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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.

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

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Penn State University Libraries

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.

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

Key characteristics to look for:

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

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

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

Reading and Evaluating Scholarly Materials

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

CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
Credo Video: How to Read Scholarly Materials (4 min.)
Credo Tutorial: How to Read Scholarly Materials
Credo Tutorial: Evaluating Information
Credo Video: Evaluating Statistics (4 min.)
Next: Finding Empirical Research in Library Databases >>
Last Updated: Feb 18, 2024 8:33 PM
URL: https://guides.libraries.psu.edu/emp

University of Memphis Libraries
Research Guides

Empirical Research: Defining, Identifying, & Finding

Defining Empirical Research
Introduction

The Results Section

Database Tools
Search Terms
Image Descriptions

The Results section provides the outcomes of the research process detailed in the Methods section.

What Criteria to Look For

The Results section is where the authors talk about the primary data that has been collected and analyzed. Most articles will not have the complete, unanalyzed primary data, but instead have data that has already gone through some analysis for patterns, trends, and cause and effect.

Finding the Criteria

The section is by far most-commonly called "Results," though there are a few variations such as "Findings." There may be some differences in what to look for between quantitative and qualitative research.

Quantitative Research

You will be looking for data that is primarily numerical.
There are likely tables and figures sharing and summarizing the data. These may also be included in appendixes at the end of the article or online.

Qualitative Research

You will be looking for data that is likely textual or visual.
Tables and figures may be included but are less common.
Some qualitative articles do not have a single Results section but provide multiple headings for the results organized by theme.

In some research disciplines, there is a growing movement towards open data where the authors will share their complete primary data in an online repository for other researchers to access and evaluate. In this case, the article will include where to find that open data. This is still is not standard for a lot of research, and for research on human behavior, especially qualitative research, there may be confidentiality and privacy issues that prevent sharing the primary data.

Single "3. Results" heading that begins on page 91.
Quantitative research that includes a lot of statistical analysis.
Includes two figures (Figure 2 & 3 on page 92) and one table (Table 3 on page 93), as well as additional tables and figures in online appendixes.
Single "Results" section that begins on page 574. It is organized into subheadings.
Includes 2 figures (Figure 1 & Figure 2 on page 576) and 2 tables (Table 1 on page 574 & Table 2 on page 575)
The Results section is thematically labeled "LGBTQ College Students and Hookup Culture" and starts on page 1006. There are then three subheadings organized by the themes discussed.
Qualitative research where the data consists of text from interview transcripts.
<< Previous: Methods
Next: Discussion >>
Last Updated: Apr 2, 2024 11:25 AM
URL: https://libguides.memphis.edu/empirical-research

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Research: Overview & Approaches

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

Introduction to Empirical Research

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

Interpretive Research
Action-Based Research
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.

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Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
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<< Previous: Scholarly and Professional Relationships
Next: Interpretive Research >>
Last Updated: Apr 25, 2024 4:11 PM
URL: https://guides.lib.purdue.edu/research_approaches

Identifying Empirical Research Articles

Identifying empirical articles.

Searching for Empirical Research Articles

What is Empirical Research?

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

Primary and Secondary Sources

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

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

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

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

Parts of an Empirical Research Article

Parts of an empirical article.

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

Introduction

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

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

Results

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

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

Learn the IMRaD Layout: How to Identify an Empirical Article

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

Next: Searching for Empirical Research Articles >>
Last Updated: Nov 16, 2023 8:24 AM

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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
Annotations
Article Cards
Organizing Sources
Writing an Outline
Citing Sources

Finding Empirical Research

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

Finding Empirical Research in PsycInfo

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

Slideshow showing how to find empirical research in APA PsycInfo

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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.

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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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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By incorporating Appinio into your data collection toolkit, you can unlock a world of possibilities and elevate the impact of your empirical research. Ready to revolutionize your approach to data collection?

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Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

Bar Charts and Histograms : Used to display the distribution of categorical 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?

Introducing Appinio , the real-time market research platform revolutionizing how companies gather consumer insights for their empirical research endeavors. With Appinio, you can conduct your own market research in minutes, gaining valuable data to fuel your data-driven decisions.

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.

Here's why Appinio is the go-to solution for empirical research:

From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
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Rapid Response Times : With an average field time of under 23 minutes for 1,000 respondents, Appinio delivers rapid results, allowing you to gather data swiftly and efficiently.
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Empirical evidence: A definition

Empirical evidence is information that is acquired by observation or experimentation.

The scientific method

Types of empirical research, identifying empirical evidence, empirical law vs. scientific law, empirical, anecdotal and logical evidence, additional resources and reading, bibliography.

Empirical evidence is information acquired by observation or experimentation. Scientists record and analyze this data. The process is a central part of the scientific method , leading to the proving or disproving of a hypothesis and our better understanding of the world as a result.

Empirical evidence might be obtained through experiments that seek to provide a measurable or observable reaction, trials that repeat an experiment to test its efficacy (such as a drug trial, for instance) or other forms of data gathering against which a hypothesis can be tested and reliably measured.

"If a statement is about something that is itself observable, then the empirical testing can be direct. We just have a look to see if it is true. For example, the statement, 'The litmus paper is pink', is subject to direct empirical testing," wrote Peter Kosso in " A Summary of Scientific Method " (Springer, 2011).

"Science is most interesting and most useful to us when it is describing the unobservable things like atoms , germs , black holes , gravity , the process of evolution as it happened in the past, and so on," wrote Kosso. Scientific theories , meaning theories about nature that are unobservable, cannot be proven by direct empirical testing, but they can be tested indirectly, according to Kosso. "The nature of this indirect evidence, and the logical relation between evidence and theory, are the crux of scientific method," wrote Kosso.

The scientific method begins with scientists forming questions, or hypotheses , and then acquiring the knowledge through observations and experiments to either support or disprove a specific theory. "Empirical" means "based on observation or experience," according to the Merriam-Webster Dictionary . Empirical research is the process of finding empirical evidence. Empirical data is the information that comes from the research.

Before any pieces of empirical data are collected, scientists carefully design their research methods to ensure the accuracy, quality and integrity of the data. If there are flaws in the way that empirical data is collected, the research will not be considered valid.

