journal and thesis difference

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Thesis vs Journal Article: A Comprehensive Comparison

In the world of academia, two prominent forms of scholarly writing are the Thesis and the Journal Article . While both contribute to the advancement of knowledge and showcase research skills, they have distinct characteristics and serve different purposes. Understanding the differences between a thesis and a journal article is crucial for researchers, scholars, and students. In this article, we will provide a detailed and insightful comparison of these two forms of academic writing, examining their purpose, structure, audience, and publication process.

A thesis serves as a comprehensive demonstration of a student’s ability to conduct independent research, analyze data, and contribute original insights to their field of study. Its primary purpose is to fulfill the requirements for the completion of a degree, whether it be a master’s or a doctoral program. A thesis delves deep into a specific research problem, addressing gaps in existing knowledge and making a unique contribution to the field.

On the other hand, a journal article focuses on the dissemination of research findings to the wider academic community. Its purpose is to contribute to the existing body of knowledge, engage in scholarly discourse, and facilitate further research. Journal articles are typically more specific in scope, targeting a particular research question or hypothesis, and highlighting the significance of the findings within the context of the field.

Thesis and journal articles follow different structures to fulfill their respective purposes.

A thesis typically consists of several chapters, including an introduction, literature review, methodology, results, discussion, and conclusion. These chapters are interconnected, forming a cohesive narrative that showcases the student’s research journey. Additionally, a thesis may include supplementary sections such as an abstract, acknowledgments, and appendices. The structure of a thesis allows for an extensive exploration of the research problem, thorough analysis of the findings, and comprehensive discussion of their implications.

In contrast, a journal article adheres to a more concise and standardized structure. It typically includes an abstract, introduction, methodology, results, discussion, and conclusion. Journal articles are focused and aim to present the research in a clear and concise manner within the limited word count set by the target journal. The structure of a journal article emphasizes brevity, with a specific focus on the key findings and their implications.

Audience and Publication

Theses and journal articles differ in their target audience and publication process.

Theses are primarily intended for academic evaluation and examination. They are typically assessed by a committee of professors or experts in the field. Theses contribute to the existing body of knowledge within a specific discipline, but they are not usually published in academic journals. While some theses may be made available through institutional repositories, their primary audience is the academic community within the student’s institution.

Journal articles, on the other hand, target a broader audience of researchers, scholars, and practitioners in the field. They undergo a rigorous peer-review process, where experts in the field assess the quality, validity, and contribution of the research. Successful publication in a reputable journal allows researchers to share their findings with the wider academic community, receive feedback, and contribute to ongoing scholarly discussions.

Length and Depth

Another significant difference between theses and journal articles lies in their length and depth.

Theses are typically longer and more extensive in terms of content. They require students to conduct comprehensive research, provide detailed literature reviews, and present thorough analyses. The length of a thesis can vary depending on the field and degree level, ranging from tens to hundreds of pages. This length allows for an in-depth exploration of the research problem and the incorporation of relevant theoretical frameworks and methodologies.

Journal articles, on the other hand, are generally shorter and more concise. They aim to present the research findings within the constraints of the target journal’s word count limitations. Journal articles can range from a few thousand words to around 8,000 words, depending on the journal’s requirements. The brevity of journal articles necessitates clear and focused writing, emphasizing the key findings, their interpretation, and their implications for the field. While the depth of analysis may be more limited compared to a thesis, journal articles are expected to provide sufficient information for other researchers to understand and build upon the presented research.

Citation and Referencing

Both theses and journal articles require accurate and comprehensive referencing to acknowledge the contributions of other researchers and provide credibility to the work.

In the case of theses, referencing is typically more extensive, as they involve comprehensive literature reviews and incorporate a broader range of sources. Theses follow specific citation styles, such as APA, MLA, or Chicago, as per the guidelines provided by the institution or department.

Journal articles also adhere to specific citation styles determined by the target journal. However, the referencing in journal articles tends to be more focused on the specific literature and studies directly relevant to the research question at hand. The emphasis is on providing adequate support for the key arguments and findings presented in the article.

The thesis and the journal article are distinct forms of academic writing, each with its own purpose, structure, audience, and publication process.

Theses demonstrate a student’s research capabilities and contribute original insights to their field of study. They are comprehensive in scope, consisting of several chapters that explore the research problem in depth. Theses are primarily evaluated by academic committees and are not typically published in academic journals.

Journal articles, on the other hand, aim to disseminate research findings to the wider academic community. They focus on specific research questions and contribute to existing knowledge. Journal articles follow a concise and standardized structure, adhering to the guidelines of the target journal. They undergo rigorous peer review and are published in reputable journals to reach a broader audience.

Understanding the distinctions between theses and journal articles enables researchers, scholars, and students to approach each form of writing with the appropriate structure, depth, and style required for their intended purpose and audience. Both theses and journal articles play vital roles in advancing knowledge and fostering academic discourse within their respective fields.

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How to Write a Journal Article from a Thesis

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You are almost done with your PhD thesis and want to convert it into a journal article. Or, you’re initiating a career as a journal writer and intend to use your thesis as a starting point for an article. Whatever your situation, turning a thesis into a journal article is a logical step and a process that eventually every researcher completes. But…how to start?

The first thing to know about converting a thesis into a journal article is how different they are:

Thesis Characteristics:

• Meets academic requirements
• Reviewed by select committee members
• Contains chapters
• Lengthy, no word limits
• Table of contents
• Lengthy research of literature
• IRB approval described in detail
• Description and copies of tools used
• All findings presented
• Verb tenses may vary

Journal Article Characteristics:

• Meets journalistic standards
• Reviewed by a panel of “blind” reviewers
• Word limits
• Manuscript format
• Succinct research of literature
• IRB described in 1 to 3 sentences
• Essential and succinct tool information
• Selected findings presented
• Verb tenses are fairly consistent

Converting your thesis to a journal article may be complex, but it’s not impossible.

A thesis is a document of academic nature, so it’s more detailed in content. A journal article, however, is shorter, highlighting key points in a more succinct format. Adapting a thesis for conversion into a journal article is a time-consuming and intricate process that can take you away from other important work. In that case, Elsevier’s Language Editing services may help you focus on important matters and provide a high-quality text for submission in no time at all.

If you are going to convert a thesis into a journal article, with or without professional help, here is a list of some of the steps you will likely have to go through:

1. Identify the best journal for your work

• Ensure that your article is within the journal’s aim and scope. How to find the right journal? Find out more .
• Check the journal’s recommended structure and reference style

2. Shorten the length of your thesis

• Treat your thesis as a separate work
• Paraphrase but do not distort meaning
• Select and repurpose parts of your thesis

3. Reformat the introduction as an abstract

• Shorten the introduction to 100-150 words, but maintain key topics to hold the reader’s attention.
• Use the introduction and discussion as basis for the abstract

4. Modify the introduction

• If your thesis has more than one research question or hypothesis, which are not all relevant for your paper, consider combining your research questions or focusing on just one for the article
• Use previously published papers (at least three) from the target journal as examples

5. Tighten the methods section

• Keep the discussion about your research approach short

6. Report main findings in the results

• Expose your main findings in the results section in concise statements

7. Discussion must be clear and concise

• Begin by providing an interpretation of your results: “What is it that we have learned from your research?”
• Situate the findings to the literature
• Discuss how your findings expand known or previous perspectives
• Briefly present ways in which future studies can build upon your work and address limitations in your study

8. Limit the number of references

• To choose the most relevant and recent
• To format them correctly
• Consider using a reference manager system (e.g. Mendeley ) to make your life easier

If you are not a proficient English speaker, the task of converting a thesis into a journal article might make it even more difficult. At Elsevier’s Language Editing services we ensure that your manuscript is written in correct scientific English before submission. Our professional proofers and editors check your manuscript in detail, taking your text as our own and with the guarantee of maximum text quality.

Language editing services by Elsevier Author Services:

How to Choose a Journal to Submit an Article

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Adapting a Dissertation or Thesis Into a Journal Article

Dissertations or theses are typically required of graduate students. Undergraduate students completing advanced research projects may also write senior theses or similar types of papers. Once completed, the dissertation or thesis is often submitted (with modifications) as a manuscript for publication in a scholarly journal. Thus, the dissertation or thesis often provides the foundation for a new researcher’s body of published work.

Writers will first want to determine whether the work in their dissertation or thesis merits publication. If it does, we then provide guidance on how to adapt a dissertation or thesis for submission to a journal.

Adapting a dissertation or thesis into a journal article is covered in the seventh edition APA Style Publication Manual in Section 12.1

Deciding to submit a dissertation or thesis for publication

When deciding whether to publish the work in your dissertation or thesis, first consider whether the findings tell a compelling story or answer important questions. Whereas dissertations and theses may present existing knowledge in conjunction with new work, published research should make a novel contribution to the literature. For example, some of your original research questions might be suitable for publication, and others may have been sufficiently addressed in the literature already. Likewise, some of your results may warrant additional experiments or analyses that could help answer the research questions more fully, and you may want to conduct these analyses before seeking publication.

You may also want to consider such factors as whether the current sample size provides sufficient power to adequately inform the analyses and whether additional analyses might clarify ambiguous findings. Consultation with colleagues can help evaluate the potential of the manuscript for publication as well as the selection of an appropriate journal to which to submit it. For information on selecting and prioritizing a journal (and tips for avoiding predatory or deceptive journals), see Sections 12.2 to 12.4 of the Publication Manual .

Adapting a dissertation or thesis for publication

Once a decision is made to convert your dissertation or thesis into a manuscript for submission to a journal, you will want to focus attention on adapting it for publication. By attending to brevity and focus, writing style, relevant literature review and data analyses, and appropriate interpretation of the results or findings, you can enhance the fit of your manuscript for journal publication. Editors and reviewers readily recognize an article that has been hastily converted; careful attention when reformatting the dissertation or thesis is likely to increase the manuscript’s potential for serious consideration and eventual publication.

There are several steps writers seeking to prepare their dissertation or thesis for publication can take beforehand:

• Look at articles in the field and in relevant journals to see what structure and focus are appropriate for their work and how they are formatted.
• Request and consider the input of advisors, colleagues, or other coauthors who contributed to the research on which the dissertation or thesis is based.
• Review an article submitted to a journal alongside their advisor (with permission from the journal editor) or serve as a reviewer for a student competition to gain firsthand insight into how authors are evaluated when undergoing peer review.

