives for strengthening food security and sovereignty: Learning from a community-based case study of Miskoziibiing (Bloodvein River First Nation), Manitoba, Canada Provisionally Accepted

• 1 Other, Canada
• 2 University of Winnipeg, Canada

The final, formatted version of the article will be published soon.

Despite food security being a significant challenge among many First Nations communities on Turtle Island, there needs to be more empirical, community-based research that underscores the role of traditional food systems and associated values and teachings in Manitoban communities through an Indigenous lens. This research addresses that gap by building upon Indigenous perspectives and knowledges on the status and future directions of food security and sovereignty in Misko-ziibiing (Bloodvein River First Nation). Guided by Indigenous research protocol and using a qualitative research approach, ten in-depth interviews with Bloodvein River First Nation (BVR) and Winnipeg Elders were conducted. Data was also sourced through discussions with local council members, participant observation, and field visits during 2017. The fundamental values and traditional teachings associated with food sovereignty within the community are aligned with the spirit of sharing, including sharing ethics and protocols, social learning within the community, and intergenerational transmission. In recent years, changing environmental, developmental activity, government policies and laws, lifestyle changes and affordability dynamics have continued to threaten the self-determination and food sovereignty of Indigenous peoples in the community. Their perspectives, teachings, and voices are rarely present in any scholarly work. Enhanced intergenerational transmission of traditional teachings, education and language revitalization, and local leadership involvement can strengthen these social and cultural values to enhance Indigenous food security and sovereignty in Miskoziibiing. This research identifies the knowledge and views of Elders, hunters, trappers and fishers, contributing to the current studies associated with traditional food systems and teachings. Strengthening social and cultural traditions and values is vital in working towards Indigenous food governance, sovereignty, and revitalization of their Indigenous food systems.

Keywords: Indigenous values1, social values2, cultural values3, food sovereingty4, food system governance5, Indigenous food security6, traditional foods7, self-determination8

Received: 13 Oct 2023; Accepted: 22 Apr 2024.

Copyright: © 2024 Young, Shukla and Wilson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Shailesh Shukla, University of Winnipeg, Winnipeg, R3B 2E9, Manitoba, Canada

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38 trillion dollars in damages each year: World economy already committed to income reduction of 19 % due to climate change

Even if CO 2 emissions were to be drastically cut down starting today, the world economy is already committed to an income reduction of 19 % until 2050 due to climate change, a new study published in Nature finds. These damages are six times larger than the mitigation costs needed to limit global warming to two degrees. Based on empirical data from more than 1,600 regions worldwide over the past 40 years, scientists at the Potsdam Institute for Climate Impact Research (PIK) assessed future impacts of changing climatic conditions on economic growth and their persistence.

"Strong income reductions are projected for the majority of regions, including North America and Europe, with South Asia and Africa being most strongly affected. These are caused by the impact of climate change on various aspects that are relevant for economic growth such as agricultural yields, labour productivity or infrastructure," says PIK scientist and first author of the study Maximilian Kotz. Overall, global annual damages are estimated to be at 38 trillion dollars, with a likely range of 19-59 trillion dollars in 2050. These damages mainly result from rising temperatures but also from changes in rainfall and temperature variability. Accounting for other weather extremes such as storms or wildfires could further raise them.

Huge economic costs also for the United States and European Union

"Our analysis shows that climate change will cause massive economic damages within the next 25 years in almost all countries around the world, also in highly-developed ones such as Germany, France and the United States," says PIK scientist Leonie Wenz who led the study. "These near-term damages are a result of our past emissions. We will need more adaptation efforts if we want to avoid at least some of them. And we have to cut down our emissions drastically and immediately -- if not, economic losses will become even bigger in the second half of the century, amounting to up to 60% on global average by 2100. This clearly shows that protecting our climate is much cheaper than not doing so, and that is without even considering non-economic impacts such as loss of life or biodiversity."

