empirical thesis meaning

How do I write an empirical bachelor's or master's thesis?

An empirical thesis is an academic research in which certain information and data from reality (experience = empiricism) are independently collected to answer a certain question.

There are copious types of data collection, such as surveys, interviews, observations, text analyses, experiments, test series, simulations and modifications of these methods.

What are the advantages of an empirical thesis?

• Collect your own data
• Personal contribution is clearly defined
• Sometimes a higher grade is possible
• Clear guidelines for action
• Many methodological sources
• Chance to show your creativity
• You can learn more
• You can gain reputation and credibility

What are the disadvantages of empirical work?

• You are dependent on others
• Time needed to learn new methods
• More time may be required
• Somewhat uncertain ending
• Possibly a larger workload
• Possible costs for interviews and experts

What does a topic for an empirical thesis look like?

Like other types of work. The difference lies in the data sources and methods. Try our topic trip.

What does an outline in an empirical thesis look like? What chapters does it contain?

The topic trip provides you with a complete sample layout that even includes page numbers per chapter.

• Introduction
• State of research
• Methodology

What are the challenges of an empirical thesis and how do I overcome them?

1. you must locate a real research gap.

You have to ask a real question that has never been answered before in the way you plan to do so. To do this, you have to evaluate real scientific studies. Books are not proper sources, not even by a long shot. The studies are 95% in English, have their own unique terminology and require a lot of knowledge in the subject area because the analyses come from experts who have been researching such questions for a long time. So it’s best to first find the research gap because so much has already been researched. This is not easy even for experienced researchers.

The Thesis Guide takes you by the hand and leads you through this process step-by-step by providing an example topic. You absolutely MUST write a proposal. We can show you HOW and WHAT belongs in there!

2. You must work with new methods!

Most likely this is your first empirical analysis. The methods are new, you don’t have much time and you have to create a questionnaire or conduct an interview. But HOW???? You have to attract participants and collect data. But HOW????

The Thesis Guide provides you with an overview of the methods and detailed instructions for working with them. You also have concrete examples and templates of all kinds.

3. You must gain real NEW insight!

You cannot use old literature for writing your own findings. An empirical analysis is creative and you must add something new. Sometimes the NEW knowledge is apparently only clear at the end but not with us! The Thesis Guide will help you know from very early on with what the results and findings will be.

The Thesis Guide will help you see the end of the work right at the beginning, using proven patterns and examples for the beginning, guiding questions, detailed questions and formulation of objectives. This makes YOUR results clear, right from the start. It even makes work fun!

What is the best way to start an empirical thesis?

Start with the research question, topic and the appropriate sources! What answers are you looking for? Then follow this standardized procedure in the Aristolo Thesis Guide:

• Write a proposal,
• Specifically filter books and write theory chapters,
• Survey the state of research by means of study evaluation and write chapters,
• Consider and describe analytical methods (research methods),
• Obtain and evaluate information, data and arguments from sources,
• Gain new insight by means of analyses
• Draw conclusions, write the chapter on results and complete the thesis.

How can the Aristolo Thesis Guide help with your empirical thesis?

The Thesis Guide helps by providing detailed descriptions of the contents of every chapter with micro questions, sample formulations, all kinds of aids, file templates for all kinds of tasks such as interview guidelines, questionnaire templates etc. Good luck writing your text!

Silvio and the Aristolo Team

PS: Check out the interactive Guide for writing a bachelor or master thesis in 31 days.

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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

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

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

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

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

LEARN ABOUT: Behavioral Research

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

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

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

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

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

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

LEARN ABOUT: Qualitative Research Questions and Questionnaires

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

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

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

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

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

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

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

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

LEARN ABOUT: Level of Analysis

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

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

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

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

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

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

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

LEARN ABOUT: Action Research

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

LEARN ABOUT: Qualitative Interview

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

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

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

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

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

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

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

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

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

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

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

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

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

Step #1: Define the purpose of the research

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

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

Step #2 : Supporting theories and relevant literature

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

Step #3: Creation of Hypothesis and measurement

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

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

Step #4: Methodology, research design and data collection

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

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

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

Step #6: Conclusion

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

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

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

LEARN MORE: Population vs Sample

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

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

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

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

LEARN ABOUT:  Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

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

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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

Get science-backed answers as you write with Paperpal's Research feature

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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Related Reads:

• How to Write a Scientific Paper in 10 Steps 
• What is a Literature Review? How to Write It (with Examples)
• What is an Argumentative Essay? How to Write It (With Examples)
• Ethical Research Practices For Research with Human Subjects

Ethics in Science: Importance, Principles & Guidelines 

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Empirical Research: Defining, Identifying, & Finding

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

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

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

Characteristics of Empirical Research

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

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

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

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

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

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

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

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

Characteristics of Quantitative Research 

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

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

Characteristics of Qualitative Research

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

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

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

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

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

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

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

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EDU 700: Educational Research for Thesis Research Project: What is Empirical Research?

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Empirical Research

Empirical research articles report on research based on actual observations or experiments. This type of research may make use of either a quantitative or qualitative research design. Quantitative research is research which generates numerical data and looks to create relationships between two or more variables. Qualitative research looks to critically and objectively analyze behaviors, beliefs, feelings, or values. 

Key characteristics of empirical research articles include: specific research questions, definition of a problem, behavior, or phenomenon, a complete description of the process used to study the population or phenomenon, including selection criteria, controls, as well as a description of the instruments used such as interviews, tests or surveys. 

Empirical articles normally include the following elements:

1. An Introduction : The Introduction provides a brief summary of the research.

2. Methodology : The methodology describes how the research was conducted -- including who the participants are, how they were selected, the steps taken to design the study, what the participants did, and the types of measurements used. 

3. Results : The results section describes the outcomes of the measures of the study.

4. Discussion : The discussion section contains interpretation and implications of the study.

5. Conclusion

6. References

Note: Empirical   research articles may combine these elements so that one section of the paper may handle two or three of these areas at once.

Empirical research articles are usually lengthy (>7 pages) and can be difficult to read -- particularly if you are not a practitioner in the field of study in which they are written.  Whenever you are in doubt always speak to your professor or ask a librarian for advice. 

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

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

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

What is Empirical Research? 

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

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

Characteristics of Empirical Research

• Research Questions

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

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

• Definition of the Research Variables

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

• Description of the Research Methodology

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

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

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

Uses of Empirical Research

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

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

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

The Empirical Research Cycle

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

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

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

This information is useful for further research. 

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

Examples of Empirical Research 

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

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

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

Empirical Research Data Collection Methods

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

The following are common methods of gathering data in empirical research

• Survey/ Questionnaire

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

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

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

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

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

• Observation

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

How to collect Empirical Research Data with Questionnaire

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

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

Sign in to Formplus

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

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

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

Edit Form Title

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

Edit Form  

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

Customize Form

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

• Share your Form Link with Respondents

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

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

Empirical vs Non-Empirical Research

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

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

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

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

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

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

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

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

Advantages of Empirical Research 

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

Disadvantages of Empirical Research 

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

Conclusion 

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

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

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

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

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

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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 35min read

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

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

What is Empirical Research?

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

Characteristics of Empirical Research

Empirical research is characterized by several key features:

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

Importance of Empirical Research

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

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

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

How to Conduct Empirical Research?

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

1. Select a Research Topic

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

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

2. Formulate Research Questions

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

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

3. Review Existing Literature

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

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

4. Define Variables

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

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

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

Empirical Research Design

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

Types of Empirical Research

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

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

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

Experimental Design

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

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

Observational Design

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

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

Survey Design

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

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

Case Study Design

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

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

Qualitative vs. Quantitative Research

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

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

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

Data Collection for Empirical Research

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

Sampling Methods

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

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

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

Data Collection Instruments

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

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

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Data Collection Procedures

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

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

Ethical Considerations

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

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

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

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

Empirical Research Data Analysis

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

Data Preparation

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

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

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

Descriptive Statistics

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

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

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

Inferential Statistics

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

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

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

Qualitative Data Analysis

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

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

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

Data Visualization

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

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

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

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

How to Report Empirical Research Results?

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

1. Write the Research Paper

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

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

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

2. Create Visuals and Tables

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

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

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

3. Interpret Findings

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

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

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

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

Examples of Empirical Research

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

Social Sciences

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

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

Environmental Science

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

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

Business and Economics

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

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

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

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

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

Conclusion for Empirical Research

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

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

• distinguishing between what IS and IS NOT empirical literature
• how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility.   Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

• your course instructor’s lectures
• political statements
• advertisements
• newspapers & magazines (journalism)
• television news reports & analyses (journalism)
• many websites, Facebook postings, Twitter tweets, and blog postings
• the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying.  However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

• Accept as is
• Accept with minor revisions
• Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

• Journal of Children & Poverty
• Journal of Poverty
• Journal of Poverty and Social Justice
• Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber.  Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

• Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
• AgeLine—multidisciplinary index of aging-related content for over 600 journals
• Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
• Google Scholar—broad search tool for scholarly literature across many disciplines
• MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
• Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
• PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
• SocINDEX—publications in sociology
• Social Sciences Abstracts—multiple disciplines
• Social Work Abstracts—many areas of social work are covered
• Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

• Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
• Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
• Census Bureau at https://www.census.gov
• Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
• Child Welfare Information Gateway at https://www.childwelfare.gov
• Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
• Forum on Child and Family Statistics at https://www.childstats.gov
• National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
• National Institute on Aging (NIA at https://www.nia.nih.gov
• National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
• National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
• National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
• National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
• National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
• National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
• National Institute of Justice (NIJ) at https://www.nij.gov
• Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
• United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease,  the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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The difference between empirical and discussion chapters (and how to write them)

Jun 7, 2019

It is a common misconception that the empirical chapters are the place for your analysis. Often this confuses the reader. In fact, you need to split the empirical facts and discussion of those facts into two distinct sections. 