The original research reported in a dissertation and thesis can then be reformatted for journal submission following one of two general strategies: the multiple-paper strategy or the conversion strategy.

Multiple-paper strategy

The quickest strategy for converting (or “flipping”) a dissertation or thesis into one or more publishable articles is to use a multiple-paper format when initially writing the dissertation or thesis. This involves structuring the dissertation or thesis used to fulfill the requirements for a degree as a series of shorter papers that are already formatted for journal submission (or close to it). These papers are usually each the length of a journal article, conceptually similar, and come from the same overarching project—but can stand alone as independent research reports. Consult your university’s editorial office to confirm that this is an approved format for your dissertation or thesis and to obtain the specific guidelines.

Conversion strategy

A second strategy is to reformat and convert a dissertation or thesis into a journal article after completing your dissertation or thesis defense to fit the scope and style of a journal article. This often requires adjustments to the following elements:

• Length: Brevity is an important consideration for a manuscript to be considered for journal publication, particularly in the introduction and Discussion sections. Making a dissertation or thesis publication-ready often involves reducing a document of over 100 pages to one third of its original length. Shorten the overall paper by eliminating text within sections and/or eliminating entire sections. If the work examined several research questions, you may consider separating distinct research questions into individual papers; narrow the focus to a specific topic for each paper.
• Abstract: The abstract may need to be condensed to meet the length requirements of the journal. Journal abstract requirements are usually more limited than college or university requirements. For instance, most APA journals limit the abstract length to 250 words.
• Introduction section: One of the major challenges in reformatting a dissertation or thesis is paring down its comprehensive literature review to a more succinct one suitable for the introduction of a journal article. Limit the introductory text to material relating to the immediate context of your research questions and hypotheses. Eliminate extraneous content or sections that do not directly contribute to readers’ knowledge or understanding of the specific research question(s) or topic(s) under investigation. End with a clear description of the questions, aims, or hypotheses that informed your research.
• Method section: Provide enough information to allow readers to understand how the data were collected and evaluated. Refer readers to previous works that informed the current study’s methods or to supplemental materials instead of providing full details of every step taken or the rationale behind them.
• Results section: Be selective in choosing analyses for inclusion in the Results section and report only the most relevant ones. Although an unbiased approach is important to avoid omitting study data, reporting every analysis that may have been run for the dissertation or thesis often is not feasible, appropriate, or useful in the limited space of a journal article. Instead, ensure that the results directly contribute to answering your original research questions or hypotheses and exclude more ancillary analyses (or include them as supplemental materials). Be clear in identifying your primary, secondary, and any exploratory analyses.
• Discussion section: Adjust the discussion according to the analyses and results you report. Check that your interpretation and application of the findings are appropriate and do not extrapolate beyond the data. A strong Discussion section notes area of consensus with and divergence from previous work, taking into account sample size and composition, effect size, limitations of measurement, and other specific considerations of the study.
• References: Include only the most pertinent references (i.e., theoretically important or recent), especially in the introduction and literature review, rather than providing an exhaustive list. Ensure that the works you cite contribute to readers’ knowledge of the specific topic and to understanding and contextualizing your research. Citation of reviews and meta-analyses can guide interested readers to the broader literature while providing an economical way of referencing prior studies.
• Tables and figures: Make sure that tables or figures are essential and do not reproduce content provided in the text.

Difference Between | Descriptive Analysis and Comparisons

Search form, difference between journal, paper and thesis.

Key Difference: The main difference between journal, paper and thesis is that journal is an article which consists of some specific criteria. A paper is an informative sheet. Thesis includes a deep study under the guidance of some respected person. 

A journal is an article type of content which consists of a specific format wherein the thoughts are put into words. A journal is an experience booklet which is to be updated regularly. Journals are informative book logs. They have a pre-defined form of content which are to be followed accordingly. Journals are used for referring purposes.

It contains the desired amount of information which is used for reference. Journals are also reference-books. After studying some specific contents, the content is written in a generalized form and molded into the journal. Journals are specific record keeping booklets. They are periodicals which publish the particular topics. They consist of the reviews regarding any articles or research.

The journals and thesis consists of bunch of papers arranged in a systematic format. The content in a paper gives a systematic approach to the user. Many such research papers together contribute in the work of thesis and journal. The certification obtained in any field acts as a proof in the form of a paper. It narrates the entire subject relevant to the topics.

The meaning of the word thesis means a long piece of writing on a particular subject that is done to earn a degree at a university.

Thesis is a deep study which is done under the guidance of an experienced person; mostly thesis is written after the research is carried on in a particular field. Thesis is especially written in the field of doctorate, post-graduation, masters, etc. After doing the appropriate research in the relevant field along with the master’s degree the individual is allowed to write the relevant experience in the form of a thesis.

Thesis includes research papers and work documents which would be affiliated by a recognized doctorate individual or by a university. A thesis document should be an approved document; its range may vary according to the type of research done. Thesis writing is a proposed form of writing. It includes the detailed study material right from the origin till its final product. It also explains the current status of the product. Theses are mostly written in order to secure the research.

Comparison between Journal, Paper and Thesis:

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11 Differences Between a Thesis and an Article

Journal article and thesis are two important formats of reports in academia. There are certain differences between the thesis and article. The aim of the thesis is totally different from a journal article. Being a researcher , understanding the differences between both kind of academic writing is very essential.

Differences Between a Thesis and an Article

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Hope, this article helps you to distinguish the difference between a thesis and an article.

What is the Difference Between Thesis and Dissertation?

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Really it is very very important for a Researcher. Without knowledge of writing an article and thesis research is a dilemma .It is fruitless. Many Many Thanks for your Support in this regard.

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• What Is a Thesis? | Ultimate Guide & Examples

What Is a Thesis? | Ultimate Guide & Examples

Published on September 14, 2022 by Tegan George . Revised on April 16, 2024.

A thesis is a type of research paper based on your original research. It is usually submitted as the final step of a master’s program or a capstone to a bachelor’s degree.

Writing a thesis can be a daunting experience. Other than a dissertation , it is one of the longest pieces of writing students typically complete. It relies on your ability to conduct research from start to finish: choosing a relevant topic , crafting a proposal , designing your research , collecting data , developing a robust analysis, drawing strong conclusions , and writing concisely .

Thesis template

You can also download our full thesis template in the format of your choice below. Our template includes a ready-made table of contents , as well as guidance for what each chapter should include. It’s easy to make it your own, and can help you get started.

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Table of contents

Thesis vs. thesis statement, how to structure a thesis, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your thesis, other interesting articles, frequently asked questions about theses.

You may have heard the word thesis as a standalone term or as a component of academic writing called a thesis statement . Keep in mind that these are two very different things.

• A thesis statement is a very common component of an essay, particularly in the humanities. It usually comprises 1 or 2 sentences in the introduction of your essay , and should clearly and concisely summarize the central points of your academic essay .
• A thesis is a long-form piece of academic writing, often taking more than a full semester to complete. It is generally a degree requirement for Master’s programs, and is also sometimes required to complete a bachelor’s degree in liberal arts colleges.
• In the US, a dissertation is generally written as a final step toward obtaining a PhD.
• In other countries (particularly the UK), a dissertation is generally written at the bachelor’s or master’s level.

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The final structure of your thesis depends on a variety of components, such as:

• Your discipline
• Your theoretical approach

Humanities theses are often structured more like a longer-form essay . Just like in an essay, you build an argument to support a central thesis.

In both hard and social sciences, theses typically include an introduction , literature review , methodology section ,  results section , discussion section , and conclusion section . These are each presented in their own dedicated section or chapter. In some cases, you might want to add an appendix .

Thesis examples

We’ve compiled a short list of thesis examples to help you get started.

• Example thesis #1:   “Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807” by Suchait Kahlon.
• Example thesis #2: “’A Starving Man Helping Another Starving Man’: UNRRA, India, and the Genesis of Global Relief, 1943-1947″ by Julian Saint Reiman.

The very first page of your thesis contains all necessary identifying information, including:

• Your full title
• Your full name
• Your department
• Your institution and degree program
• Your submission date.

Sometimes the title page also includes your student ID, the name of your supervisor, or the university’s logo. Check out your university’s guidelines if you’re not sure.

Read more about title pages

The acknowledgements section is usually optional. Its main point is to allow you to thank everyone who helped you in your thesis journey, such as supervisors, friends, or family. You can also choose to write a preface , but it’s typically one or the other, not both.

Read more about acknowledgements Read more about prefaces

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An abstract is a short summary of your thesis. Usually a maximum of 300 words long, it’s should include brief descriptions of your research objectives , methods, results, and conclusions. Though it may seem short, it introduces your work to your audience, serving as a first impression of your thesis.

Read more about abstracts

A table of contents lists all of your sections, plus their corresponding page numbers and subheadings if you have them. This helps your reader seamlessly navigate your document.

Your table of contents should include all the major parts of your thesis. In particular, don’t forget the the appendices. If you used heading styles, it’s easy to generate an automatic table Microsoft Word.

Read more about tables of contents

While not mandatory, if you used a lot of tables and/or figures, it’s nice to include a list of them to help guide your reader. It’s also easy to generate one of these in Word: just use the “Insert Caption” feature.

Read more about lists of figures and tables

If you have used a lot of industry- or field-specific abbreviations in your thesis, you should include them in an alphabetized list of abbreviations . This way, your readers can easily look up any meanings they aren’t familiar with.

Read more about lists of abbreviations

Relatedly, if you find yourself using a lot of very specialized or field-specific terms that may not be familiar to your reader, consider including a glossary . Alphabetize the terms you want to include with a brief definition.

Read more about glossaries

An introduction sets up the topic, purpose, and relevance of your thesis, as well as expectations for your reader. This should:

• Ground your research topic , sharing any background information your reader may need
• Define the scope of your work
• Introduce any existing research on your topic, situating your work within a broader problem or debate
• State your research question(s)
• Outline (briefly) how the remainder of your work will proceed

In other words, your introduction should clearly and concisely show your reader the “what, why, and how” of your research.

