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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

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

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

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

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

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

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

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.

LEARN ABOUT: 12 Best Tools for Researchers

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

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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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Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

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Understanding the Empirical Method in Research Methodology

empirical review in research methodology meaning

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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University of Memphis Libraries
Research Guides

Empirical Research: Defining, Identifying, & Finding

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

Introduction
Database Tools
Search Terms
Image Descriptions

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

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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
Creative & Experimental Approaches

Your Librarian

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

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

Study on radiation transfer in human skin for cosmetics
Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

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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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PSYC 301: Intro to Research Methods

Advanced Search Strategies
Tracking the Research Process
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Organizing Sources
Writing an Outline
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Finding Empirical Research

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

Finding Empirical Research in PsycInfo

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

Slideshow showing how to find empirical research in APA PsycInfo

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What is Empirical Research?

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

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

Key characteristics to look for:

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

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

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

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

Using PsycInfo

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Empirical research in the social sciences and education.

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

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

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

Key characteristics to look for:

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

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

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

Reading and Evaluating Scholarly Materials

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

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

Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

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

Key characteristics of empirical research include:

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

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

Empirical Research: Qualitative vs. Quantitative

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

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Quantitative Research

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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Empirical Research

Introduction, what is empirical research, attribution.

Finding Empirical Research in Library Databases
Designing Empirical Research
Case Sudies

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

Key characteristics to look for:

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

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

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

Portions of this guide were built using suggestions from other libraries, including Penn State and Utah State University libraries.

Next: Finding Empirical Research in Library Databases >>
Last Updated: Jan 10, 2023 8:31 AM
URL: https://enmu.libguides.com/EmpiricalResearch

The Vagueness of Integrating the Empirical and the Normative: Researchers’ Views on Doing Empirical Bioethics

Original Research
Open access
Published: 08 November 2023

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T. Wangmo ORCID: orcid.org/0000-0003-0857-0510 1 ,
V. Provoost 2 &
E. Mihailov 3

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The integration of normative analysis with empirical data often remains unclear despite the availability of many empirical bioethics methodologies. This paper sought bioethics scholars’ experiences and reflections of doing empirical bioethics research to feed these practical insights into the debate on methods. We interviewed twenty-six participants who revealed their process of integrating the normative and the empirical. From the analysis of the data, we first used the themes to identify the methodological content. That is, we show participants’ use of familiar methods explained as “back-and-forth” methods (reflective equilibrium), followed by dialogical methods where collaboration was seen as a better way of doing integration. Thereafter, we highlight methods that were deemed as inherent integration approaches, where the normative and the empirical were intertwined from the start of the research project. Second, we used the themes to express not only how we interpreted what was said but also how things were said. In this, we describe an air of uncertainty and overall vagueness that surrounded the above methods. We conclude that the indeterminacy of integration methods is a double-edged sword. It allows for flexibility but also risks obscuring a lack of understanding of the theoretical-methodological underpinnings of empirical bioethics research methods.

Acceptable objectives of empirical research in bioethics: a qualitative exploration of researchers’ views

Combining Empirical Data and Normativity: Possible or Not?

A systematic review of empirical bioethics methodologies.

Avoid common mistakes on your manuscript.

Introduction

Empirical bioethics is an interdisciplinary activity that centres around the integration of empirical findings with normative (philosophical) analysis (Ives, Dunn, and Cribb 2017 ). Mertz and colleagues ( 2014 ) posited that “empirical research in EE [empirical ethics] is not an end in itself, but a required step towards a normative conclusion or statement with regard to empirical analysis, leading to a combination of empirical research with ethical analysis and argument” (p. 1). Thegrowth of this field is often attributed to a dissatisfaction with a purely philosophical approach, perceived as being insufficient to address bioethical issues (Hedgecoe 2004 ; Hoffmaster 2018 ) and hence a belief that an empirically informed bioethics is better suited to deal with the complexity of human practices. A consensus paper put forward by European empirical ethics scholars aimed to reach standards of practice for those working in and wanting to do empirical bioethics (Ives, et al. 2018 ). Concerning integration, the standards included the need to (1) clearly state how the theoretical position was chosen for integration, (2) explain and justify how the method of integration was carried out, and (3) be transparent in informing how the method of integration was executed.

Despite consensus that empirical research is relevant to bioethical argument (Mihailov, et al. 2022 ; Musschenga 2005 ; Sulmasy and Sugarman 2010 ; Rost and Mihailov 2021 ), integrating empirical research with normative analysis remains challenging. An often and long discussed way of integration is the (wide) reflective equilibrium (Daniels 1979 ), which has been tailored to serve empirical bioethics projects by several scholars (Ives and Draper 2009 ; Van Thiel and Van Delden 2010 ; de Vries and van Leeuwen 2010 ). Briefly, (wide) reflective equilibrium is a two-way dialogue between ethical principles/values/judgement and practice (empirical data). It is carried out by the researcher, “the thinker.” In this process, the thinker goes back and forth between the normative underpinnings and empirical facts (data available from the study or other sources) until he or she can produce moral coherence (an “equilibrium”).

A systematic review of integrative empirical bioethics identified thirty-two methodologies (Davies, et al. 2015 ). Amongst others, these include (wide) reflective equilibrium (Ives 2014 ; Van Thiel and Van Delden 2010 ; de Vries and van Leeuwen 2010 ), dialogical empirical ethics (Widdershoven, Abma, and Molewijk 2009 ; Abma, et al. 2010 ), reflexive balancing (Ives 2014 ), integrative empirical ethics (Molewijk, et al. 2003 ), hermeneutical approach to bioethics (Rehmann-Sutter, Porz, and Scully 2012 ), symbiotic ethics (Frith 2012 ), and grounded moral analysis (Dunn, et al. 2012 ). Davies and colleagues ( 2015 ) categorized the identified methodologies into, inter alia, (1) dialogical, where there is a reliance on a dialogue between the stakeholders (e.g., researchers and participants) to reach a shared understanding of the analysis and the conclusion (e.g., inter-ethics); (2) consultative, which comprises analysis of the data by the researcher, who is the external thinker and works independently to develop a normative conclusion (e.g., reflexive balancing, reflective equilibrium), and (3) those that combine the two (e.g., hermeneutics).

The wide variety of integration methodologies available illustrates considerable uncertainty about the particular aims, content, and domain of application (Davies, et al. 2015 ; Wangmo and Provoost 2017 ). Furthermore, the steps that guide the integration process are often unspecific (Davies, et al. 2015 ; Huxtable and Ives 2019 ). For example, if an ethicist acts as facilitator and applies ethical theory to enrich the dialogical process for decision-making in concrete situations (Abma, et al. 2010 ), one may wonder whether the application of ethical theories was up to the subjective appreciation of the ethicist. In reflective equilibrium, there are pressing issues of how much weight should be given to empirical data and ethical theory. The existing methodologies thus risk being frustratingly vague and insufficiently determinate in practical contexts (Arras 2009 ; Dunn, et al. 2008 ). All in all, the multiplicity of methodological paths and their lack of clarity gives rise to a debate about appropriate methodologies (Hedgecoe 2004 ; Ives and Draper 2009 ; Ives, Dunn, and Cribb 2017 ).

In a survey of bioethics scholars in twelve European countries, Wangmo and Provoost ( 2017 ), found that one-third of the respondents (total respondents N = 200) attempted to integrate the normative with the empirical. Their findings indicate that not everyone in the field of bioethics did or intended to engage in this kind of interdisciplinary work. A reason could be the methodological diversity and complications pointed to above. It is of importance to further clarify and, where necessary, develop (new) integration methodologies that address the needs in the field. In this explorative qualitative study, we set out to investigate how researchers perform the integration of empirical data with normative analysis and how they evaluate that process. Our hope is to learn from the experiences and reflections of researchers who engaged in empirical bioethics research and to feed these insights from practice into the debate on methods.

Sampling and Study Participants

To form our participant sample pool, we conducted a systematic search of peer-reviewed publications in two databases—PubMed and SCOPUS—and used the following key terms: “Empirical Bioethics” OR “Empirical Ethics” OR “Interdisciplinary Ethics” OR “Interdisciplinary Empirical Ethics” OR “empirical-normative” OR “normative-empirical” OR “Empirical research in Bioethics.” The literature search resulted in 334 results, from which we removed 143 results because they were duplicates or did not match our inclusion criteria. A sample pool of 191 papers were left. A separate Google Scholar search using the same terms lead to thirteen extra papers, resulting in a total sample pool of 204 papers.

Starting from this sample pool, we first aimed for a maximum variation sample of scholars according to the type of paper they had authored. Therefore, the 204 results were categorized into three groups: (a) Empirical: ninety-four; (b) Methodological: seventy-four; and (c) Empirical-Argumentative: thirty-six. Empirical papers were those that used purely empirical social science methodology. The methodological papers were those that discussed and/or used empirical bioethics research. Empirical-argumentative papers were those that produced empirical results along with an attempt to use them in an argumentative manner to make certain claims. These three categories were ordered alphabetically to allow simple random selection of the first authors of those included publications. Secondly, we also purposefully selected papers to aim for a balanced distribution of male versus female scholars. We carried out two rounds of selection which identified first authors of eighty-five publications who were invited to participate in our study. A total of twenty-four scholars agreed to participate. We interviewed two additional participants who were referred to us by a participant. See table 1 for participant information.