In this post I want to explain why, more often that not, you need have  separate  empirical and discussion chapters and why you shouldn’t combine them. Then, I’ll talk you through practical steps to employ when you’re writing each up. 

What is the difference between an empirical and discussion chapter?

Whilst they are closely related, they occupy two very different spaces within a PhD thesis.

We can understand a PhD as having   four distinct sections :

1. Introduction – this is where you introduce and outline the entire study.

2. Background – this where you lay the groundwork for your thesis (in your literature review, theory framework and methods).

3. Core – this is where you present your findings.

4. Synthesis  – this is where you relate the core to the background.

The empirical chapter(s) is/are where you present the facts of your study. They occupy the core of the thesis. 

The discussion chapter though is where you interpret and discuss your findings in relation to the thesis and wider discipline. That is why is occupies the synthesis stage of the research.

Your job when writing your discussion then is to interrogate and critically engage with the findings and relate them to the research aims, objectives, research questions and gap. Most of your cutting-edge analysis and engagement with your findings will take place here.

The job of a discussion chapter is therefore to critically examine your findings with reference to the discussion in the background chapters of the thesis (introduction, literature review, theoretical framework and methods) and to make judgments as to what has been learnt in your work. In essence, the job of a discussion chapter is to tell the readers what your findings (may) mean.

The reason for this distinction between empirics and discussion is to make life easier for your examiner. They are looking at whether you are capable of both presenting observations in line with your methodology, and interpreting their significance in the context of the thesis as a whole.

However, as with everything in the PhD, there will be exceptions to this. Some theses, particularly those from within the liberal arts and social sciences, may not need a separate discussion section because the nature of their study may mean that empirics and discussion are intertwined.  It is also worth pointing out that although not every thesis will have a discussion chapter, all theses will contain discussions of some sort, however short.

How do I write an empirical chapter?

It’s important to note that the form your empirical chapter will take – or indeed if you have one at all – will depend heavily on the nature of your thesis and the discipline in which you’re working. Someone working in a more theoretical space – for example, philosophers or the theoretical mathematicians – might rely less on first-hand empirical data and more on proofs. Those working on quantitative studies will have more data-driven, empiric-heavy empirical chapters.

Broadly speaking then, the emphasis in the empirical section or chapter is on factual recount and summary. You’ll be categorising your findings into particular themes and using a variety of visual elements (tables, figures, charts, and so on) to present your results. You need to show the reader what your data ‘looks like’.

You need to do it well, too. If you present data in a messy way, your examiner might think that your thinking is messy.

By the time you have finished your empirical chapter, your reader should be able to answer six questions:

1. What are the results of your investigations?

2. How do the findings relate to previous studies?

3. Was there anything surprising or that didn’t work out as planned?

4. Are there any themes or categories that emerge from the data?

5. Have you explained to the reader why you have reached particular conclusions?

6. Have you explained the results?

You are providing sufficient detail that others can draw their own inferences and construct their own explanations. Think of it as presenting the case for a jury.

That means that an empirical discussion should:

1. Tell the reader how the data was collected, with reference to the methods chapter/section

2. Tell them how they can access it if they wanted to replicate your study

3. Discuss what the results look like (using visual aids, such as tables, diagrams, graphs and so on)

4. Provide rich summaries of the findings

5. Discuss the gaps in the findings and analysis

6. Analyse the results

7. Discuss the implications of your findings

8. Discuss the limitations of the findings

Your PhD thesis. All on one page. 

Use our free PhD structure template to quickly visualise every element of your thesis. 

How do I write a discussion chapter?

Many students struggle to write up their discussion chapters . The reason is that they lack the confidence needed to make the kinds of knowledge claims required. The discussion chapter is where you start to develop your scholarly authority, and where you start to make truth claims about your interpretation of what’s going on. By implication, that means it is where you start to agree or disagree with existing literature and theoretical ideas. 

Another reason why students struggle is that they fail to realise the significance of their findings or, put differently, they don’t think their findings are significant enough in their own right. By the time we come to write our discussion, we are so conditioned by the findings that we may not realise that they are significant and do, in fact, make a contribution. There’s often an expectation gap here; students expect their contribution to be big, whereas, more often than not, the contribution a PhD actually makes is small and specific. 

As with every stage of your thesis, you must relate your discussion section/chapter to the background. Specifically, you need to relate it to the empirical chapter, aims and objectives, research questions, the gap in the literature and, if relevant, your theoretical framework. There will, therefore, be a lot of signposting to other parts of the thesis; doing so is necessary if you want to show the examiner that you can relate your findings to the broader context of your thesis and discipline.

One of the biggest obstacles is synthesising your empirical data and being able to critically discuss it in relation to this broader context. The authors of   How To Write A Better Thesis   offer up an effective technique you can use if you’re struggling to do this. They refer to it as a ‘mud-map’:

1. You can start by writing a long list of everything you have found.

2. See if you can sort and organise this list. Categorise each finding based on whether it is speculative or based in empirical fact. This is important because your discussion will need to be somewhat (but not too) speculative.

3. Try to categorise your different findings into themes.

4. Now try to find linkages between these themes.

5. Organise these themes into different section headings for the discussion chapter, and try to come up with sub-headings. 

When it comes to writing your discussion chapter, you can start by writing a few sentences that summarise the most important results. 

One danger when writing discussion sections is that they can be too wordy, offer too much interpretation and lack a clear structure.  To avoid this, you should make sure that every element of your discussion section addresses one of the following questions: 

• What are the relationships between observations? (The mud-map you developed earlier will help here.)
• Are there any trends and generalisations amongst the results? Are there any exceptions to these? 
• What are the causes of, or mechanisms behind, the underlying patterns you have uncovered?
• Do your results agree or disagree with previous work? 
• How do your findings relate to the theoretical framework you developed, if applicable? 
• How do the findings relate to the hypotheses you developed, if applicable? 
• What other explanations could there be for your results? This issue is more pertinent if you are engaging in theory creation/inductive reasoning.
• What do we now know as a result of your research that we didn’t know before? 
• What is the significance of these findings? 
• Why should we care about the findings?

When your discussion chapter is finished, your reader should be able to answer the following questions:

1. How do the findings relate to the theory and methods discussed previously?   

2. Why you have reached particular conclusions?   

3. How do your findings relate to the gaps in the literature you identified earlier?   

4. What implications do the findings have for the discipline and for existing understanding?

5. How do the findings relate to your research questions/aims and objectives?   

A particularly important theme that I want you to always bear in mind is that your interpretation and discussion of the findings needs to be done in such a way that it relates back to the aims, objectives, research questions, gap and any theory. Running through your thesis will be a central argument – your thesis statement – and it is in the empirical and, particularly, the discussion chapter, that you will present all of the evidence and logical argument necessary to support that argument.

Unsurprisingly then, these core sections of the thesis are the most important, as they are where you make your contribution. Of course, you have outlined what your contribution is in the introduction, so what you are arguing is no surprise. But, it is in the core of the thesis where you drill down into the detail and critically engage with that contribution, using your data to rigorously support your line of argument.

Hello, Doctor…

Sounds good, doesn’t it?  Be able to call yourself Doctor sooner with our five-star rated How to Write A PhD email-course. Learn everything your supervisor should have taught you about planning and completing a PhD.

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10 comments.

Really great advice. I love simple, clear guidance. Especially when writing something that’s pretty complicated!!

Thanks for the kind words. I’m glad you found it useful.

It’s really guiding me a lot.

This is really great. I have had serious challenges putting up a convincing empirical chapter.

Glad you enjoyed it!

Very insightful guidelines for PhD candidates. So appreciative for such work.

Thanks for the kind words. I’m glad you’re finding the resources useful.

It’s really useful guidance. It helps a lot in simplifying the complex and messy ideas when starting the writing process.

Thanks for the kind words.

Very instructive.

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Empirical Research

Introduction, what is empirical research, attribution.

• Finding Empirical Research in Library Databases
• Designing Empirical Research
• Case Sudies

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

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

Key characteristics to look for:

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

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

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

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

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

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What Is Empirical Research? Definition And Tips For Students

So what is an empirical research article? What does empirical research entail? What are good examples of empirical research topics? These are common questions most students find themselves asking whenever a research paper instructs them to read and analyze empirical articles or evidence to answer a particular question. Such questions also arise when it comes to graduation papers and many students just ask writers  to help. If this points out your current dilemma, then you are in good company because below is a well-detailed guide on all you need to know about empirical research.