Read more about introductions

A literature review helps you gain a robust understanding of any extant academic work on your topic, encompassing:

• Selecting relevant sources
• Determining the credibility of your sources
• Critically evaluating each of your sources
• Drawing connections between sources, including any themes, patterns, conflicts, or gaps

A literature review is not merely a summary of existing work. Rather, your literature review should ultimately lead to a clear justification for your own research, perhaps via:

• Addressing a gap in the literature
• Building on existing knowledge to draw new conclusions
• Exploring a new theoretical or methodological approach
• Introducing a new solution to an unresolved problem
• Definitively advocating for one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework, but these are not the same thing. A theoretical framework defines and analyzes the concepts and theories that your research hinges on.

Read more about theoretical frameworks

Your methodology chapter shows your reader how you conducted your research. It should be written clearly and methodically, easily allowing your reader to critically assess the credibility of your argument. Furthermore, your methods section should convince your reader that your method was the best way to answer your research question.

A methodology section should generally include:

• Your overall approach ( quantitative vs. qualitative )
• Your research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment
• Any tools or materials you used (e.g., computer software)
• The data analysis methods you chose (e.g., statistical analysis , discourse analysis )
• A strong, but not defensive justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. These two sections work in tandem, but shouldn’t repeat each other. While your results section can include hypotheses or themes, don’t include any speculation or new arguments here.

Your results section should:

• State each (relevant) result with any (relevant) descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Explain how each result relates to the research question
• Determine whether the hypothesis was supported

Additional data (like raw numbers or interview transcripts ) can be included as an appendix . You can include tables and figures, but only if they help the reader better understand your results.

Read more about results sections

Your discussion section is where you can interpret your results in detail. Did they meet your expectations? How well do they fit within the framework that you built? You can refer back to any relevant source material to situate your results within your field, but leave most of that analysis in your literature review.

For any unexpected results, offer explanations or alternative interpretations of your data.

Read more about discussion sections

Your thesis conclusion should concisely answer your main research question. It should leave your reader with an ultra-clear understanding of your central argument, and emphasize what your research specifically has contributed to your field.

Why does your research matter? What recommendations for future research do you have? Lastly, wrap up your work with any concluding remarks.

Read more about conclusions

In order to avoid plagiarism , don’t forget to include a full reference list at the end of your thesis, citing the sources that you used. Choose one citation style and follow it consistently throughout your thesis, taking note of the formatting requirements of each style.

Which style you choose is often set by your department or your field, but common styles include MLA , Chicago , and APA.

Create APA citations Create MLA citations

In order to stay clear and concise, your thesis should include the most essential information needed to answer your research question. However, chances are you have many contributing documents, like interview transcripts or survey questions . These can be added as appendices , to save space in the main body.

Read more about appendices

Once you’re done writing, the next part of your editing process begins. Leave plenty of time for proofreading and editing prior to submission. Nothing looks worse than grammar mistakes or sloppy spelling errors!

Consider using a professional thesis editing service or grammar checker to make sure your final project is perfect.

Once you’ve submitted your final product, it’s common practice to have a thesis defense, an oral component of your finished work. This is scheduled by your advisor or committee, and usually entails a presentation and Q&A session.

After your defense , your committee will meet to determine if you deserve any departmental honors or accolades. However, keep in mind that defenses are usually just a formality. If there are any serious issues with your work, these should be resolved with your advisor way before a defense.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The conclusion of your thesis or dissertation shouldn’t take up more than 5–7% of your overall word count.

If you only used a few abbreviations in your thesis or dissertation , you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimizing confusion about abbreviations unfamiliar to your reader.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

A thesis is typically written by students finishing up a bachelor’s or Master’s degree. Some educational institutions, particularly in the liberal arts, have mandatory theses, but they are often not mandatory to graduate from bachelor’s degrees. It is more common for a thesis to be a graduation requirement from a Master’s degree.

Even if not mandatory, you may want to consider writing a thesis if you:

• Plan to attend graduate school soon
• Have a particular topic you’d like to study more in-depth
• Are considering a career in research
• Would like a capstone experience to tie up your academic experience

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Home > Blog > Tips for Online Students > Dissertation vs Thesis: The Differences that Matter

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Dissertation vs Thesis: The Differences that Matter

Updated: June 19, 2024

Published: April 26, 2020

As a graduate student, you will have many different types of challenging coursework and assignments. However, the biggest project that you’ll work on when earning your master’s or doctoral degree will be your thesis or dissertation . The differences between a dissertation vs thesis are plenty. That’s because each of these pieces of writing happen at different times in one’s educational journey.

Let’s break down what a dissertation and thesis are so that you have a strong handle on what’s expected. For both a thesis and a dissertation, there is an obvious fluency and understanding of the subject one studies.

Let’s take a look at their similarities and differences.

Photo by  Glenn Carstens-Peters  on  Unsplash

What is a dissertation.

When you enter a doctoral program to earn a PhD, you will learn a lot about how to conduct your own research. At the culmination of your degree program, you’ll produce a dissertation.

A dissertation is a lengthy piece of written work that includes original research or expanded research on a new or existing topic. As the doctoral student, you get to choose what you want to explore and write about within your field of study.

What is a Thesis?

A thesis is also a scholarly piece of writing, but it is for those who are graduating from a master’s program. A thesis allows students to showcase their knowledge and expertise within the subject matter they have been studying.

Main Differences Between a Thesis vs. Dissertation

The biggest difference between a thesis and a dissertation is that a thesis is based on existing research.

On the other hand, a dissertation will more than likely require the doctoral student to conduct their own research and then perform analysis. The other big difference is that a thesis is for master’s students and the dissertation is for PhD students.

Structural Differences Between a Thesis and a Dissertation

Structurally, the two pieces of written analysis have many differences.

• A thesis is at least 100 pages in length
• A dissertation is 2-3x that in length
• A thesis expands upon and analyzes existing research
• A dissertation’s content is mostly attributed to the student as the author

Research Content and Oral Presentation

Once completed, some programs require students to orally present their thesis and dissertation to a panel of faculty members.

Typically, a dissertation oral presentation can take several hours. On the other hand, a thesis only takes about an hour to present and answer questions.

Let’s look at how the two scholarly works are similar and different:

Similarities:

• Each is considered a final project and required to graduate
• Both require immense understanding of the material
• Written skills are key to complete both
• Neither can be plagiarized
• Both are used to defend an argument
• Both require analytical skills
• You will have to draft, rewrite, and edit both pieces of writing
• For both, it is useful to have another person look over before submission
• Both papers are given deadlines

Differences:

• A dissertation is longer than a thesis
• A dissertation requires new research
• A dissertation requires a hypothesis that is then proven
• A thesis chooses a stance on an existing idea and defends it with analysis
• A dissertation has a longer oral presentation component

The Differences in Context: Location Matters

The united states.

In the US, everything that was previously listed is how schools differentiate between a thesis and a dissertation. A thesis is performed by master’s students, and a dissertation is written by PhD candidates.

In Europe, the distinction between a thesis and dissertation becomes a little more cloudy. That’s because PhD programs may require a doctoral thesis to graduate. Then, as a part of a broader post-graduate research project, students may complete a dissertation.

Photo by  Russ Ward  on  Unsplash

The purpose behind written research.

Each piece of writing is an opportunity for a student to demonstrate his or her ability to think critically, express their opinions in writing, and present their findings in front of their department.

Graduate degrees take a lot of time, energy, and hard work to complete. When it comes to writing such lengthy and informative pieces, there is a lot of time management that is involved. The purpose of both a thesis and a dissertation are written proof that you understand and have mastered the subject matter of your degree.

Degree Types

A doctoral degree, or PhD, is the highest degree that one can earn. In most cases, students follow the following path to achieve this level of education: Earn a bachelor’s degree, then a master’s, and then a PhD. While not every job title requires this deep educational knowledge, the salaries that come along with each level of higher education increase accordingly.

Earning Your Degree

Whether you are currently a prospective student considering earning your higher education degree or a student enrolled in a master’s or doctoral program, you know the benefits of education.

However, for some, earning a traditional degree on-campus doesn’t make sense. This could be because of the financial challenges, familial obligations, accessibility, or any other number of reasons.

For students who are seeking their higher education degrees but need a flexible, affordable, and quality alternative to traditional college, take a look at the programs that the University of the People has to offer.

University of the People is an entirely online, US accredited and tuition-free institution dedicated to higher education. You can earn your Master’s in Business Administration or your Master’s in Education . Not to mention, there are a handful of associate’s and bachelor’s degree programs to choose from as well.

If you want to learn more, get in touch with us !

The Bottom Line

Regardless of where and when you earn your master’s or doctoral degree, you will likely have to complete a thesis or dissertation. The main difference between a thesis and dissertation is the level at which you complete them. A thesis is for a master’s degree, and a dissertation is for a doctoral degree.

Don’t be overwhelmed by the prospect of having to research and write so much. Your educational journey has prepared you with the right time management skills and writing skills to make this feat achievable!

In this article

At UoPeople, our blog writers are thinkers, researchers, and experts dedicated to curating articles relevant to our mission: making higher education accessible to everyone. Read More

• Library Research Strategies
• The research process
• Defining your topic and crafting your research question
• Identifying search terms from your question
• Broaden or narrow your search
• Find background information
• Search across multiple article databases
• Search individual article databases
• When you know in advance which publication you are looking for
• Interlibrary loan
• Search for books and ebooks
• Locating books and ebooks after you search
• Types of sources

Distinguishing between different types of journal articles

• Components of a scholarly article, and things to consider when reading one
• Critically evaluating articles & other sources
• Literature reviews (opens a new guide) This link opens in a new window
• Annotated bibliographies
• Writing tools
• Citing sources (opens a new guide) This link opens in a new window
• Understanding & Avoiding Plagiarism (opens a new guide) This link opens in a new window

Ask a Librarian

Visit a librarian at the  Reference Desk  on the first floor of Ablah Library or contact a  Subject Librarian  to make an appointment.  See the full schedule  of library hours for the main library (Ablah) and for the Chemistry and Music branch libraries. Ablah Library hours are also available on an automated phone line: 316-978-3581.

