empirical research analysis

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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.

LEARN ABOUT: Behavioral Research

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

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

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

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

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

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

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

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

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

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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.

LEARN ABOUT: Action Research

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

Learn More: Data Collection Methods: Types & Examples

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

LEARN MORE: Population vs Sample

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

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

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

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

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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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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 36min read

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

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

What is Empirical Research?

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

Characteristics of Empirical Research

Empirical research is characterized by several key features:

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

Importance of Empirical Research

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

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

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

How to Conduct Empirical Research?

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

1. Select a Research Topic

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

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

2. Formulate Research Questions

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

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

3. Review Existing Literature

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

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

4. Define Variables

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

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

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

Empirical Research Design

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

Types of Empirical Research

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

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

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

Experimental Design

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

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

Observational Design

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

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

Survey Design

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

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

Case Study Design

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

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

Qualitative vs. Quantitative Research

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

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

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

Data Collection for Empirical Research

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

Sampling Methods

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

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

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

Data Collection Instruments

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

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

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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.

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

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

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

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

How to Report Empirical Research Results?

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

1. Write the Research Paper

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

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

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

2. Create Visuals and Tables

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

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

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

3. Interpret Findings

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

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

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

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

Examples of Empirical Research

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

Social Sciences

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

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

Environmental Science

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

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

Business and Economics

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

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

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

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

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

Conclusion for Empirical Research

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

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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

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• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
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Empirical Research

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

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

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Bibliography

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

Google Scholar  

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

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

Article   Google Scholar  

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

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

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

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

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

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

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

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

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

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Njoku, E.T. (2020). Empirical Research. In: Leeming, D.A. (eds) Encyclopedia of Psychology and Religion. Springer, Cham. https://doi.org/10.1007/978-3-030-24348-7_200051

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Empirical Research: A Comprehensive Guide for Academics 

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

Why Empirical Research?

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

Qualitative and Quantitative Methods

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

Steps for Conducting Empirical Research

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

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

Advantages of Empirical Research

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

Disadvantages of Empirical Research

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

Tips for Empirical Writing

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

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

References:  

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

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• Data and data management

empirical analysis

• Gavin Wright

What is empirical analysis?

Empirical analysis is an evidence-based approach to the study and interpretation of information. Empirical evidence is information that can be gathered from experience or by the five senses. In a scientific context, it is called empirical research .

Empirical analysis requires evidence to prove any theory. An empirical approach gathers observable data and sets out a repeatable process to produce verifiable results. Empirical analysis often requires statistical analysis to support a claim.

The word empirical comes from the ancient Greek word empeiria , meaning experience.

How to conduct empirical analysis

Empirical analysis is based on observable data. It is mainly concerned with what can be experienced and directly observed. Well-conducted empirical analysis sets out what was initially observed, what it expects to observe during testing and what was observed during testing. If the observed results do not match the expected result, then the hypothesis is not supported by the observed data. Empirical research is concerned only with what is observed, not with what makes sense or follows logically. It is closely related to the scientific method .

Empiricism vs. rationalism

Empiricism is often contrasted with rationalism . Rationalism is a school of thought that truth can be determined by starting from simple truths, or axioms, and using logic and reasoning alone to build up to larger truths without needing to verify the truths with reality. A strictly empirical approach is limited to only what can be observed and can only produce results that support, disprove or are neutral to a theory.

Both an empirical and rational approach are needed to produce practical results. A purely rational approach can produce ideas that do not agree with observable reality, while relying on empirical data alone cannot produce new ideas and insights. Making good use of both is the cornerstone of the scientific method.

Quantitative and qualitative research in empirical analysis

Empirical analysis relies on gathering data through quantitative research, qualitative research or a mix of the two.

Quantitative research is related to things that can be quantified or assigned numbers. It deals with things that can be counted or measured. It may also use multiple-choice or closed-ended questions. In quantitative research, if two different people made the same measurements, they would get the same results.

Qualitative research is related to human perception. It deals with likes, dislikes, opinions, thoughts and behavior. It is often gathered in interviews, focus groups or open-ended surveys. Qualitative research can give excellent insight into data, but due to human nature and the difficulty of gathering large amounts of unstructured information, it may not always be reliable.

As an example of quantitative and qualitative research, imagine a firm wanted to determine if its new product was easier to use then its old one, so it observes people using the product. Examples of quantitative data it can gather would be how many people successfully completed the task, how long it took the person to finish, the age of the person and a survey with a rating of one to five of how difficult the person thought the task was. Examples of qualitative data would be what an observer saw while the person was doing the task and an interview afterward.

Empirical research cycle

In 1969, Dutch researcher A.D. de Groot published his five-step empirical research cycle. It has been widely adopted as the most concise way to conduct empirical research. Each step must be conducted in sequence and is as important as the last:

• Observation. Initial observations of a phenomena are made. This sparks an idea or a line of inquiry. Initial empirical data and research into existing information can be done.
• Induction. A probable explanation of the observed phenomenon is proposed. Inductive reasoning is used to take the specific example from step one and infer a generalized explanation for it.
• Deduction. A testable hypothesis is proposed that can support the explanation. Deductive reasoning is used to take the generalized explanation and make a specific prediction that can be tested and observed.
• Testing. Quantitative and qualitative empirical data are gathered. The data is examined, often with statistical analysis. The results can support, refute or be neutral to the hypothesis. Because of the limits of empirical data and human perception, it is not said that the results prove or disapprove the hypothesis, only that they support or don't support it.
• Evaluation. The reasoning, methodology and findings of the experiment are written down, and the conclusions of the researcher are presented. Information relating to any difficulties, challenges and limits of the test are also included. It may also include further possible avenues of research.

As a simple example of the empirical research cycle, imagine you start sneezing when you visit your sister.

• Observation. I do not sneeze at home, I do sneeze at my sister's home and my sister owns a cat, while I do not have a cat.
• Induction. I may be allergic to cats.
• Deduction. I hypothesize that, if I go to the pet store and pick up a cat, I will start sneezing.
• Testing. I went to the pet store, and when I picked up the cat, I started sneezing.
• Evaluation. My trip to the pet store supports the idea that I am allergic to cats. But it was a different type of cat, and it was the same season, So, it may have been hay fever. If I wanted to gather more evidence, I should visit another person with a cat.

Empirical analysis in IT and business

Using empirical analysis is highly effective in IT and in business. These areas can be highly complex, have interrelated factors or delve into human behavior. Because of this, the behaviors of systems or why things happen can be unclear, hard to find, or even counterintuitive or seemingly irrational. Using the evidence-based approach of empirical analysis can help to remove uncertainty in the decision-making process.

Data warehouses and data lakes can create vast amounts of empirical information. By applying empirical analysis methods to this data, new insights can be found. This can include information about customer behavior or business efficiencies. Data analytics falls in this category.

Using A/B testing is a common way to do empirical research on usability. Different users are presented different designs, and by monitoring metrics, such as click-through, the best one can be found.

See also: data collection , data mining , data cleansing , data curation , data validation , big data , quantitative analyst and field of view .

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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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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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Identifying Empirical Research Articles

Identifying empirical articles.

• Searching for Empirical Research Articles

What is Empirical Research?

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

• Primary and Secondary Sources

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

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

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

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

Parts of an Empirical Research Article

Parts of an empirical article.

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

Introduction

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

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

Results 

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

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

Learn the IMRaD Layout: How to Identify an Empirical Article

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

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• Last Updated: Nov 16, 2023 8:24 AM

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Identifying Empirical Research: Home

What is empirical research.

Empirical research is research that is based on observation or experimentation. Typically empirical research is published in peer-reviewed articles by the individuals who conducted the research. Watch the video below to learn about the characteristics of empirical research! 

Identifying Empirical Research

But how do you identify empirical research? Empirical research is typically published in scholarly journals. But not everything in scholarly journals is necessarily empirical research - you still need to carefully evaluate the methods of the article to determine if it is empirical research. 

1. Carefully evaluate the article's Methods and Results sections.  Empirical articles will a) include these sections and b) explicitly state their methodologies and share their results. Evaluate the methodology - are the methods based on observation, a survey, experimentation, etc? Look for charts, data, and other representations in the results section. 

2. Look out for types of articles that are NOT empirical.  Meta-analyses, literature reviews (with no other study components), editorials/letters, book reviews, case studies, opinions. 

3. In some databases, such as PsycINFO, you can limit to empirical research under Methodology in the "Advanced Search" section. Or limit to evidence-based practice" in CINAHL. 

4. In other databases, try using keywords such as empirical research, quantitative method, qualitative method, survey, ethnography, fieldwork or other type of empirical research method.

• Last Updated: Sep 12, 2019 2:43 PM
• URL: https://libguides.usu.edu/empirical_research

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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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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Qualitative and Quantitative Research

What is "empirical research".

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

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

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

Key characteristics to look for:

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

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

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

• Reports research based on experience, observation or experiment
• Tests a hypothesis against real data
• May use quantitative research methods that generate numerical data to establish causal relationships between variables 
• May use qualitative research methods that analyze behaviors, beliefs, feelings, or values 

What does Empirical Research Look Like?

Empirical research studies will be found in peer reviewed , scholarly/academic journals. However,   not all peer reviewed articles are empirical research studies.

Carefully look over articles to determine if they are empirical. This  What Kind of Article Do I Need  guide may prove helpful in clarifying the various types of articles available through the CSU Library. Below are other indications of an empirical article. 

The abstract will mention a study, an observation, an analysis, or a number of participants or subjects.

Data is often collected through a methodology or method such as: from a survey or questionnaire, an assessment or system of measurement, or through participant interviews

As you search for empirical research studies, you will see most feature section headings like these:

• Literature Review 
• Methodology 

In addition, you will likely also see these types of articles feature more than one author and the article's length will be substantial, typically three or more pages. 

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

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

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

What is Empirical Research? 

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

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

Characteristics of Empirical Research

• Research Questions

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

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

• Definition of the Research Variables

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

• Description of the Research Methodology

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

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

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

Uses of Empirical Research

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

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

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

The Empirical Research Cycle

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

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

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

This information is useful for further research. 