Definition of Empirical Research

Empirical research is a type of research whose findings or conclusions are mainly drawn from empirical or verifiable evidence rather than rationality.

Simply put, empirical research is any research whose findings are based on observable or experimentation evidence rather than through reasoning or logic alone. For instance, if a scientist wants to find out whether soft music promotes sleep, he or she will find a group of people and divide them into two groups.

The first group will be in a space with soft music while the second one will be in an area with no music at all for observation. Once the experiment is over, the results or conclusion drawn from it will be termed as empirical evidence whether or not soft music promotes sleep.

The Objectives of Empirical Research

There is more to empirical research than just observations. In fact, these observations will be useless if the scientist does not turn them into testable questions. So, now that you know what empirical research entails, what are the objectives? Well, the main reason why you’ll be asked to conduct such research is so you can use the findings drawn from your observations to answer well-defined questions that go with or against a particular hypothesis.

In the earlier mentioned example, for instance, the hypothesis would be “soft music promotes sleep.” Thus, the aim of carrying out empirical research, in this case, would be to come up with conclusions that either accept or reject the hypotheses.

Now to achieve this, you can either use quantitative or qualitative methodologies. Here is a breakdown of what each of the mentioned entails

Quantitative Methods

Quantitative empirical research methods are often used to collect information and draw conclusions through numerical data. Quantitative methods are usually predetermined and are set in a more structured format. Some good examples include

• Longitudinal studies
• Cross-sectional studies
• Experimental research
• Causal comparative research

The above techniques are often more effective for physics or medicine.

Qualitative Methods

Qualitative empirical research methods, on the other hand, are used to gather non-numerical data. They are mainly unstructured or semi-structured and are used to find meaning or underlying reasons for a particular phenomenon. In other words, they are used to provide more insight into the problem being researched. So, is qualitative research empirical? The short answer to that is yes. It is indeed empirical but more appropriate in finding answers to social science-related questions.

Types of Empirical Research

Note, there is a difference between methodologies and types of empirical research. Methodologies are the earlier mentioned quantitative and qualitative, and they refer to the methods used to conduct or analyze data. But when it comes to types of empirical research, it can be either experimental or non-experimental.

In experimental research, a particular intervention is often used to drive a hypothesized changed in the variables of interest. In non-experimental empirical research, however, the subjects of the study are simply observed without any form of intervention. This is why it’s also referred to as informal research.

How to Identify Empirical Research Articles

Now that you know the types of empirical research as well as methodologies, how do you distinguish an empirical research paper from a regular research paper? Well, as with any other research question, empirical research questions usually have features that can help you identify them as shown below

Empirical Research Article Format

The easiest way to identify an empirical research article is through its unique format. It’s usually divided into these sections

• The Title. It offers an overview of the research and also includes the author(s) who conducted it
• An abstract. It provides a very brief yet comprehensive summary of the empirical research study. It’s usually a paragraph long.
• The Introduction. Here the author provides background information on the research problem. For instance, they talk about similar studies and explain why the research was conducted in the first place.
• The MethodsIn this section, the author offers a detailed description of all methods used to conduct the empirical research study. In some articles, it may be titled methodology.
• The Results. Here the author provides the answer to the research question.
• DiscussionThe author will then go on to give a detailed discussion of the data obtained or the results found above. They may also compare the results of their empirical research study to the results obtained by other empirical studies on similar topics.
• ReferencesHere as you may have guessed, the author lists citations of any journal articles, studies, or books mentioned or used in coming up with the results.

Keywords Used in an Empirical Research Paper

Other than the format, you can also identify an empirical research paper based on the phrases used within it. Some of the must-have keywords include:

• Measure or measurements
• Qualitative and quantitative research
• Sample size
• Methodology
• Original study or research study

Empirical Research Examples

Some of the empirical questions you could come across in empirical research include:

• According to Maslow’s hierarchy of needs, at which level are, leaders need level?
• The lop-sided sales of illegal guns among licensed handgun retailers.
• Freeway truck travel-time prediction for seamless freight planning using GPS data.
• Use empirical research to explain the entrepreneurial mindset concerning cognition and motivation.
• Are meta-analysis reviews theoretical or empirical research?

Theoretical vs. Empirical Research

Unlike empirical research, which is based on valid or observational evidence, theoretical research is more logical than observational. It is a logical exploration of a system of assumptions and involves defining how a particular system and its environment behave without the analysis of concrete data.

Therefore, theoretical research can be classified as non-empirical data as it is conducted without data. It is essential as it offers anyone conducting research a place to start. For instance, saying soft music promotes sleep gives the researcher a hypothesis to base their research proposal on and an easy way to start. However, theoretical data is only useful if empirical research is conducted.

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Underdetermination of Scientific Theory

At the heart of the underdetermination of scientific theory by evidence is the simple idea that the evidence available to us at a given time may be insufficient to determine what beliefs we should hold in response to it. In a textbook example, if I know that you spent $10 on apples and oranges and that apples cost $1 while oranges cost $2, then I know that you did not buy six oranges, but I do not know whether you bought one orange and eight apples, two oranges and six apples, and so on. A simple scientific example can be found in the rationale behind the important methodological adage that “correlation does not imply causation”. If playing violent video games causes children to be more aggressive in their playground behavior, then we should (barring complications) expect to find a correlation between time spent playing such video games and aggressive behavior on the playground. But that is also what we would expect to find if children who are prone to aggressive behavior tend to enjoy and seek out violent video games more than other children, or if propensities for playing violent video games and for aggressive playground behavior are both caused by some third factor (like being bullied or general parental neglect). So a high correlation between time spent playing violent video games and aggressive playground behavior (by itself) simply underdetermines what we should believe about the causal relationship between the two. But it turns out that this simple and familiar predicament only scratches the surface of the various ways in which problems of underdetermination can arise in the course of scientific investigation.

1. A First Look: Duhem, Quine, and the Problems of Underdetermination

2.1 holist underdetermination: the very idea, 2.2 challenging the rationality of science, 3.1 contrastive underdetermination: back to duhem, 3.2 empirically equivalent theories, 3.3 unconceived alternatives and a new induction, other internet resources, related entries.

The scope of the epistemic challenge arising from underdetermination is not limited only to scientific contexts, as is perhaps most readily seen in classical skeptical attacks on our knowledge more generally. René Descartes ([1640] 1996) famously sought to doubt any and all of his beliefs which could possibly be doubted by supposing that there might be an all-powerful Evil Demon who sought to deceive him. Descartes’ challenge appeals to a form of underdetermination: he notes that all our sensory experiences would be just the same if they were caused by this Evil Demon rather than an external world of rocks and trees. Likewise, Nelson Goodman’s (1955) “New Riddle of Induction” turns on the idea that the evidence we now have could equally well be taken to support inductive generalizations quite different from those we usually take them to support, with radically different consequences for the course of future events. [ 1 ] Nonetheless, underdetermination has been thought to arise in scientific contexts in a variety of distinctive and important ways that do not simply recreate such radically skeptical possibilities.

The traditional locus classicus for underdetermination in science is the work of Pierre Duhem, a French physicist as well as historian and philosopher of science who lived at the turn of the 20 th Century. In The Aim and Structure of Physical Theory , Duhem formulated various problems of scientific underdetermination in an especially perspicuous and compelling way, although he himself argued that these problems posed serious challenges only to our efforts to confirm theories in physics. In the middle of the 20 th Century, W. V. O. Quine suggested that such challenges applied not only to the confirmation of all types of scientific theories, but to all knowledge claims whatsoever. His incorporation and further development of these problems as part of a general account of human knowledge was one of the most significant developments of 20 th Century epistemology. But neither Duhem nor Quine was careful to systematically distinguish a number of fundamentally distinct lines of thinking about underdetermination found in their work. Perhaps the most important division is between what we might call holist and contrastive forms of underdetermination. Holist underdetermination (Section 2 below) arises whenever our inability to test hypotheses in isolation leaves us underdetermined in our response to a failed prediction or some other piece of disconfirming evidence. That is, because hypotheses have empirical implications or consequences only when conjoined with other hypotheses and/or background beliefs about the world, a failed prediction or falsified empirical consequence typically leaves open to us the possibility of blaming and abandoning one of these background beliefs and/or ‘auxiliary’ hypotheses rather than the hypothesis we set out to test in the first place. But contrastive underdetermination (Section 3 below) involves the quite different possibility that for any body of evidence confirming a theory, there might well be other theories that are also well confirmed by that very same body of evidence. Moreover, claims of underdetermination of either of these two fundamental varieties can vary in strength and character in any number of ways: one might, for example, suggest that the choice between two theories or two ways of revising our beliefs is transiently underdetermined simply by the evidence we happen to have at present , or instead permanently underdetermined by all possible evidence. Indeed, the variety of forms of underdetermination that confront scientific inquiry, and the causes and consequences claimed for these different varieties, are sufficiently heterogeneous that attempts to address “the” problem of underdetermination for scientific theories have often engendered considerable confusion and argumentation at cross-purposes. [ 2 ]

Moreover, such differences in the character and strength of various claims of underdetermination turn out to be crucial for resolving the significance of the issue. For example, in some recently influential discussions of science it has become commonplace for scholars in a wide variety of academic disciplines to make casual appeal to claims of underdetermination (especially of the holist variety) to support the idea that something besides evidence must step in to do the further work of determining beliefs and/or changes of belief in scientific contexts. Perhaps most prominent among these are adherents of the sociology of scientific knowledge (SSK) movement and some feminist science critics who have argued that it is typically the sociopolitical interests and/or pursuit of power and influence by scientists themselves which play a crucial and even decisive role in determining which beliefs are actually abandoned or retained in response to conflicting evidence. As we will see in Section 2.2, however, Larry Laudan has argued that such claims depend upon simple equivocation between comparatively weak or trivial forms of underdetermination and the far stronger varieties from which they draw radical conclusions about the limited reach of evidence and rationality in science. In the sections that follow we will seek to clearly characterize and distinguish the various forms of both holist and contrastive underdetermination that have been suggested to arise in scientific contexts (noting some important connections between them along the way), assess the strength and significance of the heterogeneous argumentative considerations offered in support of and against them, and consider just which forms of underdetermination pose genuinely consequential challenges for scientific inquiry.