Circulation Desk (Ablah Library)  316-978-3582 Reference Desk (Ablah Library)  316-978-3584 Music Library  316-978-3029 Chemistry Library  316-978-3764 Interlibrary Loan  316-978-3167

Email  a librarian with your question.

When writing a paper or conducting academic research, you’ll come across many different types of sources, including periodical articles. Periodical articles can be comprised of news accounts, opinion, commentary, scholarly analysis, and/or reports of research findings. There are three main types of periodicals that you will encounter: scholarly/academic, trade, and popular.  The chart below will help you identify which type of periodical your article comes from.

Text and chart adapted from the WSU University Libraries' How to Distinguish Between Types of Periodicals  and Types of Periodicals guides

What makes information peer-reviewed vs. scholarly vs. non-scholarly? Which type of source should I use?

• What makes information peer-reviewed vs. scholarly vs. non-scholarly?
• Which type of source should I use?

There is a nuanced distinction between peer-review and scholarship, which typically doesn't matter when evaluating sources for possible citation in your own work.  Peer-review is a process through which editors of a journal have other experts in the field evaluate articles submitted to the journal for possible publication.  Different journals have different ways of defining an expert in the field.  Scholarly works, by contrast have an editorial process, but this process does not involve expert peer-reviewers.  Rather, one or more editors, who are themselves often highly decorated scholars in a field, evaluate submissions for possible publication.  This editorial process can be more economically driven than a peer-review process, with a greater emphasis on marketing and selling the published material, but as a general rule this distinction is trivial with regard to evaluating information for possible citation in your own work.

What is perhaps a more salient way of thinking about the peer-review / scholarship distinction is to recognize that while peer-reviewed information is typically highly authoritative, and is generally considered "good" information, the absence of a peer-review process doesn't automatically make information "bad."  More specifically, the only thing the absence of a peer-review process means is that information published in this manner is not peer-reviewed.  Nothing more.  Information that falls into this category is sometimes referred to as "non-scholarly" information -- but again, that doesn't mean this information is somehow necessarily problematic.

Where does that leave you in terms of deciding what type of information to use in producing your own work?  That is a highly individual decision that you must make.  The Which type of source should I use?  tab in this box offers further guidance on answering this question, though it is important to be aware that many WSU instructors will only consider peer-reviewed sources to be acceptable in the coursework you turn in .  You can ask your instructor for his or her thoughts on the types of sources s/he will accept in student work.

Image:  Martin Grater. (2017, Nov. 1). Deep Thought. Retrieved from https://www.flickr.com/photos/152721954@N05/24304490568/. Used under the Creative Commons License.

Your topic and research question or thesis statement will guide you on which resources are best.  Sources can be defined as primary, secondary and tertiary levels away from an event or original idea. Researchers may want to start with tertiary or secondary source for background information. Learning more about a topic will help most researchers make better use of primary sources.

While articles from scholarly journals are often the most prominent of the sources you will consider incorporating into your coursework, they are not the only sources available to you.  Which sources are most appropriate to your research is a direct consequence of they type of research question you decide to address.  In other words, while most university-level papers will require you to reference scholarly sources, not all will.  A student in an English course writing a paper analyzing Bob Dylan's lyrics, for example, may find an interview with Dylan published in Rolling Stone magazine a useful source to cite alongside other scholarly works of literary criticism.

The WSU University Libraries' What Sources Should I Use? handout, as well as the other sub-tabs under the  Evaluating information  section of this guide (which is indeed the section you are currently viewing) offer further guidance on understanding and identifying scholarly resources, and comparing them against different criteria to evaluate if they will be of value to your research.  How many non-scholarly works (if any) you are at liberty to cite alongside scholarly ones is often a question to ask of your professor.  Some may not want you to cite any, whereas others may be ok with some non-scholarly works cited alongside scholarly ones.

Image:  Brett Woods. (2006, Jan. 6). Deep Thoughts. Retrieved from https://www.flickr.com/photos/brettanicus/87653641/. Used under the Creative Commons License.

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Dissertation vs Thesis: Understanding the Key Differences

Table of Contents

Undergraduate and graduate students often find themselves navigating the intricacies of higher education, including the requirement to complete a research project as part of their academic journey.

This can be a source of confusion, particularly when it comes to distinguishing between a dissertation and a thesis. While both terms are used interchangeably in some educational institutions, they actually have distinct meanings and purposes. Let's delve into the world of dissertations and theses, exploring their differences, purposes, and unique attributes.

Defining the Terms: Dissertation and Thesis

What is a dissertation.

A dissertation is a research project that marks the culmination of a doctoral program. It requires students to conduct original research and contribute new knowledge to their field of study. Dissertations are typically longer and more comprehensive than theses, as they delve deep into a specific topic, often spanning several chapters.

When embarking on a dissertation, students are expected to choose a research topic that is both relevant and original. This topic should contribute to the existing body of knowledge in their field and address a gap in current research. To accomplish this, students must thoroughly review the existing literature and identify areas that require further investigation.

What is a Thesis?

A thesis, on the other hand, is usually associated with a master's degree program. It is a research project that allows students to explore a particular topic in depth. Theses are generally shorter than dissertations, spanning fewer chapters. While they also require original research, the depth and scope may be narrower compared to a dissertation.

Similar to a dissertation, a thesis requires students to select a research topic and conduct a thorough literature review. However, due to the shorter length of a thesis, the literature review may not be as extensive as that of a dissertation. Nevertheless, it is still important for students to demonstrate a comprehensive understanding of the existing knowledge on their chosen topic.

Key Differences Between Dissertation and Thesis

Purpose and structure.

The primary difference between a dissertation and a thesis lies in their purpose and structure. A dissertation aims to contribute new knowledge to a specific field of study and is typically a more extensive and comprehensive project. It involves an in-depth exploration of a research problem or question, often requiring the collection and analysis of primary data. The structure of a dissertation usually includes multiple chapters, such as an introduction, literature review, methodology, results, discussion, and conclusion.

Furthermore, a dissertation often requires the student to engage in critical thinking, originality, and creativity to develop a unique contribution to the field. It may involve the development of new theories, models, or frameworks that can advance the understanding of a particular subject area.

Length and Depth of Research

Dissertations are known for their length and the depth of research they require. Doctoral candidates spend years conducting original research, analyzing data, and crafting comprehensive arguments. The research conducted for a dissertation is typically more extensive and rigorous compared to that of a thesis.

In addition, a dissertation often involves a thorough review of existing literature to establish a theoretical framework and identify research gaps. This review helps situate the research within the broader academic context and provides a foundation for the research design and methodology. The depth of research in a dissertation allows for a more comprehensive analysis and interpretation of the findings.

Degree Level and Requirements

Another key difference between dissertations and theses is the level of education for which they are typically required. Dissertations are a common requirement for doctoral degrees, such as Ph.D. programs, while theses are more commonly associated with master's degree programs. The specific requirements for each project may vary across institutions and academic disciplines.

Doctoral candidates are expected to demonstrate a high level of expertise and mastery in their field of study. They are often required to defend their dissertation orally in front of a committee of experts in the field. This defense allows for a rigorous examination of the research, ensuring its validity and contribution to the field.

On the other hand, theses for master's degrees are usually shorter in length and may not require the same level of originality and depth of research as a dissertation. However, they still require students to demonstrate their ability to critically analyze existing literature, conduct research, and present their findings in a coherent and scholarly manner.

Choosing Between a Dissertation and a Thesis

Factors to consider.

When deciding between a dissertation and a thesis, several factors should be taken into account. Firstly, consider your academic goals and the requirements of your degree program. If you aim to pursue a doctoral degree or a research career, a dissertation may be the most appropriate choice. However, if your goals are more focused on professional advancement or further specialization within your field, a thesis may suit your needs.

Understanding Your Academic Goals

Reflect on your academic interests and aspirations, as well as the amount of time and resources you are willing to invest in your research project. Each option comes with its own set of challenges and expectations, so it is important to choose the path that aligns with your academic and career goals.

The Process: From Proposal to Defense

The dissertation process.

The dissertation process involves multiple stages, starting with the development of a research proposal. Doctoral candidates must conduct a literature review, outline their research methodology, collect and analyze data, and present their findings in a comprehensive written document. The final step typically involves defending the dissertation orally, where candidates must respond to questions and critique from a committee of experts in the field.

The Thesis Process

The thesis process follows a similar trajectory but on a smaller scale. Students will still need to develop a research proposal, conduct a literature review, collect and analyze data, and present their findings in a written document. The defense process for a thesis may involve an oral presentation or an examination by a committee, depending on the institution's requirements.

The Role of Advisors in Dissertations and Theses

Guidance and support in the dissertation journey.

Dissertation advisors play a crucial role in guiding students through the research process. They provide valuable mentorship, offer insights into the field, and help refine research questions and methodologies. Advisor-advisee relationships are essential for the successful completion of a dissertation, as advisors provide ongoing feedback and support during the entire research journey.

Mentorship in the Thesis Process

Similarly, thesis advisors offer mentorship and guidance to students pursuing a master's degree. They help students refine their research topic, provide feedback on drafts, and ensure that the research project meets the required standards. Working closely with a knowledgeable advisor can significantly enhance a student's thesis experience and contribute to the overall success of the project.

Understanding the differences between dissertations and theses is essential for students embarking on their research journey. By considering their academic goals and evaluating the resources available, students can make an informed decision when choosing between a dissertation and a thesis. Additionally, the guidance and support provided by advisors throughout the process are invaluable and contribute to the successful completion of these significant research endeavors.

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Dissertation vs Thesis: Understanding the Key Differences

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Embarking on an academic journey often involves deciphering the complexities of advanced research projects like dissertations and theses. While these terms might seem interchangeable, they possess distinctive changes in the world of higher education. A fundamental distinction between a thesis and a dissertation lies in their approach to research. A thesis typically involves synthesizing existing research and knowledge in your field, while a dissertation requires conducting original research, addressing research gaps, and making a substantive contribution to the academic domain. 

As you continue your higher education journey in academia, it’s critical to decipher the fundamental differences between dissertation and thesis that set them apart.

What Is a Thesis?