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

Examples of Empirical Research 

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

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

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

Empirical Research Data Collection Methods

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

The following are common methods of gathering data in empirical research

• Survey/ Questionnaire

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

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

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

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

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

• Observation

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

How to collect Empirical Research Data with Questionnaire

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

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

Sign in to Formplus

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

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

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

Edit Form Title

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

Edit Form  

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

Customize Form

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

• Share your Form Link with Respondents

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

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

Empirical vs Non-Empirical Research

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

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

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

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

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

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

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

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

Advantages of Empirical Research 

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

Disadvantages of Empirical Research 

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

Conclusion 

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

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

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

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

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

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Research Guide

Chapter 6 the empirical analysis.

Any quantitative research in economics is centered on the analysis we perform on the data we collected. This is the most crucial part of the paper and will define if our work is a success or not (this is, of course linked to having a good research question and a plausible hypothesis).

In this section, I provide a set of guidelines of some of the elements to keep in mind when conducting quantitative research. This material, of course, is not exhaustive as there are many elements we need to take into account, but it may provide you with some structure as to what are the issues we need to keep in mind.

6.1 The Data

There are two different types of data that exist. Experimental data is collected when an experiment or study is conducted to examine the effects of a given policy or intervention. One example may be when looking if there is an increase in vaccination when providing incentives. One group may not receive any sort of incentive, whereas another group may receive a monetary incentive and another one an in-kind incentive. Data is collected to ensure that all the arms in the study have a similar configuration, so when the study is conducted, we can verify that the true effects come from the treatment (the incentives) and not from a different factor affecting the configuration of the sample.

The most popular sort of data, however, is observational data. This information is collected by either administrative sources (think of the U.S. Census data or the World Bank). This data is collected using surveys or accessing historical records. Sometimes, it is hard to use this data for econometric analysis as there is no random assignment of a treatment, so it is harder to elicit the true effect . However, there are multiple tools that we can use to deal with these issues and estimate causal effects.

6.1.1 Data configuration

6.1.1.1 cross-sectional data.

Cross-sectional data includes data on different subjects (individuals, households, government units, countries) for a single time period . This means that we only have one level of analysis and one observation per subject (the i ). This type of data allows us to learn more about the relationship among different variables.

One example of this type of data is the survey on smallholder farmers collected in the Ivory Coast in 2015 by the World Bank, where about 2,500 smallholder farmers were surveyed to ask questions about farming practices, investment and access to financial services.

6.1.1.2 Time-series Data

In this case, data for a single subject is collected during multiple time periods. In this case, the main unit of analysis will be based on time (the t ).

The most common type of data used for this type of analysis is macroeconomic data (GDP, unemployment, etc.) and is highly used to do forecasting.

6.1.1.3 Panel Data

Panel, or longitudinal, data includes multiple observations for each subject Mostly, we are going to see that data is collected for the same object during multiple time periods, so we will see that for the same i , we will have data for multiple t ’s.

This data is highly used in econometrics. One example is, for instance, the number of violent crimes per county (the i ) for the period between 2000 and 2020 (the t ).

It is extremely important to understand the configuration of your data, as this will define the type of econometric analysis that you can conduct.

6.1.2 Describing your Variables

After we have identified the configuration of our data, it is necessary that we think deeper about the configuration of the variables that we will use in our analysis. It is crucial that you identify their characteristics, as well as their distribution. This will then help you evaluate if you need to conduct any sort of transformation to your variables, and understand how to interpret the coefficients of your regressions. Here, I am just including the most relevant aspects of this steps, but you can read Nick Hunington-Kelin’s book for more details.

6.1.2.1 Types of Variables

• Continuous variables : In theory, this variables can include any value, but sometimes they may be censored in some way (for instance, some variables cannot be negative). Some examples of this type of variable are income, for example.
• Count variables : Most times, we treat this variables in the same way as we treat continuous variables, but in this case, these variables represent how many or how much there is of a certain variable (they count). When we plot them, it is clear that these variables are not continuous.
• Categorical variables : Multiple times, surveys include questions that have a pre-set number of values or where the respondent needs to provide an answer that can then be grouped in a given category. For instance, ethnicity, religion, age group, etc. Many times, these variables are or can be transformed into binary (or indicator) variables. A clear example of the former is sex, but a new set of variables for different religions can be created to identify Christians, Jewish, Muslims, and so forth. Depending on the original category, a new set of dichotomous variables can be created to identify if a person identifies with one of these religions.
• Qualitative Variables : Sometimes, responses require a more detailed explanation and therefore cannot be grouped into categories (at least not on first sight). For instance, the ACLED data, a source on conflict data, includes a variable that explains the details of a given conflict event.

6.1.3 Visualizing your Data

After you identify the type of variables you are using in your analysis, it is key that you understand their distribution. What are the different values that a variable can take? How often these values occur?

This can be done in multiple ways. The easiest one is to generate a table for the variable. In Stata, this is done with:

To tabulate a variable in R, you can use:

You can also plot your variables to obtain a clear visualization of their distribution. You can use histograms for non-continuous variables, and density plots for continuous variables.

6.1.4 Distribution

Many times, it is important to know more about the different moments of the distribution of your variables: mean, variance (or standard deviation), skewness, and sometimes, the kurtosis.

Although a visual representation of your data is very useful in these cases, obtaining a table with this information may also be necessary, to also obtain the range of your data, as well as other important characteristics.

In Stata, you can obtain a set of descriptive statistics using:

In R, you can get a range of descriptive statistics using

Why is this important? Because remember, we are trying to draw some inferences from the sample we have and apply it to the real world (to the whole population we are analyzing). Many times, we have some idea of theoretical distribution of the variables we are interested in In most cases, it is plausible to assume a normal distribution (remember the Central Limit Theorem ). This is one of the reasons we prefer larger samples than smaller ones. In some cases, we may get a distribution that is skewed to the right and has a very fat right-tail, but once we obtain the natural logarithm, it becomes normal. This refers to a log-normal distribution. As we proceed with analysis and do hypothesis testing, remember that you are using a limited sample to learn more about a bigger population.

6.2 Initial Description of a Relationship

Once we know how our specific variables are distributed, we may be interested in learning more about how they are linked. We want to see how our independent variable(s) is(are) linked to the dependent variable.

The most straightforward way to do this is by using a scatterplot, where we plot the independent and dependent variable and see how they correlate.

We may also look at some conditional distributions and plot histograms and scatterplots, looking at a subsample of the data or plotting it for different groups.

In addition, we can obtain an initial image on the relationship between X and Y doing a simple OLS regression (with no control variables). We may even plot this fitted OLS line.

For more examples and a more detailed description, please check Nick Hunington-Kelin’s book .

6.3 Handouts

How to Interpret Coefficients?

How to... Conduct empirical research

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

On this page

What is empirical research, the research question, the theoretical framework, sampling techniques, design of the research.

• Methods of empirical research
• Techniques of data collection & analysis
• Reporting the findings of empirical research
• Further information

Typically, empirical research embodies the following elements:

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

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

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

Let's look at some examples:

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

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

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

RQ1:  What objectives underpinned the corporate brand?

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

RQ3:  How was the desired rearticulation promoted to shareholders?

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

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

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

Or sometimes the RQ is stated as a general objective:

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

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

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

or as a proposition:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Interpretivism

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

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

Inductive and deductive approaches

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

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

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

Sampling may be done either:

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

Useful articles

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

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

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

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

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What's in the rest?

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

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Reference Guide: Searching for Empirical Articles

• Open Access Journals
• Requesting Items from OneSearch
• Submitting an ILL request manually
• Checking on your Requests/Loans
• Google Scholar
• Faculty Resources
• Primary & Secondary Sources
• Looking up if it is Peer-Reviewed
• Grey Literature
• Videos & Tutorials
• Searching for Empirical Articles
• Impact Factors
• Annotated Bibliography vs. Literature Review

What is Empirical Research?

Empirical research  is conducted based on observed and measured phenomena and derives knowledge from actual experience, rather than from theory or belief.  Empirical research articles are examples of primary research.

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)
• The article abstract  mentions a study, observation, analysis, # of participants/subjects .
• The article includes  charts ,  graphs , or  statistical analysis .
• The article is substantial in size, likely to be  more than 5 pages  long.
• The article contains the following sections (the exact terms may vary): abstract, introduction, methodology , results , discussion, references.
• Empirical research is often (but not always) published in peer-reviewed academic journals.

Finding Empirical Research in the Databases

Most databases will not have a simple way to only look at empirical research. In the window below are some suggestions for specific databases, but here are some good rules of thumb to follow:

Search subject-specific databases - Multipurpose databases can definitely contain empirical research, but it's almost always easier to use the databases devoted to your topic, which should have more topical results and will respond better to your keywords.

Select "Peer-reviewed Journals" - Not all empirical research is published in academic journals. Grey literature is a great place to search, particularly in the health sciences. However, grey literature can be difficult to identify, so it is recommended to search the databases until you are more comfortable identifying empirical literature.

Check the abstract / methods - Most articles will not have the phrase "empirical research" in their title, or even in the whole article. A better place to get an idea of what the article contains is by looking at the abstract and the methods section. In the abstract, there will usually be a description of what was done in the article. If there isn't, look in the methods. Ideally, you can get an idea of whether original research is being conducted or if it's reviewing it from other sources.

Consider your keywords - Think about what types of methods are used in empirical research and incorporate those into your keywords. or example, searching for "sleep loss" will certainly bring back many articles about that subject, but "sleep loss and study" might yield some results describing studies being conducted on sleep loss.

The box to the right features some typical methods of conducting empirical research that you might consider including in your search terms.

Empirical research search terms

• observation
• questionnaire
• participants

Specific database examples

• CINAHL Plus
• APA PsychINFO
• Science Direct
• Linguistics and Language Behavior Abstracts
• CINAHL Complete This link opens in a new window CINAHL, the Cumulative Index to Nursing & Allied Health Literature, is a comprehensive research tool for nursing, allied health, public health, biomedicine, and related fields. It provides indexing for articles from 5,400 journals in the fields of nursing and allied health. This database provides full text access to more than 1,300 journals dating back to 1937.
• Use the "Advanced Search"
• Type your keywords into the search boxes
• Below the search windows, check off "Evidence-Based Practice" in the "Special Interests" menu
• Choose other limits, such as published date, if needed
• Click on the "Search" button
• Empirical Research
• Experimental Studies
• Nonexperimental Studies
• Qualitative Studies
• Quantitative Studies
• PubMed This link opens in a new window A comprehensive index to biomedical and life sciences journals with citations to over 18 million articles back to 1948. Note: To limit to full-text articles, search PUBMED CENTRAL.