2. Holist Underdetermination and Challenges to Scientific Rationality

Duhem’s original case for holist underdetermination is, perhaps unsurprisingly, intimately bound up with his arguments for confirmational holism: the claim that theories or hypotheses can only be subjected to empirical testing in groups or collections, never in isolation. The idea here is that a single scientific hypothesis does not by itself carry any implications about what we should expect to observe in nature; rather, we can derive empirical consequences from an hypothesis only when it is conjoined with many other beliefs and hypotheses, including background assumptions about the world, beliefs about how measuring instruments operate, further hypotheses about the interactions between objects in the original hypothesis’ field of study and the surrounding environment, etc. For this reason, Duhem argues, when an empirical prediction is falsified, we do not know whether the fault lies with the hypothesis we originally sought to test or with one of the many other beliefs and hypotheses that were also needed and used to generate the failed prediction:

A physicist decides to demonstrate the inaccuracy of a proposition; in order to deduce from this proposition the prediction of a phenomenon and institute the experiment which is to show whether this phenomenon is or is not produced, in order to interpret the results of this experiment and establish that the predicted phenomenon is not produced, he does not confine himself to making use of the proposition in question; he makes use also of a whole group of theories accepted by him as beyond dispute. The prediction of the phenomenon, whose nonproduction is to cut off debate, does not derive from the proposition challenged if taken by itself, but from the proposition at issue joined to that whole group of theories; if the predicted phenomenon is not produced, the only thing the experiment teaches us is that among the propositions used to predict the phenomenon and to establish whether it would be produced, there is at least one error; but where this error lies is just what it does not tell us. ([1914] 1954, 185)

Duhem supports this claim with examples from physical theory, including one designed to illustrate a celebrated further consequence he draws from it. Holist underdetermination ensures, Duhem argues, that there cannot be any such thing as a “crucial experiment” (experimentum crucis): a single experiment whose outcome is predicted differently by two competing theories and which therefore serves to definitively confirm one and refute the other. For example, in a famous scientific episode intended to resolve the ongoing heated battle between partisans of the theory that light consists of a stream of particles moving at extremely high speed (the particle or “emission” theory of light) and defenders of the view that light consists instead of waves propagated through a mechanical medium (the wave theory), the physicist Foucault designed an apparatus to test the two theories’ competing claims about the speed of transmission of light in different media: the particle theory implied that light would travel faster in water than in air, while the wave theory implied that the reverse was true. Although the outcome of the experiment was taken to show that light travels faster in air than in water, [ 3 ] Duhem argues that this is far from a refutation of the hypothesis of emission:

in fact, what the experiment declares stained with error is the whole group of propositions accepted by Newton, and after him by Laplace and Biot, that is, the whole theory from which we deduce the relation between the index of refraction and the velocity of light in various media. But in condemning this system as a whole by declaring it stained with error, the experiment does not tell us where the error lies. Is it in the fundamental hypothesis that light consists in projectiles thrown out with great speed by luminous bodies? Is it in some other assumption concerning the actions experienced by light corpuscles due to the media in which they move? We know nothing about that. It would be rash to believe, as Arago seems to have thought, that Foucault’s experiment condemns once and for all the very hypothesis of emission, i.e., the assimilation of a ray of light to a swarm of projectiles. If physicists had attached some value to this task, they would undoubtedly have succeeded in founding on this assumption a system of optics that would agree with Foucault’s experiment. ([1914] 1954, p. 187)

From this and similar examples, Duhem drew the quite general conclusion that our response to the experimental or observational falsification of a theory is always underdetermined in this way. When the world does not live up to our theory-grounded expectations, we must give up something , but because no hypothesis is ever tested in isolation, no experiment ever tells us precisely which belief it is that we must revise or give up as mistaken:

In sum, the physicist can never subject an isolated hypothesis to experimental test, but only a whole group of hypotheses; when the experiment is in disagreement with his predictions, what he learns is that at least one of the hypotheses constituting this group is unacceptable and ought to be modified; but the experiment does not designate which one should be changed. ([1914] 1954, 187)

The predicament Duhem here identifies is no mere rainy day puzzle for philosophers of science, but a methodological challenge that consistently arises in the course of scientific practice itself. It is simply not true that for practical purposes and in concrete contexts there is always just a single revision of our beliefs in response to disconfirming evidence that is obviously correct, most promising, or even most sensible to pursue. To cite a classic example, when Newton’s celestial mechanics failed to correctly predict the orbit of Uranus, scientists at the time did not simply abandon the theory but protected it from refutation by instead challenging the background assumption that the solar system contained only seven planets. This strategy bore fruit, notwithstanding the falsity of Newton’s theory: by calculating the location of a hypothetical eighth planet influencing the orbit of Uranus, the astronomers Adams and Leverrier were eventually led to discover Neptune in 1846. But the very same strategy failed when used to try to explain the advance of the perihelion in Mercury’s orbit by postulating the existence of “Vulcan”, an additional planet located between Mercury and the sun, and this phenomenon would resist satisfactory explanation until the arrival of Einstein’s theory of general relativity. So it seems that Duhem was right to suggest not only that hypotheses must be tested as a group or a collection, but also that it is by no means a foregone conclusion which member of such a collection should be abandoned or revised in response to a failed empirical test or false implication. Indeed, this very example illustrates why Duhem’s own rather hopeful appeal to the ‘good sense’ of scientists themselves in deciding when a given hypothesis ought to be abandoned promises very little if any relief from the general predicament of holist underdetermination.

As noted above, Duhem thought that the sort of underdetermination he had described presented a challenge only for theoretical physics, but subsequent thinking in the philosophy of science has tended to the opinion that the predicament Duhem described applies to theoretical testing in all fields of scientific inquiry. We cannot, for example, test an hypothesis about the phenotypic effects of a particular gene without presupposing a host of further beliefs about what genes are, how they work, how we can identify them, what other genes are doing, and so on. In the middle of the 20 th Century, W. V. O. Quine would incorporate confirmational holism and its associated concerns about underdetermination into an extraordinarily influential account of knowledge in general. As part of his famous (1951) critique of the widely accepted distinction between truths that are analytic (true by definition, or as a matter of logic or language alone) and those that are synthetic (true in virtue of some contingent fact about the way the world is), Quine argued that all of the beliefs we hold at any given time are linked in an interconnected web, which encounters our sensory experience only at its periphery:

The totality of our so-called knowledge or beliefs, from the most casual matters of geography and history to the profoundest laws of atomic physics or even of pure mathematics and logic, is a man-made fabric which impinges on experience only along the edges. Or, to change the figure, total science is like a field of force whose boundary conditions are experience. A conflict with experience at the periphery occasions readjustments in the interior of the field. But the total field is so underdetermined by its boundary conditions, experience, that there is much latitude of choice as to what statements to reevaluate in the light of any single contrary experience. No particular experiences are linked with any particular statements in the interior of the field, except indirectly through considerations of equilibrium affecting the field as a whole. (1951, 42–3)

One consequence of this general picture of human knowledge is that all of our beliefs are tested against experience only as a corporate body—or as Quine sometimes puts it, “The unit of empirical significance is the whole of science” (1951, p. 42). [ 4 ] A mismatch between what the web as a whole leads us to expect and the sensory experiences we actually receive will occasion some revision in our beliefs, but which revision we should make to bring the web as a whole back into conformity with our experiences is radically underdetermined by those experiences themselves. To use Quine’s example, if we find our belief that there are brick houses on Elm Street to be in conflict with our immediate sense experience, we might revise our beliefs about the houses on Elm Street, but we might equally well modify instead our beliefs about the appearance of brick, our present location, or innumerable other beliefs constituting the interconnected web. In a pinch, we might even decide that our present sensory experiences are simply hallucinations! Quine’s point was not that any of these are particularly likely or reasonable responses to recalcitrant experiences (indeed, an important part of his account is the explanation of why they are not), but instead that they would serve equally well to bring the web of belief as a whole in line with our experience. And if the belief that there are brick houses on Elm Street were sufficiently important to us, Quine insisted, it would be possible for us to preserve it “come what may” (in the way of empirical evidence), by making sufficiently radical adjustments elsewhere in the web of belief. It is in principle open to us, Quine argued, to revise even beliefs about logic, mathematics, or the meanings of our terms in response to recalcitrant experience; it might seem a tempting solution to certain persistent difficulties in quantum mechanics, for example, to reject classical logic’s law of the excluded middle (allowing physical particles to both have and not have some determinate classical physical property like position or momentum at a given time). The only test of a belief, Quine argued, is whether it fits into a web of connected beliefs that accords well with our experience on the whole . And because this leaves any and all beliefs in that web at least potentially subject to revision on the basis of our ongoing sense experience or empirical evidence, he insisted, there simply are no beliefs that are analytic in the originally supposed sense of immune to revision in light of experience, or true no matter what the world is like.