A thesis is a long-term academic research paper that presents an in-depth review of existing research on a specific subject. It requires extensive research, data collection, analysis, and critical interpretation of the findings. Although some undergraduate programs may mandate a thesis, it is more commonly expected in postgraduate studies, such as upon completing a master’s degree . 

What Is a Dissertation?

A dissertation is a longer and more comprehensive research project based on original research. It’s a substantial piece of academic writing required for the completion of a doctoral degree . The dissertation is the culmination of years of research, study, and expertise, contributing valuable insights in a particular field of study. 

Dissertation vs Thesis: Key Differences

Both theses and dissertations serve as culminating projects for program graduation, demanding a profound grasp of the research subject and analytical skills to substantiate findings. Despite these commonalities, significant differences set them apart. The key differences include: 

Academic and Research Requirements

The academic requirements for a doctoral dissertation are clearly defined hypotheses, a detailed methodology section, rigorous data analysis, and a critical discussion of the results in the context of existing literature.

In contrast, the requirements for a master’s thesis include a thorough literature review, methodology, data analysis, and a conclusion that summarizes the findings.

Research requirements are also more extensive for dissertations compared to theses, as they include conducting original experiments and studies.

Length and Complexity

The length of a dissertation or a thesis varies according to the topic and the method of analysis, or it can vary based on the departmental requirements. Usually, a master’s thesis is around 40-80 pages, sometimes up to 100 pages. A dissertation, however, is significantly longer, ranging from 100 to 300 pages and sometimes up to 400. 

Dissertations and theses also vary on the level of complexity. While the thesis requires a thorough literature review and analysis of existing research, it’s less complex than a dissertation. Moreover, the original research is limited, contrary to dissertations involving extensive original research (experiments and studies). 

Research Objectives

The research objectives are an essential component that can guide the research project and highlight the specific goals to achieve. Moreover, they outline the grounds for pursuing a particular topic.

However, they can vary depending on the academic level and the nature of the research. In a master’s thesis, the research objectives usually focus on addressing specific research questions related to the chosen topic, to deepen the understanding of existing theories within the field of study. Contrarily, in a doctoral dissertation, the research objectives are broader and are formulated to address complex research gaps, propose new theories, and contribute to the academic field. 

Timeframe and Milestones

The timeframe depends on internal factors like the student’s work pace and personal circumstances and external factors like the complexity of the subject, availability of resources, and academic level. While we can’t give a definitive answer, it usually takes a couple of semesters or 1-2 years to complete a master’s thesis. On the contrary, a doctoral dissertation is more time-consuming and can take 4-7 years to complete. 

Although there are many similarities between the milestones for completing a thesis and a dissertation, such as topic selection, literature review, and data collection, their differences lie in the higher complexity of doctoral dissertations, including original research, review by the advisory committee, and publication. 

Review and Approval Process

The review and approval process is vital to evaluate the quality of research, analysis, and presentation. Although the overall process is similar for both a master’s thesis and a doctoral dissertation, the scrutiny applied to dissertations is notably more rigorous. For a master’s thesis, a committee of professors or advisors within the university’s department evaluates the thesis for adherence to academic standards, clarity, and coherence. Following the review, approval is granted to the thesis if the evaluators are content with the student’s work. On the other hand, for a doctoral dissertation, the review process includes a committee of experts in the field, often external reviewers, to assess the dissertation’s quality, methodology, and contribution to the academic field. The approval process is more challenging as it may require defending the dissertation orally and answering questions from the committee members and to a broader audience than a master’s thesis presentation.

Potential for Publication

Lastly, one of the main differences between a dissertation and a thesis is the potential for publication. As the dissertation is more extensive and requires original research contributing to the academic field, theses are less likely to be published in a peer-reviewed journal. Although a master’s thesis is more narrowly focused, it can still be published as a single article. In contrast, a doctoral dissertation is generally equivalent to at least three articles. 

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The Scope of Dissertation vs Thesis

While both require extensive research and dedication, the thesis has a narrower scope on a specific topic within the field of study. It aims to illustrate the student’s mastery of the subject matter, ability to synthesize existing research, and ability to present coherent arguments. On the other hand, the dissertation has a broader scope , encompassing a comprehensive analysis of a complex research problem or exploring interconnected topics in the field. It aims to showcase the student’s expertise and ability to conduct original and independent research and contribute new knowledge in the field. 

As we draw the curtain in exploring the factors that differentiate a thesis and a dissertation, it becomes evident that both academic endeavors are crucial in unraveling discoveries. A master’s thesis showcases analytical finesse and a deep understanding of existing theories. And a doctoral dissertation displays new groundbreaking insights, original research, and innovative methodologies. Therefore, while they may have substantial differences, they remain a driving force in shaping the future of academia. 

Frequently Asked Questions (FAQs): What is the main difference between a dissertation and a thesis?

While many factors differentiate a dissertation and a thesis, the main difference is their academic level. A thesis is usually required at the end of a master’s degree program. In contrast, a dissertation is necessary to complete a doctoral degree.

How long does it take to complete a thesis or a dissertation?

The time it takes to complete a thesis or a dissertation depends on the complexity of the research, the availability of resources, and the student’s work pace. Usually, it takes a couple of semesters to complete a thesis at the end of a master’s program. In contrast, completing a doctoral dissertation can take four to seven years.

Is there a difference in the level of research expected in a dissertation vs a thesis?

Although a thesis requires a significant amount of research literature review and analysis of existing studies, a dissertation demands a higher level of original research through identifying research gaps, developing new hypotheses, conducting experiments, and collecting and analyzing data. 

Are there any specific publication or accessibility differences between dissertations and theses?

Yes, there are specific publication and accessibility differences between dissertations and theses. Doctoral dissertations are usually published and made public through university libraries or digital repositories. Whereas a master’s thesis is less frequently published in academic journals compared to doctoral dissertations. Theses are often made available within the university’s library or department.

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Thesis Vs. Dissertation — Know the difference and similarities!

The academic world is filled with many different types of writing assignments, each with its own unique set of requirements and expectations. One common source of confusion for students is the distinction between a thesis and a dissertation. Both are long-form academic works, but there are several key differences between the two that are important to understand.

In Shakespeare’s day, a candidate for a master’s degree would write a thesis, an original paper in which he maintained a certain proposition. Whereas, completion of a doctoral program required submission and defense of a dissertation. He would read his thesis to his committee, after which he sat in silence while two faculty members gave point-by-point refutations of everything the candidate said.

The focus here was on the student’s ideas and his ability to arrange and express them clearly. If a student wished to advance further in academia he could pursue a dissertation. This was more of a  literature review . He would read widely in a particular area and write up his findings, discussing the various authorities and their opinions. The point was to demonstrate that he was well-versed in the literature of the field. While the confusion between the two terms is understandable, we shall tackle the dissertation vs. thesis topic in this article and provide unambiguous insights on it.

Table of Contents

What Is a Thesis?

A thesis is a critically written scholarly piece of research work. Typically, it is submitted by students graduating from a master’s program. The purpose of a thesis is to allow students to showcase their knowledge and expertise within the subject matter they have been studying as part of the program.

What Is a Dissertation?

A dissertation is a comparatively lengthier piece of scholarly writing that accounts for your research work throughout the doctoral program. A researcher earns the Ph.D. after submitting and defending his/her dissertation. It includes all information about the original research or expanded research on a new or existing topic conducted by the Ph.D. candidate.

Dissertation vs. Thesis: Differences

• The primary difference between a thesis and a dissertation is the time when they are completed. As mentioned earlier, a thesis is presented at the culmination of a master’s program, whereas, a dissertation is presented to earn a Ph.D.
• A thesis is a compilation of research ensuring that the researcher is well-informed and has knowledge about the research topic learned in the study program. On the other hand, a dissertation provides an opportunity for the researcher to contribute new theories and information to the existing literature in the research field.
• A thesis is a presentation of learned and existing information, while the purpose of a dissertation is to develop a unique concept and defend it based on theoretical and practical results.
• A master’s thesis is approximately 100 pages in length. However, a Ph.D. dissertation should be much longer than a thesis and must include background and research information. A dissertation must include your research proposal, grant proposal, literature review , ideation of research topic, and every other minute detail about your research. Ideally, a dissertation inclusive of all details mentioned above should be three times the length of a master’s thesis.

Dissertation vs. Thesis: Similarities

• Both a thesis and a dissertation are considered final projects and are required to graduate from respective programs.
• The thesis and dissertation both require a deep and accurate understanding of the research problem.
• Both forms of scholarly written pieces must address specific research questions.
• Academic writing skills are imperative for a thesis as well as a dissertation.
• Ethical practices must be followed while collating and documenting research data.
• Plagiarism is not accepted in either.
• Both require analytical skills to support the findings.
• It is essential that both undergo intense dissertation/ thesis editing  and  critical proofreading  before final submission.

Dissertation vs. Thesis: Europe

In Europe, the original distinction between a thesis and a dissertation has been largely retained. A doctoral thesis is a focused piece of original research that is performed to obtain a Ph.D. A dissertation is part of a broader post-graduate research project.

However, the thesis has evolved since original research nowadays requires plenty of background research . So, a thesis will contain extensive citations and references to earlier work, although the focus remains on the original work that comes out of it.

Dissertation vs. Thesis: USA

In the United States, the definition of a thesis is almost the opposite of that in Europe. Because a thesis is shorter than a dissertation it gradually came to mean a preliminary degree on the way to a doctorate. A thesis is now performed to earn a Master’s degree. In scientific fields, a master’s candidate takes advanced coursework and gains hands-on experience in a research project but does not direct the project to the same extent that he would in a doctoral program. In a master’s project, the student’s ideas are welcomed and expected but the focus is on obtaining technical expertise, not doing original research. Engineering students commonly obtain Master’s degrees and seldom go on to get PhDs. In other fields such as Chemistry, the opposite is true, with a Master’s degree no longer being required as the first step for a doctorate. Almost everyone I know who received a Master’s degree in Chemistry got one because they dropped out of graduate school and wrote their truncated research as a Master’s project.

