what is empirical analysis in research

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Empirical Research: Defining, Identifying, & Finding

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

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research 

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

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Empirical Research: 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.

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

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

LEARN ABOUT: Action Research

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

LEARN MORE: Population vs Sample

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

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

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

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

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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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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.

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

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

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

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

How to Report Empirical Research Results?

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

1. Write the Research Paper

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

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

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

2. Create Visuals and Tables

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

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

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

3. Interpret Findings

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

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

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

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

Examples of Empirical Research

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

Social Sciences

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

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

Environmental Science

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

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

Business and Economics

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

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

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

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

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

Conclusion for Empirical Research

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

How to Collect Data for Empirical Research?

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Research: Overview & Approaches

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

Introduction to Empirical Research

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

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

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

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Empirical Research: 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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Ethics in Science: Importance, Principles & Guidelines 

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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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Introduction to Empirical Data Analysis

• First Online: 14 October 2021

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• Klaus Backhaus 6 ,
• Bernd Erichson 7 ,
• Sonja Gensler 8 ,
• Rolf Weiber 9 &
• Thomas Weiber 10  

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This chapter introduces, characterizes and classifies the eight methods of multivariate data analysis (MVA) covered in this book. When using MVA, several variables are considered simultaneously and their relationship is analyzed quantitatively. MVA aims to describe and explain these relationships or to predict future developments. Bivariate analyses that consider just two variables at a time are a special case of MVA. However, reality is usually much more complex and requires the consideration of more than just two variables. Furthermore, this chapter presents the fundamentals of empirical data analysis that are relevant to all methods discussed in the book. Since most readers will be familiar with these basics, these presentations serve primarily as a repetition or as an opportunity to look up important aspects of quantitative data analysis, such as basic statistical concepts (e.g. mean, standard deviation, covariance), the difference between correlation and causality, and the basics of statistical testing. Finally, the handling of outliers and missing values is discussed and the statistical package IBM SPSS Statistics, which is used in this book, is briefly introduced.

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Both SPSS and R use the point-biserial calculation of a correlation if one of the variables has only two calculation-relevant values.

On www.multivariate-methods.info , the reader will also find an Excel sheet with information on the calculation of the various statistical parameters using Excel.

In Excel, the mean of a variable can be calculated by: = AVERAGE(matrix), where (matrix) is the range of cells containing the data of the variable. For example, “ = AVERAGE(C6:C55)” calculates the mean of the 50 cells C6 to C55 in column C.

In Excel, the sample variance can be calculated by: \(s_{x}^{2}\)  = VAR.S(matrix). The population variance can be calculated by: \(\sigma_{x}^{2}\)  = VAR.P(matrix).

In Excel, the sample standard deviation can be calculated by: \(s_{x}^{{}}\)  = STDEV.S(matrix). The population standard deviation is calculated by: \(\sigma_{x}^{{}}\)  = STDEV.P(matrix).

Variance and standard deviation cannot be interpreted meaningfully for the variable “gender”. However, columns E and F are required for the calculation of covariance and correlations.

In Excel, the covariance can be calculated as follows: s xy  = COVARIANCE.S(matrix1;matrix2).

In Excel, the correlation between variables can be calculated as follows: r xy  = CORREL(matrix1;matrix2).

Cf. the correlation of binary variables with metrically scaled variables in Sect.  1.1.2.2 .

For statistical testing, also see Sect.  1.3 .

The p-value may be calculated in Excel as follows: p = TDIST(ABS(t);N−2;2) or p=1–F.DIST(F;1;n–2;1).

The central limit theorem states that the sum or mean of n independent random variables tends toward a normal distribution if n is sufficiently large, even if the original variables themselves are not normally distributed. This is the reason why a normal distribution can be assumed for many phenomena.

In Excel we can calculate the critical value \(t_{\alpha /2}\) for a two-tailed t-test by using the function T.INV.2 T(α;df). We get: T.INV.2 T(0.05;99) = 1.98. The values in the last line of the t-table are identical with the normal distribution. With df = 99 the t-distribution comes very close to the normal distribution.

In Excel we can calculate the p-value by using the function T.DIST.2 T(ABS( t emp );df). For the variable in our example we get: T.DIST.2 T(ABS(−1.90);99) = 0.0603 or 6.03%

In Excel we can calculate the critical value \(t_{\alpha }\) for the lower tail by using the function T.INV(α;df). We get: T.INV(0.05;99) = –1.66. For the upper tail we have to switch the sign or use the function T.INV(1–α;df).

In Excel we can calculate the p-value for the left tail by using the function T.DIST(temp;df;1). We get: T.DIST(−1.90;99;1) = 0.0302 or 3%. The p-value for the right tail is obtained by the function T.DIST.RT(temp;df).

Cf., e.g., Hastie et al. 2011 , Pearl and Mackenzie 2018 ; Gigerenzer 2002 .

The histogram was created with Excel by selecting “Data/Data Analysis/Histogram”. In SPSS, histograms are created by selecting “Analyze/Descriptive Statistics/Explore”.

In SPSS we can create boxplots (just like histograms) by selecting “Analyze/Descriptive Statistics/Explore”.

Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research . Chicago: Rand McNelly.

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Gigerenzer, G. (2002). Calculated rsks . New York: Simon & Schuster.

Green, P. E., Tull, D. S., & Albaum, G. (1988). Research for marketing decisions (5th ed.). Englewood Cliffs (NJ): Prentice Hall.

Hastie, T., Tibshirani, R., & Friedman, J. (2011). The elements of statistical learning . New York: Springer.

Pearl, J., & Mackenzie, D. (2018). The book of Why—The new science of cause and effect . New York: Basic Books.

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Tukey, J. W. (1977). Exploratory data analysis . Massachusetts: Addison-Wesley.

Watson, J., Whiting, P. F. & Brush, J. E. (2020). Interpreting a covid-19 test result. British Medical Journal, 12 May 2020, 369:m1808.

Further reading

Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2007). Essentials of modern business statistics with Microsoft Excel . Mason (OH): Thomson.

Darren, G., & Mallery, P. (2021). IBM SPSS Statistics 27 step by step: A simple guide and reference (17th ed.). New York: Routledge.

Field, A., Miles, J., & Field, Z. (2012). Discovering sstatistics using R . London: Sage.

Fisher, R. A. (1990). Statistical methods, experimental design, and scientific inference . Oxford: Oxford University Press.

Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). New York: Norton & Company.

Sarstedt, M., & Mooi, E. (2019). A concise guide to market research: The process, data, and methods using IBM SPSS statistics (3rd ed.). Berlin: Springer.

Wonnacott, T. H., & Wonnacott, R. J. (1977). Introductory statistics for business and economics (2nd ed.). Santa Barbara: Wiley.

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Institute of Business-to-Business Marketing, Marketing Center Münster, University of Münster, Münster, Germany

Klaus Backhaus

Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

Bernd Erichson

Chair for Value-Based-Marketing, Marketing Center Münster, University of Münster, Münster, Germany

Sonja Gensler

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Rolf Weiber

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Backhaus, K., Erichson, B., Gensler, S., Weiber, R., Weiber, T. (2021). Introduction to Empirical Data Analysis. In: Multivariate Analysis. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-32589-3_1

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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

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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?