Data Collection

All selected first authors received an email from EM informing them about the study, its purpose, the researchers, and the voluntary nature of the study. All non-responders received one reminder. No incentive was given to participate in the study. The interviews were carried out using Zoom in light of the pandemic and because our participants were from different countries. The interviews were completed between April 2020 and January 2021 and were on average sixty minutes long (range forty-five to ninety minutes).

To structure the discussion, we used an interview guide composed of three sections. The first part of the interview was geared towards generally understanding the type of research carried out by the participants. Therefore, this part of the interview was not limited to the research presented in the paper via which they were selected. The second part aimed at their attitudes towards the purpose of empirical research in bioethics, using a series of eight statements to which they were invited to respond (Mihailov, et al. 2022 ). The third section sought participants’ experiences of doing empirical bioethics (i.e., integration), the advantages and challenges to carrying out empirical bioethics study, and their views on the empirical turn in bioethics. During the data collection process, the research team met twice to discuss the interview guide based on reading two of the first four interviews. This resulted in minor adjustments to the interview guide. For the interview guide and further information on the study method, please refer to the first paper from this project (Mihailov, et al. 2022 ).

Data Analysis

Audio recordings were transcribed verbatim. All anonymized transcripts were imported into qualitative data analysis software, MAXQDA. Two authors (EM and TW) carefully read and coded several interviews independently and discussed the coding process and code labels used for the entire data corpus. This pre-coding followed a thematic analysis (TA) framework (Braun and Clark, 2006 ; Guest, et al. 2012 ) in light of its fit with the explorative nature of the overall project. Thereafter, a more specific analysis of the data related to integration methods took place in order to meet the aim of this paper.

The first author created and analysed a data set pertaining to participants’ experience, opinions, and their use of particular methods of integrating the normative and the empirical. Themes and sub-themes were developed based on authors’ discussion of the data related to the integration process. Using these themes and sub-themes, TW drafted the study results in a detailed and descriptive way for the co-authors (VP and EM) to gain the richness and depth of this specific content. After several rounds of iterations and discussions among the authors (process described in the next paragraph), we agreed on the result interpretations as presented in the next section.

Briefly, our analytical approach combines TA with a hermeneutics of faith or empathy and a hermeneutics of suspicion. Such approach has been used in other studies (Huxley et al. 2011 ). Whereas a hermeneutics of faith aims at better understanding what the participant described, a hermeneutics of suspicion aims to find out hidden or latent meanings. Our team integrated two types of hermeneutics that were reflected in the researcher roles: a hermeneutics of faith or empathy (EM, the interviewer and TW, the first author), a hermeneutics of suspicion (VP) and a mixture of both (TW). Integrating various analytic roles in one team has the advantage that different readings of the data can be used to challenge each other’s views, whilst still keeping track of those different interpretations. In the results section, these layers of interpretation are interwoven. We start with interpretations close to the participants’ accounts (the first two themes predominantly resulted from a hermeneutics of faith, where we also added critical notes at the end). As the results section progresses, critical interpretations that go beyond the data surface are given more weight (hermeneutics of suspicion). At the same time, we simultaneously keep underlining the scholars’ experiences in their own terms. We present data as block-quotes to support our analysis. Shorter expressions of the participants are given in the text using italic print between quotation marks.

Ethics Approval

The study was approved by the Research Ethics Commission of the University of Bucharest. All participants provided their informed consent to participate in this study and to record their interviews.

We identified four themes related directly to our research question. The first theme “the back-and forth methods” relays the scholars’ accounts of using a reflective equilibrium method or similar. The second theme “collaboration as doing integration” deals with dialogical methods and the views of scholars who thought that collaboration was a better way of organizing integration. In reporting these two themes, we also illustrate the inherently vague manner in which the participants discussed their use of integration methods. Both theme labels were also chosen to reflect the simplified way several of the scholars conveyed their integration process. Thereafter, we continue with two additional themes, where we focus in on these accounts of participants’ chosen methods and how they were used. For this, we first present the theme “Integration as inherently ingrained from the start of the project; but is it integration?” In this theme, we start by critically looking at participants’ process of how the integration is done. Finally, we move further to unpack the ambiguity with which some participants spoke of engaging with these methods. In the theme “the integration method as a particular opaque intelligence” we highlight participants’ plea for creativity and flexibility. Here we note that although the participants are making a good point, this plea may at the same time reveal hesitance and uncertainty in talking about how they chose and applied the method they used.

Theme 1: The “back-and-forth” methods

Several participants described their method as cyclical and included terms like “back-and-forth” between the conceptual framework and the empirical data. They alluded to their method as reflective equilibrium. Here, the participants noted that their research begins with a conceptual understanding of the ethical issues relevant for the topic or question. This was followed by the collection of empirical data based on the ethical concern teased out from conceptual work and going back to the conceptual to evaluate how it must be changed or adapted. Important in this backward and forward process was the notion of “ revising ” the theory and that this was an iterative process.

While doing the back-and-forth method of integration, one participant distinguished the normative and the empirical work, with the former being the core and the empirical elements being used to shape the normative concept. This reflective equilibrium method was also seen as a way of trying to understand why practice and theory are different; hence, it includes the need to go back-and-forth iteratively between what is happening in practice and why it does or does not conform to what is set out in theory.

My approach would be to start with the normative bit. Do(ing) research around that area. Have that firmly consolidated. With that, I could develop the empirical research bit: method, structure, instrument, population, whatever ... the design of the empirical bit. But probably that—the ongoing findings from this empirical bit, empirical research—would be continuously informing the normative bit that I already had then. And—as I mentioned before—for the output and the final outcomes, I think that probably starts by seeing how the empirical changed the shape of this normative “stone” [laughs]. (P18, SSE) I think it’s kind of a reflective equilibrium thing going on and ... if it turns out that people who are on the front lines making certain kinds of moral decisions systematically think about a case a certain way, and that’s different, you know, they are sensitive to factors that maybe my theory thinks shouldn’t be important, it’s not obvious what should happen. Maybe I need to update my theory …. Or it might be that I come up with an account of why it is that they are systematically wrong, that their intuitions are corrupted in some way, or they’re responsive to factors that shouldn’t be normatively relevant. (P9, EE)

The iterative process was also seen as something that cannot be set into stone since one may have to go through several rounds of going backward and forward. Thus, a participant said that although this method is in essence a simple one, it cannot be recipe-like. This method was described as a creative process, explicitly set apart from empirical methods that follow a strict and preset schedule.

You know, this isn’t like science, where, you know, you have this type of data, do this statistical step, and follow x, y, z .... It is a creative process. You do your conceptual work; you look at the data. “No, that doesn’t work. Something’s not right. Doesn’t fit.” You go back to your concepts, reorganize them, look at your data again and other information you might have. So, it’s this iterative process of interpreting, reinterpreting data—you might have to go and seek more information, you know, if there’s certain gaps in what you ... to solve certain dilemmas you come up with. But yeah, I mean, it’s that simple. You just ... look at your data, try to ... gain meaning from that data and then conceptualize it and keep going backwards and forwards. (P7, ERB)

Within the participants’ accounts of doing such “back-and-forth” integration work, we were surprised by how often their descriptions expressed hesitance and uncertainty. This vagueness becomes clearest in this part of the discussions where an actual method of doing empirical bioethics was described. It was evident in the use of language such as “it’s kind of a […] thing,” and “a bit of . ” Also, the participants used expressions such as “trying” and “we reflect a bit and balance a bit” when explaining how they used the method. These wording suggest a lack of confidence towards their own role in the methodological process.

I think basically my advice is some kind of evaluation of judgement is a normative one, philosophical normative one, but I try to use empirical [data] as in some kind of understanding, or I try to apply those normative into the practice, and also when the real or the empirical data, empirical knowledge, has some different implication or different meaning, then I could go back for my normative one. So, it’s kind of the reflective equilibrium thing. (P25, ERB) So, what we normally say is that we use a bit of the method of reflective equilibrium, trying to combine all kinds of considerations [of the people you are studying or the issue of the study], and norms and values and principles and professional norms and individual norms. And try to mix those and weigh those and come to an equilibrium. (P19, ERB)

Theme 2: Collaboration as doing integration without a distinct integration method

Some participants said that integration can be done through collaboration, in which two or more researchers with different skills (normative analysis and empirical method) would come together to formulate the research question and conduct the study. Participants reporting this mode of integration used a dialogical encounter. It was advised that the researchers with different backgrounds should know each other’s trade and work closely together, although in some ways also staying distinct. Calling for collaboration, one participant felt that although each researcher within their respective disciplines needs their own methods, there is no particular need for a standardized overarching method.

Well, first of all it’s an interdisciplinary work. So, you need the methodology, and you need the experts in their fields. For the empirical part you really need experienced social scientists, who know how to do empirical research in a valid manner. And for the normative part you need philosophers and people who are used ... are familiar with how to approach a normative question. And I think what is also important is that they know from each other and their different methodology and work. … So, integration sounds a little bit as if all things come together ... kind of a ménage. But there ... I see it more as staying distinct but working very closely and interactively together. But still with different methodology and [remaining] aware that they are different. (P20, ERB)

One participant explained this collaborative integration as a communicative process where the normative conclusions drawn are the result of discussions with the study participants, stakeholders, and even journal readers and other audiences. This participant’s collaboration method made a clear differentiation between the empirical and the theoretical parts. That is, the empirical phase stops after finishing the data collection and the (first-level) interpretation of those collected data. Thereafter, the empirical results are taken through a process of discussions with different stakeholders, a collaborative process that in theory is unending as it continues even after the publication of the study findings.