In a Nutshell

Needless to say, the dissertation vs. thesis facts are real. Therefore, using one term instead of another is not acceptable as an academic. One must remember the purpose of each and use them accordingly. However, one is not undermined by the other. Whether you are writing a thesis or a dissertation, both must be done with the same seriousness. Both require critical technical and soft skills. Improving your time management and academic writing skills plays a major role in acing both forms of scholarly writing.

How do you decipher dissertation vs. thesis? Should the interchanged usage of these terms be acceptable? How is your approach to writing a thesis different from that of a dissertation? What are the other differences associated with the thesis and dissertation? Let us know in the comments section below!

Frequently Asked Questions

"Dissertation" and "thesis" are used interchangeably but differ in: Academic Level: Thesis for master's, dissertation for doctoral degrees (US). Scope and Depth: Thesis shorter, demonstrates mastery; dissertation extensive, original research. Originality: Thesis may involve original analysis; dissertation presents significant new insights. Time and Effort: Dissertations require more resources and time than theses.

The length of a dissertation varies depending on factors like academic discipline, research topic, institution, and country. Generally, dissertations are longer than theses, ranging from 10,000 to over 100,000 words. However, word count alone does not reflect the quality or depth of the research. Guidelines from the academic institution should be consulted for specific requirements.

The length of a thesis varies depending on factors like academic discipline, research topic, institution, and country. Generally, the word count ranges from around 10,000 to 50,000 words. Specific guidelines from the academic institution should be consulted for precise requirements.

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Thesis vs. Dissertation: What’s the difference?

Thesis and dissertation are extensive research papers that differ in terms of their requirements, length, and purpose, with the former being associated with a master's degree and the latter with a doctoral degree, but are often used interchangeably.

Updated on September 15, 2023

A thesis and a dissertation are both extensive research papers, and both require literature searches and novel findings, but the two differ in various ways. Their definitions also differ across regions. Typically, in North America, a thesis is required for the completion of a master’s degree, while a dissertation is required for the completion of a doctoral degree. The former is long, while the latter is longer and more intensive.

Despite these differences, the two terms are often used interchangeably, especially among those who haven’t completed one or the other. Here, we’ll compare the components, length, and purpose of these two academic documents to clearly understand the differences between these important papers in the life of a graduate student.

What’s a thesis?

The term “thesis” explained here is generally consistent with how the word is used in North America to describe this substantive research paper.

A thesis is an extended argument (PDF). It is a research-based document that displays the student’s/author’s knowledge and understanding of a specific subject within their field of study. It generally presents findings on a particular topic. 

See this and this (PDFs) for examples. These superb master’s theses from Canada will give you an idea of the size and format of these papers.

Who would write a thesis?

You generally write a thesis if you’re undertaking a research-oriented master's degree program (as opposed to a practical program, which may require a capstone, internship, exam, etc.). 

The thesis is the essential part of a program’s research component, demonstrating the student's ability to critically analyze the literature and complete independent research. The process of writing a thesis involves exploring a specific research question, conducting a comprehensive literature review, collecting and analyzing data, and presenting findings in a structured and cohesive way.

A thesis' specific requirements and expectations differ depending on the academic institution, department, and program.

Components of a thesis

A thesis is typically presented in chapters. How many chapters will vary, but a common structure is:

• Introduction: Presents the research topic, purpose, and objectives, setting the context for the work.
• Literature review: Comprehensive survey of existing scholarly material related to the research topic, highlighting key theories and findings.
• Methodology: Describes the methods, procedures, and tools used in doing the research.
• Research: The actual performing of the study, collecting, and analyzing data relevant to the research question.
• Findings and conclusions: Gives the results obtained and explains their significance in relation to the research question.
• Limitations and future research: Outlines the study’s shortcomings and suggests potential areas for future investigation.

Within that structure, and in addition to those parts, a thesis may also include: 

• Cover page: Contains the thesis title, author's name, institution, department, date, and other relevant information
• Abstract : A brief summary of the thesis, highlighting the research objectives, methods, key findings, and conclusions.
• Certificates of own work
• Certificate of readiness to be included in the library
• Certificate that the research has not been presented to another university
• Acknowledgments
• Table of contents: List of the main sections, subsections, and corresponding page numbers.
• Index of figures and tables
• References: A comprehensive list of all the sources cited in the thesis, following a specific citation style (e.g., APA, MLA).
• Appendices (optional): Additional materials include:
• Abbreviations and/or acronyms used
• Questionnaire or interview schedule/s (if used)
• Data acquired in the form of transcripts or numeric tables
• Research protocol
• Ethics protocol

What’s a dissertation?

This is also viewed from a North American perspective, where a dissertation is usually the main research work toward completing a research-based doctoral program.

A dissertation is a comprehensive and in-depth research project completed as part of the requirements for a doctoral degree. It’s a substantial piece of original work that contributes new knowledge to a specific field of study.  Naturally, when it’s completed as the major requirement for earning a PhD, it’s longer, more detailed, and the expectations are higher.

Dissertations themselves can add to the literature in the field. For this reason, some students choose to publish them and have them indexed. The research and the data acquired while working on a dissertation can potentially lead to more publications and help define the researcher’s growing area of expertise.

See this and this (PDFs)  top-ranking dissertation on ProQuest for good examples.

Who would write a dissertation?

Completion and defense of a dissertation is a standard requirement for doctoral students to earn a PhD or another doctorate such as an EdD or DM. But some specialized degrees, such as a PsyD (Doctor of Psychology), JD (Juris Doctor) or DPT (Doctor of Physical Therapy) may have practice-based requirements in place of a research project, as these courses of study are geared more toward practical application.

Components of a dissertation

A dissertation’s components are generally the same as those of a thesis. You can look at the list above for a thesis to see what typically goes into a dissertation. But, if compared with a master’s thesis, most aspects are longer and more rigorous.

The word count requirements for theses can vary significantly, but doctoral dissertations often range 40,000–80,000 words or, per Harvard , 100–300 pages.

Differences between a thesis and a dissertation

As already touched on, the key differences are in where the two documents are used, length, and rigor. There are also regional differences.

A thesis typically demonstrates a master’s degree program student's grasp and presentation of a specific subject in their field of study. It normally involves a literature review, data analysis, and original research, but it is usually shorter and less comprehensive than a dissertation. The standards for rigor and novelty may also be lower.

A dissertation requires more extensive research, original contributions to the field, and a deeper exploration of the research topic. A dissertation is typically the output associated with a doctoral degree program.

The main differences in structure between a thesis and a dissertation are in the scope and complexity.

The word count requirement for theses and dissertations can vary depending on the institution and program.

A thesis is usually 20,000–40,000 words. However, there have been cases of mathematics dissertations that were only a few pages long!

Doctoral dissertations may range 60,000 to upward of 100,000 words, and exceed 100 pages. Many universities, however, seek around 80,000 words.

Oversight and process

A thesis may simply be submitted to the student's instructor, though rigorous thesis programs require a committee and defense. A dissertation will nearly always require the student to choose a chair, a committee, and then go through a more rigorous defense and revision (if necessary).

• Committee: Master's thesis committees usually have fewer members (typically 2–3) than doctoral dissertation committees (often 4–5, or even more).
• Guidance: Master's students often receive more detailed direction from advisers than doctoral students, who are expected to work more independently.
• Review: Dissertation reviews are typically more rigorous, often involving external reviewers, while thesis reviews are usually internal.
• Defense: A dissertation defense is generally more intense and formal, as it often involves a presentation to the wider academic community, while a thesis defense might be more confined and informal.
• Revision: The revision process for a doctoral dissertation is typically more extensive, given the larger scope of the project and higher stakes involved, compared with those for a master's thesis.

Regional differences

The terms' use varies among (and even within) countries. Here are some general regional differences:

In the United Kingdom, a thesis is commonly associated with both master's and doctoral degree programs. For example, the University College London  refers to a thesis for EngD, MPhil, MD(Res), and PhD degrees. At the University of Nottingham , a dissertation is written for a research master’s degree.

In Australia and New Zealand , “thesis” is generally used to refer to a substantial research project completed for a higher degree, though not limited to a master’s (you’ll find ample references to a “PhD thesis”).

In Latin American countries,  the thesis is commonly used to refer to both master's and doctoral research projects.

Closing thoughts

Both theses and dissertations are necessary documents for students in graduate programs. Despite the differences in expectations, and even in definitions of these papers, the student-author must do a diligent and rigorous job to earn their degree.

Here are a few helpful resources if you want to get into greater detail:

• Writing the Winning Thesis or Dissertation: A Step-By-Step Guide
• 100 PhD rules of the game to successfully complete a doctoral dissertation (PDF)
• Theses and Dissertations: A Guide to Writing in Social and Physical Sciences

Perfect the English on your thesis or dissertation

Whether you’re submitting a thesis or a dissertation, if it’s in English, it should:

• Have no grammatical or spelling mistakes
• Use field-appropriate language
• Concisely and clearly communicate your research.

That’s what AJE expert editors will do for you. Within days, you can receive an expert English edit of your work. The editor will be familiar with your field of study and will comprehensively improve both the language quality and the delivery of your message. Look into AJE English Editing .

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Masters Thesis vs. PhD Dissertation: Key Differences

Whether you are a graduate student just starting out in academia or a professor advising a student, making the distinction between a dissertation and a thesis is critically important to writing a strong dissertation and becoming a stronger writer. Unfortunately, the difference remains unclear since the terms are used interchangeably by graduate students, doctoral researchers, academic publishers & universities.

If you’re not sure whether you’re writing a thesis or a dissertation, this article will help you understand the differences between the two whether you’re a PhD or master’s degree student.

Main Differences Between a Dissertation and a Thesis

While theses and dissertations share many similarities (they are both advanced graduate research papers), they actually refer to two different types of academic writing, and their differences include important concepts such as scope, purpose, length, and research requirements.

Most importantly, the difference between a thesis and a dissertation depends on the level of education. Far beyond being a simple essay, a thesis is for graduate students pursuing a master’s degree while a dissertation is written by doctoral students, also referred to as PhD candidates.

There are a few key differences between a thesis versus a dissertation.

The biggest difference between a thesis and a dissertation is that a thesis makes arguments based on existing research. Meanwhile, a dissertation often requires the PhD candidate to conduct research and then perform an analysis.