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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

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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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Empirical research in the social sciences and education.

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

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Introduction: What is Empirical Research?

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

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

Key characteristics to look for:

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

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

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

Reading and Evaluating Scholarly Materials

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

• CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
• IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
• Credo Video: How to Read Scholarly Materials (4 min.)
• Credo Tutorial: How to Read Scholarly Materials
• Credo Tutorial: Evaluating Information
• Credo Video: Evaluating Statistics (4 min.)
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What is "empirical research".

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

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

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

Key characteristics to look for:

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

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

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

Table of Contents

Have you ever wondered how scientists gather evidence to support their theories? Or what steps researchers take to ensure that their findings are reliable and not just based on speculation? The answer lies in a cornerstone of scientific investigation known as the empirical method . This approach to research is all about collecting data and observing the world to form solid, evidence-based conclusions. Let’s dive into the empirical method’s fascinating world and understand why it’s so critical in research methodology.

What is the empirical method?

The empirical method is a way of gaining knowledge by means of direct and indirect observation or experience. It’s fundamentally based on the idea that knowledge comes from sensory experience and can be acquired through observation and experimentation. This method stands in contrast to approaches that rely solely on theoretical or logical means.

The role of observation in the empirical method

Observation is at the heart of the empirical method. It involves using your senses to gather information about the world. This could be as simple as noting the color of a flower or as complex as using advanced technology to observe the behavior of microscopic organisms. The key is that the observations must be systematic and replicable, providing reliable data that can be used to draw conclusions.

Data collection: qualitative and quantitative

Different types of data can be collected using the empirical method:

• Qualitative data – This data type is descriptive and conceptual, often collected through interviews, observations, and case studies.
• Quantitative data – This involves numerical data collected through methods like surveys, experiments, and statistical analysis.

Empirical vs. experimental methods

While the empirical method is often associated with experimentation, it’s important to distinguish between the two. Experimental methods involve controlled tests where the researcher manipulates one variable to observe the effect on another. In contrast, the empirical method doesn’t necessarily involve manipulation. Instead, it focuses on observing and collecting data in natural settings, offering a broader understanding of phenomena as they occur in real life.

Why the distinction matters

Understanding the difference between empirical and experimental methods is crucial because it affects how research is conducted and how results are interpreted. Empirical research can provide a more naturalistic view of the subject matter, whereas experimental research can offer more control over variables and potentially more precise outcomes.

The significance of experiential learning

The empirical method has deep roots in experiential learning, which emphasizes learning through experience. This connection is vital because it underlines the importance of engaging with the subject matter at a practical level, rather than just theoretically. It’s a hands-on approach to knowledge that has been valued since the time of Aristotle.

Developing theories from empirical research

One of the most significant aspects of the empirical method is its role in theory development . Researchers collect and analyze data, and from these findings, they can formulate or refine theories. Theories that are supported by empirical evidence tend to be more robust and widely accepted in the scientific community.

Applying the empirical method in various fields

The empirical method is not limited to the natural sciences. It’s used across a range of disciplines, from social sciences to humanities, to understand different aspects of the world. For instance:

• In psychology , researchers might use the empirical method to observe and record behaviors to understand the underlying mental processes.
• In sociology , it could involve studying social interactions to draw conclusions about societal structures.
• In economics , empirical data might be used to test the validity of economic theories or to measure market trends.

Challenges and limitations

Despite its importance, the empirical method has its challenges and limitations. One major challenge is ensuring that observations and data collection are unbiased. Additionally, not all phenomena are easily observable, and some may require more complex or abstract approaches.

The empirical method is a fundamental aspect of research methodology that has stood the test of time. By relying on observation and data collection, it allows researchers to ground their theories in reality, providing a solid foundation for knowledge. Whether it’s used in the hard sciences, social sciences, or humanities, the empirical method continues to be a critical tool for understanding our complex world.

How do you think the empirical method affects the credibility of research findings? And can you think of a situation where empirical methods might be difficult to apply but still necessary for advancing knowledge? Let’s discuss these thought-provoking questions and consider the breadth of the empirical method’s impact on the pursuit of understanding.

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Research Methodology

1 Introduction to Research in General

• Research in General
• Research Circle
• Tools of Research
• Methods: Quantitative or Qualitative
• The Product: Research Report or Papers

2 Original Unity of Philosophy and Science

• Myth Philosophy and Science: Original Unity
• The Myth: A Spiritual Metaphor
• Myth Philosophy and Science
• The Greek Quest for Unity
• The Ionian School
• Towards a Grand Unification Theory or Theory of Everything
• Einstein’s Perennial Quest for Unity

3 Evolution of the Distinct Methods of Science

• Definition of Scientific Method
• The Evolution of Scientific Methods
• Theory-Dependence of Observation
• Scope of Science and Scientific Methods
• Prevalent Mistakes in Applying the Scientific Method

4 Relation of Scientific and Philosophical Methods

• Definitions of Scientific and Philosophical method
• Philosophical method
• Scientific method
• The relation
• The Importance of Philosophical and scientific methods

5 Dialectical Method

• Introduction and a Brief Survey of the Method
• Types of Dialectics
• Dialectics in Classical Philosophy
• Dialectics in Modern Philosophy
• Critique of Dialectical Method

6 Rational Method

• Understanding Rationalism
• Rational Method of Investigation
• Descartes’ Rational Method
• Leibniz’ Aim of Philosophy
• Spinoza’ Aim of Philosophy

7 Empirical Method

• Common Features of Philosophical Method
• Empirical Method
• Exposition of Empiricism
• Locke’s Empirical Method
• Berkeley’s Empirical Method
• David Hume’s Empirical Method

8 Critical Method

• Basic Features of Critical Theory
• On Instrumental Reason
• Conception of Society
• Human History as Dialectic of Enlightenment
• Substantive Reason
• Habermasian Critical Theory
• Habermas’ Theory of Society
• Habermas’ Critique of Scientism
• Theory of Communicative Action
• Discourse Ethics of Habermas

9 Phenomenological Method (Western and Indian)

• Phenomenology in Philosophy
• Phenomenology as a Method
• Phenomenological Analysis of Knowledge
• Phenomenological Reduction
• Husserl’s Triad: Ego Cogito Cogitata
• Intentionality
• Understanding ‘Consciousness’
• Phenomenological Method in Indian Tradition
• Phenomenological Method in Religion

10 Analytical Method (Western and Indian)

• Analysis in History of Philosophy
• Conceptual Analysis
• Analysis as a Method
• Analysis in Logical Atomism and Logical Positivism
• Analytic Method in Ethics
• Language Analysis
• Quine’s Analytical Method
• Analysis in Indian Traditions

11 Hermeneutical Method (Western and Indian)

• The Power (Sakti) to Convey Meaning
• Three Meanings
• Pre-understanding
• The Semantic Autonomy of the Text
• Towards a Fusion of Horizons
• The Hermeneutical Circle
• The True Scandal of the Text
• Literary Forms