So, we were very interested in how they [their participants] narrate what they experience, and we saw, that [they] have typical […] narratives, with which they identify. […] That was the empirical approach and then at the end there was another [approach] between the results from the empirical part and the more theoretical or bioethical discussion, where we had regular interactions with the two parts of our team and some of the members, myself included, per parts of the empirical theme and of the theoretical theme and so we had this exchange of perspective and that led then to the publications. It’s a communication process. I think bioethics is always a conversation, also when we just write up papers, we are in a conversation, just one step in a conversation. So, your question how to integrate, is how to proceed in more comprehensive conversation with the audience, the readers of our papers, we are addressing. (P4, EE)

What these participants relayed is that the integration occurred through the process of collaboration. In these accounts, there is no specific integration method used during this collaboration and no plea for an overarching method of integration. Another way of stakeholder collaboration leading to integration was described as dialogical encounter during workshops. Here the key idea was that the research team along with their invited experts deliberate on the aggregate findings and reach a consensus as to what could be the key message of the overall work. Here again, we notice how no specific method (used during such collaboration) was brought forward.

Yeah. We tend to do a little bit of reflection ourselves on the data to come up with a conceptual map or model or policy recommendations and then we try to iterate that with the group, because we realize that, you know, we have a responsibility together. Right? And so balancing our ideas offered people ... it’s a good way of assessing whether ... when we are making the shift from what the “is” is to perhaps what the “ought” should be. Having different perspectives there is important. And we do that and depending on the project, sometimes we built in a formal consensus process, another time we just want to test our ideas to see how they ... If other people endorse them or can make some suggestions to improve them. (P3, ERB) I think ... I don’t think we need one [a specific method]. I really, I don’t. I don’t actually think we need one. Because a lot of people do a lot of good work—either empirically or normatively—and there are people who get along and so ... I think that is the empiricist and the normative […] and I also very hate to “pick” ... I think we have a lot of people who do both really well. But what I WISH ... is that instead of looking for a recipe to be able to integrate ... that people with different expertise would just work together more often. (P15, ERB)

Overall, we saw a similar vagueness in their description of the “how” of integration. For example, in the quote above, the participant talks of “balancing” that is done among the invited stakeholders as part of their discussion. It remains unclear how exactly such collaboration occurs and how to confirm the value of the outcomes reached. Also in this quote, we note the language of indeterminacy we described above (e.g., “try to iterate” ).

Theme 3: Integration as inherently ingrained from the start of the project; but is it integration?

Several scholars did not consider it necessary to use a specific method of integration. They reported that, for them, the normative and empirical parts of a study are interwoven within the different phases of the research process. According to these participants, the normative and empirical cannot be teased out. This is because these are inherently linked from the start of the study, with the research question and the research project being, in and of itself, normatively oriented. The empirical and the normative are constantly informing one another: “ you cannot separate the normative from the empirical. When doing empirical work, you already do a lot of normative work as well. So yeah it’s for me it’s integrated anyway” (P12, EE). Adding to the above quote, the same participant stated, “ No, it’s always both [normative and empirical], you cannot separate actually. But it also depends on what you understand as normative analysis of course .”

However, some scholars who felt that they were also doing this type of integration in empirical bioethics, to our view, are mistaken. This is because they were either (1) describing what looked to be purely theoretical research activities or (2) presenting what looked to be purely empirical activities as both empirical and normative. For instance, one participant argued that the normative and the empirical are not distinguishable in that there is no separation between the normative and empirical. This scholar talked about a feature of this approach, where “ no data is gathered ” as it was a process of doing philosophical work in context. The claim was that the entire research is situated in the world of “oughts,” thereby making it possible to come to an “ought” statement without having to trouble oneself with the is-ought gap. What this scholar sees as “integration” looks like context-sensitive normative argumentation.

So, the integration account is basically the production of a certain kind of an argument in a certain kind of context. And that’s why the integration that I defend, I guess, is, it’s so, it’s about normative reasoning of a certain kind, taking place in a certain kind of context, in situ. Which is why I resist the idea of, as seeing descriptive and normative phases. If you take that view, you’re basically saying something I think more profoundly about how, that data can produce an understanding of the ethics or something like that or that data can profoundly impact on our political positions. I don’t think that’s what the data is doing, insofar as what data is doing on my account on integration, it’s much more about how we can make better, how we can make arguments that have a particular kind of fall. (P6, EE)

A few other participants’ empirical bioethics work seemed to us as merely descriptive-oriented research activities on ethically relevant topics. One participant stated how the normative and empirical are not distinguishable and that somehow the analysis process is when normative thinking takes place. In this, however, no normative undertaking of the data was evident. Within their descriptions, we also found statements that conveyed vagueness in how this process of integrating the empirical and the normative was done. For instance, a participant regarded several parts of the research process, interpreting and discussing the research data, as normative in nature because it could not be disentangled from normative presuppositions.

Yeah. So, the way I do data analysis is by listening to the audio of interviews and also reading transcripts. And so ... often by the time I’ve gotten to the point of analysis I already have ... interpretative themes … So, it really is an integrated theoretical and empirical process. (P16, SSE). I wouldn’t know how to distinguish the empirical and the normative because ... what you can do empirically is deeply dependent on ... normative ... presuppositions. Ehm ... and then of course, what you actually do when you ask people for responses, and when you do ... your statistical analysis, I mean that’s not […] that’s only partly normative in the epistemic sense, but not in the moral sense. Ehm so, that’s obviously empirical then. But again—as soon as you start interpreting and discussing the empirical results—you’re back in the normative arena so, that really goes hand in hand. (P23, TE)

In another example, participants explained how in a descriptive type of study on an ethical topic, the normative work still played a role by referring to a thematic map that was based on normative concepts. However, one could claim that by describing the normative part as doing “ an empirical analysis in an ethically relevant way” they actually place this research activity fully within the empirical domain.

I mean, the normative and the empirical, what I actually, I’m not so much concerned with that question, even though that may be a little bit, um, bit weird. Um, I often think a little bit different, I think like what can I contribute for the empirical and what can I contribute from the applied ethics, perspective so to say. It doesn’t necessarily have to be normative, um, it just needs to be in the realm of ethics so to say, so again if I talk about [ethical topic of the participant’s research], I, I’m also just interested in what do they [researchers] think is their [values on the ethical topic], how do they frame their [value on the ethical topic], and by asking them about [the ethical topic] I ask them about their actions, what they do, why they do it, what is their normative basis, all those things, and by that I already ensure the ethical debate, to some degree. (P5, ERB) If you are doing the interviews, I would say, this is more the point where you are on the empirical parts …. Though I would still say, it’s very helpful to have the normative background assisting, when you are doing the interviews and hearing out what are the normative interesting things that people say. So, still it is not completely gone, the normative background. When you are analysing the data, then I would say, you have the empirical part for one, because you have to do this in an empirically solid manner, but you also have the normative part included, because you want to analyse the data not just in a sociological way, but you want to analyse this in an ethically relevant way. (P2, EE)

Theme 4: The integration method as a particular opaque intelligence

Within this theme, we illustrate how the vagueness in the methods used was more explicitly brought forward as a feature of these methods. Participants who have done empirical bioethics or sought to do it described how one can go from one step to the next to reach the normative conclusion. Their use of terminologies to describe this vague process pointed to something mysterious: an “ opaque A.I. ” and a “ big leap .” The process was seen as something that was difficult to explain. One participant claimed it could not be put into precise methodological rules. We pointed to this argument above when we reported the case participants made against recipe-like methods. Here, the participant explicitly raised the view that this process remains open to post-hoc justification.

What does integration really mean? How do you articulate this—kind of—magic box, where data goes in and then you come out conclusions?. It’s a particular opaque A.I., where you—kind of—plug in the data and this conclusion comes out. … And, that’s not a transparent process, we don’t know how our brains work, we don’t know how we make connections. So all we can do is perhaps be transparent about the steps we’re taking to get the information, be reflexive about how we use information, and then articulate the reasons for our conclusions. But I think—as I said earlier—there will always tend to be post-hoc justifications. (P22, EE) And then the big leap ... and the big leap is probably the one that you are curious about. The big leap toward what is the good thing to do. … But yet again, I have always thought that that methods [reflective equilibrium] falls short in giving clear sight of the black box, of the end, of the conclusion, ... I don’t have an answer whether or not we really get a clear view what happens when we take the “jump” from what we see, what we think, towards what we think would be the right thing to do, what we ought to do. (P19, ERB).

Accounts where we saw this vagueness presented as a feature of the method also expressed a need for a creative process that would require some flexibility. In the same line another participant noted: “ I feel that if we did have a recipe for integration, it would almost be sad ... people might feel that they are finding the ‘holy grail,’ but then you limiting yourself to just one way of thinking” (P15, ERB). Several participants underscored the need for flexibility and not to be restricted by too many rules. They said that much of empirical bioethics seeks to integrate work from two disciplines that have indeterminate processes, i.e., qualitative research and theoretical ethics. They thus emphasized the challenges of articulating two methods that are themselves opaque into one that is not.