The scientific method often involves lab experiments that are repeated over and over, and these experiments result in quantitative data in the form of numbers and statistics. However, that is not the only process used for gathering information to support or refute a theory.

This methodology mostly applies to the natural sciences. "The role of empirical experimentation and observation is negligible in mathematics compared to natural sciences such as psychology, biology or physics," wrote Mark Chang, an adjunct professor at Boston University, in " Principles of Scientific Methods " (Chapman and Hall, 2017).

"Empirical evidence includes measurements or data collected through direct observation or experimentation," said Jaime Tanner, a professor of biology at Marlboro College in Vermont. There are two research methods used to gather empirical measurements and data: qualitative and quantitative.

Qualitative research, often used in the social sciences, examines the reasons behind human behavior, according to the National Center for Biotechnology Information (NCBI) . It involves data that can be found using the human senses . This type of research is often done in the beginning of an experiment. "When combined with quantitative measures, qualitative study can give a better understanding of health related issues," wrote Dr. Sanjay Kalra for NCBI.

Quantitative research involves methods that are used to collect numerical data and analyze it using statistical methods, ."Quantitative research methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques," according to the LeTourneau University . This type of research is often used at the end of an experiment to refine and test the previous research.

Identifying empirical evidence in another researcher's experiments can sometimes be difficult. According to the Pennsylvania State University Libraries , there are some things one can look for when determining if evidence is empirical:

Can the experiment be recreated and tested?
Does the experiment have a statement about the methodology, tools and controls used?
Is there a definition of the group or phenomena being studied?

The objective of science is that all empirical data that has been gathered through observation, experience and experimentation is without bias. The strength of any scientific research depends on the ability to gather and analyze empirical data in the most unbiased and controlled fashion possible.

However, in the 1960s, scientific historian and philosopher Thomas Kuhn promoted the idea that scientists can be influenced by prior beliefs and experiences, according to the Center for the Study of Language and Information .

— Amazing Black scientists

— Marie Curie: Facts and biography

— What is multiverse theory?

"Missing observations or incomplete data can also cause bias in data analysis, especially when the missing mechanism is not random," wrote Chang.

Because scientists are human and prone to error, empirical data is often gathered by multiple scientists who independently replicate experiments. This also guards against scientists who unconsciously, or in rare cases consciously, veer from the prescribed research parameters, which could skew the results.

The recording of empirical data is also crucial to the scientific method, as science can only be advanced if data is shared and analyzed. Peer review of empirical data is essential to protect against bad science, according to the University of California .

Empirical laws and scientific laws are often the same thing. "Laws are descriptions — often mathematical descriptions — of natural phenomenon," Peter Coppinger, associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology, told Live Science.

Empirical laws are scientific laws that can be proven or disproved using observations or experiments, according to the Merriam-Webster Dictionary . So, as long as a scientific law can be tested using experiments or observations, it is considered an empirical law.

Empirical, anecdotal and logical evidence should not be confused. They are separate types of evidence that can be used to try to prove or disprove and idea or claim.

Logical evidence is used proven or disprove an idea using logic. Deductive reasoning may be used to come to a conclusion to provide logical evidence. For example, "All men are mortal. Harold is a man. Therefore, Harold is mortal."

Anecdotal evidence consists of stories that have been experienced by a person that are told to prove or disprove a point. For example, many people have told stories about their alien abductions to prove that aliens exist. Often, a person's anecdotal evidence cannot be proven or disproven.

There are some things in nature that science is still working to build evidence for, such as the hunt to explain consciousness .

Meanwhile, in other scientific fields, efforts are still being made to improve research methods, such as the plan by some psychologists to fix the science of psychology .

" A Summary of Scientific Method " by Peter Kosso (Springer, 2011)

"Empirical" Merriam-Webster Dictionary

" Principles of Scientific Methods " by Mark Chang (Chapman and Hall, 2017)

"Qualitative research" by Dr. Sanjay Kalra National Center for Biotechnology Information (NCBI)

"Quantitative Research and Analysis: Quantitative Methods Overview" LeTourneau University

"Empirical Research in the Social Sciences and Education" Pennsylvania State University Libraries

"Thomas Kuhn" Center for the Study of Language and Information

"Misconceptions about science" University of California

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Empirical Research: Quantitative & Qualitative

Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

Quantitative vs. Qualitative
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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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Understanding the Empirical Method in Research Methodology

Table of Contents

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

What is the empirical method?

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

The role of observation in the empirical method

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

Data collection: qualitative and quantitative

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

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

Empirical vs. experimental methods

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

Why the distinction matters

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

The significance of experiential learning

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

Developing theories from empirical research

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

Applying the empirical method in various fields

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

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

Challenges and limitations

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

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

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

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Research Methodology

1 Introduction to Research in General

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

2 Original Unity of Philosophy and Science

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

3 Evolution of the Distinct Methods of Science

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

4 Relation of Scientific and Philosophical Methods

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

5 Dialectical Method

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

6 Rational Method

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

7 Empirical Method

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

8 Critical Method

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

9 Phenomenological Method (Western and Indian)

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

10 Analytical Method (Western and Indian)

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

11 Hermeneutical Method (Western and Indian)

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

12 Deconstructive Method

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

13 Method of Bibliography

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

14 Method of Footnotes

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

15 Method of Notes Taking

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

16 Method of Thesis Proposal and Presentation

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

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Qualitative and Quantitative Research

What is "empirical research".

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

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

Key characteristics to look for:

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

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

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies
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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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Consumer Trust: Meta-Analysis of 50 Years of Empirical Research

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Mansur Khamitov, Koushyar Rajavi, Der-Wei Huang, Yuly Hong, Consumer Trust: Meta-Analysis of 50 Years of Empirical Research, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 7–18, https://doi.org/10.1093/jcr/ucad065

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Trust is one of the highly important concepts of consumer research; yet it is characterized by a striking lack of generalizations and consensus regarding the relative strength of its antecedents, consequences, and moderators. To close this important gap, the current research reports a comprehensive large-scale meta-analysis shedding light on a wide variety of the antecedents, consequences, and moderators of the individual consumer’s trust and their relative importance. Empirical generalizations are based on 2,147 effect sizes from 549 studies across 469 manuscripts in numerous disciplines, representing a total of 324,834 respondents in 71 countries over a five-decade span (1970–2020). The key findings are thus that (1) integrity-based (vs. reliability-based) antecedents are more effective in driving trust, and (2) trust is more effective in improving primarily attitudinal (vs. primarily behavioral) outcomes. Moderation analyses unpack further heterogeneity. Notably, both integrity-based and reliability-based antecedents have become stronger drivers of consumer trust in recent years. Theoretical and practical contributions are discussed in addition to advancing important future directions.