There are 2 ways to find empirical articles in PubMed:

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

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

Another alternative is to construct a more sophisticated search:

• From PubMed's main screen, click on "Advanced" link underneath the search box
• On the Advance Search Builder screen type your keywords into the search boxes
• Change one of the empty boxes from "All Fields" to "Publication Type"
• To the right of Publication Type, click on "Show Index List" and choose a methodology that interests you. You can choose more than one by holding down the "Ctrl" or "⌘" on your keyboard as you click on each methodology
• APA PsycINFO This link opens in a new window Available via EBSCO. The American Psychological Associations (APA) notable database for locating abstracts of scholarly journal articles, book chapters, books, and dissertations. This resource is the largest of its kind dedicated to peer-reviewed literature in behavioral science and mental health, and it also includes information about the psychological aspects of related fields such as medicine, psychiatry, nursing, sociology, education, pharmacology, technology, linguistics, anthropology, business, and law. Material is drawn from over 2,000 periodicals in more than 20 languages.

To find empirical articles in PsycINFO:

• Scroll down the page to "Methodology," and choose "Empirical Study." There are more specific methodologies below.
• Choose other limits, such as publication date, if needed

To find empirical articles in ScienceDirect:

• Click on "Advanced Search" to the right of the search windows
• On next page, click on "Show all fields"
• Under "Article Types," select "Research Articles," or any other type of article which might be helpful.
• Slick Search
• Case Studies
• Qualitative Analysis
• Quantitative Analysis
• Statistical Analysis
• ERIC This link opens in a new window Abstracts (and in some cases, full-text) articles, reports, book reviews and government documents covering all aspects of education from 1966 to the present
• Action Research
• Ethnography
• Evaluation Methods
• Evaluation Research
• Experiments
• Focus Groups
• Field Studies
• Mail Surveys
• Mixed Methods Research
• Naturalistic Observation
• Online Surveys
• Participant Observation
• Participatory Research
• Qualitative Research
• Questionnaires
• Statistical Studies
• Telephone Surveys

Empirical Articles - Sample Research Tips -- CAS & PSYC 101 / PSYC 341 IN-PERSON & ONLINE -- ACCESSIBLE VERSION

This  guide  helps to identify the major parts of an empirical article and covers sample strategies for locating them through databases such as  APA PsycInfo  and  ERIC . There are also general tips applicable to other databases.

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Quantitative and Empirical Research vs. Other Types of Research: Quantitative Research

• Quantitative Research
• Other Types of Research
• What are Scholarly Journals?

     P rofessors often want you to use scholarly journal articles for your assignments.

     Sometimes, they will require you to use scholarly journal articles that contain quantitative research .

DEFINITIONS

QUANTITATIVE

     Quantitative research looks at factors that can actually be measured in some way, in other words, quantified . It produces numerical results that can be analyzed statistically.

     Quantitative research commonly involves experimentation, surveys, or questionnaires in the context of a large, randomly selected group.

     The term  empirical research  is often used as a synonym for quantitative research, but strictly speaking, empirical research is simply any form of research based upon direct observation. It might also be quantitative, but it might not.

PLEASE NOTE: Some professors use these two terms interchangeably.  When this occurs, they are usually referring to articles that fit the quantitative description above.

HINT: Don't use the words "quantitative" or "empirical" in your keyword searches.  They usually do not appear in article titles, abstracts, or subject words.  Instead, check the articles you find to see if some sort of numerical measuring and statistical analysis is present along with the characteristics listed on the right.

CHARACTERISTICS OF QUANTITATIVE RESEARCH

      W atch for these features when determining if an article has quantitative research. They may appear in the abstract, or you may need to skim the text of the article to find them.

• Introduction : a statement of background or purpose (what was being studied and why). May review prior studies on the same topic.
• Description of the design and/or method of the study (the experimental group or sample , control, variables, number of test subjects, test conditions, etc.)
• Results , or report of the findings (in numeric form as tables, charts, or graphs, etc., often with statistical analysis)
• Conclusions that can be drawn from the results (may be labeled  discussion or significance )
• Footnotes and/or a bibliography
• Author credentials (degrees earned, where they work, etc.)  

SAMPLE QUANTITATIVE RESEARCH ARTICLES

• Relations Between Trait Impulsivity, Behavioral Impulsivity, Physiological Arousal, and Risky Sexual Behavior Among Young Men
• Nocturnal Heart Rate Variability in Patients Treated with Cognitive–Behavioral Therapy for Insomnia.
• Characterisation of Mainstream and Passive Vapors Emitted by Selected Electronic Cigarettes

• Next: Other Types of Research >>
• Last Updated: Apr 6, 2023 8:16 AM
• URL: https://libguides.csusb.edu/quantitative

ORIGINAL RESEARCH article

Research on the impact of technology mergers and acquisitions on corporate performance: an empirical analysis based on china’s pharmaceutical industry.

• 1 School of Business Administration, Shenyang Pharmaceutical University, Shenyang, China
• 2 Drug Regulatory Research Base of NMPA, Research Institute of Drug Regulatory Science, Shenyang Pharmaceutical University, Shenyang, China

There is intense competition among pharmaceutical companies with the rapid growth of the global pharmaceutical industry. In recent years, China has continuously increased the reform of the medical system. Technology mergers and acquisitions (M&A) in China’s pharmaceutical industry have emerged in this complex policy and economic background. This paper conducts an empirical study from the dual perspectives of financial performance and innovation performance, based on unbalanced panel data of Chinese listed pharmaceutical firms from 2012 to 2022. The impact of technology M&A on firm performance is analyzed in terms of the heterogeneity of firm characteristics. Meanwhile, the relationship between R&D investment in technology M&A and firm performance is examined. The results show that technology M&A can promote the performance of pharmaceutical companies, and R&D investment has a mediating effect on the impact of technology M&A on corporate performance. Based on the above findings, this study enriches the relevant literature on technology M&A in the pharmaceutical industry, provides warnings and suggestions for pharmaceutical companies to improve corporate performance through technology M&A, and provides reference materials for future policy formulation.

1 Introduction

Competition among enterprises is intensifying in the context of global economic integration ( 1 ). Governments have intensified their scientific and technological innovation backing to gain a competitive edge. It is an important indicator that the innovation capacity of pharmaceutical companies can measure a country’s strength in science and technology innovation ( 2 ). The pharmaceutical market has opportunities and challenges when China is economically transforming and upgrading ( 3 ). Pharmaceutical companies can improve their innovation performance in two ways: internal research and development (R&D) and external mergers and acquisitions (M&A). It is relatively slow to depend on internal R&D to enhance technology because of limitations in technical resources and research and development professionals ( 4 ). Therefore, there is a growing tendency among pharmaceutical companies to pursue advanced pharmaceutical development technologies to acquire specialized technological resources from external sources and to use technology mergers and acquisitions to achieve leapfrog innovation ( 5 ).

In recent years, China has been increasing its efforts to reform the pharmaceutical system and to develop technological innovations in pharmaceutical enterprises ( 6 ). There have been mergers and acquisitions of companies in the pharmaceutical industry in China’s complex policy and economic background. It is increasingly recognized among pharmaceutical companies that they can gain access to advanced technologies and products by engaging in technological mergers and acquisitions, which allows them to establish an advantageous market position ( 7 ). However, China’s pharmaceutical industry is relatively dispersed regarding industrial structure. Although the number of pharmaceutical enterprises is large, the scale is generally small, and the innovation ability is relatively weak. There is an apparent gap between Chinese pharmaceutical companies and large multinational pharmaceutical companies in developed countries regarding research and development capabilities, innovation capabilities, financial strength, etc. ( 8 ). China has implemented many measures to promote and facilitate the technological advancement of pharmaceutical companies in recent years. As an illustration, the government will offer tax advantages, financial assistance, and other policy measures to stimulate firms to enhance their investment in research and development. These policies create a favorable external environment and conditions for Chinese pharmaceutical businesses to engage in technology mergers and acquisitions, making such mergers and acquisitions an essential means to boost the development of the pharmaceutical industry.

The earliest concept of technology mergers and acquisitions (M&A) is Williamson’s 1975 proposal that technology M&A is a mergers and acquisitions activity with the primary goal of acquiring the target’s technological resources ( 9 ). Technology M&A is a highly effective technique for companies to quickly get innovative resources and strengthen their ability to innovate technologically in response to changes in their business models ( 10 ). Mergers and acquisitions among enterprises originated in the United States and have since expanded worldwide ( 11 ). Scholars from various countries have researched technology mergers and acquisitions, with the most influential researchers appearing in Sweden and the United States. Jacobsson and Granstrand revealed in 1983 and 1984, respectively, that small firms in technology M&As have seller characteristics in deal offers in the M&A market due to their advanced technology patents. Granstrand ( 12 ) discussed the role of technology M&A in the mergers and acquisitions process using the “theory of technology-based enterprises.” Wang and Han ( 13 ) examined how the absorptive capacity of American companies might influence technological innovation as a moderator. Enterprises’ absorptive capacity enables them to incorporate and utilize external technological information as internal knowledge effectively. It is a relatively late research on technology mergers and acquisitions in China. Wu et al. ( 14 ) examined the developmental trajectory of China’s firms’ technological prowess, as demonstrated through their adoption of new technologies, expansion of production capacity, and ability to innovate. The primary purpose of leading companies engaging in M&A is to address their deficiencies in specific areas, enhance the diversified growth of their technology research and development, and lay the foundation for comprehensive technological innovation in the future. Based on existing research on technology M&A, one view is that technology mergers and acquisitions can efficiently address R&D disadvantages and enhance the knowledge capacities of acquiring organizations ( 15 ). Simultaneously, technology M&A provides exit channels other than IPOs for the founders of target firms, thus reducing their entrepreneurial risk ( 16 ). This initiative aims to incentivize target firms to enhance their investment in research and development and intensify their efforts in technological knowledge innovation ( 7 ). Furthermore, Ghosh et al. ( 8 ) propose that technology M&A offers a faster way to obtain external technological resources than internal research and development. It is an effective way for enterprises to master the technical knowledge of the target enterprise and absorb high-tech talents. Another view is that technology M&A may hinder the improvement of internal research and development capabilities if firms rely too much on external technological resources. The enterprise’s intangible resources are not effectively accumulated, and the absorption of external knowledge resources will be negatively affected ( 17 ). Szucs ( 18 ) argues it will diminish the enterprise’s ability to innovate independently if the primary objective of technological mergers and acquisitions is to evade market competition rather than efficiently incorporate and utilize the acquired technology.