And I think qualitative researchers have been ... struggling with this for a long time, and I think a lot of what we’re doing now mirrors the difficulties that qualitative researchers have been having—particularly in medicine—where they’re being challenged to explain a method. … And we have to explain method, but you can’t explain how your brain got there. With empirical bioethics, we’re working with qualitative research AND we’re working with ... theoretical ethics, so it’s doubly challenging to articulate two uhm very opaque processes. (P22, EE)

In 2015, Davies and colleagues summarized thirty-two empirical bioethics integrative methodologies that combine normative analysis and empirical data obtained using social-science research. Following this, scholars have discussed the integration of the normative and life sciences research (Mertz and Schildmann 2018 ), using critical realism in empirical bioethics (McKeown 2017 ), and integrating experimental philosophical bioethics and normative ethics (Earp, et al. 2020 ; Mihailov, et al. 2021 ). In line with the systematic review of empirical bioethics methodologies’ two broad categories of dialogical and consultative processes of integration (Davies, et al. 2015 ), our participants indicated two familiar approaches. The first one is based on a reflective equilibrium–type process, and the other, an interdisciplinary collaboration between and among different stakeholders.

In addition, several participants suggested integration was inherent with the normative and empirical intertwined within the overall research process. Our participants’ accounts of inherent integration shared some similarities with, for example, moral case analysis (Dunn, et al. 2012 ), integrated empirical ethics (Molewijk, et al. 2003 ), and dialogical empirical ethics (Landeweer, et al. 2017 ; Widdershoven, et al. 2009 ). The shared similarities were in the sense that there were no separate normative and empirical parts to be distinguished in a project and that the project itself was normatively oriented. However, we should be critical of this view. The mere fact the empirical and the normative is inseparably intertwined throughout a research process does not mean (1) that these claims cannot be conceptually separated and (2) that such a method is free of methodological concerns. For instance, there would still be the need to specify what moral principles demand in a particular situation, decide which ethical theory to use, or make normative judgements with the help of empirical data (Frith 2012 ; Salloch, et al. 2015 ). Apart from that, several of these “inherently integrated” methods lacked a clear normative side and the enterprises described seemed purely empirical. Upon closer analysis, one could interpret some of the accounts of “integration was always inherently present” as a way of avoiding looking into the black box.

Furthermore, within these “inherently integrated” approaches, a few scholars described their descriptive research on ethical issues as empirical bioethics. Based on the available definition of empirical bioethics (Ives, Dunn, and Cribb 2017 ; Mertz, et al. 2014 ) and the standards offered by Ives and colleagues ( 2018 ), the works of these participants would thus not count as empirical bioethics. This is because there was no evidence of any integration happening. In our opinion, this mismatch between the practice of some scholars and what is “agreed” to in the literature as empirical bioethics may be pointing to the fact that empirical work in bioethics is in essence heterogeneous (Ives, Dunn, and Cribb 2017 ; Mertz, et al. 2014 ). For one, it is possible that scholars look at their projects as fitting an empirical bioethics because they start from research questions relating to the normative and because their projects, even with purely descriptive parts (and papers), are aimed to eventually lead to normative conclusions. But also in that case, we need to be clear about the nature of such particular (sub)projects and about the absence of integration efforts in these parts. Second, it is possible that scholars have different perspectives on the matter than the one expressed in the standards paper (Ives, et al. 2018 ). In that case as well, these must be brought out in the open. Third, some scholars may simply be mistaken when they consider their projects to be empirical bioethics. Their mistaken belief might be based on the idea that the empirical findings were at some point integrated in normative reasoning, which results in a normative claim. This simply might not be the case. This then, more than anything, would point to the need for transparency about and agreement on the use of methods. A heterogeneity of approaches in the field should be applauded. However, for all of them, we need to be able to identify where and how the integration happens. In the remaining part of this discussion, we focus on the overall vague manner in which our participants talked about their methods and what that implies for the field of empirical bioethics.

Vagueness of Integration Methods Used

Reflective equilibrium, broadly construed, is a deliberative process that seeks coherence between attitudes, beliefs, and competing ethical principles (Daniels 2020 ). A standard objection against reflective equilibrium methodology is that it is insufficiently determinate in practical contexts to be action-guiding or to help decide between conflicting views (Arras 2009 ; Paulo 2020 ; Raz 1982 ). The iterative process of going back-and-forth between the normative and the empirical to come to a coherent account, similarly, is fraught with indeterminate indications. The way study participants relayed their approaches and explained their practices underscored the vagueness they felt. It further showed the difficulties even scholars with expertise in using these methods had in illustrating the “how” in an exact manner.

Such vagueness was also evident in collaboration methods of integration reported by our study participants. This collaboration involves an iterative and deliberative process of sharing information and engaging with different perspectives (Rehmann-Sutter, et al. 2012 ). It requires ongoing dialogue between social scientists and bioethicists. Their practical know-how guides the conclusion about the normative significance of empirical data. Even though the experience and implicit know-how of the experts can be rich in content and varied, how the communication process is done and who decides the outcome often remains indeterminate. This was noted in the voices of our participants.

The difficulty in clearly explaining the “how” of the integration process is something that researchers who have carried out an integration or wished to do so are likely to be familiar with. Several scholars have pointed to this unclear process as well (Ives and Draper 2009 ; Mertz and Schildmann 2018 ; Strong, et al. 2010 ). One explanation for this finding may be that, given the numerous tailored versions of the reflective equilibrium methodology for empirical bioethics (de Vries and van Leeuwen 2010 ; Ives 2014 , Ives and Draper 2009 ; Van Thiel and Van Delden 2010 ; Savulescu, et al. 2021 ), there may be confusion surrounding how to make a choice and how to implement it in practice. As noted earlier, there are many available empirical bioethics methodologies (Davies, et al. 2015 ), and it has been suggested that each researcher could be using his or her own version (Wangmo and Provoost 2017 ). This situation, to us, points in two directions. First, it may convey a general need to remain flexible and open to creativeness, key components of the normative reasoning that is central to the integration method. We may thus have to stop looking for a method that is akin to empirical standards, especially those of quantitative methods, and recognize that the empirical and normative integration is in many ways a normative enterprise, which does not follow an exact method. Second, the wide variation of approaches makes it even clearer that we need to seek more methodological clarity on the overarching level. This is where the debate on standards (Ives, et al. 2018 ), for instance, has been an added value. It allows for heterogeneity while at the same time striving to create more clarity. In fact, we point out that the integration methods are inherently indeterminate and that this is a good thing. That said, an acceptance of the indeterminate character of this integration does not absolve us from the need to identify the foundations of what we are doing in a theoretical-methodological way.

The study findings confirm the image of an indeterminate process. As research on this topic is developing, it is ever more clear that the scholars involved come from a wide variation of disciplines. This is another argument as to why this indeterminate character is indispensable. The findings thus substantiate what has already been written about the indeterminate status of the methods used in empirical bioethics (Arras 2009 ; Davies, et al. 201 5 ; Dunn, et al. 2008 ; Huxtable and Ives 2019 ), despite efforts to delimit and standardize empirical bioethics work (Mertz, et al. 2014 ; Ives, et al. 2018 ). One way of reading the vagueness we encountered is the scholars’ struggle to explain their own integration process, and perhaps even a lack of full comprehension of that process. Another interpretation is one that is in line with the wish for creativity and flexibility, and a level of indeterminacy in the methods we look for, namely an expression of leaving things open. Creativity can be a medicine against the belief that precise and transparent standards can account for such a “maze of interactions” (Feyerabend 2010 ) between experts with fertile know-hows. Too much standardization misses how particular research situations inspire novel ways of seeing the ethical relevance of empirical data. We should nevertheless be aware that the indeterminate nature of any integrative methodology makes it subject to risks of post-hoc rationalizations and motivated reasoning (Ives and Dunn 2010 ; Mihailov 2016 ). In the end, demands for creativity—however valid—should go hand in hand with demands for a thorough theoretical foundation as well as practical understanding of the method at hand.

The Normative Nature of Integrative Methodologies

Reflective equilibrium is a deliberation method that helps us come to a conclusion about what we ought to do (Daniels 1996 ; Rawls 1951 , 1971 ). If we describe the integration process only in terms of going back-and-forth between data and theory, or in terms of collaboration between different experts, we risk obscuring the normative nature of using empirical data to help elaborate ethical prescriptions, which is the goal of doing such an integration (Ives and Draper 2009 ; Mertz, et al. 2014 ). Researchers often talk about integration as if it is a process half empirical and half normative or something that just needs normative reasoning alongside empirical data. But the very act of integration is normative in nature. While facts are essential for addressing bioethical issues, the task of integration ultimately depends on normative assumptions about the normative weight of moral intuitions.

Our data show that many of our participants rely on a reflective equilibrium characterized in their explanations mostly by moving back-and-forth between empirical results about moral attitudes and intuitions. Although the cyclical thinking is an important part of reflective equilibrium, there is more to it. Often, however, our participants did not move beyond this aspect. Ideas of coherence between moral intuitions and moral principles, and the fundamental willingness to adjust moral principles in light of what we discover were rarely touched upon. Perhaps what we see here is that several study participants embarked on an intuitive account of a—sometimes simplified—reflective equilibrium inspired methodology. At least in the interviews, it was not shown that they were fully aware of theoretical commitments to coherence, giving normative weight to moral intuitions, and screening them for bias.