12 Deconstructive Method

• The Seminal Idea of Deconstruction in Heidegger
• Deconstruction in Derrida
• Structuralism and Post-structuralism
• Sign Signifier and Signified
• Writing and Trace
• Deconstruction as a Strategic Reading
• The Logic of Supplement
• No Outside-text

13 Method of Bibliography

• Preparing to Write
• Writing a Paper
• The Main Divisions of a Paper
• Writing Bibliography in Turabian and APA
• Sample Bibliography

14 Method of Footnotes

• Citations and Notes
• General Hints for Footnotes
• Writing Footnotes
• Examples of Footnote or Endnote
• Example of a Research Article

15 Method of Notes Taking

• Methods of Note-taking
• Note Book Style
• Note taking in a Computer
• Types of Note-taking
• Notes from Field Research
• Errors to be Avoided

16 Method of Thesis Proposal and Presentation

• Preliminary Section
• Presenting the Problem of the Thesis
• Design of the Study
• Main Body of the Thesis
• Conclusion Summary and Recommendations
• Reference Material

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• Open access
• Published: 28 May 2024

Relational aggression in romantic relationship: empirical evidence among young female adults in Malaysia

• Mohammad Rahim Kamaluddin 2 ,
• Shalini Munusamy 1 ,
• Chong Sheau Tsuey 2 &
• Hilwa Abdullah & Mohd Nor 2  

BMC Psychology volume  12 , Article number:  305 ( 2024 ) Cite this article

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Aggressive behaviour in romantic relationship is a social problem of great concern. Studies related to the influence of psychosocial factors on relational aggression are still limited. Furthermore, these factors have not been widely studied in the local context, resulting in the issue of relational aggression among young female adults still not being addressed. This study aims to explore whether psychosocial factors such as big five personality traits, adult attachment style and loneliness could predict relational aggression in romantic relationships among young female adults in Malaysia. In addition, this study aims to identify the moderating effect of social support in the relationship between psychosocial factors and relational aggression in romantic relationship.

A quantitative research approach was used with 424 young female adults in Malaysia aged between 18 and 30 years old (mean age = 24.18) were recruited through multistage sampling design by completing a questionnaire consisting of the Big Five Inventory (BFI), Experiences in Close Relationships Scale II (ECRS-II), Revised UCLA Loneliness Scale, Measure of Relational Aggression and Victimization (MRAV) and Multidimensional Scale of Perceived Social Support (MSPSS).

Multiple regression analysis predicted significant relationship between agreeableness personality, loneliness, avoidant attachment style and anxious attachment style with relational aggression in romantic relationships. Hierarchical regression analysis found a significant effect of social support as a moderator between loneliness with relational aggression in romantic relationships.

Conclusions

Thus, the results show that young female adults with low level of agreeableness, high level of loneliness, avoidant attachment style and anxious attachment style are at a higher risk of engaging in relational aggression in romantic relationships. The implication of this study can help in understanding the psychosocial factors that form the basis of relational aggression in romantic relationships. Hence, the gap in knowledge warrants further research.

Peer Review reports

The development of romantic relationships among early adulthood is crucial in forming views about intimate relationships and exhibiting intimacy, power, and control [ 1 ]. Emerging adulthood is a key developmental stage for creating a healthy romantic relationship. Some romantic relationships involve aggressive behaviour between partners, which can manifest in various forms such as physical, non-physical, direct, or indirect aggression, overt or covert aggression [ 2 ]. Aggressive behaviour is a criminogenic trait linked to various violent crimes including dating violence [ 3 ]. Physical aggression involves intentionally using physical force to hurt the partner, ranging from mild actions like pushing to severe violence like choking, slapping or weapon use [ 4 ]. Emotional abuse is also a common form of abuse in romantic relationships [ 5 ]. The online dating scam is another alarming form of dating violence that can result in financial loss and severe emotional and psychological suffering ( 6 – 7 ). Relational aggression is a form of non-physical and covert aggression, involves threatening others by manipulating and acting to jeopardize romantic relationships [ 8 ]. Unlike physical aggression, relational aggression occurs without any physical force or physically threatening the individual and can be considered a type of psychological aggression, targeting perceptions, feelings, or behaviour in romantic relationship [ 9 ]. Relational aggression can be indirect, such as through negative facial expressions or spreading rumors about a partner. While there has been extensive research on physical aggression and violence in romantic relationships [ 10 , 11 , 12 ], there is relatively less research on relational aggression in romantic relationships.

Relational aggression in romantic relationships might appear as threats to end the relationship if the other person doesn’t cooperate, flirting with other people to make the other person envious, or treating the other person silently while upset [ 9 ]. In terms of relational aggression, females who utilized high levels of relational aggression had a strong tendency to see other people’s acts as hostile and malevolent, whereas males did not [ 13 ]. Examining relational aggression and its relationship with adaptive functioning in females may shed light on the critical mechanisms involved in females’ dating violence. In this study, we hope to study the psychosocial factors most related with relational aggression in females by looking at components known to relate to aggression in females, such as individual characteristics and environmental factors. There is little evidence from research on female gender to differentiate the experience of relational aggression in romantic relationships, female perpetrators will be the greatest risk of this aggressive behaviour and young female adults may experience greater psychological stress than men ( 13 – 14 ). Therefore, this study focuses only on female samples and will be done using Malaysian samples. Despite research, little is known about how relational aggression originate, persist, and have an impact on romantic relationships, including whether men and women experience these issues differently ( 13 – 14 ). Romantic relational aggression has also been linked to relationship quality, violence, psychosocial maladjustment, impulsivity, hostile attribution biases, loneliness, emotional sensitivity to relational incitements, and abuse history [ 13 ].

In addition, this study emphasizes the psychosocial aspect of a person that can cause the tendency to behave aggressively in romantic relationships. It is important to identify the psychosocial aspects of a person who tends to engage in relational aggression in romantic relationships. The link between relational aggression and psychosocial factors such as loneliness, attachment styles, and personality type has been established ( 15 – 16 ). Personality traits of aggressors have been known to be associated with dating violence ( 15 – 16 ). This study used the “Big Five” personality model (extraversion, agreeableness, openness, conscientiousness, and neuroticism) as one of the psychosocial factors. Each main trait from this model can be divided into several aspects to provide a more detailed analysis of a person’s personality. Several theorists argue that personality variable is an important predictor of aggressive behaviour in romantic relationship [ 17 , 18 , 19 ]. Agreeableness dimensions are often associated with aggressive behaviour [ 18 , 20 , 21 ]. Besides that, a study conducted by Ulloa et al. (2016) found individuals with a high neuroticism personality tend to be victims in relational aggression during intimate relationships [ 22 ]. The findings of this study are also supported by other research that neuroticism trait as the main personality trait that gives a strong influence on relational aggression ( 23 – 24 ).