Trust is one of the highly important concepts of consumer research. Trust is crucial in all aspects of our daily lives, such as commercial and social transactions, because it reduces perceived uncertainty regarding intentions and capabilities of other entities. Past research in marketing recognizes the significance of trust—albeit with a slightly richer tradition in the B2B setting, from extensive study of the nature of trust between business customers, to the point that two meta-analyses on that topic have emerged ( Geyskens, Steenkamp, and Kumar 1998 ; Palmatier et al. 2006 ). Given the synthesized and illustrious evidence accumulated in the B2B setting, it is puzzling that no systematic meta-analysis was conducted on the nuanced role of the individual consumer’s trust. 1

RQ1: What is the (relative) impact of a broad set of antecedents of consumer trust? RQ2: Under what conditions do antecedents of consumer trust become more effective?

RQ3: What is the (relative) impact of consumer trust on a broad set of downstream consequences?

Overall, our meta-analysis of 2,147 individual effects derived from 549 studies across 469 manuscripts from 1970 to 2020 offers generalizable insights into antecedents and consequences of consumer trust, along with future implications. The work provides a big-tent investigation of consumer-trust research that highlights its multi-disciplinary nature using the meta-analytic lens.

Consumer trust is defined as “a consumer’s confidence in […] reliability and integrity” of the target of trust ( De Wulf, Odekerken-Schröder, and Iacobucci 2001 , 36). In order to identify drivers of consumer trust, it is important to consider what trust consists of. While there are small differences in how consumer trust is conceptualized in past research, it is commonly accepted that it encompasses consumers’ beliefs about how reliably and with integrity an entity would deliver on its stated promise(s) ( Garbarino and Johnson 1999 ; McKnight, Choudhury, and Kacmar 2002 ). Thus, factors that drive inferences of an entity’s reliability or integrity are particularly relevant in generating consumer trust. That is, a systematic classification of prior consumer trust literature simply cannot be considered complete without accounting for both integrity-based trust antecedents (IBTA) and reliability-based trust antecedents (RBTA). Theoretical support for this underlying grouping can be found in numerous seminal consumer trust papers: intentions toward the consumers versus reliability ( Delgado‐Ballester and Munuera‐Alemán 2001 ), benevolence and integrity versus ability and dependability ( Sirdeshmukh, Singh, and Sabol 2002 ), honesty versus reliability and safety ( Chaudhuri and Holbrook 2001 ), can be counted on to be good to the consumer versus confidence and reliability ( Garbarino and Johnson 1999 ), and behaving in the long-term interest of the customer versus confidence and reliability ( Crosby, Evans, and Cowles 1990 ). 2 , 3 Furthermore, our review of past studies on consumer trust led us to two general groups of outcomes associated with consumer trust: primarily attitudinal consequences (PAC) and primarily behavioral consequences (PBC).

After determining general groups of antecedents and consequences based on past research, we chose specific antecedents and consequences based on (1) how frequently a construct appears in past research on individual consumer’s trust and (2) whether a construct fits our theoretical framework (e.g., IBTA or RBTA for antecedents). 4 Therefore, we objectively focus on the most prevalent antecedents and consequences of consumer trust, as determined by past research. In doing so, in accordance with Palmatier et al. (2006) , we retain antecedents that appear in at least 10% of the past studies on consumer trust. 5

These considerations led us to eight antecedents of consumer trust: IBTA include three constructs (attachment, ethicality and social responsibility [SR], reputation) and RBTA encompass five constructs (marketing investments, perceived value, competence, perceived risk, perceived quality). 6 For the nine consequences in our study, PAC include five constructs (self-concept connection, evaluation, engagement, attitudinal loyalty, satisfaction) and PBC have four constructs (behavioral loyalty, willingness to pay, purchase intention, market performance). 7

In web appendix A , we define and describe these constructs, report their common aliases, and highlight sample studies that examined their respective relationship with consumer trust. Figure 1 illustrates our theoretical framework. We next briefly discuss how and why the antecedents fit within the two buckets and how they affect consumer trust (for a more detailed review of research on drivers of consumer trust, see web appendix B ). The discussion on the relationships between consumer trust and its consequences can be found in web appendix C .

THEORETICAL FRAMEWORK

Antecedents of Consumer Trust

Turning to how the eight underlying antecedents fit within the two buckets and in turn drive consumer trust, we start with the three IBTA. Through ongoing encounters and interactions, consumers often form a connection with the business entity and develop attachment to the entity, which has been shown to impact consumer trust ( Bidmon 2017 ) by affecting consumers’ perceptions of the sincere relational motives of the cherished entity ( Khamitov et al. 2019 ).

Growing consumer consciousness in the 21st century has encouraged businesses to focus on ethicality and SR . The ethicality of a business entity (i.e., the commitment to doing the right thing) and investments in CSR activities influence consumers’ trust by signaling to them that the entity is moral, honest, benevolent, less likely to cheat, and likely to be of high integrity ( Diallo and Lambey-Checchin 2017 ). Relatedly, the reputation of a business entity—being highly respected and getting known for having the consumer’s best interests at heart—has been shown to significantly enhance consumer trust ( Johnson and Grayson 2005 ).

In terms of the five RBTA, businesses invest in various marketing activities to create and communicate value and expertise to their consumers. Different forms of sale-independent marketing investments influence consumer trust through conveying capability (e.g., signaling superiority; Rajavi, Kushwaha, and Steenkamp 2019 ). Perceived value has been shown to affect consumer trust by making consumers presume that the entity has the reliability and resources to come up with offerings that provide superior value to them ( Wu and Huang 2023 ).