The need to clarify the essential normative nature of integration appeals to normatively trained bioethicists, who may be in a better position to debate and assess how empirical input should be integrated into normative recommendations. We are not claiming that bioethics should be the arena of philosophers. Empirical research in bioethics is widespread (Borry, et al. 2006 ; Wangmo, et al. 2018 ), and scholarly perceptions about who belongs in the field are no longer exclusivist. There is thus a need to look at empirical bioethics projects in a broader way, including studies where empirical data are gathered but not used directly as part of a normative argumentation. Such empirical data may thus contribute to a larger body of work aimed at reaching normative conclusions. They can include, for example, empirical studies that explore stakeholders’ views relating to bioethical matters and explain how people arrive at certain reasoning patterns or studies that reveal the lived experience of stakeholders and explore how moral questions are experienced in practice (Mihailov, et al. 2022 ). To our view, despite the central role of normative know-how to integration, this does not mean that integration efforts need to be exclusively the work of ethicists or that empirical researchers will be unable to engage in it.

Limitations

Our findings are, first and foremost, not generalizable, as they are based on an exploratory qualitative study design. The data come from a small non-representative sample of researchers. Other scholars, with different or greater experience in using particular (interdisciplinary) integration methods may have different opinions. They could perhaps have provided us with more concrete information about the way they carried out such integration. Also, only one of our participants described him/herself as a normative researcher. It would have been interesting to have more participants who were normatively oriented to include their views on how empirical data can be of use to the adaptation or formation of normative recommendations. Second, we asked scholars to tell us the process they use in integrating the normative and the empirical. This is a challenge task in and of itself. Not only did the scholars have limited time for the interview, but also it is generally difficult to explain how exactly this process pans out post-hoc. We thus acknowledge that we presented the participants with questions which were in no way easy for them to address in a single conversation. Because we wanted to focus on the scholars’ own reports, we did not confront them with approaches adopted by others in as systematic way. We did not also engage in a critical assessment of the reported method at the time of the interview. It would be interesting for further research to include such an approach and, for instance, study this using focus group methods. Using confrontation with other approaches or other views could offer the opportunity for a more critical reflection. For this paper, however, we opted to enrich the ongoing debate first and foremost with the accounts of the scholars. Third, we underline that a minority of our participants had already published methodological papers related to empirical bioethics as evident from the EBE sample. We did not ask the scholars to discuss the method that they have written about or most liked, nor did we ask them to discuss the paper that led to their identification for this study. During the interviews, however, we sought to address acquiescence and social desirability by using Socratic questioning and probing, to provide time for participants to explain their method of integration.

Conclusion: Ambiguity Waiting to Be Disentangled

We set out to find more about the “how” of the integration methods used by scholars in empirical bioethics. Our hope was to provide input for the ongoing debate on methods and perhaps even some practical support for those considering empirical bioethics projects. Although we shed some light onto the way integration methods were used by different bioethics scholars, we especially bring forth the vagueness and uncertainties in their accounts. The main challenge was not the heterogeneity of methods but rather the indeterminate nature of integration methodologies. On a practical level, this finding may express the need for flexibility and variation in approaches rather than a need for recipe-like instructions. Such a clear-cut method will likely neither be possible nor appreciated. Philosopher of science Paul Feyerabend once said that methodological rules “are ambiguous in the way certain drawings are ambiguous” ( 2001 , 39). The ambiguity of integration methods does not make them less appealing, just as the ambiguity of drawings does not make them less beautiful. Therefore, we may be wiser to accept some degree of indeterminacy, while simultaneously striving for clarity and transparency in terms of the theoretical-methodological underpinnings.

Data Availability

Anonymized data relevant to evaluate the results presented in this paper can be made available upon request.

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Acknowledgements

We sincerely acknowledge the two anonymous reviewers for their insightful comments and for how they constructively challenged the discussion of the study findings.

The authors thank the study participants for their time and sharing their views. The study was supported by the Swiss National Science Foundation, IZSEZ0_190015.

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Wangmo, T., Provoost, V. & Mihailov, E. The Vagueness of Integrating the Empirical and the Normative: Researchers’ Views on Doing Empirical Bioethics. Bioethical Inquiry (2023). https://doi.org/10.1007/s11673-023-10286-z

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SYSTEMATIC REVIEW article

The role of counseling for non-traditional students in formal higher education: a scoping review provisionally accepted.

1 Department of Pedagogy and Primary Education, National and Kapodistrian University of Athens, Greece

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

Since the mid-20th century, the number of adult students enrolled in formal higher education (HE) programs has significantly increased. The profile of non-traditional students differs significantly from that of traditional students in terms of their characteristics, learning methods, obstacles and challenges, motivations for learning, and conditions for effective learning. Unlike traditional students, adult students often balance family, work, and educational responsibilities, necessitating a more nuanced approach to support and guidance. However, most HE institutions primarily serve the needs of traditional student populations, which results in limited support available to adult students. This scoping review aimed to explore and map the existing literature on the role of adult (or non-traditional) students counseling in the context of formal HE. We focused on literature related to academic advising for non-traditional students in formal HE, restricting our search to both empirical and non-empirical articles published in peer-reviewed journals between 2010 and 2022. Employing Arksey and O'Malley's scoping review method and the PRISMA-ScR Checklist, we searched four databases (EBSCOhost, Crossref, Semantic Scholar, and ERIC), supplemented by a manual search. Of the 1,330 articles identified and screened, 25 studies met the eligibility criteria. Our review included 17 empirical and eight non-empirical studies, with the majority conducted in the USA (21 of 25). Thematic analysis revealed five key research areas (or themes): academic advising practices, perceptions of advising, technology, and advising, advising models, and academic success. The most common research theme, advising practices for adult (undergraduate and doctoral) students, constituted 52% of the studies (n=13). Drawing from our analysis, we discuss current trends and future development in advising non-traditional students within formal HE settings. The added value of academic advising for adult students is explored, and any potential gaps in research literature knowledge are identified.

Keywords: Academic advising, Counseling, non-traditional students, adult students, higher education

Received: 25 Dec 2023; Accepted: 16 May 2024.

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

* Correspondence: Ms. Panagiota Stamou, National and Kapodistrian University of Athens, Department of Pedagogy and Primary Education, Athens, Greece

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An overview of methodological approaches in systematic reviews

Prabhakar veginadu.

1 Department of Rural Clinical Sciences, La Trobe Rural Health School, La Trobe University, Bendigo Victoria, Australia

Hanny Calache

2 Lincoln International Institute for Rural Health, University of Lincoln, Brayford Pool, Lincoln UK

Akshaya Pandian

3 Department of Orthodontics, Saveetha Dental College, Chennai Tamil Nadu, India

Mohd Masood

Associated data.

APPENDIX B: List of excluded studies with detailed reasons for exclusion

APPENDIX C: Quality assessment of included reviews using AMSTAR 2

The aim of this overview is to identify and collate evidence from existing published systematic review (SR) articles evaluating various methodological approaches used at each stage of an SR.

The search was conducted in five electronic databases from inception to November 2020 and updated in February 2022: MEDLINE, Embase, Web of Science Core Collection, Cochrane Database of Systematic Reviews, and APA PsycINFO. Title and abstract screening were performed in two stages by one reviewer, supported by a second reviewer. Full‐text screening, data extraction, and quality appraisal were performed by two reviewers independently. The quality of the included SRs was assessed using the AMSTAR 2 checklist.

The search retrieved 41,556 unique citations, of which 9 SRs were deemed eligible for inclusion in final synthesis. Included SRs evaluated 24 unique methodological approaches used for defining the review scope and eligibility, literature search, screening, data extraction, and quality appraisal in the SR process. Limited evidence supports the following (a) searching multiple resources (electronic databases, handsearching, and reference lists) to identify relevant literature; (b) excluding non‐English, gray, and unpublished literature, and (c) use of text‐mining approaches during title and abstract screening.

The overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process, as well as some methodological modifications currently used in expedited SRs. Overall, findings of this overview highlight the dearth of published SRs focused on SR methodologies and this warrants future work in this area.