In addition to personality traits, other factors such as the level of loneliness are also considered to be a strong predictive factor of relational aggression especially the tendency to be a victim [ 25 ]. Generally, loneliness can be associated with individuals having a lack of social support as well as showing no interest in social networks [ 25 ]. Many studies have linked aggressive behaviour with loneliness [ 26 , 27 , 28 ]. Loneliness is defined as a negative emotional response to the discrepancy between the desired and achieved quality of one’s social network [ 27 ]. In addition, relational aggression is caused by the loneliness faced by an individual [ 28 ]. Individuals who are lonely describe themselves negatively and have negative ideas about others. As a result, loneliness leads to a bad perception of oneself, such as being unwanted and unaccepted by others, and it leads to aggression, which is a means of using force to influence other people in interpersonal relationships [ 29 ]. Individuals with high level of loneliness are at high risk of engaging in relational aggression in romantic relationship ( 30 – 31 ). Another psychosocial aspect often associated with relational aggression is attachment style. Attachment style is said to be able to shape the probability of an individual being involved in incidents of relational aggression in romantic relationship.

An expanding corpus of research has highlighted attachment theory as a crucial paradigm for comprehending emotional and interpersonal processes that take place across the lifespan [ 32 , 33 , 34 ]. The foundation of attachment theory is the idea of an attachment behavioural system, in which attachment actions are grouped together to strengthen a particular attachment figure. A sense of personal security within the relationship can be established or maintained by intimate partner violence, according to the attachment theory. People feel startled when they sense a threat to their attachment connection, and the ensuing anxiety causes them to act in ways that protect their attachment system [ 35 ]. Individuals with different attachment style also have an influence strongly to the involvement of individuals in the occurrence of aggression ( 36 – 37 ). Besides that, individuals with avoidant attachment shows high relational aggression in romantic relationship ( 38 – 39 ). Besides that, individuals who often exhibit anxious attachment to their partners such as fear of rejection and dependency on their partner are more likely to experience relational aggression in romantic relationships ( 40 – 41 ).

The potentially moderating role of Social Support

In relation to that, social support is used as a moderator based on previous literature studies [ 42 , 43 , 44 ]. Social support is also defined as interpersonal relationships and support provided by social groups that aim to provide well-being to individuals [ 42 ]. Social support from family and friends is important in contributing to positive psychological health among early adulthood and influences the act of aggressive behaviour [ 45 ]. Previous studies have shown that social support has a significant relationship with big personality traits, especially with extraversion and agreeableness [ 45 , 46 , 47 , 48 , 49 ]. In addition, a few studies also found that family members with agreeableness trait also provide more social support [ 46 , 47 , 48 ]. Besides that, people who experience loneliness interact less with friends and family than people who do not feel lonely. In other words, the less social support a person has, the higher the level of loneliness [ 50 ]. According to earlier research, there have been negative association between relational aggression and social support as well positive association between relational aggression and psychosocial maladjustment during major developmental stages including childhood, adolescence, and young adulthood [ 51 , 52 , 53 ].

According to research, individuals with little social support from their parents were more likely to engage in verbal, physical, and relational aggression [ 54 ] whereas individuals who reported high perceived social support from peers were less likely to engage in overt and relational aggression [ 55 ]. Besides that, individuals who have supporting friends and family have lower relational aggression. Family and peer support can help to mitigate the harmful effects from using relational aggression behaviour in their romantic relationship. Adults with high levels of social support outperformed those with low levels of family and peer support in exhibiting relational aggression behaviour in romantic relationships [ 56 ]. Although both relational aggression and social support are empirically connected to maladjustment, research on the interaction effect of psychosocial factors and social support on relational aggression is still limited ( 57 – 58 ).

Besides that, a study done in US had found that there is no evidence of social support act as a moderator between psychosocial factors and dating violence [ 59 ]. Only a small amount is allocated in the extent literature to research the triad of the relationship. In accordance with that, this study will further explore to develop an understanding of the role of social support in the association between psychosocial factors and relational aggression. Among several theories of social behaviour, for this study we have used Albert Bandura (1986) social cognitive theory to help provide researchers with a comprehensive framework to understand the factors that may influence aggressive human behaviour. Although Bowlby (1969) prioritized and focused on understanding the nature of caregiver’s relationship with his infant, at the same time he also believed that bonding features are present in human life experience from “cradle to grave” [ 30 ]. Besides that, attachment style and social support combine the theory-based prediction that people with an insecure attachment style are more likely to evaluate others’ reactions negatively [ 60 ].

This study can give awareness to young female adults about the issue of relational aggression that can happen in a romantic relationship. This is because relational aggression is an issue that is not given attention in romantic relationships by women and only aggressive behaviour such as physical and sexual is considered more harmful in romantic relationships. This study can give awareness to young female adults about the characteristics of an individual who practices relational aggression in a romantic relationship and can help in finding a solution from practicing relational aggression in romantic relationship. This study can also help young adults to identify this issue so that it does not continue and affect romantic relationships in adulthood. Relational aggression is known to be a relevant social problem factor which can be a precursor to abusive romantic relationships in later adulthood [ 61 ].

A conceptual framework in this study was built based on the social cognitive theory introduced by Albert Bandura in 1986, attachment theory developed by John Bowlby (1907–1990) and the big five personality theory developed in 1949 by D. W. Fiske (1949) as well as from the findings of research on previous studies in the field of psychosocial factors and relational aggression in romantic relationship. In general, this study aims to explore whether psychosocial factors could predict relational aggression in romantic relationships. There is not much direct research that examines covert set of manipulative behaviors in romantic relationships such as relational aggression. Besides that, there are only a few studies conducted in Malaysia about relational aggression in romantic relationships compared to studies conducted in Western countries [ 53 , 54 , 55 , 60 , 61 , 62 ]. Therefore, it is important to conduct this study using respondents from Malaysia so that it can help psychologists and other parties involved to identify individuals using relational aggression in romantic relationships and from being involved in psychological problems.

The present study

This study was designed to explore whether psychosocial factors such as big five personality traits, attachment style and loneliness could predict relational aggression in romantic relationship among young female adults in Malaysian context and aims to extend findings from previous studies in this field. The researchers hypothesize that psychosocial factors, such as personality trait, attachment styles, and loneliness, will play a significant role in determining the presence and severity of relational aggression in romantic relationships. In addition, it is believed that social support will act as a moderating factor in the relationship between psychosocial factors and relational aggression. As a result, this study aims to shed light on the drivers behind relational aggression in romantic relationships and to better understand the relationship between psychosocial factors and relational aggression. This study is regarded novel because there are no known studies on relational aggression in romantic relationship in the Malaysian context as this will be the first Malaysian study to define the relational aggression in romantic relationship among the sample of young female adults in Malaysia.