Quine recognized, of course, that many of the logically possible ways of revising our beliefs in response to recalcitrant experiences that remain open to us nonetheless strike us as ad hoc, perfectly ridiculous, or worse. He argues (1955) that our actual revisions of the web of belief seek to maximize the theoretical “virtues” of simplicity, familiarity, scope, and fecundity, along with conformity to experience, and elsewhere suggests that we typically seek to resolve conflicts between the web of our beliefs and our sensory experiences in accordance with a principle of “conservatism”, that is, by making the smallest possible number of changes to the least central beliefs we can that will suffice to reconcile the web with experience. That is, Quine recognized that when we encounter recalcitrant experience we are not usually at a loss to decide which of our beliefs to revise in response to it, but he claimed that this is simply because we are strongly disposed as a matter of fundamental psychology to prefer whatever revision requires the most minimal mutilation of the existing web of beliefs and/or maximizes virtues that he explicitly recognizes as pragmatic in character. Indeed, it would seem that on Quine’s view the very notion of a belief being more central or peripheral or in lesser or greater “proximity” to sense experience should be cashed out simply as a measure of our willingness to revise it in response to recalcitrant experience. That is, it would seem that what it means for one belief to be located “closer” to the sensory periphery of the web than another is simply that we are more likely to revise the first than the second if doing so would enable us to bring the web as a whole into conformity with otherwise recalcitrant sense experience. Thus, Quine saw the traditional distinction between analytic and synthetic beliefs as simply registering the endpoints of a psychological continuum ordering our beliefs according to the ease and likelihood with which we are prepared to revise them in order to reconcile the web as a whole with our sense experience as a whole.

It is perhaps unsurprising that such holist underdetermination has been taken to pose a threat to the fundamental rationality of the scientific enterprise. The claim that the empirical evidence alone underdetermines our response to failed predictions or recalcitrant experience might even seem to invite the suggestion that what systematically steps into the breach to do the further work of singling out just one or a few candidate responses to disconfirming evidence is something irrational or at least arational in character. Imre Lakatos and Paul Feyerabend each suggested that because of underdetermination, the difference between empirically successful and unsuccessful theories or research programs is largely a function of the differences in talent, creativity, resolve, and resources of those who advocate them. And at least since the influential work of Thomas Kuhn, one important line of thinking about science has held that it is ultimately the social and political interests (in a suitably broad sense) of scientists themselves which serve to determine their responses to disconfirming evidence and therefore the further empirical, methodological, and other commitments of any given scientist or scientific community. Mary Hesse suggests that Quinean underdetermination showed why certain “non-logical” and “extra-empirical” considerations must play a role in theory choice, and claims that “it is only a short step from this philosophy of science to the suggestion that adoption of such criteria, that can be seen to be different for different groups and at different periods, should be explicable by social rather than logical factors” (1980, 33). Perhaps the most prominent modern day defenders of this line of thinking are those scholars in the sociology of scientific knowledge (SSK) movement and in feminist science studies who argue that it is typically the career interests, political affiliations, intellectual allegiances, gender biases, and/or pursuit of power and influence by scientists themselves which play a crucial or even decisive role in determining precisely which beliefs are abandoned or retained when faced with conflicting evidence (classic works in SSK include Bloor 1991, Collins 1992, and Shapin and Schaffer 1985; in feminist science studies, see Longino, 1990, 2002, and for a recent review, Nelson 2022). The shared argumentative schema here is one on which holist underdetermination ensures that the evidence alone cannot do the work of picking out a unique response to failed predictions or recalcitrant experience, thus something else must step in to do the job, and sociologists of scientific knowledge, feminist critics of science, and other interest-driven theorists of science each have their favored suggestions close to hand. (For useful further discussion, see Okasha 2000. Note that historians of science have also appealed to underdetermination in presenting “counterfactual histories” exploring the ways in which important historical developments in science might have turned out quite differently than they actually did; see, for example, Radick 2023.)

In response to this line of argument, Larry Laudan (1990) argues that the significance of such underdetermination has been greatly exaggerated. Underdetermination actually comes in a wide variety of strengths, he insists, depending on precisely what is being asserted about the character, the availability, and (most importantly) the rational defensibility of the various competing hypotheses or ways of revising our beliefs that the evidence supposedly leaves us free to accept. Laudan usefully distinguishes a number of different dimensions along which claims of underdetermination vary in strength, and he goes on to insist that those who attribute dramatic significance to the thesis that our scientific theories are underdetermined by the evidence defend only the weaker versions of that thesis, yet draw dire consequences and shocking morals regarding the character and status of the scientific enterprise from much stronger versions. He suggests, for instance, that Quine’s famous claim that any hypothesis can be preserved “come what may” in the way of evidence can be defended simply as a description of what it is psychologically possible for human beings to do, but Laudan insists that in this form the thesis is simply bereft of interesting or important consequences for epistemology— the study of knowledge . Along this dimension of variation, the strong version of the thesis asserts that it is always normatively or rationally defensible to retain any hypothesis in the light of any evidence whatsoever, but this latter, stronger version of the claim, Laudan suggests, is one for which no convincing evidence or argument has ever been offered. More generally, Lauden argues, arguments for underdetermination turn on implausibly treating all logically possible responses to the evidence as equally justified or rationally defensible. For example, Laudan suggests that we might reasonably hold the resources of deductive logic to be insufficient to single out just one acceptable response to disconfirming evidence, but not that deductive logic plus the sorts of ampliative principles of good reasoning typically deployed in scientific contexts are insufficient to do so. Similarly, defenders of underdetermination might assert the nonuniqueness claim that for any given theory or web of beliefs, either there is at least one alternative that can also be reconciled with the available evidence, or the much stronger claim that all of the contraries of any given theory can be reconciled with the available evidence equally well. And the claim of such “reconciliation” itself disguises a wide range of further alternative possibilities: that our theories can be made logically compatible with any amount of disconfirming evidence (perhaps by the simple expedient of removing any claim(s) with which the evidence is in conflict), that any theory may be reformulated or revised so as to entail any piece of previously disconfirming evidence, or so as to explain previously disconfirming evidence, or that any theory can be made to be as well supported empirically by any collection of evidence as any other theory. And in all of these respects, Laudan claims, partisans have defended only the weaker forms of underdetermination while founding their further claims about and conceptions of the scientific enterprise on versions much stronger than those they have managed or even attempted to defend.

Laudan is certainly right to distinguish these various versions of holist underdetermination, and he is equally right to suggest that many of the thinkers he confronts have derived grand morals concerning the scientific enterprise from much stronger versions of underdetermination than they are able to defend, but the underlying situation is somewhat more complex than he suggests. Laudan’s overarching claim is that champions of holist underdetermination show only that a wide variety of responses to disconfirming evidence are logically possible (or even just psychologically possible), rather than that these are all rationally defensible or equally well-supported by the evidence. But his straightforward appeal to further epistemic resources like ampliative principles of belief revision that are supposed to help narrow the merely logical possibilities down to those which are reasonable or rationally defensible is itself problematic, at least as part of any attempt to respond to Quine. This is because on Quine’s holist picture of knowledge such further ampliative principles governing legitimate belief revision are themselves simply part of the web of our beliefs, and are therefore open to revision in response to recalcitrant experience as well. Indeed, this is true even for the principles of deductive logic and the (consequent) demand for particular forms of logical consistency between parts of the web itself! So while it is true that the ampliative principles we currently embrace do not leave all logically or even psychologically possible responses to the evidence open to us (or leave us free to preserve any hypothesis “come what may”), our continued adherence to these very principles , rather than being willing to revise the web of belief so as to abandon them, is part of the phenomenon to which Quine is using underdetermination to draw our attention, and so cannot be taken for granted without begging the question. Put another way, Quine does not simply ignore the further principles that function to ensure that we revise the web of belief in one way rather than others, but it follows from his account that such principles are themselves part of the web and therefore candidates for revision in our efforts to bring the web of beliefs into conformity (by the resulting web’s own lights) with sensory experience. This recognition makes clear why it will be extremely difficult to say how the shift to an alternative web of belief (with alternative ampliative or even deductive principles of belief revision) should or even can be evaluated for its rational defensibility. Each proposed revision is likely to be maximally rational by the lights of the principles it itself sanctions. [ 5 ] Of course we can rightly say that many candidate revisions would violate our presently accepted ampliative principles of rational belief revision, but the preference we have for those rather than the alternatives is itself simply generated by their position in the web of belief we have inherited, and the role that they themselves play in guiding the revisions we are inclined to make to that web in light of ongoing experience.