1. INTRODUCTION

Evidence synthesis is a prerequisite for knowledge translation. 1 A well conducted systematic review (SR), often in conjunction with meta‐analyses (MA) when appropriate, is considered the “gold standard” of methods for synthesizing evidence related to a topic of interest. 2 The central strength of an SR is the transparency of the methods used to systematically search, appraise, and synthesize the available evidence. 3 Several guidelines, developed by various organizations, are available for the conduct of an SR; 4 , 5 , 6 , 7 among these, Cochrane is considered a pioneer in developing rigorous and highly structured methodology for the conduct of SRs. 8 The guidelines developed by these organizations outline seven fundamental steps required in SR process: defining the scope of the review and eligibility criteria, literature searching and retrieval, selecting eligible studies, extracting relevant data, assessing risk of bias (RoB) in included studies, synthesizing results, and assessing certainty of evidence (CoE) and presenting findings. 4 , 5 , 6 , 7

The methodological rigor involved in an SR can require a significant amount of time and resource, which may not always be available. 9 As a result, there has been a proliferation of modifications made to the traditional SR process, such as refining, shortening, bypassing, or omitting one or more steps, 10 , 11 for example, limits on the number and type of databases searched, limits on publication date, language, and types of studies included, and limiting to one reviewer for screening and selection of studies, as opposed to two or more reviewers. 10 , 11 These methodological modifications are made to accommodate the needs of and resource constraints of the reviewers and stakeholders (e.g., organizations, policymakers, health care professionals, and other knowledge users). While such modifications are considered time and resource efficient, they may introduce bias in the review process reducing their usefulness. 5

Substantial research has been conducted examining various approaches used in the standardized SR methodology and their impact on the validity of SR results. There are a number of published reviews examining the approaches or modifications corresponding to single 12 , 13 or multiple steps 14 involved in an SR. However, there is yet to be a comprehensive summary of the SR‐level evidence for all the seven fundamental steps in an SR. Such a holistic evidence synthesis will provide an empirical basis to confirm the validity of current accepted practices in the conduct of SRs. Furthermore, sometimes there is a balance that needs to be achieved between the resource availability and the need to synthesize the evidence in the best way possible, given the constraints. This evidence base will also inform the choice of modifications to be made to the SR methods, as well as the potential impact of these modifications on the SR results. An overview is considered the choice of approach for summarizing existing evidence on a broad topic, directing the reader to evidence, or highlighting the gaps in evidence, where the evidence is derived exclusively from SRs. 15 Therefore, for this review, an overview approach was used to (a) identify and collate evidence from existing published SR articles evaluating various methodological approaches employed in each of the seven fundamental steps of an SR and (b) highlight both the gaps in the current research and the potential areas for future research on the methods employed in SRs.

An a priori protocol was developed for this overview but was not registered with the International Prospective Register of Systematic Reviews (PROSPERO), as the review was primarily methodological in nature and did not meet PROSPERO eligibility criteria for registration. The protocol is available from the corresponding author upon reasonable request. This overview was conducted based on the guidelines for the conduct of overviews as outlined in The Cochrane Handbook. 15 Reporting followed the Preferred Reporting Items for Systematic reviews and Meta‐analyses (PRISMA) statement. 3

2.1. Eligibility criteria

Only published SRs, with or without associated MA, were included in this overview. We adopted the defining characteristics of SRs from The Cochrane Handbook. 5 According to The Cochrane Handbook, a review was considered systematic if it satisfied the following criteria: (a) clearly states the objectives and eligibility criteria for study inclusion; (b) provides reproducible methodology; (c) includes a systematic search to identify all eligible studies; (d) reports assessment of validity of findings of included studies (e.g., RoB assessment of the included studies); (e) systematically presents all the characteristics or findings of the included studies. 5 Reviews that did not meet all of the above criteria were not considered a SR for this study and were excluded. MA‐only articles were included if it was mentioned that the MA was based on an SR.

SRs and/or MA of primary studies evaluating methodological approaches used in defining review scope and study eligibility, literature search, study selection, data extraction, RoB assessment, data synthesis, and CoE assessment and reporting were included. The methodological approaches examined in these SRs and/or MA can also be related to the substeps or elements of these steps; for example, applying limits on date or type of publication are the elements of literature search. Included SRs examined or compared various aspects of a method or methods, and the associated factors, including but not limited to: precision or effectiveness; accuracy or reliability; impact on the SR and/or MA results; reproducibility of an SR steps or bias occurred; time and/or resource efficiency. SRs assessing the methodological quality of SRs (e.g., adherence to reporting guidelines), evaluating techniques for building search strategies or the use of specific database filters (e.g., use of Boolean operators or search filters for randomized controlled trials), examining various tools used for RoB or CoE assessment (e.g., ROBINS vs. Cochrane RoB tool), or evaluating statistical techniques used in meta‐analyses were excluded. 14

2.2. Search

The search for published SRs was performed on the following scientific databases initially from inception to third week of November 2020 and updated in the last week of February 2022: MEDLINE (via Ovid), Embase (via Ovid), Web of Science Core Collection, Cochrane Database of Systematic Reviews, and American Psychological Association (APA) PsycINFO. Search was restricted to English language publications. Following the objectives of this study, study design filters within databases were used to restrict the search to SRs and MA, where available. The reference lists of included SRs were also searched for potentially relevant publications.

The search terms included keywords, truncations, and subject headings for the key concepts in the review question: SRs and/or MA, methods, and evaluation. Some of the terms were adopted from the search strategy used in a previous review by Robson et al., which reviewed primary studies on methodological approaches used in study selection, data extraction, and quality appraisal steps of SR process. 14 Individual search strategies were developed for respective databases by combining the search terms using appropriate proximity and Boolean operators, along with the related subject headings in order to identify SRs and/or MA. 16 , 17 A senior librarian was consulted in the design of the search terms and strategy. Appendix A presents the detailed search strategies for all five databases.

2.3. Study selection and data extraction

Title and abstract screening of references were performed in three steps. First, one reviewer (PV) screened all the titles and excluded obviously irrelevant citations, for example, articles on topics not related to SRs, non‐SR publications (such as randomized controlled trials, observational studies, scoping reviews, etc.). Next, from the remaining citations, a random sample of 200 titles and abstracts were screened against the predefined eligibility criteria by two reviewers (PV and MM), independently, in duplicate. Discrepancies were discussed and resolved by consensus. This step ensured that the responses of the two reviewers were calibrated for consistency in the application of the eligibility criteria in the screening process. Finally, all the remaining titles and abstracts were reviewed by a single “calibrated” reviewer (PV) to identify potential full‐text records. Full‐text screening was performed by at least two authors independently (PV screened all the records, and duplicate assessment was conducted by MM, HC, or MG), with discrepancies resolved via discussions or by consulting a third reviewer.

Data related to review characteristics, results, key findings, and conclusions were extracted by at least two reviewers independently (PV performed data extraction for all the reviews and duplicate extraction was performed by AP, HC, or MG).

2.4. Quality assessment of included reviews

The quality assessment of the included SRs was performed using the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews). The tool consists of a 16‐item checklist addressing critical and noncritical domains. 18 For the purpose of this study, the domain related to MA was reclassified from critical to noncritical, as SRs with and without MA were included. The other six critical domains were used according to the tool guidelines. 18 Two reviewers (PV and AP) independently responded to each of the 16 items in the checklist with either “yes,” “partial yes,” or “no.” Based on the interpretations of the critical and noncritical domains, the overall quality of the review was rated as high, moderate, low, or critically low. 18 Disagreements were resolved through discussion or by consulting a third reviewer.

2.5. Data synthesis

To provide an understandable summary of existing evidence syntheses, characteristics of the methods evaluated in the included SRs were examined and key findings were categorized and presented based on the corresponding step in the SR process. The categories of key elements within each step were discussed and agreed by the authors. Results of the included reviews were tabulated and summarized descriptively, along with a discussion on any overlap in the primary studies. 15 No quantitative analyses of the data were performed.

From 41,556 unique citations identified through literature search, 50 full‐text records were reviewed, and nine systematic reviews 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 were deemed eligible for inclusion. The flow of studies through the screening process is presented in Figure 1 . A list of excluded studies with reasons can be found in Appendix B .

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Study selection flowchart

3.1. Characteristics of included reviews

Table 1 summarizes the characteristics of included SRs. The majority of the included reviews (six of nine) were published after 2010. 14 , 22 , 23 , 24 , 25 , 26 Four of the nine included SRs were Cochrane reviews. 20 , 21 , 22 , 23 The number of databases searched in the reviews ranged from 2 to 14, 2 reviews searched gray literature sources, 24 , 25 and 7 reviews included a supplementary search strategy to identify relevant literature. 14 , 19 , 20 , 21 , 22 , 23 , 26 Three of the included SRs (all Cochrane reviews) included an integrated MA. 20 , 21 , 23

Characteristics of included studies

SR = systematic review; MA = meta‐analysis; RCT = randomized controlled trial; CCT = controlled clinical trial; N/R = not reported.

The included SRs evaluated 24 unique methodological approaches (26 in total) used across five steps in the SR process; 8 SRs evaluated 6 approaches, 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 while 1 review evaluated 18 approaches. 14 Exclusion of gray or unpublished literature 21 , 26 and blinding of reviewers for RoB assessment 14 , 23 were evaluated in two reviews each. Included SRs evaluated methods used in five different steps in the SR process, including methods used in defining the scope of review ( n = 3), literature search ( n = 3), study selection ( n = 2), data extraction ( n = 1), and RoB assessment ( n = 2) (Table 2 ).

Summary of findings from review evaluating systematic review methods

There was some overlap in the primary studies evaluated in the included SRs on the same topics: Schmucker et al. 26 and Hopewell et al. 21 ( n = 4), Hopewell et al. 20 and Crumley et al. 19 ( n = 30), and Robson et al. 14 and Morissette et al. 23 ( n = 4). There were no conflicting results between any of the identified SRs on the same topic.

3.2. Methodological quality of included reviews

Overall, the quality of the included reviews was assessed as moderate at best (Table 2 ). The most common critical weakness in the reviews was failure to provide justification for excluding individual studies (four reviews). Detailed quality assessment is provided in Appendix C .

3.3. Evidence on systematic review methods

3.3.1. methods for defining review scope and eligibility.