Participants

An online survey was conducted with a total of 424 females from early adulthood stage, aged between 18 and 30 years old in Malaysia. According to DOSM (2021), the total population of women in early adulthood in Malaysia is 15,758.2(‘000). From the entire population in each state, the respondents aged between 18 and 30 were selected in this study using Raosoft formula. Proportionate stratified random sampling was used to recruit respondents from 13 states in Malaysia to get sufficient sample size from each state through Raosoft formula calculation in July 2022. Then, convenience sampling was used to select a study sample from the population to get a sufficient sample from each state where an advertisement was posted in social media. Inclusion criteria: [ 1 ] participants must be Malaysian; [ 2 ] female participants aged between 18 to 30 years old only; [ 3 ] currently in a romantic relationship for more than 3 months; [ 4 ] must answer all questions in relation to the most recent partner or romantic relationship; [ 5 ] informed and voluntary participation in the study. The study sample for this research consists of different races, occupation, and education background so that they will have equal opportunity to be selected as a respondent.

Instruments

Big five inventory (bfi).

The Malay version of Big Five Inventory (BFI; 63) which was developed by Muhammad et al., [ 63 ] was used to measure the five basic personality dimensions, namely extraversion, agreeableness, conscientiousness, openness, and neuroticism. The 44-item BFI is rated on a 5-point Likert Scale from 1 (strongly disagree) to 5 (strongly agree). After reverse scoring, the mean score of each subscale is obtained. The Malay version of the BFI shows good internal consistency, convergent and discriminant validity [ 63 ]. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.78 to 0.88 with a mean of 0.81.

UCLA loneliness scale-3

The Malay version of the Rusell’s [ 64 ] UCLA Loneliness Scale [ 65 ] was used to measure loneliness. This tool consists of 20 items and is rated on a 4-point Likert scale from 1 (Never) to 4 (Always). Loneliness was assessed by averaging the scores of all items with higher scores indicating higher levels of loneliness. The internal reliability of this scale in the current study reported with a Cronbach’s alpha of 0.83.

Experiences in close relationships– II (ECR-II)

The Experiences in Close Relationships Scale II (ECRS-II; 67) assessed individual differences in anxious attachment style (i.e., the extent to which individuals feel secure versus insecure about romantic partner relationships and reactions) and avoidant attachment style (i.e., the extent to which individuals feel uncomfortable with having close relationships with others versus feel safe to rely on others). The Malay version of the ECR-II [ 66 ] was used in this study. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.82 to 0.83 with a mean of 0.83.

Measure of relational aggression and victimization (MRAV)

This instrument was developed by Linder et al. [ 67 ]. This 56-item instrument consists of six subscales that measure six dimensions of aggression, namely relational aggression, physical aggression, relational victimization, physical sacrifice, exclusivity, and prosocial behaviour. For this study, only the subscales of relational aggression (5 items) were used. Items in this tool are rated on a 7-point Likert-type scale from 1 (Not at all True) to 7 (Very True). This questionnaire was translated into Malay language using Forward-Backward translation method and followed by content validation. CVR technique was used to measure the content validity of this questionnaire. The CVR was in the range 0.7-1 for all items and the overall mean CVR values were 0.83. According to Rahim et al. [ 68 ], in the context of measuring psychological test, tools which are available in their own native language will be more appropriate and measurement will be more accurate compared to other languages. The internal reliability of this scale in the current study was high with a Cronbach’s alpha calculation of 0.88 with a mean of 0.89.

Multidimensional scale of perceived social support (MSPSS)

This questionnaire was developed by Zimet et al. [ 69 ] and was used to measure social support of an individual. The MSPSS consists of 12 items assessing three specific sources of social support namely family, friends, and others. This test tool uses a 7-point Likert scale where (1 = strongly disagree, 7 = strongly agree). In this study, the Malay version of the MSPSS tool was used which was translated and validated by Ng et al., [ 70 ]. The internal reliability of this scale in the current study was high, with a Cronbach’s alpha calculation of 0.93.

The survey was conducted from July 1 to July 26, 2022. According to Connelly [ 71 ], previous studies suggest that the sample size of the pilot study should be 10% of the sample size used for the actual study. Therefore, a pilot study was carried out before the real study with 44 respondents in the state of Selangor. The researcher chose Selangor because it is the state where the researcher is currently living, and this will make it easier to carry out the study. In the actual study, 424 participants were recruited based on Table  2 . This study was approved by the Research Ethics Committee of The National University of Malaysia (No: 2022 − 549). All participants were informed of the research objectives and their rights on the first screen (voluntary participation, the right to withdraw at any time and anonymity). This study was not conducted with any minors. At the start of the test, informed permission was acquired, this study only moved forward if the subject ticked the box that said, “Yes, I offer my consent to participate.” The participants’ privacy was guaranteed by the test’s anonymity and the numerical coding of their replies.

Data analysis

Descriptive statistics and inferential statistics were calculated using SPSS 26.0. For inferential statistics multiple regression and hierarchical regression has been used in this study. Multiple regression was used to explore whether psychosocial factors such as big five personality traits, attachment style and loneliness could predict relational aggression in romantic relationship. A single dependent variable and numerous independent variables can be analysed using the statistical method known as multiple regression. The value of R, the multiple correlation coefficient, is shown in the “R” column. The “R Square” column displays the R 2 value, also known as the coefficient of determination, which is the percentage of the dependent variable’s variance that can be explained by the independent variables. R can be thought of as one indicator of the accuracy of the dependent variable’s prediction [ 72 ]. It is the proportion of variation accounted for by the regression model above and beyond the mean model. Hierarchical regression was used to study the effect of social support as a moderator in the relationship between psychosocial factors (personality trait, attachment style and loneliness) with relational aggression in romantic relationship. The moderation effect analysis was carried out using SPSS hierarchical regression. The hierarchical regression is a more appropriate method for determining whether a quantitative variable has a moderating effect on the relationship between two other quantitative variables [ 72 ]. If the moderation test result fell within the 95% confidence interval and contained 0, it meant that the moderation impact of social support was not significant; if it did not, it meant that the moderation effect of social support was substantial. In this study, p  <.05 was regarded as statistically significant. In this study, SPSS 26.0 software were used to analyse the data.

Descriptive statistics

A total of 500 participants have completed the online survey but only 424 (M ± SD = 24.18 ± 3.21 years) participants’ responses were included after 76 questionnaires were rejected from this study as it did not meet the inclusion criteria. The highest level of education obtained by the participants is degree education. 18.2% of participants had engaged in aggression towards their romantic partner.

Inferential statistics

Table  2 shows the results of a multiple regression analysis in predicting relational aggression based on big five personality traits, attachment styles, and loneliness among young female adults in Malaysia. Among the five subscales of personality trait, agreeableness showed a significant predictor. In addition, loneliness, avoidant attachment style, and anxious-attachment style also showed significant prediction with relational aggression. Overall, the results of the regression analysis showed that agreeableness, loneliness, avoidant attachment style, and anxious attachment style together can predict 30.3% of the variance in relational aggression (R²=0.303), where [F (3,269) = 22.561, p  < 0 0.05]. The subscale of agreeableness showed negative prediction (β=-0.305, p  <.05) with relational aggression whereas loneliness (β = 0.364, p  <.05), avoidant attachment style (β = 0.420, p  <.05), and anxious attachment style (β = 0.321, p  <.05) showed positive prediction with relational aggression. These findings showed that higher level of agreeableness trait contributes to lower level of relational aggression in romantic relationships. Besides that, high levels of loneliness, avoidant attachment style, and anxious attachment style contribute to higher level of relational aggression in romantic relationship.