To sum up, the extant literature on technological M&A and enterprise performance lacks consensus and is confined to a singular perspective. Only a few numbers of researchers have empirically investigated technological M&A in the pharmaceutical industry. Different firms are affected differently by undertaking technology M&As. It is essential to consider the diversity of innovation subjects, whose characteristics such as property rights, size, and geographical location should also not be ignored. Therefore, it is important to further investigate the effect of technology M&A on firm performance and understand the mechanisms by which technology M&A enhances firm performance. This paper empirically analyses the impact of technology M&A on firm performance using a fixed-effects approach based on unbalanced panel data of listed pharmaceutical firms in China from 2012 to 2022. In addition, it comprehensively examines the diversity among enterprises with varying ownership characteristics, sizes, and geographic locations. It then further analyses the role of R&D investment as a mediator between technology M&A and firm performance. It offers rich materials for China’s pharmaceutical industry to launch technology M&As, aiding firms in gaining a deeper understanding of technological expansion through M&A to enhance their innovation capabilities. Furthermore, this article analyzes the outcomes of the present government’s strategy to encourage technological advancement in China and offers pertinent information for future policy development using the most recent sample data.

Compared with the existing results, this research offers possible contributions as follows: Firstly, it is the inaugural empirical study that examines financial performance and innovation performance from a dual perspective. The extensive literature on company performance mostly concentrates on individual performance indicators. This paper integrates the two variables into a single variable for research and analysis to systematically evaluate the influence of technological mergers and acquisitions on the performance of pharmaceutical manufacturing companies. It complements the limited theory and empirical evidence available in this field. Secondly, this paper explores the relationship between technology M&A and firm performance and frames the study of R&D inputs to explore its mechanism as a mediating variable. Existing literature has conducted a great deal of research and studies from the perspective of the respective impact of R&D inputs and technology M&A on firm performance, and some scholars have made several studies from the relevant aspects of individual firms. This paper will provide R&D inputs into the study of technology M&A and its impact on company performance, enriching the relevant literature. Thirdly, there are fewer studies on technology M&A in China’s pharmaceutical companies. This paper specifically studies the impact of technology M&A on firm performance in the pharmaceutical industry with Chinese characteristics (heterogeneity) to provide a realistic reference for the current innovation practice of pharmaceutical enterprises.

The rest of the paper is organized as follows. In the “Literature Review and Research Hypotheses” section, this paper reviews the previous literature and puts forward the hypotheses of this paper. The “Research Design” section provides data sources, variable selection, and model setup. The “Empirical Analysis” and “Heterogeneity Analysis” sections discuss the results. Finally, “Conclusions and Recommendations, Shortcomings and Prospects” is given.

2 Literature review and research hypothesis

2.1 technology mergers and acquisitions and firm performance.

With the growing requirement for enterprise innovation, obtaining external technical resources has become a major incentive for companies to combine and acquire. In theory, the mechanism by which companies rely on acquisitions to enhance innovation output is mainly reflected in two aspects. One is the selection mechanism; it is more efficient for companies with poor innovation capabilities to acquire innovation by acquiring companies with substantial expertise or ready-made patents than a direct investment in independent innovation ( 19 ). Cassiman et al. ( 20 ) argues that the principal merger party will purposefully select target firms that possess their missing technological knowledge so that they can update their existing knowledge after the technology acquisition. The study conducted by Chen ( 21 ) shows that there is a very important role in the development of new ideas and the existing knowledge base of firms in enhancing innovation core competitiveness after M&A. The second is synergy; it will be enlarged to cover the knowledge stock of the leading merging company after a technology M&A. It is beneficial for enterprises with a deep stock of knowledge to absorb external technological resources, which enhances their innovation capacity and increases the firm’s innovation output ( 22 ). Zhang et al. ( 23 ) showed that technology M&A can avoid the knowledge cocoon trap and innovation path dependence generated by long-term independent research and development, which can rapidly update and expand the existing knowledge stock of enterprises. Enterprises have complementary technological resources to enhance innovation power because of the synergistic effect.

Technology mergers and acquisitions are an effective strategy for enterprises to acquire innovative resources and enhance corporate performance rapidly. According to Zhao ( 24 ), an analysis of M&A cases across various industries in the United States between 1984 and 1997 revealed that M&A transactions driven by the goal of technology innovation are a common phenomenon. It is through technology M&As that companies, especially those with a weak innovation capacity before the M&A, will increase the number of patents obtained. Entezarkheir and Moshiri ( 25 ) argues that M&A significantly positively affects corporate innovation, and heterogeneity will exist across industries. Chinese scholars have shown that technology mergers and acquisitions clearly influence firms’ innovation performance ( 26 ). Qu ( 27 ) analyzed the intrinsic link between a company’s technology M&A and innovation performance and found that complementary and substitutive technology M&As significantly boost the firm’s innovation performance. Also, the study conducted by Yang and Zhou ( 28 ) demonstrated that the impact of technical innovation resulting from technology M&As becomes more evident when the acquired firm experiences significant growth. Wu et al. ( 29 ) investigated the effect of knowledge integration on firms’ innovation performance based on different technology M&A modes. Enterprises will change their knowledge base regarding width and depth when they carry out two modes of technology M&A. Therefore, the performance of innovation in enterprises will yield varying outcomes.

Additionally, Nesta and Saviotti ( 30 ) conducted an empirical study on the pharmaceutical industry and concluded that the higher the acquired firm’s technological R&D base, the more effective it is in improving the R&D capability of the primary acquiring firm after the merger. Also, it is more conducive for firms with a solid long-term technological R&D base to enhance post-merger innovation performance. Lin and Jang ( 31 ) examined merger and acquisition data from the United States pharmaceutical industry and argued that complementarities between firms can improve technological development and innovation. Firms should find companies in the same industry as theirs that match their size and technology for strategic integration. Hao and Ren ( 32 ) studied the evolution of issues related to technology M&A in high-tech enterprises. They proposed that the impact of technology M&A on the technological integration of enterprises varies depending on the industry. Technology M&A particularly promotes R&D in the pharmaceutical industry and suggests relevant countermeasures for enterprises and authorities. Yu and Wang ( 33 ) used a double-difference method to compare the innovation performance of technology M&As between firms that executed M&As and firms that did not perform M&As throughout different policy stages. The study results showed that firms engaging in technology M&As could improve their innovation performance in the short run before implementing the policy. Still, the innovation effect was negative in the long run. After implementing the policy, firms that engage in technology M&As show adverse innovation effects in the short term. The study results provide a realistic reference for the future decision-making of enterprises and the establishment of national policies.

Based on the combination of the above theories and literature, this paper proposes the following research hypotheses:

H1 : Technology mergers and acquisitions positively affect firm performance.

2.2 The mediating role of R&D investment

There are two basic approaches to innovation for enterprises: closed innovation and open innovation. Closed innovation is mainly based on internal R&D, and R&D investment is the core bloodline of firms’ innovation activities ( 22 ), and firm performance is closely related. It can enhance the enterprise’s independent R&D capability, which starts from within the enterprise to invest enterprise resources in R&D activities. Enterprises can master the core technology and form their core competitiveness through independent R&D to occupy a favorable position in the market ( 34 ). One of the most important avenues for open innovation is technology M&A, which allows for rapid access to external technological resources and core knowledge capabilities and improves the firm’s innovation ability ( 35 ). Compared with the long time, high risk, and high investment required for independent R&D, technology M&A can more rapidly acquire the technological knowledge the target firm holds ( 4 ). Firms relying solely on internal R&D to realize innovation can increase riskiness in the context of the increasing speed of innovation iteration ( 36 ). There is a growing realization in enterprises that innovation does not only come from within the enterprise; external resource integration is also an essential part ( 37 ).

Williamson’s Transaction cost theory (9) states that external technological resource acquisition replaces internal R&D skills. Companies experience transaction costs when they acquire external technology resources, which impact the incorporation of these resources within the company. Cohen and Levinthal ( 6 ) contend that the firm’s internal R&D capacity plays a significant role in assimilating and innovating external resources. However, it will negatively impact the company’s performance due to an excessive dependence on external technology resources and a lack of logical incorporation of externally acquired technological resources. Hitt et al. ( 38 ) and Jensen ( 39 ) propose that the reduction in R&D spending by innovative companies following mergers and acquisitions diminishes their level of R&D intensity. Firms interrupt their existing development plans to use the target company’s resources better and spend a lot of time on strategic adjustments at the managerial level, slowing down technological innovation in the company. It has also been argued that inadequate integration measures are not taken after a merger or acquisition, or if there is inertia in autonomous innovation due to technology purchase, this can negatively impact a firm’s innovation performance ( 40 ). In addition, Wang and Ma ( 36 ) discovered that the R&D expenditure of the dominant party involved in a merger and acquisition has a moderating effect on the process using a multiple regression model. This moderation promotes the combination of resources and collaborative innovation. Gandal and Scotchmer ( 41 ) highlighted that corporate governance issues influence the optimal selection of R&D investment by decision-makers, which subsequently impacts the efficiency of using external technological resources.

In summary, this paper argues that after technology mergers and acquisitions, adequate integration measures are taken on acquired technological resources by increasing R&D investment, which leads to a growth trend in firm performance. Based on this analysis, this paper proposes the hypothesis:

H2 : R&D investment mediates the effect of technology mergers and acquisitions on firms’ innovation performance.