Two SRs investigated the effect of excluding data obtained from gray or unpublished sources on the pooled effect estimates of MA. 21 , 26 Hopewell et al. 21 reviewed five studies that compared the impact of gray literature on the results of a cohort of MA of RCTs in health care interventions. Gray literature was defined as information published in “print or electronic sources not controlled by commercial or academic publishers.” Findings showed an overall greater treatment effect for published trials than trials reported in gray literature. In a more recent review, Schmucker et al. 26 addressed similar objectives, by investigating gray and unpublished data in medicine. In addition to gray literature, defined similar to the previous review by Hopewell et al., the authors also evaluated unpublished data—defined as “supplemental unpublished data related to published trials, data obtained from the Food and Drug Administration or other regulatory websites or postmarketing analyses hidden from the public.” The review found that in majority of the MA, excluding gray literature had little or no effect on the pooled effect estimates. The evidence was limited to conclude if the data from gray and unpublished literature had an impact on the conclusions of MA. 26

Morrison et al. 24 examined five studies measuring the effect of excluding non‐English language RCTs on the summary treatment effects of SR‐based MA in various fields of conventional medicine. Although none of the included studies reported major difference in the treatment effect estimates between English only and non‐English inclusive MA, the review found inconsistent evidence regarding the methodological and reporting quality of English and non‐English trials. 24 As such, there might be a risk of introducing “language bias” when excluding non‐English language RCTs. The authors also noted that the numbers of non‐English trials vary across medical specialties, as does the impact of these trials on MA results. Based on these findings, Morrison et al. 24 conclude that literature searches must include non‐English studies when resources and time are available to minimize the risk of introducing “language bias.”

3.3.2. Methods for searching studies

Crumley et al. 19 analyzed recall (also referred to as “sensitivity” by some researchers; defined as “percentage of relevant studies identified by the search”) and precision (defined as “percentage of studies identified by the search that were relevant”) when searching a single resource to identify randomized controlled trials and controlled clinical trials, as opposed to searching multiple resources. The studies included in their review frequently compared a MEDLINE only search with the search involving a combination of other resources. The review found low median recall estimates (median values between 24% and 92%) and very low median precisions (median values between 0% and 49%) for most of the electronic databases when searched singularly. 19 A between‐database comparison, based on the type of search strategy used, showed better recall and precision for complex and Cochrane Highly Sensitive search strategies (CHSSS). In conclusion, the authors emphasize that literature searches for trials in SRs must include multiple sources. 19

In an SR comparing handsearching and electronic database searching, Hopewell et al. 20 found that handsearching retrieved more relevant RCTs (retrieval rate of 92%−100%) than searching in a single electronic database (retrieval rates of 67% for PsycINFO/PsycLIT, 55% for MEDLINE, and 49% for Embase). The retrieval rates varied depending on the quality of handsearching, type of electronic search strategy used (e.g., simple, complex or CHSSS), and type of trial reports searched (e.g., full reports, conference abstracts, etc.). The authors concluded that handsearching was particularly important in identifying full trials published in nonindexed journals and in languages other than English, as well as those published as abstracts and letters. 20

The effectiveness of checking reference lists to retrieve additional relevant studies for an SR was investigated by Horsley et al. 22 The review reported that checking reference lists yielded 2.5%–40% more studies depending on the quality and comprehensiveness of the electronic search used. The authors conclude that there is some evidence, although from poor quality studies, to support use of checking reference lists to supplement database searching. 22

3.3.3. Methods for selecting studies

Three approaches relevant to reviewer characteristics, including number, experience, and blinding of reviewers involved in the screening process were highlighted in an SR by Robson et al. 14 Based on the retrieved evidence, the authors recommended that two independent, experienced, and unblinded reviewers be involved in study selection. 14 A modified approach has also been suggested by the review authors, where one reviewer screens and the other reviewer verifies the list of excluded studies, when the resources are limited. It should be noted however this suggestion is likely based on the authors’ opinion, as there was no evidence related to this from the studies included in the review.

Robson et al. 14 also reported two methods describing the use of technology for screening studies: use of Google Translate for translating languages (for example, German language articles to English) to facilitate screening was considered a viable method, while using two computer monitors for screening did not increase the screening efficiency in SR. Title‐first screening was found to be more efficient than simultaneous screening of titles and abstracts, although the gain in time with the former method was lesser than the latter. Therefore, considering that the search results are routinely exported as titles and abstracts, Robson et al. 14 recommend screening titles and abstracts simultaneously. However, the authors note that these conclusions were based on very limited number (in most instances one study per method) of low‐quality studies. 14

3.3.4. Methods for data extraction

Robson et al. 14 examined three approaches for data extraction relevant to reviewer characteristics, including number, experience, and blinding of reviewers (similar to the study selection step). Although based on limited evidence from a small number of studies, the authors recommended use of two experienced and unblinded reviewers for data extraction. The experience of the reviewers was suggested to be especially important when extracting continuous outcomes (or quantitative) data. However, when the resources are limited, data extraction by one reviewer and a verification of the outcomes data by a second reviewer was recommended.

As for the methods involving use of technology, Robson et al. 14 identified limited evidence on the use of two monitors to improve the data extraction efficiency and computer‐assisted programs for graphical data extraction. However, use of Google Translate for data extraction in non‐English articles was not considered to be viable. 14 In the same review, Robson et al. 14 identified evidence supporting contacting authors for obtaining additional relevant data.

3.3.5. Methods for RoB assessment

Two SRs examined the impact of blinding of reviewers for RoB assessments. 14 , 23 Morissette et al. 23 investigated the mean differences between the blinded and unblinded RoB assessment scores and found inconsistent differences among the included studies providing no definitive conclusions. Similar conclusions were drawn in a more recent review by Robson et al., 14 which included four studies on reviewer blinding for RoB assessment that completely overlapped with Morissette et al. 23

Use of experienced reviewers and provision of additional guidance for RoB assessment were examined by Robson et al. 14 The review concluded that providing intensive training and guidance on assessing studies reporting insufficient data to the reviewers improves RoB assessments. 14 Obtaining additional data related to quality assessment by contacting study authors was also found to help the RoB assessments, although based on limited evidence. When assessing the qualitative or mixed method reviews, Robson et al. 14 recommends the use of a structured RoB tool as opposed to an unstructured tool. No SRs were identified on data synthesis and CoE assessment and reporting steps.

4. DISCUSSION

4.1. summary of findings.

Nine SRs examining 24 unique methods used across five steps in the SR process were identified in this overview. The collective evidence supports some current traditional and modified SR practices, while challenging other approaches. However, the quality of the included reviews was assessed to be moderate at best and in the majority of the included SRs, evidence related to the evaluated methods was obtained from very limited numbers of primary studies. As such, the interpretations from these SRs should be made cautiously.

The evidence gathered from the included SRs corroborate a few current SR approaches. 5 For example, it is important to search multiple resources for identifying relevant trials (RCTs and/or CCTs). The resources must include a combination of electronic database searching, handsearching, and reference lists of retrieved articles. 5 However, no SRs have been identified that evaluated the impact of the number of electronic databases searched. A recent study by Halladay et al. 27 found that articles on therapeutic intervention, retrieved by searching databases other than PubMed (including Embase), contributed only a small amount of information to the MA and also had a minimal impact on the MA results. The authors concluded that when the resources are limited and when large number of studies are expected to be retrieved for the SR or MA, PubMed‐only search can yield reliable results. 27

Findings from the included SRs also reiterate some methodological modifications currently employed to “expedite” the SR process. 10 , 11 For example, excluding non‐English language trials and gray/unpublished trials from MA have been shown to have minimal or no impact on the results of MA. 24 , 26 However, the efficiency of these SR methods, in terms of time and the resources used, have not been evaluated in the included SRs. 24 , 26 Of the SRs included, only two have focused on the aspect of efficiency 14 , 25 ; O'Mara‐Eves et al. 25 report some evidence to support the use of text‐mining approaches for title and abstract screening in order to increase the rate of screening. Moreover, only one included SR 14 considered primary studies that evaluated reliability (inter‐ or intra‐reviewer consistency) and accuracy (validity when compared against a “gold standard” method) of the SR methods. This can be attributed to the limited number of primary studies that evaluated these outcomes when evaluating the SR methods. 14 Lack of outcome measures related to reliability, accuracy, and efficiency precludes making definitive recommendations on the use of these methods/modifications. Future research studies must focus on these outcomes.

Some evaluated methods may be relevant to multiple steps; for example, exclusions based on publication status (gray/unpublished literature) and language of publication (non‐English language studies) can be outlined in the a priori eligibility criteria or can be incorporated as search limits in the search strategy. SRs included in this overview focused on the effect of study exclusions on pooled treatment effect estimates or MA conclusions. Excluding studies from the search results, after conducting a comprehensive search, based on different eligibility criteria may yield different results when compared to the results obtained when limiting the search itself. 28 Further studies are required to examine this aspect.

Although we acknowledge the lack of standardized quality assessment tools for methodological study designs, we adhered to the Cochrane criteria for identifying SRs in this overview. This was done to ensure consistency in the quality of the included evidence. As a result, we excluded three reviews that did not provide any form of discussion on the quality of the included studies. The methods investigated in these reviews concern supplementary search, 29 data extraction, 12 and screening. 13 However, methods reported in two of these three reviews, by Mathes et al. 12 and Waffenschmidt et al., 13 have also been examined in the SR by Robson et al., 14 which was included in this overview; in most instances (with the exception of one study included in Mathes et al. 12 and Waffenschmidt et al. 13 each), the studies examined in these excluded reviews overlapped with those in the SR by Robson et al. 14

One of the key gaps in the knowledge observed in this overview was the dearth of SRs on the methods used in the data synthesis component of SR. Narrative and quantitative syntheses are the two most commonly used approaches for synthesizing data in evidence synthesis. 5 There are some published studies on the proposed indications and implications of these two approaches. 30 , 31 These studies found that both data synthesis methods produced comparable results and have their own advantages, suggesting that the choice of the method must be based on the purpose of the review. 31 With increasing number of “expedited” SR approaches (so called “rapid reviews”) avoiding MA, 10 , 11 further research studies are warranted in this area to determine the impact of the type of data synthesis on the results of the SR.