For hierarchical regression analysis, only those variables that were significant in the multiple regression analyses were entered into hierarchical regression models which are agreeableness trait, loneliness, avoidant attachment style, and anxious attachment style. Table  3 shows the hierarchical regression analysis where R² value for Model 1 is 0.097, F (25.735) = 22.545, p  <.05. This means that the agreeableness dimension accounts for 9.7% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.098, F (17.410) = 15.240, p  <.05. This means that social support and agreeableness dimensions contribute as much as 9.8% of the variance to relational aggression in romantic relationships. These results showed that the percentage of variance only increases by 0.1% (9.8%– 9.7%) with the presence of a moderator in this model. The results in Table  3 showed that the dimension of agreeableness as a predictor is significant with a value of β =-0.296, t = -6.333, p  <.05. While social support as a predictor is not significant with β value = -0.062, t = -1.331, p  >.05. After entering the moderator, the interaction term of social support and agreeableness is not significant with a value of β = -0.406, t = − 0.816 and p  >.05. The agreeableness subscale was a significant predictor in the first block ( p  <.05) but did not reach significance in the second block ( p  =.415).

Table  4 shows the hierarchical regression analysis where R² value for Model 1 is 0.135, F (35.826) = 32.761, p  <.05. This means that the loneliness level dimension accounts for 13.5% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.146, F (25.874) = 23.915, p  <.05. This means that social support and loneliness level dimensions contribute as much as 14.6% of the variance to relational aggression in romantic relationships. These results show that the percentage of variance only increases by 1.1% (14.6%– 13.5%) with the presence of a moderator in this model. The results in Table  4 show that the dimension of loneliness as a predictor is significant with a value of β = 0.383, t = 7.767, p  <.05. While social support as a predictor is not significant with a value of β = 0.048, t = 0.964, p  >.05. After entering the moderator, the interaction term of social support and loneliness is significant with a value of β = 0.550, t = 2.349 and p  <.05. The loneliness subscale was a significant predictor in all blocks ( p  <.05), with p  =.019 in the second block.

Table  5 shows the hierarchical regression analysis where R² value for Model 1 is 0.231, F (40.936) = 42.014, p  <.05. This means that the attachment style dimension accounts for 23.1% of the variance in relational aggression. While the R² value obtained for Model 2 is 0.237, F (25.225) = 25.976, p  <.05. This means that social support and attachment style dimensions account for 23.7% of the variance in relational aggression in romantic relationships. These results show that the percentage of variance only increases by 0.6% (23.7%– 23.1%) with the presence of a moderator in this model. The results in Table  5 show that the dimension of avoidant attachment style as a predictor is significant with a value of β = 0.368, t = 8.345, p  <.05 and the dimension of anxious attachment style as a predictor is significant with a value of β = 0.244, t = 5.364, p  <.05. While social support as a predictor is.

not significant with a value of β = 0.20, t = 0.447, p  >.05. After entering the moderator, the interaction term of social support and attachment style was not significant on the relational aggression with values ​​of β = 0.155, t = 0.676, p  >.05 and β = 0.520, t = 2.925, p  >.05. The ECR’s anxious and avoidant subscale were significant predictor in the first block ( p  <.05) but did not reach significance in the second block ( p  =.328;0.105).

The participants that have been selected for this study are young female adults between the age of 18 to 30 (M ± SD = 22.08 ± 3.21 years) who are currently in a romantic relationship for more than three months. Regression analysis was done, and it was found that only agreeableness trait showed significant predictor on relational aggression in romantic relationship and the other four dimensions of the big five personality in the psychosocial factor variable, which are extraversion, openness, neuroticism, and conscientiousness are not predictors or contributors to relational aggression in romantic relationships. Therefore, the findings prove that the importance of the relative contribution of personality traits of agreeableness. Generally, in an interpersonal context, personality is known to play an important role in determining the likelihood of engaging in an aggressive act. Negative emotions are generally harmful to romantic relationships. The result from our study is contradictory with the research findings by Burton et al. [ 73 ] where they have found that higher relational aggression was associated with higher levels of neuroticism and lower level of conscientiousness.

In addition, in some studies it has been found that individuals who tend to engage in relational aggression are more likely to show lower traits of agreeableness, openness and conscientiousness [ 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. In our study, none of the big five personality traits except for agreeableness show significant prediction towards relational aggression in romantic relationships. This may be due to in general agreeableness traits may have stronger predictive utility than other personality traits ( 78 – 79 ). It has also been shown that agreeableness trait is negatively associated with relational aggression [ 80 , 81 , 82 ]. Agreeableness characterized as cooperation and understanding is an aspect related to motivation to maintain positive interpersonal relationships [ 83 ]. Likewise, the relationship between agreeableness and mind suggests that the former is responsible for processing social information.

Furthermore, agreeableness supports altruism while relational aggression is a type of destructive and hostile behaviour that has anti-social tendencies [ 84 ]. Therefore, this can further explain the evidence we found that agreeableness trait is associated with a negative influence on relational aggression. The trait of agreeableness has also been referred to as adaptability or reliability. There are differences in the interpretation of the dimension of agreeableness. The trait of agreeableness is considered reliable whereas Asian people generally support a collectivist culture, emphasizing social harmony and avoiding conflict [ 84 ]. Agreeableness represents the obligation to act as a group member and to make sacrifices. This cultural difference can lead to the irrelevance of agreeableness traits against relational aggression among young female adults in Malaysia. Besides that, those with higher levels of neuroticism are thought to be more likely to be aggressive. This individual is considered to have fewer stable emotions. Therefore, people who exhibit many neurotic personality traits are more prone to emotional instability and more prone to conflict with others. Conversely, agreeableness and aggressiveness are consistently negatively correlated [ 84 ].

Loneliness shows positively significant prediction towards relational aggression in romantic relationships. This is consistent with the study done by Prinstein et al., [ 55 ] which revealed that both relationally aggressive children and youth are more likely to be depressed, lonely, anxious, and socially isolated. However, according to the study done by Povedano et al., [ 85 ] found that the relationship between loneliness and relational aggression is significant and positive for boys, but not for girls. The involvement in violent behaviour would not act as a buffer for victimized girls experiencing strong feelings of loneliness, whereas it would be for boys. Lonely people usually have a negative perception of others’ intentions and behaviours in their interpersonal relationships. Along with these findings, lonely people tend to assume that their interpersonal failures stem from unchangeable and undesirable traits in their own personality, and they have a negative interpretation of other people’s intentions and interactions. Individuals who have developed a negative perception of themselves because of loneliness, feeling undesirable and unaccepted by others may resort to relational aggression, a powerful tool in which one uses force in interpersonal relationships to control other people [ 27 ].