Thus, if we accept Quine’s general picture of knowledge, it becomes quite difficult to disentangle normative from descriptive issues, or questions about the psychology of human belief revision from questions about the justifiability or rational defensibility of such revisions. It is in part for this reason that Quine famously suggests (1969, 82; see also p 75–76) that epistemology itself “falls into place as a chapter of psychology and hence of natural science.” His point is not that epistemology should simply be abandoned in favor of psychology, but instead that there is ultimately no way to draw a meaningful distinction between the two. (James Woodward, in comments on an earlier draft of this entry, pointed out that this makes it all the harder to assess the significance of Quinean underdetermination in light of Laudan’s complaint or even know the rules for doing so, but in an important way this difficulty was Quine’s point all along!) Quine’s claim is that “[e]ach man is given a scientific heritage plus a continuing barrage of sensory stimulation; and the considerations which guide him in warping his scientific heritage to fit his continuing sensory promptings are, where rational, pragmatic” (1951, 46), but the role of these pragmatic considerations or principles in selecting just one of the many possible revisions of the web of belief in response to recalcitrant experience is not to be contrasted with those same principles having rational or epistemic justification. Far from conflicting with or even being orthogonal to the search for truth and our efforts to render our beliefs maximally responsive to the evidence, Quine insists, revising our beliefs in accordance with such pragmatic principles “at bottom, is what evidence is” (1955, 251). Whether or not this strongly naturalistic conception of epistemology can ultimately be defended, it is misleading for Laudan to suggest that the thesis of underdetermination becomes trivial or obviously insupportable the moment we inquire into the rational defensibility rather than the mere logical or psychological possibility of alternative revisions to the holist’s web of belief.

In fact, there is an important connection between this lacuna in Laudan’s discussion and the further uses made of the thesis of underdetermination by sociologists of scientific knowledge, feminist epistemologists, and other vocal champions of holist underdetermination. When faced with the invocation of further ampliative standards or principles that supposedly rule out some responses to disconfirmation as irrational or unreasonable, these thinkers typically respond by insisting that the embrace of such further standards or principles (or perhaps their application to particular cases) is itself underdetermined, historically contingent, and/or subject to ongoing social negotiation. For this reason, they suggest, such appeals (and their success or failure in convincing the members of a given community) should be explained by reference to the same broadly social and political interests that they claim are at the root of theory choice and belief change in science more generally (see, e.g., Shapin and Schaffer, 1985). On both accounts, then, our response to recalcitrant evidence or a failed prediction is constrained in important ways by features of the existing web of beliefs. But for Quine, the continuing force of these constraints is ultimately imposed by the fundamental principles of human psychology (such as our preference for minimal mutilation of the web, or the pragmatic virtues of simplicity, fecundity, etc.), while for constructivist theorists of science such as Shapin and Schaffer, the continuing force of any such constraints is limited only by the ongoing negotiated agreement of the communities of scientists who respect them.

As this last contrast makes clear, recognizing the limitations of Laudan’s critique of Quine and the fact that we cannot dismiss holist underdetermination with any straightforward appeal to ampliative principles of good reasoning by itself does nothing to establish the further positive claims about belief revision advanced by interest-driven theorists of science. Conceding that theory choice or belief revision in science is underdetermined by the evidence in just the ways that Duhem and/or Quine suggested leaves entirely open whether it is instead the (suitably broad) social or political interests of scientists themselves that do the further work of singling out the particular beliefs or responses to falsifying evidence that any particular scientist or scientific community will actually adopt or find compelling. Even many of those philosophers of science who are most strongly convinced of the general significance of various forms of underdetermination remain deeply skeptical of this latter thesis and thoroughly unconvinced by the empirical evidence that has been offered in support of it (usually in the form of case studies of particular historical episodes in science).

Appeals to underdetermination have also loomed large in recent philosophical debates concerning the place of values in science, with a number of authors arguing that the underdetermination of theory by data is among the central reasons that values (or “non-epistemic” values) do and perhaps must play a central role in scientific inquiry. Feminist philosophers of science have sometimes suggested that it is such underdetermination which creates room not only for unwarranted androcentric values or assumptions to play central roles in the embrace of particular theoretical possibilities, but also for the critical and alternative approaches favored by feminists themselves (e.g. Nelson 2022). But appeals to underdetermination also feature prominently in more general arguments against the possibility or desirability of value-free science. Perhaps most influentially, Helen Longino’s “contextual empiricism” suggests that a wide variety of non-epistemic values play important roles in determining our scientific beliefs in part because underdetermination prevents data or evidence alone from doing so. For this and other reasons she concludes that objectivity in science is therefore best served by a diverse set of participants who bring a variety of different values or value-laden assumptions to the enterprise (Longino 1990, 2002).

3. Contrastive Underdetermination, Empirical Equivalents, and Unconceived Alternatives

Although it is also a form of underdetermination, what we described in Section 1 above as contrastive underdetermination raises fundamentally different issues from the holist variety considered in Section 2 (Bonk 2008 raises many of these issues). John Stuart Mill articulated the challenge of contrastive underdetermination with impressive clarity in A System of Logic , where he writes:

Most thinkers of any degree of sobriety allow, that an hypothesis...is not to be received as probably true because it accounts for all the known phenomena, since this is a condition sometimes fulfilled tolerably well by two conflicting hypotheses...while there are probably a thousand more which are equally possible, but which, for want of anything analogous in our experience, our minds are unfitted to conceive. ([1867] 1900, 328)

This same concern is also evident in Duhem’s original writings concerning so-called crucial experiments, where he seeks to show that even when we explicitly suspend any concerns about holist underdetermination, the contrastive variety remains an obstacle to our discovery of truth in theoretical science:

But let us admit for a moment that in each of these systems [concerning the nature of light] everything is compelled to be necessary by strict logic, except a single hypothesis; consequently, let us admit that the facts, in condemning one of the two systems, condemn once and for all the single doubtful assumption it contains. Does it follow that we can find in the ‘crucial experiment’ an irrefutable procedure for transforming one of the two hypotheses before us into a demonstrated truth? Between two contradictory theorems of geometry there is no room for a third judgment; if one is false, the other is necessarily true. Do two hypotheses in physics ever constitute such a strict dilemma? Shall we ever dare to assert that no other hypothesis is imaginable? Light may be a swarm of projectiles, or it may be a vibratory motion whose waves are propagated in a medium; is it forbidden to be anything else at all? ([1914] 1954, 189)

Contrastive underdetermination is so-called because it questions the ability of the evidence to confirm any given hypothesis against alternatives , and the central focus of discussion in this connection (equally often regarded as “the” problem of underdetermination) concerns the character of the supposed alternatives. Of course the two problems are not entirely disconnected, because it is open to us to consider alternative possible modifications of the web of beliefs as alternative theories between which the empirical evidence alone is powerless to decide. But we have already seen that one need not think of the alternative responses to recalcitrant experience as competing theoretical alternatives to appreciate the character of the holist’s challenge, and we will see that one need not embrace any version of holism about confirmation to appreciate the quite distinct problem that the available evidence might support more than one theoretical alternative. It is perhaps most useful here to think of holist underdetermination as starting from a particular theory or body of beliefs and claiming that our revision of those beliefs in response to new evidence may be underdetermined, while contrastive underdetermination instead starts from a given body of evidence and claims that more than one theory may be well-supported by that evidence. Part of what has contributed to the conflation of these two problems is the holist presuppositions of those who originally made them famous. After all, on Quine’s view, we simply revise the web of belief in response to recalcitrant experience, and so the suggestion that there are multiple possible revisions of the web available in response to any particular evidential finding just is the claim that there are in fact many different “theories” (i.e. candidate webs of belief) that are equally well-supported by any given body of data. [ 6 ] But if we give up such extreme holist views of evidence, meaning, and/or confirmation, the two problems take on very different identities, with very different considerations in favor of taking them seriously, very different consequences, and very different candidate solutions. Notice, for instance, that even if we somehow knew that no other hypothesis on a given subject was well-confirmed by a given body of data, that would not tell us where to place the blame or which of our beliefs to give up if the remaining hypothesis in conjunction with others subsequently resulted in a failed empirical prediction. And as Duhem suggests in the passage cited above, even if we supposed that we somehow knew exactly which of our hypotheses to blame in response to a failed empirical prediction, this would not help us to decide whether or not there are other hypotheses available that are also well-confirmed by the data we actually have.

One way to see why not is to consider an analogy that champions of contrastive underdetermination have sometimes used to support their case. If we consider any finite group of data points, an elementary proof reveals that there are an infinite number of distinct mathematical functions describing different curves that will pass through all of them. As we add further data to our initial set we will eliminate functions describing curves which no longer capture all of the data points in the new, larger set, but no matter how much data we accumulate, there will always be an infinite number of functions remaining that define curves including all the data points in the new set and which would therefore seem to be equally well supported by the empirical evidence. No finite amount of data will ever be able to narrow the possibilities down to just a single function or indeed, any finite number of candidate functions, from which the distribution of data points we have might have been generated. Each new data point we gather eliminates an infinite number of curves that previously fit all the data (so the problem here is not the holist’s challenge that we do not know which beliefs to give up in response to failed predictions or disconfirming evidence), but also leaves an infinite number still in contention.