4.2. Implications for future research

The findings of this overview highlight several areas of paucity in primary research and evidence synthesis on SR methods. First, no SRs were identified on methods used in two important components of the SR process, including data synthesis and CoE and reporting. As for the included SRs, a limited number of evaluation studies have been identified for several methods. This indicates that further research is required to corroborate many of the methods recommended in current SR guidelines. 4 , 5 , 6 , 7 Second, some SRs evaluated the impact of methods on the results of quantitative synthesis and MA conclusions. Future research studies must also focus on the interpretations of SR results. 28 , 32 Finally, most of the included SRs were conducted on specific topics related to the field of health care, limiting the generalizability of the findings to other areas. It is important that future research studies evaluating evidence syntheses broaden the objectives and include studies on different topics within the field of health care.

4.3. Strengths and limitations

To our knowledge, this is the first overview summarizing current evidence from SRs and MA on different methodological approaches used in several fundamental steps in SR conduct. The overview methodology followed well established guidelines and strict criteria defined for the inclusion of SRs.

There are several limitations related to the nature of the included reviews. Evidence for most of the methods investigated in the included reviews was derived from a limited number of primary studies. Also, the majority of the included SRs may be considered outdated as they were published (or last updated) more than 5 years ago 33 ; only three of the nine SRs have been published in the last 5 years. 14 , 25 , 26 Therefore, important and recent evidence related to these topics may not have been included. Substantial numbers of included SRs were conducted in the field of health, which may limit the generalizability of the findings. Some method evaluations in the included SRs focused on quantitative analyses components and MA conclusions only. As such, the applicability of these findings to SR more broadly is still unclear. 28 Considering the methodological nature of our overview, limiting the inclusion of SRs according to the Cochrane criteria might have resulted in missing some relevant evidence from those reviews without a quality assessment component. 12 , 13 , 29 Although the included SRs performed some form of quality appraisal of the included studies, most of them did not use a standardized RoB tool, which may impact the confidence in their conclusions. Due to the type of outcome measures used for the method evaluations in the primary studies and the included SRs, some of the identified methods have not been validated against a reference standard.

Some limitations in the overview process must be noted. While our literature search was exhaustive covering five bibliographic databases and supplementary search of reference lists, no gray sources or other evidence resources were searched. Also, the search was primarily conducted in health databases, which might have resulted in missing SRs published in other fields. Moreover, only English language SRs were included for feasibility. As the literature search retrieved large number of citations (i.e., 41,556), the title and abstract screening was performed by a single reviewer, calibrated for consistency in the screening process by another reviewer, owing to time and resource limitations. These might have potentially resulted in some errors when retrieving and selecting relevant SRs. The SR methods were grouped based on key elements of each recommended SR step, as agreed by the authors. This categorization pertains to the identified set of methods and should be considered subjective.

5. CONCLUSIONS

This overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process. Limited evidence was also identified on some methodological modifications currently used to expedite the SR process. Overall, findings highlight the dearth of SRs on SR methodologies, warranting further work to confirm several current recommendations on conventional and expedited SR processes.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

Supporting information

APPENDIX A: Detailed search strategies

ACKNOWLEDGMENTS

The first author is supported by a La Trobe University Full Fee Research Scholarship and a Graduate Research Scholarship.

Open Access Funding provided by La Trobe University.

Veginadu P, Calache H, Gussy M, Pandian A, Masood M. An overview of methodological approaches in systematic reviews . J Evid Based Med . 2022; 15 :39–54. 10.1111/jebm.12468 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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The FTC’s Noncompete Ban Was Long Overdue

Empirical evidence backs up the argument that trade secrets can remain protected even as talent is freely mobile.

The FTC’s new noncompete rule adopts a comprehensive prohibition on the use of noncompete clauses in any U.S. industry with any worker, including those at senior executive levels. The rule is promulgated using the FTC’s authority to determine practices that are unfair methods of competition. For those who have long argued against the use of noncompetes, this moment has been a long time coming. While the rule already faces legal challenges, company leaders would be well advised to make sure they understand what’s in the rule, its potential impact, and what it could mean for employees. Far from being an anti-business rule, the ban on noncompetes stands to spur innovation and grow markets.

The Federal Trade Commission (FTC) made history last week when it passed a new rule that fundamentally alters the landscape of employment agreements across the U.S. The agency’s noncompete rule adopts a comprehensive prohibition on the use of noncompete clauses in any industry with any worker, including those at senior executive levels. The rule is promulgated using the FTC’s authority to determine practices that are unfair methods of competition. For those like me who have long argued against the use of noncompetes, this moment has been a long time coming.

OL Orly Lobel is the Warren Distinguished Professor and director of the Center for Employment and Labor Policy (CELP) at University of San Diego and author of The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future (PublicAffairs), Talent Wants to be Free Why We Should Learn to Love Leaks, Raids and Free-Riding (Yale Press), and You Don’t Own Me (Norton).

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International Journal of Quality & Reliability Management

ISSN : 0265-671X

Article publication date: 17 May 2024

This study is intended to introduce and summarise Industry 4.0 practices in BRICS nations (the abbreviation “BRICS” is made up of the first letters of the member countries: Brazil, Russia, India, China and South Africa) and determine each nation’s current contribution to Industry 4.0 practice implementation based on past literature. As the BRICS countries continue to play an essential role in the global economy, it is significant to understand Industry 4.0, focussing on these emerging economies.

Design/methodology/approach

To assess the present research work on Industry 4.0 practices and research studies in BRICS nations, a systematic literature review (SLR) is performed using the articles available on the SCOPUS database. This study is a descriptive analysis based on the frequency and year of publications, the most influential universities, most influential journals and most influential articles. Similarly, this study consists of category analysis based on multi-criteria decision-making (MCDM) methods, research design used, research method utilised, different data analysis techniques and different Industry 4.0 technologies were used to solve different applications in the BRICS nations.

According to the analysis of past literature, the primary identified practices are centred on operations productivity, waste management, energy reduction and sustainable processes. It also found that despite the abundance of research on Industry 4.0, the major academic journal publications are restricted to a small number of industries and issues in which the manufacturing and automotive industries are front runners. The categorisation of selected papers based on the year of publication demonstrates that the number of publications has been rising. It is also found that China and India, out of the BRICS countries, have contributed significantly to Industry 4.0-related publications by contributing 61 percent of the total articles identified. Similarly, this study identified that qualitative research design is the most adopted framework for research, and empirical triangulation is the least adopted framework in this field. The categorisation of selected articles facilitates the identification of numerous gaps, such as that 67.14% of the literature research is qualitative.

Practical implications

Understanding Industry 4.0 in the BRICS nations helps to identify opportunities for international collaboration and future cooperation possibilities. This study helps to promote collaboration between BRICS countries and other nations, organisations or businesses interested in capitalising on these growing economies' assets and capabilities related to Industry 4.0 technologies. This study helps to provide essential insights into the economic, technological and societal impacts, allowing for effective decision-making and strategic planning for a sustainable and competitive future. So, this contribution links the entire world in terms of the better utilisation of resources, the reduction of downtime, improving product quality, personalised products and the development of human resource capabilities through the application of cutting-edge technologies for nearly half of the world’s population.

Originality/value

In this study, BRICS nations are selected due to their significant impact on the world regarding social, economic and environmental contributions. In the current review, 423 articles published up to August 2022 were selected from the SCOPUS database. The comparison analysis of each BRICS nation in the form of applications of Industry 4.0, the primary area of focus, leading industry working, industry involvement with universities and the area that needs attention are discussed. To the best of our knowledge, this is the most recent SLR and meta-analysis study about Industry 4.0 in BRICS nations, which analysed the past available literature in nine different descriptive and category-wise classifications, considering a total of 423 articles. Based on this SLR, this study makes some important recommendations and future directions that will help achieve social, economic and environmental sustainability in BRICS nations.

Industry 4.0
BRICS nations
Emerging economies
Competitive advantages
Literature review
Meta-analysis

Yadav, A. , Yadav, G. and Desai, T.N. (2024), "Unlocking the potential of Industry 4.0 in BRICS nations: a systematic literature review and meta-analysis", International Journal of Quality & Reliability Management , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJQRM-06-2023-0180

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An effective and well-conducted review as a research method creates a firm foundation for advancing knowledge and facilitating theory development (Webster & Watson, 2002). By integrating findings and perspectives from many empirical findings, a literature review can address research questions with a power that no single study has.
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Similarly, this study identified that qualitative research design is the most adopted framework for research, and empirical triangulation is the least adopted framework in this field. The categorisation of selected articles facilitates the identification of numerous gaps, such as that 67.14% of the literature research is qualitative.

Empirical Research: Definition, Methods, Types and Examples

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What is Empirical Research? Definition, Methods, Examples

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Understanding the Empirical Method in Research Methodology

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