The results of this study found a positive and significant prediction between avoidant and anxious attachment styles with relational aggression in romantic relationships. It has been established that the quality of communication between parents and children plays a crucial role in the development of a secure attachment. Our findings are in line with previous research that suggests that adolescents who have a positive relationship with their parents and communicate well with them are less likely to engage in aggressive behaviours and engage in risky activities [ 86 ]. Moreover, early attachments shape not only an individual’s sense of self and view of the world, but also their social skills, overall well-being, and future relationships. This is supported by the findings of Dervishi et al., [ 87 ] who found that adolescents with anxious attachments had higher levels of physical and verbal aggression. Studies have also shown that communication between parents and teens is strongly linked to the emergence of aggressive behaviours, with better communication resulting in a higher sense of security and an active exchange with others throughout life [ 88 , 89 , 90 ]. Essentially, individuals who are highly insecure may have difficulties controlling their anger and are more likely to engage in aggressive behaviour.

Previous research has demonstrated that individuals with insecure attachment patterns, particularly the anxious type, are at risk of experiencing negative consequences [ 91 , 92 , 93 ]. This can be attributed to a negative self-concept and high levels of rejection anxiety, leading to an over-reaction of excessive anger, and hurt in conflict situations. Research suggests that individuals with anxious attachment style have a history of persistent rejection from their partners and perceive themselves as unworthy of affection [ 94 ]. This can result in a perception of partners as untrustworthy and even threatening. It has been found that young adults with anxious attachment style are more prone to experiencing anger, compared to those with a secure or preoccupied attachment style who tend to have more positive expectations of their partners. In other words, those who have a strong sense of insecurity are likely to struggle with controlling their anger, while those with these insecurities are more likely to engage in aggressive behaviour.

Hierarchical regression analysis was carried out and it was found that social support as a moderator showed no significant effect between big five personality, avoidant and anxious attachment style with relational aggression in a romantic relationship except for loneliness subscale. The behaviour’s of loved ones that are in tune with the needs of the individual who is dealing with a stressful situation are referred to as social support [ 95 ]. The availability of support in the environment, the emotional response to stressful events, and the assessment of the consequences of these events can all be positively influenced by support from loved ones. Support from loved ones help to decrease the impact of stress by solving the victim’s problems, diminishing the perceived importance of the incident, facilitating the adoption of rational thoughts, and preventing or reducing inappropriate behaviour responses. According to previous research, social support may act as a moderator and buffer the effects of aggression and family functioning [ 96 ]. Due to the positive correlation between social support and a person’s family adjustment, social support helps to balance the negative effects of relational aggression on families [ 97 ].

This study’s finding is also consistent with the finding by Fortin et al. [ 98 ], where the moderating effect of social support is not present in female victims of physical violence. Thompson et al. [ 99 ] found that less women who have experienced relational aggression perceive the availability of social support, the more severe the violence they have experienced. The victim may also begin to blame herself more and ask for less support from her loved ones as the violence intensifies due to the bidirectional pattern of violence. Additionally, it seems that continuing in a relationship while having experienced physical abuse may have an impact on how satisfied they are with the assistance they have received [ 100 ]. These victims may also require additional forms of support, such as emotional, educational, and material support, even though they are generally happy with the assistance they have received.

Therefore, fewer confidants may have led to less robust social support. As a result, having fewer confidants may have led to social support that was insufficient and did not entirely satisfy the needs of the physical abuse victims. Besides that, social support is thought to be the most important factor that could significantly reduce loneliness [ 100 ], and it may be able to predict the trajectory of loneliness [ 101 ]. Indeed, numerous studies on the roles played by various forms of social support have found that perceived social support is more useful for predicting people’s mental health and may have a bigger impact on mental health than other forms of social support [ 102 , 103 , 104 ].

Both relational aggression and social support are empirically related to levels of loneliness, empirical literature is lacking on the interactive effects of relational aggression and social support on levels of loneliness [ 53 , 105 , 106 ]. Little is devoted in the existing literature to investigating the relationship triad. Ladd and Burgess [ 52 ] suggested that social support moderates the association between aggression and adjustment because it balances the dysfunction created by aggression. Family and peer support can act as a buffer in minimizing the negative effects of relational aggression in romantic relationships [ 107 ]. Adolescents who receive social support perform better in academic tasks and social interactions than individuals who do not have family and peer support [ 108 ]. Consistent with this research, social support, in general, and family support may act as moderating factors for the relationship between levels of loneliness and relational aggression.

Next in this study, it was found that there is no relationship between the role of social support as a moderator in the relationship between attachment style and relational aggression in romantic relationships among young female adults in Malaysia. This is contrary to the results of previous studies that suggest social support act as a moderator and minimizes or increases the effect of relational aggression on parental attachment style because social support is positively related to one’s family adjustment [ 99 ] and it has been hypothesized that social support moderates the relationship between relational aggression and parenting style. However, the findings of this current study highlight that social support as a moderator, relational aggression and parenting style are one of the factors that are very influential which affects the functioning of young people based on past studies [ 104 ]. The current findings show how social support moderates as an enhancer and buffer in attachment styles and relational aggression.

Results from previous studies differ from the current study due to several factors. Based on attachment style theory by Bowlby (1969), attachment style consists of secure attachment style, anxious attachment style, and avoidance attachment style but in this study only anxious attachment style, and avoidance attachment style alone were used to assess the attachment style of young adults. Avoidant attachment style involves fear of dependence and intimacy interpersonal, excessive need for independence and reluctance to self-disclosure. Anxious attachment styles involve fear of interpersonal rejection or neglect and distress when one’s partner is absent or unresponsive. People with an anxious attachment style always feel insecure about their romantic relationships and fear of abandonment by partner. Those with an avoidant attachment style have a common need to feel loved but not prepared emotionally to be in romantic relationships. Things like this can cause someone to use relational aggression in their romantic relationships such as manipulating partners, threatening partner to end the relationship. In addition, even if that individual has high social support but it does not affect if one is oriented in an avoidant attachment style and anxious attachment style.

Besides that, the findings of this study are consistent with a recent study by Egan and Bull [ 107 ] who found that there is no effect of social support as a moderator in the relationship between personality traits and relational aggression in romantic relationships. This is different from the perception based on personality theory developed by Goldberg [ 109 ] stating that social support is significantly associated with personality characteristics, especially extraversion, agreeableness, or emotional stability [ 107 ]. In general, from childhood to late adulthood, the relationships maintained by individuals with other people are related to individual differences in personality characteristics [ 110 ]. Personality traits that define interaction style can predict social interaction, available social support, and its perception. However, a supportive social context may also predict personality traits by providing individuals with opportunities to develop social skills, maintain social relationships, and foster prosocial behaviour. If personal experiences, roles, and social relationships can influence a person’s personality traits, social support is not only a proxy for the quality of social relationships but also a resource that can help to face the social challenges faced in middle adulthood and can predict personality traits by adapting to social roles expectations and developing social skills. Therefore, the relationship between the big five personalities and perceived social support is not only unidirectional but also reciprocal.