Consumers’ interactions with a business entity, and the information that consumers obtain via different sources (e.g., news, social media) affect customers’ beliefs about competence , perceived quality , and perceived risk of these entities. Competence affects consumer trust by influencing perceptions regarding the entity’s ability to deliver and reliably satisfy consumers’ needs ( Sung and Kim 2010 ). Perceived quality drives trust by enhancing perceptions regarding the overall excellence of an offering and improving public assessments of its attributes ( Hennig-Thurau, Langer, and Hansen 2001 ). Finally, perceived risk can erode consumers’ beliefs regarding the likelihood that the business entity will reliably fulfill its promises, because an entity that increases perceived risk for consumers sends negative signals about its ability to deliver ( Pappas 2016 ).

According to the considerable amount of work focused on morality in the marketplace ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller et al. 2022 ), a vast majority of ordinary consumers are guided by moral beliefs and intuitions in the marketplace, with a huge importance placed on marketplace actors acting responsibly and with integrity, making integrity-related levers particularly influential when it comes to consumer trust. We thus posit antecedents based on integrity, on average, outperform antecedents based on reliability in driving trust.

In light of the seeming evidence that consumer trust has undergone dramatic changes in recent times ( Edelman 2021 ; Gallup 2023 ; Khamitov et al. 2019 ), we utilize year of publication to examine how the impacts of trust antecedents have changed recently. Additionally, given that the research in the consumer-trust domain has evolved from an early focus on brands and firms ( Garbarino and Johnson 1999 ) to encompass trust toward specific offerings ( Johnson and Grayson 2005 ), industries ( Diallo and Lambey-Checchin 2017 ), and even technologies ( Kim and Peterson 2017 ), we include a target of trust moderator to unpack this heterogeneity. Lastly, as extant work hinted at the potential moderating role of search, experience, and credence attributes in the context of consumer trust ( Pan and Chiou 2011 ), we employ a type of attribute moderator.

Overview of Data Collection and Coding Procedure

To ensure extensive coverage of articles that examined drivers or consequences of the individual consumer’s trust, we systematically searched several databases, including Google Scholar, ProQuest, EBSCOhost, and Web of Science. We used individual keyword phrases and their combinations such as “consumer trust,” “firm trust,” “customer mistrust,” etc., to identify studies related to consumer trust (see web appendix D for the complete list of keywords). We also manually reviewed leading journals in marketing and other disciplines to uncover additional work. We retained studies that (1) examined the individual consumer’s trust rather than an organization’s, (2) were published between 1970 and 2020, (3) had an empirical focus, and (4) reported sufficient information for direct use or indirect computation of our focal effects. We also made an effort to incorporate unpublished work (“file drawer”) by soliciting unpublished manuscripts in a blind, anonymous, confidential manner via the Association for Consumer Research’s ACR-L and American Marketing Association’s ELMAR listservs over a period of four weeks. This led to a final sample that includes 2,147 effect sizes from 549 studies across 469 manuscripts, representing a total of 324,834 respondents in 71 countries over a five-decade span. 8 Our benchmarking review of consumer research suggests that our final dataset’s scope and magnitude compare very favorably to those of other recent meta-analytic datasets (290 studies in Khamitov et al. 2019 ; 141 studies in Weingarten and Goodman 2021 ). 9

Following other meta-analytic studies ( Gremler et al. 2020 ; Palmatier et al. 2006 ), we use Pearson’s correlation coefficient as the focal effect size metric in our study. As needed, we employed conversion formulas to transform other available statistics into correlation coefficients ( Lipsey and Wilson 2001 ). We adjusted the effect sizes for measurement error using the square root of the products of the reliabilities of the two constructs, that is, consumer trust and its respective antecedent or consequence ( Hunter and Schmidt 1990 ). Finally, we weighted the resulting reliability-adjusted correlations by sample size ( Hunter and Schmidt 1990 ). For a detailed description of our data collection (i.e., literature search, inclusion criteria, PRISMA flow chart of the screening process and outcomes, coding procedure, control variables), see web appendix D .

Methodology: Hierarchical Linear Modeling

Following Raudenbush and Bryk’s (2001) recommendation, we specify a three-level hierarchical linear model (HLM) that accounts for the nested structure of data. The first level represents observations belonging to each study (i.e., the within-study effect sizes), the second level stands for different studies belonging to a paper, and the third level incorporates the distinct papers in our dataset. In our HLM model with maximum-likelihood estimation, the dependent variable represents adjusted and weighted effect sizes (correlations). The focal independent variables are eight dummies corresponding to the eight antecedents of consumer trust (RQ1). Following Gremler et al. (2020) , for each effect size, we set all dummy variables to 0, except the dummy variable corresponding to the antecedent of consumer trust, whose correlation the focal effect size is capturing (it gets a value of 1). We also control for several sample-, study-, and paper-level characteristics that we briefly discuss in the Discussion section. Additionally, we present the moderator subgroup analyses (year of publication, target of trust, type of attribute) to decompose heterogeneity (RQ2). In web appendices E and F , we detail our model specifications as well as robustness checks and publication bias analyses/corrections. We use a similar three-level HLM to examine the consequences of consumer trust.

Antecedents of Consumer Trust (RQ1)

The antecedent results appear in table 1 . The focal effects are robust to inclusion or exclusion of covariates in models A0 and A1. We focus on the results from the full model (A1).

RESULTS FOR ANTECEDENTS OF CONSUMER TRUST

p < .05;

p < .01;

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, magnitude of estimates are directly comparable across the antecedents of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the three integrity-based (the five reliability-based) antecedents to construct the aggregate variables. For the aggregate analyses presented here and in subsequent analyses, we utilize absolute values of effect sizes since some effect sizes are positive and others are negative. The estimates for the covariates and the deviance values are based on models with the eight antecedents included.

Most importantly, when we compare aggregated integrity-based antecedents with aggregated reliability-based ones, we observe a stronger magnitude for integrity-based antecedents ( b IBTA = 0.432, SE = 0.021 vs. b RBTA = 0.353, SE = 0.014, p = .002). That is, integrity-based antecedents have stronger influence on consumer trust than reliability-based antecedents. Thus, the most important aspect of trust building is establishing and conveying integrity and honesty aligned with morality in the marketplace stream ( Campbell and Winterich 2018 ; Grayson 2014 ; Philipp-Muller, Teeny, and Petty 2022 ), central premise of which is that consumers perceive and care about the business morality.