3 Materials and methods

3.1 sample selection and data source.

This paper selects the information on M&A events of Chinese A-share listed pharmaceutical companies from 2012 to 2022 as the research sample. Based on data availability and accuracy, this paper excludes ST and *ST, PT, and companies with missing relevant data. For multiple M&A events of the same company in the same year, the first M&A event in a year is selected. Finally, 1,418 unbalanced panel observations for 145 firms that meet the requirements are obtained. To eliminate the impact of outliers on the study, this paper uses Stata16.0 software to perform bilateral shrinkage of the relevant variables. The financial data of listed companies are mainly obtained from the China Stock Market and Accounting Research Database (CSMAR, https://data.csmar.com/ ) and Juchao Information Network 1 to find the annual reports of enterprises. Patent data comes from the China Research Data Service Platform (CNRDS, https://www.cnrds.com/ ).

3.2 Variable selection and definition

3.2.1 explained variable.

The paper studies the effects of technology mergers and acquisitions on enterprise performance by thoroughly analyzing innovation and financial performance variables. It refers to the study by Gu and Xie ( 42 ) which selects return on assets (ROA) as the metric to evaluate corporate financial performance. Patents are highly effective in measuring innovation performance, as they provide significant exclusivity and can explain the rise in performance output resulting from technological innovation. A greater quantity of patent applications typically signifies a higher degree of innovation performance exhibited by a company. Experts commonly assert that the quantity of patent applications provides a more accurate indication of the extent of innovation compared to the number of grants. This is due to the fact that patent approvals necessitate evaluation and payment of yearly fees, leading to greater unpredictability and volatility. Thus, this study uses the total number of patent applications increased by one as the logarithm of the innovation performance indicator for measurement.

3.2.2 Explanatory variables

Technology mergers and acquisitions (Tma) is a dummy variable, assigned a value of 1 if a technology merger or acquisition occurs in an enterprise. Otherwise, it is assigned a value of 0. Technology mergers and acquisitions provide a direct means of accessing the technological resources of the target firm and achieving the substitution and complementation of production technology. This paper defines technology M&A according to Ahuja and Katila ( 43 ). Technology M&A refers to M&A events involving listed businesses that meet one of the following three criteria: (i) the announcement of the M&A by the businesses listed clearly states that the goal of the M&A is to get technology. (ii) The target company possesses patented technology within 5 years prior to the date of the M&A announcement. (iii) The listed companies are classified as high-tech enterprises. This study utilizes the CSMAR database to extract the 2012–2022 M&A information table of listed businesses. We next manually examine the relevant papers to ascertain if the M&A events fall under the category of technological M&A. After applying the aforementioned criteria, we obtained a total of 293 samples of M&A events that satisfy the specified conditions.

3.2.3 Mediating variable

Research and development (RD) investment. To measure R&D investment, the empirical practice of Guo ( 44 ) measures the R&D intensity of enterprises by the proportion of R&D expenditure to operating revenue.

3.2.4 Control variable

The magnitude of a company’s assets directly impacts its capacity to effectively incorporate post-merger technology. Information asymmetry can create market flaws that lead to financing limits for organizations. Companies with large levels of debt typically incur more risks, which in turn restrict their involvement in technological mergers and acquisitions. Companies that experience higher rates of growth in their operating revenue typically possess stronger skills for achieving growth. They are more likely to be preferred by the capital market in mergers and acquisitions. In this paper, we cite the works of Hui et al. ( 45 ), Wang and Huang ( 46 ), and Hong ( 47 ) to regulate the variables of firm size, asset-liability ratio, financing constraints, operating income growth rate, and total asset turnover. This is done to enhance the scientific rigor and dependability of the study by managing other potential confounding factors. See Table 1 for specific measurements.

Table 1 . Variables and their definitions.

3.3 Model construction

3.3.1 fixed effects model.

To test the impact of technology mergers and acquisitions on corporate performance, this paper refers to the research of Hou ( 48 ). It combines theoretical analyses and the design of research indicators to construct the following econometric model:

Where i denotes individual firms, t denotes the year, and Y it s dependent variables are financial performance (Roa) and innovation performance (Invia). The independent variables are technology mergers and acquisitions (Tma) and all other possible control variables (controls), respectively. firmi and year t denote individual and time-fixed effects, respectively, and ε i , t is a random error term. Considering that the individual perspective of pharmaceutical enterprises is not affected by time (enterprise ownership, high-tech enterprise qualification) and the time perspective is not affected by individual changes in the enterprise (industrial structure, GDP growth, years of education of the provincial population, and the macroeconomic environment), and thus the empirical design includes the enterprise individual fixed effects and the year fixed effects, the constructed model (1) is a bidirectional fixed effects model.

3.3.2 Mediating effect model

To explore the relationship between technology mergers and acquisitions, R&D investment, and enterprise performance, according to the three-step mediating effect model proposed by Wen et al. ( 49 ), this paper adds the mediating variable R&D investment (Rd) based on the above-fixed effect model (1), and constructs the model as follows:

In Equation (2) , RD it is the mediating variable R&D input, and the rest of the symbols have the same meaning as in Equation (1) above Equation (3) , is based on Eq. (1) , with the addition of the variable of R&D investment, which is used to test the effect of technology mergers and acquisitions and R&D investment on corporate performance.

4 Empirical analyses

4.1 descriptive statistics analysis.

The descriptive statistics of each variable are shown in Table 2 . It can be seen that the return on assets (Roa) of enterprises ranges from −0.154 to 0.226, with an average value of 0.061, indicating that the financial status of enterprises varies greatly. The number of invention patent applications (Invia) ranges from 0 to 1.857, with an average of 0.893, indicating some variation in firms’ innovation performance and that most pharmaceutical firms have low innovation performance. For the explanatory variables, the mean value of Technology Mergers and Acquisitions (Tma) is 0.207, and the variance is 0.405. The range of Research and Development (RD) is 0.32 ~ 23.06, and the variance is 3.892, which is a significant difference, indicating a great difference in Research and Development (RD) among enterprises. There is a great fluctuation in R&D investment in each company and each year. The maximum value of the capital debt ratio (Lev) is 0.815, and the minimum value is 0.042, indicating that the debt capacity of listed companies is not uniform. The minimum value of enterprise growth (Gro) is -0.46, and the maximum value is 1.162, indicating that listed companies’ development ability and growth opportunities in China’s pharmaceutical manufacturing industry vary more significantly. The value of financing constraint (SA) ranges from -4.747 to-3.233, and the larger the value of SA, the larger the financing constraint. The average value of financing constraint is-3.887, which shows that enterprises generally face the dilemma of financing constraint.

Table 2 . Descriptive statistics.

4.2 Correlation analysis

The results of the Pearson correlation analysis between the variables are shown in Table 3 . It can be seen that the correlation coefficient between technology mergers and acquisitions (Tma) and enterprise financial performance (Roa) is 0.351, and the coefficient with innovation performance (Invia) is 0.336; there is a significant positive correlation. This indicates that with the increase in technology mergers and acquisitions, enterprise performance will also improve, which initially verifies H1. Research and development investment (RD) also has a significant positive correlation with enterprise performance (Roa) and innovation performance (Invia), which initially verifies H2. The study results show that the variance inflation factor VI F value is less than 10, and the absolute value of correlation coefficients between the rest of the variables is less than 0.8, indicating that the variables passed the multiple covariance test.

Table 3 . Correlation analysis.

4.3 Benchmark regression results

A fixed effects model is used to test the impact of technology mergers and acquisitions on pharmaceutical firms’ performance, and the regression results of model (1) are shown in Table 4 . Column (1)–Column (4) regressions are all controlled for individual and time effects. Columns (1) and (2) are the effects of technology mergers and acquisitions on the financial performance of enterprises. In column (1), the regression coefficient of technology mergers and acquisitions (Tma) on Roa is 0.0552, which is significant at the 1% statistical level. In column (2), after adding control variables, the coefficient is 0.0149 and still significant at the 1% level, which means that technology mergers and acquisitions can significantly contribute to the enhancement of the financial performance of pharmaceutical enterprises. This implies that technology mergers and acquisitions can significantly promote the financial performance of pharmaceutical companies. Columns (3) and (4) show the effect of technology M&A on innovation performance. Column (3) does not include control variables, and column (4) adds control variables. The regression coefficients of technology M&A are 0.3885 and 0.0881, respectively, which are significant at the 1 percent confidence level, indicating that technology M&A can also enhance the innovation performance of pharmaceutical enterprises; this validates H1. Based on theoretical analysis, enterprises that acquire technology resources for M&As pay more attention to integrating technology and knowledge to quickly absorb the acquired enterprise’s technical knowledge. It can better enhance corporate performance by expanding the scale of their basic knowledge. In addition, by comparing the regression findings in columns (2) and (4), it is evident that technology M&A has a more pronounced impact on pharmaceutical firms’ innovation performance than its effect on financial performance. It could be because technology mergers and acquisitions may prompt firms to adjust their patent policies. Companies can evaluate and modify their patent portfolios based on market demand and technological advancements throughout the merger and acquisition process. As a result, companies may choose to augment their patent applications to align with the changing market conditions and competitive forces.

Table 4 . Benchmark regression results.

4.4 Mediating effects of R&D inputs

Table 5 shows the regression results of research and development investment (RD) as a mediating variable. According to the three-step method of mediating effect, columns (1) and (4) are the results of the benchmark regression of Tma on firm performance, consistent with the above results. Column (2) shows the effect of technology mergers and acquisitions on RD, and the regression coefficient of RD is 1.134 and is significantly positive at the 1% level. It indicates that technology mergers and acquisitions positively impact enterprise R&D investment, and enterprise R&D investment is significantly improved after the enterprise carries out technology mergers and acquisitions. Firms obtain new R&D capabilities and technological knowledge through technology mergers and acquisitions. To fully use these new R&D capabilities, firms increase their R&D investment to develop more innovative products and technologies. Columns (3) and (5) show the effects of technology M&A on firms’ financial performance and innovation performance after adding the mediating variable of RD, respectively. The coefficients of Tma on Roa are 0.00999, which is significantly positive at a 10% statistical level, respectively. The coefficient of Tma on Invia is 0.0673, which is significantly positive at a 5% statistical level. This indicates that R&D investment mediates the effect of technology mergers and acquisitions on enterprise performance, thus verifying the above H2. After a technological merger and acquisition, boosting R&D investment can expedite the company’s integration and assimilation of external technology resources and core knowledge skills. Enterprises can enhance their level of technological innovation by bolstering internal research and development efforts and aggressively leveraging them. Simultaneously, the surge in research and development spending resulting from technological M&A aids firms in effectively adapting to shifts in market demand, thereby enhancing their performance.