Limitations

As for limitations, all data used in this study were self-reported. The sensitive nature of some questions may have caused some participants to succumb to the social desirability bias and report. For instance, lower rates of relational aggression than their actual behaviour. Despite this, participants provided anonymous answers, making it less likely that they were prompted to provide biased answers. Furthermore, due to recall issues and inaccurate reporting it’s possible that both estimates of psychosocial factors and relational aggression contain measurement error. Another limitation for this study is the cross-sectional nature of these data, which precludes inferences about causal relationships is another drawback of this study.

Additionally, caution should be used when extrapolating the findings to all female samples since the participants in this study were a homogeneous sample of young female adults. Due to the study’s cross-sectional design, it is also impossible to draw conclusions about the cause-and-effect relationship between social support as a moderator in between psychosocial factors and relational aggression. To address the temporal ordering of people’s levels of social support from family and friends and their participation in relational aggression, longitudinal studies are required. Besides that, young female adults were not questioned regarding the opinions or involvement of friends in relational aggression. According to earlier studies, teenagers who have friends who engage in dating violence run a higher risk of doing so themselves [ 111 ]. Moreover, data was collected at one time point, so cause-and-effect conclusions could not be made. Besides that, the difference between the psychosocial factor’s groups couldn’t be identified clearly in relation to relational aggression in romantic relationship as only multiple regression has been conducted. A post hoc test can help in identifying the differences between specific groups and give a more meaningful finding.

Future studies

Future studies are needed on the impact of multiple placements, including their effects on unstable living situations, sibling attachment, adoption, frequent school changes, and difficulties. For instance, if an individual grew up in a family that shamed or condemned emotional expression or in a home with an abusive parent, this may associate anger with fear, danger, or damaged relationships, which will cause to develop more negative perception of their relationship with their parents and siblings. This study only focuses on female samples. Even though there are differences between the genders, both genders naturally experience anger. Men are thought to be more prone to rage despite evidence that women are more emotionally expressive. In addition, more research on gender disparities is necessary.

The current study suggests that preventive measures need to be taken to stop the symptoms of anger from getting worse. Uncontrollable anger can cause several problems, such as erratic behaviour, assault, abuse, addictions, and legal troubles. In these circumstances, anger impairs decision-making, harms relationships, and has other negative effects. Besides that, to manage anger and deal with triggers without repressing and storing it, as well as to deal without causing emotional harm, it’s crucial to recognize the warning signs of anger. Anger management techniques include breathing exercises under supervision, cognitive behavioural therapy, imagery, problem-solving, and the development of interpersonal and communication skills. Besides that, the findings of this study indicate that aimed at reducing and/or preventing relational aggression among young female adults should consider agreeableness traits ( 112 – 113 ). Young female adults who were less agreeable were likely to experience relational aggression. The findings highlight the need for additional research to pinpoint specific characteristics of the lower level of agreeableness female population that put them at risk for relational aggression in a romantic relationship.

The current study was novel in its examination of social support as a moderator of the association between psychosocial factor and relational aggression in romantic relationships. Future studies will need to test these associations further. Based on the findings from this study, there’s no evidence to support the prediction that social support would moderate this association, but future research with a better measure of social support or using different moderator variable may provide different results. Future research should investigate variables that are not included in this study that are possible predictors of relational aggression in romantic relationships. A post hoc test can be conducted further in identifying the differences between specific groups and give a more meaningful finding.

Relational forms of aggression tend to rise during adolescence (115), in part because more complex cognitive abilities are developed during this period that are necessary for successfully manipulating the relationships of others. We discovered a significant correlation between aggression and social support, which is crucial during adolescence. This research suggests that for some people, attachment style and relational aggression are highly overlapping, and possibly reciprocal. However, for some people, personality traits appear to be differentially linked to relational aggression. These results point to the need for additional research examining the moderating effects of significant correlates as well as a more nuanced strategy for relational forms of aggression during early adulthood’s prevention and intervention. Therefore, efforts to prevent young female adults from engaging in relational aggression should concentrate on all females and not just those who have been identified as perpetrators or victims. All females will be better equipped to spot relational aggression signs and help their friends if they are informed about the warning signs of relational aggression. Early adulthood could be taught about the warning signs of relational aggression through community-wide campaigns and in high school. This study will help to create awareness on the existence of relational aggression, public will be able to tackle this issue at an earlier stage rather than later and individuals will be able to identify the difference between a toxic and a non-toxic relationship.

In conclusion, many participants in this study reported having violent-free romantic relationships even though there are individuals who reported being the perpetrators of relational aggression. The current study was a first step in determining how psychosocial factors and relational aggression in romantic relationships are related to one another. Findings indicate that social support is also an important factor in understanding females’ relational aggression in romantic relationship. At the same time, results demonstrated that social support from friends and/or family has no significant effect with personality traits and attachment styles with relational aggression. This finding raises questions as to what may provide support to young female adults in relational aggression in romantic relationships. The current study’s greatest strength is the dialogue it has sparked about the importance of social support in romantic relationships between young female adults who is experiencing loneliness. This raised awareness could serve as a starting point for further study as well as the creation of programs and regulations that cater to the requirements of this population. It is necessary to create and carry out programs that encourage healthy dating interactions and inform young adults about dating violence which focuses on relational aggression. The findings also provide evidence for the significance of parental modelling in the development of romantic relationships in young adults. The findings are supported by social learning theory (Bandura, 1971), the concepts of which might be employed in investigating other areas of psychosocial factors on young adults’ relationships in the future.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Big Five Inventory

Experiences in Close Relationships– II

Multidimensional Scale of Perceived Social Support

Measure of Relational Aggression and Victimization

UCLA Loneliness Scale

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Kamaluddin, M.R., Munusamy, S., Sheau Tsuey, C. et al. Relational aggression in romantic relationship: empirical evidence among young female adults in Malaysia. BMC Psychol 12 , 305 (2024). https://doi.org/10.1186/s40359-024-01670-4

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Why Economic Inequality Undermines Political Trust: An Analysis of Mechanisms

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Shuai Jin, Yue Hu, Tianguang Meng, Why Economic Inequality Undermines Political Trust: An Analysis of Mechanisms, Public Opinion Quarterly , Volume 88, Issue 2, Summer 2024, Pages 337–358, https://doi.org/10.1093/poq/nfae013

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Research suggests that economic inequality reduces political trust after the public recognizes the inequality and perceives it as a failure of the political system in Western democracies. This study challenges this presumed “output evaluation model” (OEM) both theoretically and empirically. We provide an alternative mediator evaluation model (MEM) contending that objective inequality affects political trust through government-performance mediators, without requiring accurate public perception of inequality or specific regime types. With nationwide economic inequality and public opinion data from China, we examined both the OEM implication and four MEM mechanisms through impartial governance, responsiveness, judicial fairness, and anti-corruption efforts. Findings indicate that the mediating mechanisms, rather than direct inequality, shape political trust, with robust evidence even after addressing endogeneity. This study broadens the understanding of the intricate relationship between systemic conditions and individual perceptions, offering significant insights into the dynamics of trust in political institutions in a general sense.

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