When looking at specific antecedents of trust, reputation emerges as the strongest driver ( b = 0.460, SE = 0.031, p < .001), followed by ethicality and SR ( b = 0.426, SE = 0.032, p < .001). Reputation is likely the strongest driver of consumer trust, since reputation is and has for a while been the most valuable marketplace currency according to the notion of reputation economy ( Rifkin, Corus, and Kirk 2022 ), which underscores that the consumption marketplace is an environment where trust toward firms and brands is built on reputational considerations of track record and the promise(s) they deliver. The ethicality and SR results are in line with the importance and relevance of moral theories and concepts in marketplace environment ( Diallo and Lambey-Checchin 2017 ) and are consistent with the theme of a recent issue of JCP on marketplace morality ( Campbell and Winterich 2018 ).

The next three antecedents, while relatively weaker in terms of their strength, also emerge as strong and positive: attachment ( b = 0.408, SE = 0.030, p < .001), perceived quality ( b = 0.407, SE = 0.034, p < .001), and perceived value ( b = 0.353, SE = 0.021, p < .001). Attachment’s strong effect reinforces consumer–brand relationship theory as it pertains to attachment figures ( Khamitov et al. 2019 ). The quality finding reinforces the relationship marketing theories ( Palmatier et al. 2006 ), whereas the relatively strong positive effect for perceived value highlights the importance of ensuring that consumer needs and wants are fulfilled. Marketing investments ( b = 0.256, SE = 0.032, p < .001), competence ( b = 0.209, SE = 0.087, p = .016), and the non-significant perceived risk ( b = −0.120, SE = 0.073, p = .102) are the weakest drivers of consumer trust. The last finding is especially surprising given that risk is traditionally strongly linked to trust in the extant consumer-trust literature ( Elliott and Yannopoulou 2007 ). This outcome suggests that the identified risk is a weaker determinant of trust than expected, likely because a vast majority of consumption situations in our meta-analytic dataset entail minimal levels of risk 10 ; hence, the risk-reducing capability may not be particularly relevant when it comes to driving trust. 11

Moderating Conditions (RQ2)

Trust across time.

We conducted the year of publication moderation analyses on antecedents of consumer trust by comparing meta-analytic coefficients in recent studies (published after 2015) versus older studies (published before 2015). 12 We conjectured that the change in trends in older versus more recent studies would be manifested in the future: antecedents that have recently become stronger determinants of trust will continue to play an even more important role in the future. We present the detailed results in table 2 . We find that the magnitude of the effectiveness of IBTA ( p = .031) and RBTA ( p = .033) has both significantly increased over time, although less so for the RBTA. That is, different antecedents of trust are more effective in driving consumer trust in today’s marketplace than in the past. This outcome is consistent with the observation of consumer scholars that the roles of trust and other consumer relationship constructs have strengthened over time ( Khamitov et al. 2019 ) and is a silver lining for practitioners and managers who strive to enhance trust.

CHANGE IN EFFECTIVENESS OF ANTECEDENTS OF CONSUMER TRUST ACROSS TIME

p < .001; Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

We report the results for each specific antecedent in web appendix H . Interestingly, we find that marketing investments have grown in importance in recent years (although in a marginally significant way: p = .098), which is a testament to the continued effects of the positive signals that marketing mix instruments convey ( Rajavi et al. 2019 ).

Target of Trust

Target of trust plays an important role when it comes to the relative influence of antecedents on consumer trust ( table 3 ). We focus primarily on big-picture differences in (magnitude of) effects of drivers of trust by comparing average IBTA versus RBTA effects. Though on average there is no significant difference in the strength of effects of IBTA versus RBTA for specific offerings and technologies, IBTA are significantly more effective in driving trust toward brands/firms and industries as compared to RBTA. Being intangible entities, brands are increasingly viewed by many consumers as a series of normatively binding expectations that are ethically akin to brand promises ( Bhargava and Bedi 2022 ) and are expected to be honest and well-intentioned relational agents ( Khamitov et al. 2019 ), making it easier to drive trust by conveying integrity. As for industries, because a number of industries (fuel and energy, banking, aviation, tobacco, and alcohol) over the years have left consumers with the impression that some industries lack integrity ( Darke and Ritchie 2007 ), if and when a certain industry can convince consumers of its moral uprightness, such efforts are particularly effective in driving trust.

SPLIT-SAMPLE ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF TRUST ENTITY

p < .001; ψ: number of effect sizes. For average IBTA and RBTA effects, we focused on absolute value of effect sizes. Because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Out of 983 effect sizes for antecedents, we were not able to categorize 134 of them into any of the above four categories (e.g., target of trust was an employee). A full table with estimates for each antecedent is presented in web appendix H .

Comparing average IBTA and RBTA effects across different entities is also worthwhile. While there is no significant difference in IBTA effects across brands/firms and specific offerings (all pairwise p -values >.10), IBTA are significantly stronger (weaker) in driving trust toward industries (technologies). This implies a particularly strong role for industry integrity (aligned with the discussion above), which is unlike the relatively weaker technology benevolence mandate. Also, while RBTA are similarly effective in driving trust toward brands/firms and technologies (all pairwise p -values >.10), they are stronger (weaker) in driving trust toward specific offerings (industries). We conjecture that unlike with other trust entities, consumers’ responses to specific product/service offerings are influenced more heavily by an offering’s perceived practical and functional reliability in meeting their requirements.

Type of Attribute

We performed the type of attribute moderator analyses by comparing IBTA and RBTA meta-analytic coefficients for not-search versus search, not-experience versus experience, and not-credence versus credence attributes. We provide the results in table 4 . There is only one statistically significant difference: the magnitude of the effectiveness of IBTA is significantly stronger for non-experience attributes than for experience attributes ( p = .006). That is, if quality or other characteristics remain unknown until consumption (i.e., experience attributes), whether a good has higher or lower integrity is unlikely be diagnostic when it comes to trusting the good.

ANALYSIS OF ANTECEDENTS OF CONSUMER TRUST BASED ON TYPE OF ATTRIBUTE

p < .001; because inclusion of covariates did not influence the findings in our main analysis ( table 1 ), we did not include covariates. Absolute values of effect sizes were used in aggregating antecedents to IBTA and RBTA.

Put differently, if the consumer can evaluate a good only by way of experience, communicating integrity and ethicality may not be that meaningful for trust-building ( Grabner-Kraeuter 2002 ).