Of course, generating and testing fundamental scientific hypotheses is rarely if ever a matter of finding curves that fit collections of data points, so nothing follows directly from this mathematical analogy for the significance of contrastive underdetermination in most scientific contexts. But Bas van Fraassen has offered an extremely influential line of argument intended to show that such contrastive underdetermination is a serious concern for scientific theorizing more generally. In The Scientific Image (1980), van Fraassen uses a now-classic example to illustrate the possibility that even our best scientific theories might have empirical equivalents : that is, alternative theories making the very same empirical predictions, and which therefore cannot be better or worse supported by any possible body of evidence. Consider Newton’s cosmology, with its laws of motion and gravitational attraction. As Newton himself realized, exactly the same predictions are made by the theory whether we assume that the entire universe is at rest or assume instead that it is moving with some constant velocity in any given direction: from our position within it, we have no way to detect constant, absolute motion by the universe as a whole. Thus, van Fraassen argues, we are here faced with empirically equivalent scientific theories: Newtonian mechanics and gravitation conjoined either with the fundamental assumption that the universe is at absolute rest (as Newton himself believed), or with any one of an infinite variety of alternative assumptions about the constant velocity with which the universe is moving in some particular direction. All of these theories make all and only the same empirical predictions, so no evidence will ever permit us to decide between them on empirical grounds. [ 7 ]

Van Fraassen is widely (though mistakenly) regarded as holding that the prospect of contrastive underdetermination grounded in such empirical equivalents demands that we restrict our epistemic ambitions for the scientific enterprise itself. His constructive empiricism holds that the aim of science is not to find true theories, but only theories that are empirically adequate: that is, theories whose claims about observable phenomena are all true. Since the empirical adequacy of a theory is not threatened by the existence of another that is empirically equivalent to it, fulfilling this aim has nothing to fear from the possibility of such empirical equivalents. In reply, many critics have suggested that van Fraassen gives no reasons for restricting belief to empirical adequacy that could not also be used to argue for suspending our belief in the future empirical adequacy of our best present theories. Of course there could be empirical equivalents to our best theories, but there could also be theories equally well-supported by all the evidence up to the present which diverge in their predictions about observables in future cases not yet tested. This challenge seems to miss the point of van Fraassen’s epistemic voluntarism: his claim is that we should believe no more but also no less than we need to take full advantage of our scientific theories, and a commitment to the empirical adequacy of our theories, he suggests, is the least we can get away with in this regard. Of course it is true that we are running some epistemic risk in believing in even the full empirical adequacy of our present theories, but this is the minimum we need to take full advantage of the fruits of our scientific labors, and the risk is considerably less than what we assume in believing in their truth: as van Fraassen famously suggests, “it is not an epistemic principle that one might as well hang for a sheep as a lamb” (1980, 72).

In an influential discussion, Larry Laudan and Jarrett Leplin (1991) argue that philosophers of science have invested even the bare possibility that our theories might have empirical equivalents with far too much epistemic significance. Notwithstanding the popularity of the presumption that there are empirically equivalent rivals to every theory, they argue, the conjunction of several familiar and relatively uncontroversial epistemological theses is sufficient to defeat it. Because the boundaries of what is observable change as we develop new experimental methods and instruments, because auxiliary assumptions are always needed to derive empirical consequences from a theory (cf. confirmational holism, above), and because these auxiliary assumptions are themselves subject to change over time, Laudan and Leplin conclude that there is no guarantee that any two theories judged to be empirically equivalent at a given time will remain so as the state of our knowledge advances. Accordingly, any judgment of empirical equivalence is both defeasible and relativized to a particular state of science. So even if two theories are empirically equivalent at a given time this is no guarantee that they will remain so, and thus there is no foundation for a general pessimism about our ability to distinguish theories that are empirically equivalent to each other on empirical grounds. Although they concede that we could have good reason to think that particular theories have empirically equivalent rivals, this must be established case-by-case rather than by any general argument or presumption.

One fairly standard reply to this line of argument is to suggest that what Laudan and Leplin really show is that the notion of empirical equivalence must be applied to larger collections of beliefs than those traditionally identified as scientific theories—at least large enough to encompass the auxiliary assumptions needed to derive empirical predictions from them. At the extreme, perhaps this means that the notion of empirical equivalents (or at least timeless empirical equivalents) cannot be applied to anything less than “systems of the world” (i.e. total Quinean webs of belief), but even that is not fatal: what the champion of contrastive underdetermination asserts is that there are empirically equivalent systems of the world that incorporate different theories of the nature of light, or spacetime, or whatever (for useful discussion, see Okasha 2002). On the other hand, it might seem that quick examples like van Fraassen’s variants of Newtonian cosmology do not serve to make this thesis as plausible as the more limited claim of empirical equivalence for individual theories. It seems equally natural, however, to respond to Laudan and Leplin simply by conceding the variability in empirical equivalence but insisting that this is not enough to undermine the problem. Empirical equivalents create a serious obstacle to belief in a theory so long as there is some empirical equivalent to that theory at any given time, but it need not be the same one at each time. On this line of thinking, cases like van Fraassen’s Newtonian example illustrate how easy it is for theories to admit of empirical equivalents at any given time, and thus constitute a reason for thinking that there probably are or will be empirical equivalents to any given theory at any particular time, assuring that whenever the question of belief in a given theory arises, the challenge posed to it by contrastive underdetermination arises as well.

Laudan and Leplin also suggest, however, that even if the universal existence of empirical equivalents were conceded, this would do much less to establish the significance of underdetermination than its champions have supposed, because “theories with exactly the same empirical consequences may admit of differing degrees of evidential support” (1991, 465). A theory may be better supported than an empirical equivalent, for instance, because the former but not the latter is derivable from a more general theory whose consequences include a third, well supported, hypothesis. More generally, the belief-worthiness of an hypothesis depends crucially on how it is connected or related to other things we believe and the evidential support we have for those other beliefs. [ 8 ] Laudan and Leplin suggest that we have invited the specter of rampant underdetermination only by failing to keep this familiar home truth in mind and instead implausibly identifying the evidence bearing on a theory exclusively with the theory’s own entailments or empirical consequences (but cf. Tulodziecki 2012). This impoverished view of evidential support, they argue, is in turn the legacy of a failed foundationalist and positivistic approach to the philosophy of science which mistakenly assimilates epistemic questions about how to decide whether or not to believe a theory to semantic questions about how to establish a theory’s meaning or truth-conditions.

John Earman (1993) has argued that this dismissive diagnosis does not do justice to the threat posed by underdetermination. He argues that worries about underdetermination are an aspect of the more general question of the reliability of our inductive methods for determining beliefs, and notes that we cannot decide how serious a problem underdetermination poses without specifying (as Laudan and Leplin do not) the inductive methods we are considering. Earman regards some version of Bayesianism as our most promising form of inductive methodology, and he proceeds to show that challenges to the long-run reliability of our Bayesian methods can be motivated by considerations of the empirical indistinguishability (in several different and precisely specified senses) of hypotheses stated in any language richer than that of the evidence itself that do not amount simply to general skepticism about those inductive methods. In other words, he shows that there are more reasons to worry about underdetermination concerning inferences to hypotheses about unobservables than to, say, inferences about unobserved observables. He also goes on to argue that at least two genuine cosmological theories have serious, nonskeptical, and nonparasitic empirical equivalents: the first essentially replaces the gravitational field in Newtonian mechanics with curvature in spacetime itself, [ 9 ] while the second recognizes that Einstein’s General Theory of Relativity permits cosmological models exhibiting different global topological features which cannot be distinguished by any evidence inside the light cones of even idealized observers who live forever. [ 10 ] And he suggests that “the production of a few concrete examples is enough to generate the worry that only a lack of imagination on our part prevents us from seeing comparable examples of underdetermination all over the map” (1993, 31) even as he concedes that his case leaves open just how far the threat of underdetermination extends (1993, 36).

Most philosophers of science, however, have not embraced the idea that it is only lack of imagination which prevents us from finding empirical equivalents to our scientific theories generally. They note that the convincing examples of empirical equivalents we do have are all drawn from a single domain of highly mathematized scientific theorizing in which the background constraints on serious theoretical alternatives are far from clear, and suggest that it is therefore reasonable to ask whether even a small handful of such examples should make us believe that there are probably empirical equivalents to most of our scientific theories most of the time. They concede that it is always possible that there are empirical equivalents to even our best scientific theories concerning any domain of nature, but insist that we should not be willing to suspend belief in any particular theory until some convincing alternative to it can actually be produced: as Philip Kitcher puts it, “give us a rival explanation, and we’ll consider whether it is sufficiently serious to threaten our confidence” (1993, 154; see also Leplin 1997, Achinstein 2002). That is, these thinkers insist that until we are able to actually construct an empirically equivalent alternative to a given theory, the bare possibility that such equivalents exist is insufficient to justify suspending belief in the best theories we do have. For this same reason most philosophers of science are unwilling to follow van Fraassen into what they regard as constructive empiricism’s unwarranted epistemic modesty. Even if van Fraassen is right about the most minimal beliefs we must hold in order to take full advantage of our scientific theories, most thinkers do not see why we should believe the least we can get away with rather than believing the most we are entitled to by the evidence we have.