More specifically, a thesis often takes the form of a literature review , which is a compilation of research knowledge in a particular field of study that proves one is competent in that subject. On the other hand, a dissertation is a more specific type of research paper written by those working toward a specific doctorate degree that contributes knowledge, theory, or methods to a field of study.

What is a master’s thesis?

A master’s thesis is an academic research paper that requires a greater degree of research than an undergraduate thesis or term paper. It is marked by a higher standard of writing, and students are expected to demonstrate competence, literacy, and mastery of a subject. It usually takes two or three years to complete. Finally, a master’s degree thesis is usually written in order to obtain a research degree and is not intended to be published separately.

What is a PhD dissertation?

A PhD dissertation is a substantial piece of independent research that is required of all students who are pursuing a doctorate degree. It is a piece of original work that has not been published elsewhere and, most importantly, makes a new contribution to the field. This contribution may be a new way of thinking about an existing topic or even a novel theory. The research performed for a dissertation is usually conducted over a period of several years to half a decade.

Features of a Master’s Thesis vs PhD Dissertation

Content and Structural Differences

So how is dissertation writing different from thesis writing?

Now that you know the definitions of a dissertation and thesis, let’s dive into some clear ways in which they differ in structure and other main characteristics.

How long is a thesis vs dissertation?

Length is the most obvious factor in differentiating between writing a thesis or dissertation. 

Generally, a doctoral dissertation has greater breadth, depth, and intention than a master’s thesis since it is based on original research. While the standard length of a master’s thesis is around 100 pages , a doctoral dissertation can be upwards of 400-500 pages. 

While most students can finish their PhD dissertation or thesis in as little as 1-2 years, it can take as long as 7 years depending on the school, program, and dissertation topic. As doctoral programs have their own formatting requirements, check with your school or university to find out what you need for your own dissertation or thesis. Most dissertations are organized into chapters, but the number of chapters varies as well.

Differences in research methods

A thesis and dissertation are both graduate-level research reports. This means they require students to investigate and report on a specific topic. But what is the difference in the scale of research between a master’s versus doctoral degree? The answer comes down to how much and what type of data you collect .

Data sources for a thesis vs dissertation

A master’s thesis is limited to secondary or reported knowledge . This knowledge has already been published, analyzed, and scrutinized in the literature. A thesis does not typically offer anything new in that regard. Your purpose is usually to write a comprehensive literature review on a novel or underreported topic using already-reported data.

On the other hand, a doctoral dissertation reports on  novel data  and is published so it can be scrutinized by others. It culminates in your dissertation defense.

The above lists clearly show that a PhD researcher and dissertation writer must have specific hands-on experience about not only the result of others’ research but also how the researchers obtained the data. A dissertation must venture into criticism of how other studies performed their experiments, whereas a master’s student will only report on and evaluate the results.

Differences in research scope 

As mentioned above, a thesis is more of a literature review written to demonstrate competence and mastery of a field of study. In short, you are a reliable “reporter” of information related to that subject. A thesis shows that you know the technical jargon, understand the subject, are familiar with industry tools, and can translate that information to a general audience. This is why a master’s degree is sufficient and often preferred for industry jobs.

In contrast, a doctoral dissertation goes beyond simply using the building blocks of your subject and actually creates new tools, knowledge, and theories to advance the subject as a whole. If a master’s degree holder is like a seasoned Rolling Stone journalist, then a doctorate is the band/musician who actually makes the music.

So should you pursue a thesis or a dissertation?

The benefits of earning a graduate degree are huge. According to the US Census Bureau , those with an advanced degree earn 3.7 times as much as a high school dropout, and 13.1% hold a master’s, professional, or doctorate degree. If you’re a curious undergraduate student thinking of applying to graduate school, which is the right choice?

In short, a dissertation is more focused and in-depth than a thesis. While a doctoral dissertation is based on original research, a thesis is often an extension or review of others’ research in order to demonstrate literacy. Further, a dissertation can be used as the basis or subject of a thesis, but not vice versa.

Editing a Dissertation vs Thesis

So far, we’ve focused a lot on differences such as research and purpose, but in the end, a thesis or dissertation is a written document that requires skill, focus, discipline, subject knowledge, organization, and scheduling. 

For non-native English speakers, the challenge is especially difficult since English is the lingua franca of academia and research. 

How does an editing service improve your dissertation or thesis ?

From body spacing and pagination, to font size and citation formatting, the dissertation guidelines are exhaustive. Even worse, they vary by school. So besides the actual English writing and grammar, graduate students must worry about consistency, formatting, nomenclature, and terminology. That’s quite the burden!

This is why it’s very common for graduate students, especially ESL and foreign ones, to seek out dissertation editing services that specifically cater to the academic needs of researchers and students.

Here are just a few reasons why dissertation proofreading is so helpful and what these editors do:

• Correct grammar, punctuation, syntax, and structural errors
• Offer suggestions to rewrite, remove, and revise writing
• Ensure formatting and nomenclature are consistent
• Knowledgeable academic editors with master’s and PhD degrees
• Free up your time to focus on research, revisions, and content instead of looking for mistakes
• Provide a  language editing certificate , which may be necessary for non-native English-speaking students

Lastly, most PhD advisors recommend that students seek out professional editing services , specifically thesis editing or dissertation editing , since professors prefer to assess the actual research content of a dissertation, not mundane writing errors. Any graduate student reading this knows professors don’t like their time to be wasted! 

Be sure to check out other academic resources on how to improve your academic manuscript and the benefits of proofreading and editing.

And try the Wordvice FREE Citation Generator, which provides citations for four academic formatting styles:  APA Citation Generator , MLA Citation Generator , Chicago Citation Generator , and Vancouver Citation Generator .

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Home » Dissertation vs Thesis – Key Differences

Dissertation vs Thesis – Key Differences

Dissertation vs Thesis

“Dissertation” and “ thesis ” are often used interchangeably, but there are some differences between them, depending on the context and country in which they are used. Here is a brief overview of their differences:

In the United States and Canada, a thesis is usually associated with a master’s degree, while a dissertation is associated with a doctoral degree. A thesis involves original research and is usually shorter than a dissertation, with a typical length of 50-100 pages. A dissertation, on the other hand, is a longer piece of original research, with a typical length of 100-300 pages or more.

In the United Kingdom, the opposite is true: a thesis is usually associated with a doctoral degree, while a dissertation is associated with a master’s degree. However, the terms are sometimes used interchangeably.

In some other countries, such as Australia and New Zealand, the terms “thesis” and “dissertation” are used interchangeably, and the length and content of these documents depend on the degree program and the requirements of the institution.

In general, both a thesis and a dissertation involve original research and are used to demonstrate the candidate’s expertise in a particular field of study. However, the specific requirements and expectations for each may vary depending on the degree program, institution, and country.

Both a thesis and a dissertation typically involve conducting original research and presenting findings in a formal document. They often include a literature review, methodology section, analysis of data, and conclusions based on the findings.

The purpose of both a thesis and a dissertation is to contribute to the body of knowledge in a particular field and demonstrate the student’s mastery of the subject matter. They are also important for advancing academic and professional careers in fields such as academia, research, and policy-making.

While the requirements and expectations for a thesis or dissertation may vary, it is important for students to work closely with their advisors and follow all guidelines provided by their institution. This includes adhering to formatting and citation styles, conducting ethical research, and submitting drafts and revisions in a timely manner.

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Hominins, Apes, and the Imago Dei

by Dylan Belton September 05, 2024

T he Swedish naturalist and botanist Carl Linnaeus caused a storm of controversy in the eighteenth century by classifying  homo sapiens in the same genus ( homo ) as the recently discovered orangutan and by placing the genus homo within the order of primates. Linnaeus did not deny the unique dignity of human beings. He did, however, regard it as a matter for religion, not the naturalist whose method of classification—as Linnaeus practiced it—was based primarily upon similarities and differences in structural morphology. Calling it a “humiliating truth for humans,” the preeminent French natural historian, Georges-Louis Leclerc de Buffon, saw Linnaeus’s taxonomy as dangerously blurring the lines between apes and humans.

According to Buffon, morphological resemblances are not as important as are capacities such as reason or language. If we focus instead on the latter, then the morphological resemblances indicate only, in Buffon’s striking language, that the orangutan is “a pure animal, wearing a human mask.” [1] Language (or reason) renders us different in kind (or qualitatively different) from all other living beings. Such human exceptionalism found its greatest scientific challenge in the nineteenth century with the publication of Darwin’s Origin of Species in 1859 and his The Descent of Man in 1871. Ending his silence on human evolution in the latter text, Darwin famously argued for a difference in degree between humans and the other “higher” animals: “the difference in mind between man and the higher animals, great as it is, is certainly one of degree and not of kind.” [2]

We now, of course, have far more knowledge about human origins and our evolutionary relation to other species than Linnaeus, Buffon, and even Darwin possessed. But the issue of whether there is a difference in kind or degree between ourselves and other species continues to be fraught with scientific, political, ethical, economic, and religious controversy. [3] In fact—and as will be discussed in more detail below—the issue has become even more complex given the increased knowledge about the multiple extinct hominin species that we have gained over the past century or so. Most importantly for our purposes here, the claim that there is a difference in kind has long been central to what we may call the “capacities approach” to the imago Dei within Christian theology. Stated in its classical formulation, our possession of reason and will renders us different in kind from other animals and, in turn, renders us in the imago Dei . In Aquinas’s terse formulation: “things that do not have an intellect are not made in the image of God.” [4] For this capacities approach, clarifying the differences between humans and non-humans is intrinsic to its account of the imago Dei . In other words, it operates in an interspecies-comparative framework.