Consequences of Consumer Trust (RQ3)

When we compare aggregated PAC with aggregated primarily behavioral ones in table 5 , we observe a stronger magnitude of effect for attitudinal consequences ( b PAC = 0.431, SE = 0.010 vs. b PBC = 0.353, SE = 0.015, p < .001), which makes sense because behavioral outcomes are further down the purchase funnel and might be strongly affected by other variables (e.g., price, availability, etc.), hence lowering the overall importance of trust in driving them. This finding reinforces the hierarchy of effects and attitude-behavior gap theories ( Barry and Howard 1990 ). When it comes to individual consequences of trust, the most notable results are for satisfaction ( b = 0.494, SE = 0.027, p < .001; top consequence) and attitudinal loyalty ( b = 0.404, SE = 0.014, p < .001; third strongest consequence), which are in line with the classic tripartite relationship quality theory ( Connors et al. 2021 ; Fletcher, Simpson, and Thomas 2000 ).

RESULTS FOR CONSEQUENCES OF CONSUMER TRUST

p < .001; ψ: number of effect sizes. Although the reported coefficients are unstandardized, because the effect size captures correlations, the magnitude of coefficient estimates is directly comparable across the consequences of consumer trust. Robust standard errors are reported. For the shaded rows, we combined the absolute effect sizes for the five attitudinal-based (the four behavioral-based) consequences to construct the aggregate variables. The estimates for the covariates and the deviance values are based on models with the nine consequences included.

Trust remains the most important currency in lasting relationships … . In times of turbulence and volatility, trust is what holds society together. (Edelman “Trust Barometer” 2021)

Theoretical and Practical Contributions

Closing the consumer trust gap.

Over the last five decades, numerous articles from various disciplines have expanded our understanding of the individual consumer’s trust. Although the extant research demonstrates the crucial role played by consumer trust, no consensus has been reached regarding which antecedents and consequences of the individual consumer’s trust are most powerful. Furthermore, a vast majority of such studies employ a singular focus, context, operationalization, and/or sample and, hence, have been unable to examine conditions under which antecedents of consumer trust become more rather than less effective. The present research is the first to systematically investigate the antecedents and consequences of consumer trust, as well as important moderators across a very broad body of multidisciplinary work, and to shed light on the differential strength of these antecedents and consequences. In so doing, we advance the extant literatures on both consumer trust ( Chaudhuri and Holbrook 2001 ; Darke and Ritchie 2007 ; Engeler and Barasz 2021 ; Sirdeshmukh et al. 2002 ) and empirical generalizations in consumer research ( Khamitov et al. 2019 ; Weingarten and Goodman 2021 ).

Integrity Over Reliability

From a practical standpoint, the empirical generalizations distilled by the current research can and should be used as managerial benchmarks when it comes to driving and benefiting from consumer trust. For instance, managers are encouraged to prioritize establishing integrity over conveying reliability, to strategically prioritize top drivers of consumer trust (e.g., reputation, ethicality and SR, perceived quality, attachment), and to allocate resources accordingly. Such an approach is warranted, as businesses typically have limited resources, which is why effective trust-building approaches are critical. To this end, in web appendix H , we also provide granular trust-driver results that can be used by managers in charge of a brand/firm (ethicality/SR, reputation, attachment), specific offering (competence, attachment, perceived value), industry (reputation, ethicality/SR, perceived quality), or technology (perceived value, reputation, perceived quality).

Strong Effect of Consumer Trust on Attitudinal and Behavioral Outcomes

On the surface, consumer trust is logically expected to lead to strong market performance. However, lack of systematic and generalizable evidence on the exact nature of benefits associated with consumer trust has led some experts to draw on anecdotal evidence and undermine the importance of fostering consumer trust ( Marketing Week 2021 ). Our findings stand in contrast to such claims and highlight the strong effect of consumer trust on desirable outcomes. Not only does consumer trust result in enhanced attitudinal consequences of satisfaction, attitudinal loyalty, self-concept connection, evaluations, and engagement, but it also boosts behavioral consequences like purchase intentions, behavioral loyalty, willingness to pay, and even market performance.

Consumer Trust in the Future

The increasing importance of the right antecedent levers.

The cross-time findings, alongside recent industry reports regarding change in baseline trust yield interesting insights. While reports by Edelman (2021) , Gallup (2023) , and Millward Brown (2018) suggest that baseline consumer trust has declined, our findings imply that all is not doom and gloom, and that managerial actions now have more power to move the needle and improve consumers’ trust. In other words, although, in general, many consumers have lost trust in brands, brands can more easily make up for that loss in baseline trust by engaging in the right activities (conveying integrity via a reputation campaign or CSR, increasing the quality of their offerings).

The Nuanced Impact on Downstream Consequences Over Time

How has the importance of consumer trust in driving outcomes changed? Both researchers and practitioners would benefit greatly from insights regarding the future influence of consumer trust on different outcomes. To speak to the future role of trust, we conducted additional exploratory analyses on consequences of consumer trust by comparing meta-analytic coefficients in recent versus older studies. We likewise conjectured that the change in trends in older versus more recent studies would be manifested in the future: the outcomes that trust more strongly affects in recent studies will be impacted by it strongly in the future as well. On the aggregate, we do not find significant evidence for change in the effectiveness of consumer trust in driving PAC and PBC. However, when looking at individual consequences, we find that in recent years, the effect of consumer trust on behavioral loyalty and market performance has strongly increased. Interestingly, and contrary to the claims made by some practitioners ( Marketing Week 2021 ), trust has recently become (and will most likely continue to be) more important in driving consumer purchase decisions. Additionally, the effect of consumer trust in enhancing behavioral loyalty has also increased in recent years. We present the detailed results in web appendix H .

Implications and Future Research Agenda

Probing integrity further.

The current article opens avenues for further research. First, the impressively strong impact of reputation, ethicality, and SR on consumer trust speaks to the effectiveness of inherently moral precursors of generating trust and the importance of doing the right thing . These integrity antecedents emerged the strongest among a number of contenders. Thus, scholars are encouraged to pay increased attention to studying various nuances related to how and why reputational and moral considerations influence consumer trust as well as studying the apparent importance of establishing integrity over establishing reliability in the marketplace (which is particularly meaningful amid the growing proliferation of unsuccessful sociopolitical activism efforts, greenwashing, and CSI). Related to this, past research has shown that when it comes to choosing between service providers, consumers prioritize competent ones over moral ones ( Kirmani et al. 2017 ). Our findings paint a different picture when it comes to consumer trust. Future research on tradeoffs between consumers’ trust and choice across settings is needed.