Table 5 . Regression results mediated by R&D investment.

In addition, the model passed the Sobel test and Bootstrap test (drawing self-help samples 1,000 times), and the test results are shown in Table 6 . When the explanatory variable is Roa, the direct effect coefficient is 0.009992, and the indirect effect coefficient is 0.00492, which is significantly positive at 1%. When the explanatory variable is Invia, the direct effect coefficient is 0.06782, and the indirect effect coefficient is 0.20802, both of which are significant at 1%. It indicates that research and development investment (RD) as a mediating variable promotes the positive effect of technology mergers and acquisitions on firms’ financial performance, further validating H2.

Table 6 . Mediating effect model Sobel and Bootstrap test.

4.5 Robustness tests

4.5.1 replacement of variable indicators.

Referring to the study of Wang and Huang ( 46 ), this paper replaces the dummy variable of whether the explanatory variable is mergers and acquisitions (Tma) with the ratio of the sum of the amount of all technology merger and acquisition deals initiated by listed companies in the year to the total assets (Ta) to conduct the regression again, and the results are shown in columns (1) and (2) of Table 7 . The coefficient of Roa is 0.1517, the coefficient of Invia is 0.2078, and the regression coefficients of firm performance are all significantly positive at the 1% level. This indicates that technology mergers and acquisitions positively impact firm performance, and the regression results are consistent with the benchmark regression results. The explanatory variable return on assets (Roa) is replaced by return on equity (Roe), and the regression results are shown in column (3) of Table 7 . The coefficient of Roe is 0.0159, which is significantly positive at the 10 percent level. The regression results are consistent with those of the benchmark regression results, which indicates that this paper’s conclusion on the promotional effect of technology mergers and acquisitions on corporate performance is robust.

Table 7 . Robustness test.

4.5.2 Reconstructing the sample

Referring to the empirical research method of Li and Yang ( 50 ), this paper transforms the unbalanced panel data into balanced panel data for regression to ensure sample integrity. The regression results are shown in columns (4) and (5) of Table 7 . The coefficient of Roa is 0.0244, the coefficient of Invia is 0.3121, and the coefficient of technology M&A on firm performance is still significantly positive and significant at the 1% level, indicating that the benchmark regression results are robust and reliable.

4.5.3 Replacement of measurement model

To avoid the influence of problems such as autocorrelation and heteroskedasticity, this paper refers to the study of Zhang et al. ( 51 ), adjusts the heteroskedasticity and clustering of the standard errors, and shows the results in Table 7 . From the regression results in Columns (6) and (7), it can be seen that, after the adjustments to the standard errors, the promotional effect of technology M&As on firm performance is still significant, which once again verifies the robustness of the conclusions of the paper’s study.

4.6 Endogeneity test

The endogeneity problem is usually a 3-pronged problem of omitted variables, bi-directional causality, and measurement error in the variables. To eliminate the possibility of endogeneity problems, this paper adopts instrumental variables and the dynamic panel system generalized moment estimation (SYS-GMM) method for testing. This paper refers to the study of Li et al. ( 52 ) and adopts the explanatory variables (Tma) lagged term (L.Tma) as an instrumental variable, which can keep the obvious correlation between it and the explanatory variables, and also avoid the problem of weak instrumental variables. In addition, the current period’s disturbance terms cannot affect these lagged indicators. Therefore, the instrumental variables are selected to lag the lagged terms of the explanatory variables, which can satisfy the constraints of correlation and homogeneity.

Table 8 shows the results of the instrumental variable test. After controlling for possible endogeneity issues by choosing this instrumental variable, the Roa and Invia coefficients are still positive, the level of significance remains unchanged, and technological M&A still present significant positive incentives for firm performance. The results of this test once again maintain the findings of the previous study, indicating that the results are robust and credible.

Table 8 . Endogeneity test (instrumental variable approach).

In addition, this paper incorporates the lagged one-period of the explanatory variables into the regression model to further address possible endogeneity issues through the SYS-GMM approach. In the SYS-GMM estimation, we consider the lagged one period of the explanatory variables and technology mergers and acquisitions as endogenous variables and use the lagged terms of the explanatory variables as instrumental variables. Roodman ( 53 ) emphasizes that the HansenTest is more robust than the SarganTest regarding heteroskedasticity problems in the model. Hence, this paper reports the results of the HansenTest. The results of the endogeneity test are shown in Table 9 , where we find that the regression coefficients of the explanatory variables return on total assets (Roa) lagged one period is 0.1442, which is significantly positive at the 10% level. The coefficient of the number of invention patent applications (Invia) is 0.2269, which is significantly positive at the 5% level. This indicates that there is an inertia in the firms’ technology M&A decisions and that the outcome of the decision in the previous period significantly affects the technology M&A decisions in the next period. The regression coefficients of the explanatory variable technology mergers and acquisitions (Tma) are 0.0315 and 0.3621, respectively, which are still significantly positive at the 1 percent confidence level. To enhance the reliability of the SYS-GMM estimation results, the rationality of the model setup and the validity of the instrumental variable selection are examined in this paper, respectively. Among them, the test results of AR(2) for second-order serial correlation show that the original hypothesis cannot be rejected, indicating that there is no second-order serial correlation in the residual terms of the dynamic panel. Also, the results of the HansenTest for the test of whether there is over-identification of instrumental variables indicate that the instrumental variables used in the model are appropriate.

Table 9 . Endogeneity test (SYS-GMM).

5 Heterogeneity analysis

5.1 heterogeneity in the nature of corporate equity.

Organizational structures and management styles may vary between firms due to equity variances. This paper examines the nature of equity based on information from the actual controller of listed companies in the Cathay Pacific database. The sample is divided into state-owned enterprises and non-state-owned enterprises, including private, foreign, and other types of enterprises. The data is then analyzed using a fixed effects model for regression to determine the difference in the impact of technology mergers and acquisitions on enterprise performance.

Table 10 shows the results of the equity heterogeneity test. Overall, for both state-owned and non-state-owned enterprises, technology mergers and acquisitions significantly impact enterprise performance, which verifies the robustness of the benchmark regression results. Specifically, compared to state-owned firms, technological M&As have a higher positive impact on the performance of non-state-owned enterprises. According to the theoretical study, this could be because state-owned firms have a more comprehensive range of reasons for engaging in technological mergers and acquisitions, and they prioritize objectives other than innovation performance. Non-state-owned enterprises typically encounter heightened market competition and prioritize assimilating technology and knowledge following technology mergers and acquisitions. It enables the enterprises to swiftly incorporate the acquired company’s technological expertise, enhancing benefits. In addition, non-state-owned firms exhibit greater adaptability and prowess in innovation than state-owned counterparts. State-owned firms could face additional legislative limitations and regulations, which could impede their ability to innovate. Technology M&As can offer non-state-owned companies access to fresh technology and expertise, enabling them to enhance efficiency, save expenses, and innovate new products.

Table 10 . Equity heterogeneity regression results.

5.2 Firm size heterogeneity

The size of a firm is a crucial aspect that affects how technology mergers and acquisitions impact the performance of the firm. This paper categorizes the enterprise’s total assets at the end of the period into two groups: the first group includes large-scale enterprises with total assets in the first three quartiles of the size distribution, while the second group includes small and medium-sized enterprises with the remaining total assets.

Table 11 shows the regression outcomes for various company sizes, indicating that technological M&As have a substantial impact on the performance of both large-scale and small-and medium-sized pharmaceutical companies. Specifically, technology M&As have a more significant impact on enhancing innovation performance in small and medium-sized companies than in large-scale organizations. This is because small-scale enterprises typically possess a more uniform business plan, product assortment, and a very uncomplicated management structure. They can respond and take action with incredible speed and adaptability when confronted with technology mergers and acquisitions. Furthermore, they are eager to capitalize on the opportunity presented by technology M&As to enhance technological innovation. However, the inflexibility inherent in the hierarchical structure of large-scale corporations, as opposed to small-scale enterprises, diminishes the motivation for technical innovation. In addition, large corporations own more advanced infrastructure, typically have more sophisticated innovation frameworks, and may require less emphasis on innovation. Technology mergers and acquisitions are more effective in fostering the growth of large-scale enterprises in terms of financial performance. Based on the theoretical analysis, this may be attributed to variations in the knowledge resources, the ability to integrate and absorb new information, and the capability to finance research and development among firms of varying sizes. Technology M&As frequently necessitate a significant financial commitment to support ongoing research and development spending and the integration of resources. Major corporations consistently secure additional research and development funding following a technology merger and acquisition, whereas smaller companies may encounter financial limitations shortly after completing a technology merger and acquisition. Small-sized enterprises may lack the financial resources to cover the increased expenses of mergers and acquisitions and the consequent investment in research and development. Furthermore, small-scale firms often have limited research and development capabilities and struggle to effectively integrate resources, which contrasts with the more robust capabilities of large-scale enterprises. This disparity can result in inadequate technical integration and innovation following a merger or acquisition, ultimately impacting the financial performance of the enterprises.

Table 11 . Regression results of size heterogeneity.

5.3 Regional heterogeneity

The geographical distribution of pharmaceutical firms in China is divided into several regions. Therefore, we categorize the listed pharmaceutical companies in our sample into three groups: East, Central, and West. The sample consists of 648 pharmaceutical enterprises in the East area, 279 in the Central region, and 208 in the West region. Subsequently, we assess the influence of technological mergers and acquisitions on the corporate performance of pharmaceutical firms in these three geographical areas.