Champions of contrastive underdetermination have most frequently responded by trying to establish that all theories have empirical equivalents, typically by proposing something like an algorithmic procedure for generating such equivalents from any theory whatsoever. Stanford (2001, 2006) suggests that these efforts to prove that all our theories must have empirical equivalents fall roughly but reliably into global and local varieties, and that neither makes a convincing case for a distinctive scientific problem of contrastive underdetermination. Global algorithms are well-represented by Andre Kukla’s (1996) suggestion that from any theory T we can immediately generate such empirical equivalents as T ′ (the claim that T ’s observable consequences are true, but T itself is false), T ″ (the claim that the world behaves according to T when observed, but some specific incompatible alternative otherwise), and the hypothesis that our experience is being manipulated by powerful beings in such a way as to make it appear that T is true. But such possibilities, Stanford argues, amount to nothing more than the sort of Evil Deceiver to which Descartes appealed in order to doubt any of his beliefs that could possibly be doubted (see Section 1, above). Such radically skeptical scenarios pose an equally powerful (or powerless) challenge to any knowledge claim whatsoever, no matter how it is arrived at or justified, and thus pose no special problem or challenge for beliefs offered to us by theoretical science. If global algorithms like Kukla’s are the only reasons we can give for taking underdetermination seriously in a scientific context, then there is no distinctive problem of the underdetermination of scientific theories by data, only a salient reminder of the irrefutability of classically Cartesian or radical skepticism. [ 11 ]

In contrast to such global strategies for generating empirical equivalents, local algorithmic strategies instead begin with some particular scientific theory and proceed to generate alternative versions that will be equally well supported by all possible evidence. This is what van Fraassen does with the example of Newtonian cosmology, showing that an infinite variety of supposed empirical equivalents can be produced by ascribing different constant absolute velocities to the universe as a whole. But Stanford suggests that empirical equivalents generated in this way are also insufficient to show that there is a distinctive and genuinely troubling form of underdetermination afflicting scientific theories, because they rely on simply saddling particular scientific theories with further claims for which those theories themselves (together with whatever background beliefs we actually hold) imply that we cannot have any evidence. Such empirical equivalents invite the natural response that they simply tack on to our theories further commitments that are or should be no part of those theories themselves. Such claims, it seems, should simply be excised from our theories, leaving over just the claims that sensible defenders would have held were all we were entitled to believe by the evidence in any case. In van Fraassen’s Newtonian example, for instance, this could be done simply by undertaking no commitment concerning the absolute velocity and direction (or lack thereof) of the universe as a whole. Note also that if we believe a given scientific theory when one of the empirical equivalents we could generate from it by the local algorithmic strategy is correct instead, most of what we originally believed will nonetheless turn out to be straightforwardly true.

Stanford (2001, 2006) concludes that no convincing general case has been made for the presumption that there are empirically equivalent rivals to all or most scientific theories, or to any theories besides those for which such equivalents can actually be constructed. But he goes on to insist that empirical equivalents are no essential part of the case for a significant problem of contrastive underdetermination. Our efforts to confirm scientific theories, he suggests, are no less threatened by what Larry Sklar (1975, 1981) has called “transient” underdetermination, that is, theories which are not empirically equivalent but are equally (or at least reasonably) well confirmed by all the evidence we happen to have in hand at the moment, so long as this transient predicament is also “recurrent”, that is, so long as we think that there is (probably) at least one such (fundamentally distinct) alternative available—and thus the transient predicament re-arises—whenever we are faced with a decision about whether to believe a given theory at a given time. Stanford argues that a convincing case for contrastive underdetermination of this recurrent, transient variety can indeed be made, and that the evidence for it is available in the historical record of scientific inquiry itself.

Stanford concedes that present theories are not transiently underdetermined by the theoretical alternatives we have actually developed and considered to date: we think that our own scientific theories are considerably better confirmed by the evidence than any rivals we have actually produced. The central question, he argues, is whether we should believe that there are well confirmed alternatives to our best scientific theories that are presently unconceived by us. And the primary reason we should believe that there are, he claims, is the long history of repeated transient underdetermination by previously unconceived alternatives across the course of scientific inquiry. In the progression from Aristotelian to Cartesian to Newtonian to contemporary mechanical theories, for instance, the evidence available at the time each earlier theory dominated the practice of its day also offered compelling support for each of the later alternatives (unconceived at the time) that would ultimately come to displace it. Stanford’s “New Induction” over the history of science claims that this situation is typical; that is, that “we have, throughout the history of scientific inquiry and in virtually every scientific field, repeatedly occupied an epistemic position in which we could conceive of only one or a few theories that were well confirmed by the available evidence, while subsequent inquiry would routinely (if not invariably) reveal further, radically distinct alternatives as well confirmed by the previously available evidence as those we were inclined to accept on the strength of that evidence” (2006, 19). In other words, Stanford claims that in the past we have repeatedly failed to exhaust the space of fundamentally distinct theoretical possibilities that were well confirmed by the existing evidence, and that we have every reason to believe that we are probably also failing to exhaust the space of such alternatives that are well confirmed by the evidence we have at present. Much of the rest of his case is taken up with discussing historical examples illustrating that earlier scientists did not simply ignore or dismiss, but instead genuinely failed to conceive of the serious, fundamentally distinct theoretical possibilities that would ultimately come to displace the theories they defended, only to be displaced in turn by others that were similarly unconceived at the time. He concludes that “the history of scientific inquiry itself offers a straightforward rationale for thinking that there typically are alternatives to our best theories equally well confirmed by the evidence, even when we are unable to conceive of them at the time” (2006, 20; for reservations and criticisms concerning this line of argument, see Magnus 2006, 2010; Godfrey-Smith 2008; Chakravartty 2008; Devitt 2011; Ruhmkorff 2011; Lyons 2013). Stanford concedes, however, that the historical record can offer only fallible evidence of a distinctive, general problem of contrastive scientific underdetermination, rather than the kind of deductive proof that champions of the case from empirical equivalents have typically sought. Thus, claims and arguments about the various forms that underdetermination may take, their causes and consequences, and the further significance they hold for the scientific enterprise as a whole continue to evolve in the light of ongoing controversy, and the underdetermination of scientific theory by evidence remains very much a live and unresolved issue in the philosophy of science.

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confirmation | constructive empiricism | Duhem, Pierre | epistemology: naturalism in | feminist philosophy, interventions: epistemology and philosophy of science | Feyerabend, Paul | induction: problem of | Quine, Willard Van Orman | scientific knowledge: social dimensions of | scientific realism

Acknowledgments

I have benefited from discussing both the organization and content of this article with many people including audiences and participants at the 2009 Pittsburgh Workshop on Underdetermination and the 2009 Southern California Philosophers of Science retreat, as well as the participants in graduate seminars both at UC Irvine and Pittsburgh. Special thanks are owed to John Norton, P. D. Magnus, John Manchak, Bennett Holman, Penelope Maddy, Jeff Barrett, David Malament, John Earman, and James Woodward.

Copyright © 2023 by Kyle Stanford < stanford @ uci . edu >

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Definition of empirical

Did you know.

Empirical Has Roots in Latin and Greek

When empirical first appeared as an adjective in English, it meant simply “in the manner of an empiric.” In the ancient world, empirics were members of a sect of doctors who practiced medicine using treatments observed to be clinically effective, rather than treatments based on theoretical principles. This sounds all fine and good to a modern reader, but empirics were in direct opposition to Galen, the 2nd century Greek physician whose theories and practices (including the theory of bodily humors ) dominated medicine in Europe from the Middle Ages until the mid-17th century. As the underdogs in this rivalry, empirics took some reputational hits, evidenced by the use of empiric to refer to someone who disregards or deviates from the rules of science or accepted practice; to be called an empiric was sometimes like being called a quack or charlatan. Empirical can still be used critically to describe ideas and practices that rely on experience or observation alone and without due regard for system or theory. But, perhaps in a bit of a case of “the Empirics strike back,” empirical more often keeps its narrower sense, and is used positively to describe evidence and information grounded in observation and experience, or capable of being verified or disproved by observation or experiment.

• existential
• experiential
• experimental
• observational

Examples of empirical in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'empirical.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

empiric "of physicians in ancient Greece and Rome holding that treatment should be based on observation rather than theory" (going back to Middle English emperic, borrowed from early Medieval Latin empīricus, borrowed from Greek empeirikós, "based on observation (of medical treatment), experienced") + -al entry 1 — more at empiric

1576, in the meaning defined at sense 1

Phrases Containing empirical

empirical formula

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Dictionary Entries Near empirical

Cite this entry.

“Empirical.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/empirical. Accessed 20 Apr. 2024.

Kids Definition

Kids definition of empirical, medical definition, medical definition of empirical, more from merriam-webster on empirical.

Nglish: Translation of empirical for Spanish Speakers

Britannica English: Translation of empirical for Arabic Speakers

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