To date, global projections of economic damages caused by climate change typically focus on national impacts from average annual temperatures over long-time horizons. By including the latest empirical findings from climate impacts on economic growth in more than 1,600 subnational regions worldwide over the past 40 years and by focusing on the next 26 years, the researchers were able to project sub-national damages from temperature and rainfall changes in great detail across time and space all the while reducing the large uncertainties associated with long-term projections. The scientists combined empirical models with state-of-the-art climate simulations (CMIP-6). Importantly, they also assessed how persistently climate impacts have affected the economy in the past and took this into account as well.

Countries least responsible will suffer most

"Our study highlights the considerable inequity of climate impacts: We find damages almost everywhere, but countries in the tropics will suffer the most because they are already warmer. Further temperature increases will therefore be most harmful there. The countries least responsible for climate change, are predicted to suffer income loss that is 60% greater than the higher-income countries and 40% greater than higher-emission countries. They are also the ones with the least resources to adapt to its impacts. It is on us to decide: structural change towards a renewable energy system is needed for our security and will save us money. Staying on the path we are currently on, will lead to catastrophic consequences. The temperature of the planet can only be stabilized if we stop burning oil, gas and coal," says Anders Levermann, Head of Research Department Complexity Science at the Potsdam Institute and co-author of the study.

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• Maximilian Kotz, Anders Levermann, Leonie Wenz. The economic commitment of climate change . Nature , 2024; 628 (8008): 551 DOI: 10.1038/s41586-024-07219-0

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38 trillion dollars in damages each year: World economy already committed to income reduction of 19 % due to climate change

 “Strong income reductions are projected for the majority of regions, including North America and Europe, with South Asia and Africa being most strongly affected. These are caused by the impact of climate change on various aspects that are relevant for economic growth such as agricultural yields, labour productivity or infrastructure,” says PIK scientist and first author of the study Maximilian Kotz. Overall, global annual damages are estimated to be at 38 trillion dollars, with a likely range of 19-59 trillion Dollars in 2050. These damages mainly result from rising temperatures but also from changes in rainfall and temperature variability. Accounting for other weather extremes such as storms or wildfires could further raise them.

Huge economic costs also for the United States and European Union

“Our analysis shows that climate change will cause massive economic damages within the next 25 years in almost all countries around the world, also in highly-developed ones such as Germany, France and the United States,” says PIK scientist Leonie Wenz who led the study. ”These near-term damages are a result of our past emissions. We will need more adaptation efforts if we want to avoid at least some of them. And we have to cut down our emissions drastically and immediately – if not, economic losses will become even bigger in the second half of the century, amounting to up to 60% on global average by 2100. This clearly shows that protecting our climate is much cheaper than not doing so, and that is without even considering non-economic impacts such as loss of life or biodiversity.”

To date, global projections of economic damages caused by climate change typically focus on national impacts from average annual temperatures over long-time horizons. By including the latest empirical findings from climate impacts on economic growth in more than 1,600 subnational regions worldwide over the past 40 years and by focusing on the next 26 years, the researchers were able to project sub-national damages from temperature and rainfall changes in great detail across time and space all the while reducing the large uncertainties associated with long-term projections. The scientists combined empirical models with state-of-the-art climate simulations (CMIP-6). Importantly, they also assessed how persistently climate impacts have affected the economy in the past and took this into account as well.

Countries least responsible will suffer most

 “Our study highlights the considerable inequity of climate impacts: We find damages almost everywhere, but countries in the tropics will suffer the most because they are already warmer. Further temperature increases will therefore be most harmful there. The countries least responsible for climate change, are predicted to suffer income loss that is 60% greater than the higher-income countries and 40% greater than higher-emission countries. They are also the ones with the least resources to adapt to its impacts. It is on us to decide: structural change towards a renewable energy system is needed for our security and will save us money. Staying on the path we are currently on, will lead to catastrophic consequences. The temperature of the planet can only be stabilized if we stop burning oil, gas and coal,” says Anders Levermann, Head of Research Department Complexity Science at the Potsdam Institute and co-author of the study.

Maximilian Kotz, Anders Levermann, Leonie Wenz (2024): The economic commitment of climate change. Nature. [DOI: 10.1038/s41586-024-07219-0]

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https://www.nature.com/articles/s41586-024-07219-0

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