Table 12 shows the outcomes of the examination of regional heterogeneity. The regression analysis indicates that technological M&As have a substantial impact on the performance of pharmaceutical companies in the central, eastern, and western areas. More precisely, the impact of technological M&As on the performance of companies is more noticeable in the eastern region than in the central and western areas. The reason for this could be attributed to the fact that the east part of China is primarily located along the coastline, characterized by a flat topography, efficient transportation infrastructure, and the presence of numerous prominent domestic pharmaceutical companies. Consequently, this region has become a magnet for drawing many highly skilled professionals ( 54 ). Pharmaceutical businesses in the eastern area are increasing their collaboration and engagement with major multinational pharmaceutical corporations, displaying greater agility in responding to industry dynamics and adopting a more proactive strategy toward mergers and acquisitions. Relatively speaking, China’s central and western areas began to engage with the international community later, and their infrastructure development is comparatively less advanced. The natural environmental conditions in this area are subpar, and its economic progress is sluggish, characterized by a scarcity of technology, skilled individuals, and financial resources. In addition, according to the theoretical aspect of the preceding analysis, the central and western regions exhibit a generally low level of technology, resulting in limited capacity for digestion and absorption and a comparatively poor technological knowledge base. Consequently, it is difficult to bring about major technological breakthroughs for remote regions because they may struggle to completely comprehend and integrate the company’s technological advancements following the merger and acquisition.

Table 12 . Geographical heterogeneity regression results.

6 Conclusion and recommendations

6.1 conclusion.

The macro background of China’s economic structural transformation requires enterprises to have more robust technological innovation capabilities. Technology mergers and acquisitions (M&A) are ineffective strategies to occupy a favorable market position. This paper empirically analyses the impact of technology mergers and acquisitions (M&A) on enterprise performance with the sample data of Chinese pharmaceutical-listed enterprises from 2012 to 2022. The conclusions are as follows: (i) Benchmark regression results show that technology mergers and acquisitions (M&A) have a significant positive impact on the performance of pharmaceutical enterprises. (ii) Based on the mediation effect model, it is found that there is a mediation effect of R&D investment in the impact of technology M&As on enterprise performance. (iii) Through subgroup regression, the effect of technology M&A on pharmaceutical firm performance is heterogeneous regarding the nature of equity, firm size, and geographic region. Regarding different equity natures, technology M&A has a more vital role in promoting the performance of non-state-owned enterprises than state-owned enterprises. Regarding firm size, technology M&As have a more substantial effect on the innovation performance of small and medium-sized enterprises than large firms and a stronger effect on the financial performance of large firms than small and medium-sized enterprises. Regarding different regions, technology M&As are more effective in promoting the performance of pharmaceutical enterprises in the eastern region.

6.2 Recommendations

Based on the above theoretical and empirical results, combined with the status quo of technology M&A activities and R&D investment of China’s listed pharmaceutical companies, this paper proposes suggestions from both the government and enterprise levels.

At the governmental level, it is imperative for the government to enhance the focus on technology M&As to foster enterprise innovation. Additionally, the government should provide guidance and incentives to pharmaceutical companies to improve their performance using technology M&A. Acknowledging the diversity of technology M&As in enhancing innovation performance across organizations with varying ownership structures, sizes, and geographical locations is essential. When implementing the policy, it is crucial to prioritize efficiency and balance to expedite achieving the goal of supporting technological innovation and enterprise development. It will ensure that technology mergers and acquisitions activities provide the most favorable outcomes. Specifically, the government should consider the different behavior of enterprises with different property rights in the face of technology mergers and acquisitions. The government should provide state-owned firms a permissive environment for technology mergers and acquisitions. It is recommended that the current obstacles preventing state-owned firms from participating in technology M&A be removed by streamlining the administrative license and approval processes. Enterprise innovation necessitates substantial cash; the government can implement policies such as providing financial subsidies and tax incentives. It can alleviate the difficulties encountered by enterprises in the process of technology mergers and acquisitions by strengthening the government’s financial support. Secondly, in the face of the differences between pharmaceutical production enterprises of different sizes. The Government should provide guidance and support to foster the growth of innovative and competitive small and medium-sized firms in the industry. It will modify its policy’s flexibility to enhance the diverse market demand by setting aside a specific market share for qualified businesses. The Government also stimulates large-scale enterprises to merge and acquire overseas high-tech enterprises and integrate domestic resources by establishing special funds and technical support measures to promote competitiveness and innovation in the pharmaceutical market. Finally, the government should fully consider the actual differences in the situation of pharmaceutical production enterprises in the eastern, central, and western regions. It should strengthen support for the M&A and innovation system in those areas and facilitate technical mergers and acquisitions by bringing in talent and bolstering infrastructure to boost innovation in the central and western regions. Simultaneously, the government should establish a cross-regional collaboration platform for enterprises in the eastern, central, and western areas to facilitate the sharing of resources and foster collaborative innovation. This platform encourages the involvement of enterprises from the central and western regions in merger and acquisition activities to enhance their capability and success rate in M&A endeavors.

At the company level, it is crucial for companies to recognize the significance of innovation to differentiate themselves in the competitive market. Technology M&A and investment in R&D are successful strategies for organizations to obtain innovative resources and improve their ability to innovate technologically in response to changes in their business models. State-owned enterprises should leverage their resources and fully capitalize on their strengths. They should demonstrate the bravery to expand internationally and actively engage in mergers and acquisitions to foster innovation. In addition, enterprises must comprehend cutting-edge market trends, consistently acquire and assimilate sophisticated technological expertise, and augment their consciousness of innovation and research and development capabilities. Non-state-owned enterprises must align with national strategies, persist in exploring and innovating, and enhance their investment in research and development. They should focus on innovation and quality and offer a wide range of items to cater to consumers’ diverse needs to gain a competitive edge in the market. It will help them avoid competing with similar products. Large enterprises should leverage their extensive technological expertise and consistently innovate by building upon their existing technology to improve the level of R&D and enhance the core competitiveness of firms in the industry. Small and medium-sized enterprises should evaluate their capacity for growth and determine if they can obtain external technological resources by engaging in technology mergers and acquisitions. It is essential to thoroughly assess the potential risks and advantages of technology M&As and develop a comprehensive M&A strategy that effectively incorporates technology and accelerates technology upgrading smoothly. Also, enhancing the accumulation of financial resources and human capital is imperative. They should exercise stringent control over the allocation of research and development funding, attract exceptionally skilled individuals to increase the scope of corporate knowledge, and strengthen the fundamental competitiveness of firms. In addition, it is crucial to be mindful of market trends and fully comprehend the cutting-edge advancements within the sector. Pharmaceutical enterprises in the central and western regions should enhance their collaboration with external firms that possess robust research and development capabilities, as well as universities and research institutes. By combining industry, academia, and research, they can enhance the existing technology level, breaking through the bottleneck and improving the success rate of technology M&A. At the same time, enterprises should consider improving the welfare treatment of talents and strengthening the training of talents to ensure the long-term development of enterprises.

7 Shortcomings and prospects

This study still has many shortcomings, which may also be worth further exploration. First, this paper only studied the pharmaceutical manufacturing industry in China. Although pharmaceutical manufacturing enterprises are typical representatives of the real economy and high-tech economy, the research object is still limited and can expand the scope of the study to other industries in other countries in the future. Second, due to the availability of data, this paper only considered listed pharmaceutical enterprises, and the phenomenon of technological mergers and acquisitions also exists in non-listed pharmaceutical enterprises. Future research can turn to unlisted companies to fully understand the relationship between technology M&A and firm performance. Third, this paper only analyzes the correlation from the relationship between technology M&A, R&D investment, and firm performance, but in practice, there may be other unobserved factors (such as market competition, managerial decision-making, etc.) affecting firm performance, so it is necessary to explore the correlation analysis more deeply in the future.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Author contributions

JY: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft. JL: Methodology, Validation, Writing – review & editing. SW: Supervision, Writing – review & editing. YC: Supervision, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Liaoning Provincial Department of Education’s 2024 Innovative Development Project for Scientific Research, the Liaoning Provincial Social Science Planning Fund Project (Approval No. L23BGL006), and the Philosophy and Social Science Research Base Project of Shenyang City Social Science Federation (Approval No. SYSK2024-JD-24).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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1. Jaewon, J . Multinational firms and economic integration: the role of global uncertainty. Sustain For . (2023) 15:2801–1. doi: 10.3390/SU15032801

Crossref Full Text | Google Scholar

2. Shantala, S, and Jongwook, K. Best foot forward? The importance of contractual governance mechanisms for innovation from alliances. Technovation . (2023) 127:102828. doi: 10.1016/J.TECHNOVATION.2023.102828

3. Graafland, J . Competition in technology and innovation, motivation crowding, and environmental policy. Corp Soc Responsib Environ Manag . (2020) 27:137–45. doi: 10.1002/csr.1779

4. Fenglian, W, Zongming, Z, and Hua, Z. Independent Research and Development or technology mergers and acquisitions? Decision mechanisms of Research and Development strategies in a two-stage game. Discret Dyn Nat Soc . (2021) 2021:1–9. doi: 10.1155/2021/7300128

5. Sears, J, and Hoetker, G. Technological overlap, technological capabilities, and resource recombination in technological acquisitions. Strateg Manag J . (2014) 35:48–67. doi: 10.1002/smj.2083

6. Marianthi, P, and Konstantinos, T. Healthcare reform in China: challenges and opportunities. Current medical research and opinion. (2018) 34:821–823.

Google Scholar

7. Lee, JY, Shin, K, and Kim, E. The Influence of a Firm’s Capability and Dyadic Relationship of the Knowledge Base on Ambidextrous Innovation in Biopharmaceutical M&As.Sustainability . (2019) 11:4920–4920. doi: 10.3390/su111849

8. Aigner, D, Lovell, CAK, and Schmidt, P. Formulation and estimation of stochastic frontier production function models. J Econ . (1977) 6:21–37. doi: 10.1016/0304-4076(77)90052-5

9. Williamson, O . The economic institutions of capitalism: firms, markets, relational contracting [M] . New York: The Free Press (1985).