Rethinking Certain Antecedents

Second, the relatively low average capacity of perceived risk and marketing investments to influence trust is interesting and rather surprising, implying that their effects on trust are likely to be weaker than previously thought. This former finding is different than Geyskens et al.’s (1998) finding regarding the importance of risk and uncertainty in driving trust in the B2B context. This might be because the individual consumer is less calculative than the organizational customer. In this connection, future research should investigate conditions under which risk and marketing investments hold the ground and serve as more effective drivers of the individual consumer’s trust (e.g., types or magnitude of risk and marketing investments).

Digging Deeper into the Moderators

Further, the finding that different trust entities have differential effectiveness of their respective antecedents implies that there is likely no one-size-fits-all approach to driving consumer trust. That is, depending on which target trust is directed at (brands/firms vs. specific offerings vs. industries vs. technologies), the impact of different antecedents varies quite dramatically. This is consistent with the idea of the increasingly nuanced marketplace wherein nowadays consumers have to put trust in both humans and machines, whereas humans were more of the focus in the past. Therefore, future researchers must carefully select a particular trust entity context of interest and avoid expecting uniform effects. Depending on the context, consumer trust scholars should be able to calibrate their expectations and shortlist a handful of manipulations holding the highest potential when it comes to predicting trust (e.g., manipulating competence to drive trust in crowdfunding requestors; Wang et al. 2021 ).

Interestingly, looking at temporal patterns and trajectories within our meta-analytic data for recent versus older years as well as attribute type differences spurs a number of research pathways. These trends naturally prompt the following questions: Why do we observe such increases and decreases, respectively? What are some of the factors driving this evolution over time and this IBTA effectiveness gap for experience attributes? Can scholars expect the same patterns moving forward? Future work is urged in this regard, and we explicitly call for research identifying certain conditions where trust is still highly impactful on consumer outcomes.

Consumer Trust in a Post-Truth World

Importantly, one can argue that consumers are increasingly distrustful of media in general and of social media in particular, especially in the United States with the prevalence of fake news and one’s inability to distinguish truth from lies in these contexts. Is it likely that this distrust finds its way into a general distrust of products and brands? Has time come to determine more latent ways in which trust might affect consumers’ decisions even when they do not explicitly state it as important? Relatedly, would the increasing levels of nationalism being observed across the globe lead to a distrust of foreign brands?

Calling for Greater Ecological Validity

Lastly, a fairly strong trust-market performance link warrants elaboration. On the one hand, this effect is reassuring, as it implies that the positive substantial effects of trust are not limited to attitudes and behavioral intentions. On the other hand, only a handful of included studies (i.e., 28 effect sizes) focused on market performance. Against this background, more studies of ecologically valid downstream financial and market consequences are urgently needed going forward because of their (1) superior representation of the real-world marketplace, (2) current lower sample size, and (3) higher potential to arrive at realistic, non-inflated effect sizes.

Examining Understudied Constructs

To keep the scope of our work manageable, following other meta studies we focused on the most prevalent antecedents of our focal construct. However, many other antecedents of consumer trust have been discussed in past research. A few examples are propensity to trust ( Yamagishi and Yamagishi 1994 ), warmth ( Kirmani et al. 2017 ), and familiarity ( Garbarino and Johnson 1999 ). Future research could look through other theoretical lenses and meta-analyze another set of understudied antecedents not examined in our research.

Trust Dimensionality

The dimensionality of trust warrants further investigation. Our review of past research indicates that trust is predominantly conceptualized as two-dimensional (65% of papers, web appendix I ), aligning with our findings regarding the differential effects of IBTA versus RBTA. The most commonly studied dimensions are reliability and integrity, although other dimensions such as sympathy and familiarity are also mentioned in the literature, albeit rarely ( web appendix I ). While we adopted a two-dimensional conceptualization of trust, given the limited available data in prior papers, our empirical modeling treated trust as a unidimensional construct with two groups of antecedents inspired by the most commonly examined dimensions of trust. Future work could explore the relationships between antecedents and different dimensions of trust in greater detail.

The collection and coding of data for the meta-analysis were administered at Indiana University and Georgia Institute of Technology between Fall of 2020 and Summer of 2023. The first two authors designed the coding protocol and conducted data analyses. The third and fourth authors carried out data collection under supervision of the first two authors. Data and coding were discussed on multiple occasions by all authors. The final article was jointly authored. The data are currently stored in a project directory on the Open Science Framework.

Mansur Khamitov ( [email protected] ) is an assistant professor of marketing at the Kelley School of Business, Indiana University, 1309 E 10th St, Bloomington, IN 47405, USA.

Koushyar Rajavi ( [email protected] ) is an assistant professor of marketing at the Scheller College of Business, Georgia Institute of Technology, 800 W Peachtree St NW, Atlanta, GA 30308, USA.

Der-Wei Huang ( [email protected] ) is an assistant professor of marketing at the School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) 518172, China.

Yuly Hong ( [email protected] ) is an assistant professor of marketing at NEOMA Business School, 59 rue Pierre Taittinger, 51100 Reims, France.

All authors contributed equally. The authors are grateful for funding from RATS Grant (2236360/MKHAM) to Mansur Khamitov provided by the Kelley School of Business, Indiana University. Supplementary materials are included in the web appendix accompanying the online version of this article.

We acknowledge that there have been meta-analytic studies on the role of consumer trust in specific contexts (e.g., Kim and Peterson’s study (2017) of the role of online trust in e-commerce); yet such studies are context-specific, smaller in scale, and their conclusions might not be generalizable to consumer trust in other settings or across settings. We also acknowledge Khamitov, Wang, and Thomson’s study (2019) meta-analyzing the link between brand relationships and customer loyalty that (1) focuses only on trust towards a single, specific entity (brand), (2) does not study any brand-trust antecedents, (3) explores a single brand-trust consequence (customer brand loyalty), and (4) includes fewer effect sizes (216).