10. Higgins, MJ, and Rodriguez, D. The outsourcing of R&D through acquisitions in the pharmaceutical industry. J Financ Econ . (2006) 80:351–83. doi: 10.1016/j.jfineco.2005.04.004

11. Chang, C . Big changes in North America rankings driven by M&A. Focus Catal . (2018) 2018:2. doi: 10.1016/j.focat.2018.11.062

12. Granstrand, O . The economics and management of technology trade: towards a pro-licensing era? Int J Technol Manag . (2004) 27:209–40. doi: 10.1504/IJTM.2004.003953

13. Wang, CF, and Han, Y. Linking properties of knowledge with innovation performance: the moderate role of absorptive capacity. J Knowl Manag . (2011) 15:802–19. doi: 10.1108/13673271111174339

14. Wu, GS, Wang, Y, and Xie, W. Technological growth and management of enterprises in China. Res Dev Manag . (2002) 5:34–40. doi: 10.13581/j.cnki.rdm.2002.05.008

15. Graebner, ME, and Roundy, PT. Success and failure in technology acquisitions: lessons for buyers and sellers. Acad Manag Perspect . (2010) 24:73–92. available at: https://www.jstor.org/stable/29764975

16. Graebner, ME, and Eisenhardt, KM. The Seller's side of the story: acquisition as courtship and governance as syndicate in entrepreneurial firms. Adm Sci Q . (2004) 49:366–403. doi: 10.2307/4131440

17. Guo, S, and Cai, S. Several major issues in the feasibility study of mergers and acquisitions. Technical Economy . (2004) 12:45–47.

18. Szucs, F . M&A and R&D: asymmetric effects on acquirers and targets? Res Policy . (2014) 43:1264–73. doi: 10.1016/j.respol.2014.03.007

19. Aghion, P, and Tirole, J. Formal and real authority in organizations. J Polit Econ . (1997) 105:1–29. doi: 10.1086/262063

20. Cassiman, B, Colombo, MG, Garrone, P, and Veugelers, R. The impact of M&A on the R&D process: an empirical analysis of the role of technological-and market – relatedness. Res Policy . (2005) 34:195–220. doi: 10.1016/j.respol.2005.01.002

21. Chen, FQ, Ge, YH, and Liu, HQ. Overseas M&A integration and industrial innovation: a study based on internal and external knowledge network reconfiguration. Technology Analysis & Strategic Management . (2023) 35:573–585.

22. Rhodes-Kropf, M, and Robinson, TD. The Market for Mergers and the Boundaries of the Firm The Journal of Finance . (2008) 63:1169–1211. doi: 10.1177/21582440231199560

23. Zhang, XY, Liu, YY, Rodan,, et al. The impact of innovation capability on the performance of listed companies in mergers and acquisitions. Fin Resh . (2017) 3:159–75.

24. Zhao, X . Technological innovation and acquisitions. Manag Sci . (2009) 55:1170–83. doi: 10.1287/mnsc.1090.1018

25. Entezarkheir, M, and Moshiri, S. Is innovation a factor in merger decisions? Evidence from a panel of US firms. Empir Econ . (2019) 57:1783–809. doi: 10.1007/s00181-018-1515-7

26. Han, BS . Technology mergers and acquisitions and innovation:Literature review and research outlook. Economic Management . (2017) 39:195–208. doi: 10.19616/j.cnki.bmj.2017.09.013

27. Qu, J . Research on the relationship between corporate technology mergers and acquisitions and innovation performance--an analysis of the moderating role based on strategic matching and technology gap. Sci Manag Res . (2019) 37:122–6. doi: 10.19445/j.cnki.15-1103/g3.2019.02.027

28. Yang, Q, and Zhou, SN. Can technology mergers and acquisitions bring technological innovation effect – the perspective of growth potential of acquiring companies. Sci Technol Prog Counter . (2019) 36:100–8. doi: 10.6049/kjjbydc.Q201908468

29. Wu, J, Liu, SS, Yongxiang, S, et al. The impact of knowledge base and knowledge integration on corporate innovation performance under different technology M&A modes. China Sci Technol Forum . (2020) 6:94–102. doi: 10.13580/j.cnki.fstc.2020.06.017

30. Nesta, L, and Saviotti, PP. Coherence of the Knowledge Base and the Firm's innovative performance: evidence from the U.S. pharmaceutical industry. J Ind Econ . (2005) 53:123–42. doi: 10.1111/j.0022-1821.2005.00248.x

31. Lin, CH, and Jang, SL. The impact of M&A on company innovation: evidence from the US medical device industry. Scientometrics . (2010) 84:119–31. doi: 10.1007/s11192-009-0096-9

32. Hao, QM, and Ren, HH. Research on innovation evolution performance of technology mergers and acquisitions and R&D. Sci Technol Prog Counter . (2015) 32:63–8. doi: 10.6049/kjjbydc.2015010665

33. Yu, H, and Wang, XY. The impact of technology mergers and acquisitions on corporate innovation capability – an empirical study from pharmaceutical companies. Ind Technol Econ . (2021) 40:25–34. doi: 10.3969/j.issn.1004-910X.2021.12.003

34. Lee, YG, Park, K, Kim, HJ, and Cho, SH. Creating portfolios of firm-specific energy R&D investment under market uncertainty. Energy Environ . (2023) 34:1548–63. doi: 10.1177/0958305X221092401

35. Bena, J, and Li, K. Corporate innovations and mergers and acquisitions. J Financ . (2014) 69:1923–60. doi: 10.1111/jofi.12059

36. López-Navarro, I, Rey-Rocha, J, and González-Bravo, IM. Risk Perception and Relational Capital Strategies in Corporate Research and Development. Revista Espanola de Documentacion Cientifica . (2023) 46:1–21.

37. Rögnvaldur, S, Marina, C, and Olaf, TF. The influence of performance feedback and top management team orientation on decisions about R&D in technology-based firms. Technovation . (2022) 113. doi: 10.1016/J.TECHNOVATION.2021.102420

38. Hitt, MA, Hoskisson, RE, Johnson, RA, and Moesel, DD. The market for corporate control and firm innovation. Acad Manag J . (1996) 39:1084–119. doi: 10.2307/256993

39. Jensen, MC . Takeovers: their causes and consequences. J Econ Perspect . (1988) 2:21–48. doi: 10.1257/jep.2.1.21

40. Salunke, S, Weerawardena, J, and McColl-Kennedy, RJ. The central role of knowledge integration capability in service innovation-based competitive strategy. Ind Mark Manag . (2018) 76:144–56. doi: 10.1016/j.indmarman.2018.07.004

41. Gandal, N, and Scotchmer, S. Coordinating research through research joint ventures. J Public Econ . (1993) 51:173–93. doi: 10.1016/0047-2727(93)90083-6

42. Gu, HF, and Xie, SX. Has internet finance affected liquidity creation in commercial banks? Finan Theory Pract . (2021) 42:10–8. doi: 10.16339/j.cnki.hdxbcjb.2021.06.002

43. Ahuja, G, and Katila, R. Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study. Strateg Manag J . (2001) 22:197–220. doi: 10.1002/smj.157

44. Guo, Y . Signaling mechanism of government innovation subsidies and corporate innovation. China Indust Econ . (2018) 9:98–116. doi: 10.19581/j.cnki.ciejournal.2018.09.016

45. Hui, S, Youyu, L, and Renmiao, Y. Technology M&A and enterprise innovation performance–knowledge-based mediation effect. Tech Anal Strat Manag . (2024) 36:365–77. doi: 10.1080/09537325.2022.2033720

46. Wang, CP, and Huang, ZH. Environmental regulation, financing environment and corporate technology mergers and acquisitions. J Sun Yat Sen Univ . (2022) 62:167–79. doi: 10.13471/j.cnki.jsysusse.2022.05.017

47. Hong, BH . (2022) Merger and acquisition types, financing constraints and Firms’ innovation investment. [master’s thesis]. [Shanghai] Shanghai Univ . doi: 10.27296/d.cnki.gshcu.2022.000710

48. Hou, SF . Tax incentives, innovation subsidies and technological innovation of cultural enterprises. J Shenzhen Univ . (2022) 39:51–62.

49. Wen, Z, Fang, J, Xie, J, and Ouyang, J. A methodological study of domestic mediation effects. Adv Psychol Sci . (2022) 30:1692–702. doi: 10.3724/SP.J.1042.2022.01692

50. Li, XJ, and Yang, Y. Industry heterogeneity, tax incentives and independent innovation-an empirical study of A-share listed companies in China's strategic emerging industries. Sci Technol Prog Counter . (2019) 36:60–8. doi: 10.6049/kjjbybc.LC201808760

51. Caiping, W, Jiahao, Z, Fei, J, and Zhihong, H. Has the establishment of chief technology officer improved the innovation performance of corporate technology mergers and acquisitions. Sci Technol Prog Counter :1–11. doi: 10.6049/kjjbydc.2023040324

52. Li, SM, Wang, CP, and Huang, ZH. Industrial agglomeration, technology mergers and acquisitions and knowledge spillovers. Econ Theory Econ Manag . (2022) 42:33–46.

53. Roodman, D . HowtoDoXtabond2:an introduction to difference and system Gmm in Stata. Stata J . (2009) 9:86–136. doi: 10.1177/1536867X0900900106

54. Gu, J . Determinants of biopharmaceutical R&D expenditures in China: the impact of spatiotemporal context. Scientometrics . (2021) 126:6659–80. doi: 10.1007/S11192-021-04058-Y

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Keywords: technology M&A, corporate performance, R&D, pharmaceutical industry, mediating effects

Citation: Yang J, Li J, Wang S and Chen Y (2024) Research on the impact of technology mergers and acquisitions on corporate performance: an empirical analysis based on China’s pharmaceutical industry. Front. Public Health . 12:1419305. doi: 10.3389/fpubh.2024.1419305

Received: 18 April 2024; Accepted: 19 July 2024; Published: 09 August 2024.

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Copyright © 2024 Yang, Li, Wang and Chen. 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) and the copyright owner(s) 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: Su Wang, [email protected] ; Yuwen Chen, [email protected]

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