As can be seen in our discussion of prior literature, different labels have been used to refer to similar and/or closely related components of trust (e.g., reliability, capability, ability). We acknowledge that there might be slight conceptual differences between these constructs. We utilize the integrity versus reliability dichotomy, which, in our view, most succinctly and parsimoniously represents the literature on consumer trust across different domains.

It should be noted that some perspectives on trust place more emphasis on the inherent characteristics of the trusting entity (e.g., propensity to trust in the literature on individual trust). In the current work, following a large body of research on consumer trust, we view trust as a temporary state experienced by consumers when they examine brands, products, services, etc., rather than a stable personality trait. This perspective is pertinent to practitioners, for it focuses on antecedents that business entities can modify to enhance consumer trust. Thus, ** we do not examine factors that are related to the stable nature of trust that practitioners have little or no influence over (e.g., consumers’ general propensity to trust).

On the basis of these two criteria, for instance, we excluded perceived warmth (appeared in <2% of the past studies on consumer trust) and familiarity/experience (lack of fit with our theoretical framework).

Using the 10% threshold led to fewer consequences compared to antecedents (seven vs. eight). To have more balance between the number of antecedents and consequences, and to provide more insights with respect to different marketplace outcomes tied to trust, we also included the next two commonly studied consequence variables: market performance and willingness to pay.

Our assignment of certain antecedents to IBTA versus RBTA is based on the primary mechanism in the literature. Our framework is not meant to suggest that a variable categorized as IBTA (RBTA) has no impact at all on the reliability (integrity) trust aspect.

To further justify the categorization of trust consequences/outcomes as primarily attitudinal versus primarily behavioral, we refer the reader to past research like Chaudhuri and Holbrook (2001) , Boonlertvanich (2019) , or Liu et al. (2021) where this attitudinal versus behavioral distinction is apparent and central.

We also caution that our framework does not suggest that trust would never drive any of our antecedents, such as attachment and/or reputation. To assign a construct to antecedents or consequences of consumer trust, we relied on past research and determined its role in the nomological framework based on the majority of the past research. Resultantly, our antecedents and consequences were used in the same role in more than 80% of past research. As such, our focal relationship specification between constructs represents a better-fitting depiction of the extant literature (and not a universal depiction).

Of the overall sample, 983 effect sizes from 347 studies across 310 manuscripts correspond to antecedents of trust, while 1,164 effect sizes from 459 studies across 414 manuscripts capture consequences of trust.

A full list of included papers is available at https://researchbox.org/1335&PEER_REVIEW_passcode=YPKQTP .

Our follow-up interaction analysis based on low versus high level of financial risk suggests that risk does not influence trust in the low-risk subset ( b = −0.101, p = .186), whereas in the high financial risk subset, the effect of risk is substantial ( b = −0.592, p < .001). Relatedly, while in the low physical risk subset the impact of risk on trust is b = −0.102 ( p = .176), the influence of risk on trust is much stronger under high physical risk ( b = −0.355, p = .038).

We present pairwise significance tests across coefficients of antecedents (and consequences) in web appendix G .

The 2015 year of publication threshold leads to a good balance of effect sizes for recent and older studies, as well as allowing us to focus specifically on the most recent studies that are pertinent to understanding what the future might look like.

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Andrew Adewale Alola a CREDS-Centre for Research on Digitalization and Sustainability, Inland Norway University of Applied Sciences, Lillehammer, Norway; ... the Data description with methodological approaches and discussion of the findings in sections Data and methodology and Empirical analysis and results, respectively. Lastly, the summary of ...
KG-EmpiRE: A Community-Maintainable Knowledge Graph for a Sustainable
KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). ... and timeliness of their research results. In the last two decades, several researchers provided snapshots of the "current" state and ...
Sustainability
Abstract: Rural digital governance is an inevitable requirement to improve the efficiency of rural governance, and is also an important means to realize the modernization of rural governance. In the context of the digital rural development strategy, the index measurement system of the rural digital governance level is built around the five key governance areas of "digital economy, digital ...
NTRS
Basaltic magmatism is an efficient process for bringing volatiles from a planetary interior to its surface, with the possibility of generation of a transient lunar atmosphere as abundant volcanic materials de-gassed. However, pyroclastic deposits are locations where trapped gases may be studied [e.g., 2,3]. Volatile-rich pyroclastic deposits occur over a wide surface area of the Moon ...

Empirical Research: Definition, Methods, Types and Examples

Empirical research: Definition

Types and methodologies of empirical research

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Introduction: What is Empirical Research?

Reading and Evaluating Scholarly Materials

Empirical Research: Defining, Identifying, & Finding

The Results Section

What Criteria to Look For

Finding the Criteria

Quantitative Research

Qualitative Research

Research: Overview & Approaches

Introduction to Empirical Research

Your Librarian

Identifying Empirical Research Articles

What is Empirical Research?

Parts of an Empirical Research Article

Introduction

Results

Learn the IMRaD Layout: How to Identify an Empirical Article

Empirical Research: A Comprehensive Guide for Academics

Why Empirical Research?

Qualitative and Quantitative Methods

Steps for Conducting Empirical Research

Advantages of Empirical Research

Disadvantages of Empirical Research

Tips for Empirical Writing

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Finding Empirical Research

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Slideshow showing how to find empirical research in APA PsycInfo

What is Empirical Research?

Using PsycInfo

What is Empirical Research? Definition, Methods, Examples

What is Empirical Research?

Characteristics of Empirical Research

Importance of Empirical Research

How to Conduct Empirical Research?

1. Select a Research Topic

2. Formulate Research Questions

3. Review Existing Literature

4. Define Variables

Empirical Research Design

Types of Empirical Research

Experimental Design

Observational Design

Survey Design

Case Study Design

Qualitative vs. Quantitative Research

Data Collection for Empirical Research

Sampling Methods

Data Collection Instruments

Data Collection Procedures

Ethical Considerations

Empirical Research Data Analysis

Data Preparation

Descriptive Statistics

Inferential Statistics

Qualitative Data Analysis

Data Visualization

How to Report Empirical Research Results?

1. Write the Research Paper

2. Create Visuals and Tables

3. Interpret Findings

Examples of Empirical Research

Social Sciences

Environmental Science

Business and Economics

Conclusion for Empirical Research

How to Collect Data for Empirical Research?