An increasing number of scholars within the humanities and sciences have, however, argued that it is no longer possible to uphold a difference in kind. According to the primatologist Frans de Waal, the belief in a difference in kind perpetuates because we have been habituated into the misguided practice of anthropodenial —that is, the rejection of human-like traits in other animals. [5] Indeed, for many, this practice implicates us in an anthropocentric-humanist tradition whose fruits have been ethically and ecologically catastrophic. [6]

Such claims—as well as developments within theology to be touched on at the end of this essay—cast a shadow of suspicion over the capacities approach to the imago Dei . But it seems to me that, while dismissals of the “difference in kind thesis” (DKT from now on), are quite common in both scholarly and non-scholarly works alike, such dismissals often operate without an adequate articulation of what “difference in kind” might actually mean or entail. In light of this, I want to explore the following question: what exactly does it mean to claim that human beings are different in kind from other animals and not simply different in degree? As I will suggest, there are different takes on DKT, what I call here the weak and strong versions. To be clear, it is not my intention to defend DKT. I only offer some clarifications concerning what these versions might entail and what a defense of them might look like for those who are so inclined. Having done so, I will dwell a bit in the conclusion upon what import any of this might have for those who adhere to the capacities approach to the  imago Dei .

Challenges to the Difference in Kind Thesis

DKT faces two primary and interrelated challenges. The first we may call the Darwinian challenge. This challenge stems from the axiom in evolutionary theory that all evolutionarily relevant changes are incremental. With the combination of Darwin’s account of natural selection and the new science of genetics in the twentieth century, evolutionarily relevant changes came to be seen as only those that can be passed on genetically. These genetic changes are always incremental and, moreover, random insofar as they are not teleologically ordered toward a beneficial outcome for the organism.

For this to work as a challenge to DKT, the assumption must be that incremental changes are incompatible with there being differences in kind between species. By itself, the challenge amounts to very little. It is difficult to maintain that there is only a difference in degree between, say, an elephant and the earliest vertebrates living some 500 million years ago. An elephant is not simply more of the same as these earliest fish-like vertebrates. Both are, however, part of the same evolutionary process whose changes have been incremental. Genuine novelty can and does emerge within the evolutionary process where changes are incremental.

Already here the reader might note the ambiguity surrounding the phrases “difference in degree” and “difference in kind.” By “difference in kind,” do we mean something like “genuinely new”? In what sense exactly? Each species can be said to be genuinely new. But it does not seem correct to speak about every species as being different in kind from other species, especially when we are talking about species in the same evolutionary lineages. What about “difference in degree”? Does it mean that there are similarities? Similarities in what sense exactly? After all, an elephant is in certain ways similar to the earliest vertebrates.

Darwin is of some help at this point. Following his own logic in The Descent of Man , we might say that difference in degree is not just a matter of similarity but of a causal relation to a particular evolutionary lineage. As Darwin argued, the emergence of human morality—which he considered the most distinctively human characteristic—can be explained in purely naturalistic terms. We need only assume that our ancestors lived in highly social groups, had strong social instincts, and were subject to group selection. It is then possible to give an evolutionary-causal account of how human morality—or at least what Darwin understands by this—gradually evolved from non-human ancestors.

The Darwinian challenge can therefore be reframed in the following manner: there is an evolutionary explanation of the distinctively human capacities according to which they evolve gradually within a specific evolutionary lineage. There is no need to posit, say, a special divine act or something like an immaterial soul in order to explain how we became human.

This brings us to the second interrelated challenge to DKT—what I call the ethological-paleoanthropological challenge. The gist of this challenge is the following: a wealth of data has emerged from comparative ethology and paleoanthropology over the past few decades which demonstrates that other great apes and certain extinct species in our genus homo (e.g., homo habilis, homo neanderthalenis, homo erectus , and so forth) are/were capable of types of behavior long thought of as belonging solely to the human being. To name but a few: tool-use, foresight, culture, deceit, theory of mind, organized hunting, shelter construction, burial practices, and morality. [7] This second challenge to DKT therefore amounts to the claim that the presence of such capacities in certain other species to which we are closely related (evolutionarily speaking)—i.e., the great apes and the other extinct species in the genus homo —indicates that there is only a difference in degree between ourselves and these specific species. Given what we now know about the capacities of certain other species, we can now provide an evolutionary-causal account of how we got from non-humans to humans with even more confidence than Darwin could.

Two Versions of the Difference in Kind Thesis

Responding to these challenges requires making clearer what exactly such a defender has in mind when claiming that there is a “difference in kind” between humans and other animals. As I suggest here, we can make a distinction between a weak and strong version of DKT, the former of which is relatively uncontroversial and is in fact a position held by many scientists. The strong version is more controversial and is—I believe—what most have in mind with DKT.

The Weak Version

Let us begin with the less controversial weak version. The first thing to note is that, despite the regular publication of (often bestselling) books that emphatically assert our fundamental “ape-ness” (e.g., The Naked Ape , The Third Chimpanzee ), claims about human distinctiveness in relation to the other great apes are in fact widespread in the scientific literature. As the anthropologist Robin Dunbar writes, “It is surely obvious to everyone that we are not ‘just great apes.’” [8] To offer but two examples: first, the discipline of cultural anthropology established itself upon a sharp divide between the symbolic human domain and the non-symbolic domain of the great apes. [9] Although this sharp divide is increasingly challenged within anthropology, the capacity for symbolism is still considered by many anthropologists to be distinctively human. [10]

Second and more recently, the extensively researched work of the comparative psychologist Michael Tomasello has demonstrated that humans possess a specific kind of intentionality—what he calls joint intentionality—that other apes lack. Joint (or “we”) intentionality—i.e., the ability to understand another as an intentional agent with whom I can share a goal and who can adopt a specific role in pursuit of it—is the key cognitive capacity for understanding the evolution of our genus  homo . It undergirds the unique forms of collaborative, cultural, and moral activity that we do not find among the other great apes. [11]

Much of this literature can ground what I am calling the weak version of DKT. According to this version, humans are different in kind from the other great apes (and other mammals) in the following sense: despite our being primates and our sharing many morphological and behavioral traits with other great apes, humans are also members of a different evolutionary group ( hominin ) that originated some six to seven million years ago and that—depending on the authority being consulted—includes the genera of Homo , Australopithecus , and Paranthropus . We possess distinctive characteristics that emerged during the evolutionary history of this specific hominin lineage, both prior to and after the emergence of our own genus ( homo ) roughly two to three million years ago. How exactly these distinctive characteristics are defined and when they are said to have first emerged may differ among scholars, but what is shared is the claim that humans are not simply “more great ape.” We belong to a distinct group ( hominin ) and genus ( homo ) in which—for whatever reason—specific traits developed that are not possessed by the great apes. A Planet of the Apes scenario where the great apes are envisioned as being on the verge of attaining human modes of cognition and behavior is therefore misguided.

By focusing on our evolutionary relation to the other great apes, this weak version of DKT gives some specific content to “difference in kind.” It is, however, uncontroversial within the scientific community. It is also not uncommon among philosophical treatments of human distinctiveness, many of which focus on how language—as distinct from the expressive signals of other animals—renders distinct our mode of being in the world compared to other apes. [12] This weak version also borders on triviality, since it entails that all other living beings are different in kind from those they do not share a specific evolutionary lineage with. Human beings are therefore nothing special in this weak version of DKT.

The Strong Version

This weak version is not what many have in mind when claiming that humans are different in kind from other animals. Usually intended is what I call the strong version of DKT. In light of what we now know about other species in the genus homo , this version can be characterized as consisting of two claims: first, that, when it comes to behavioral and cognitive capacities, there is a difference in kind between not just ourselves and the other great apes but also other species in the genus homo .

Second, that  homo sapiens represents something like a breakthrough to a new ontological domain (for lack of a better phrase). The difference between human beings and all other animals is framed here as akin to the difference between the plant domain and the animal domain. In more classical Aristotelian terminology that has been so influential on Catholic understandings of human nature, human beings alone have a rational soul. Other living beings possess only vegetative and/or sensible souls. In more recent metaphysical language articulated by, for instance, Max Scheler, the human being represents a breakthrough to the realm of “spirit” ( Geist ), while all other living beings remain in the realm of “life” ( Leben ). [13] Those who uphold this position can explain this breakthrough by means of either a natural-evolutionary process or special divine intervention.

These are more controversial claims. The first claim is, however, not in fact foreign to the scientific community. It is not possible to go back in time and see exactly what the other species in our genus were like. For those of a more speculatively sober disposition, this might rule out the possibility of any defense of the strong version of DKT. But perhaps this is an overly restrictive position on what we can or cannot say about these species. Based upon archaeological evidence, many paleoanthropologists do make inferences about the social lives and cognitive capacities of extinct species in the genus homo . Tool creation, for instance, is present in other species in the genus homo , but we find that some later tools are more complex and perhaps even give evidence of the utilization of aesthetic criteria in their construction. For some at least, this suggests the presence of different cognitive capacities in the species responsible for the more complex tools.

Furthermore (and as noted earlier), it is also a common position among anthropologists that symbolism is a distinctive mark of our own species ( homo sapiens ) who—genetically and anatomically speaking at least—emerges on the evolutionary scene roughly 300,000-150,000 years ago. Examples of early evidence of symbolism include bead technology and engraved objects. By 50,000 years ago, we find in the archaeological record what may be called a “symbolic (or ‘cultural’) explosion,” evidenced by, for instance, cave paintings and stone or wood figurines. [14] Moreover, strong cases have been made that language and symbolism co-evolved. This is, of course, a philosophical matter, since there is no way to go back in time to prove this. Nonetheless, if it is the case, then it suggests that we are the only species with language. [15]

Perhaps a defender of the hard version of DKT could make his or her stand here. That is, the defender might assert that, in relation to the other species in the genus homo, homo sapiens as a symbolic-linguistic species is something different in kind. To borrow from Ernst Cassirer’s formulation, homo sapiens alone is homo symbolicus . [16]

This position is not without its scientific advocates. It faces difficulties, however. For instance, the archaeological record is imperfect and ambiguous: imperfect in the sense that evidence of more sophisticated symbolism in other species may simply be lost to us; ambiguous in the sense that, depending on what one counts as “symbolic,” there is evidence of symbolic-like behavior going back roughly 400,000 years ago. [17] What are we to make of this deep history? Older indications of symbolism might not be as elaborate as, say, the cave paintings in the Chauvet Cave in France, but the more sophisticated indications of symbolism do not arrive de novo . The presence of symbolism does, moreover, not by itself indicate qualitatively different innate cognitive capacities.

This falls prey to what Kim Sterelny calls the “simple reflection model”—that is, an assumption that differences in cultural expressions in the archaeological record directly reflect differences in intrinsic cognitive capacities. [18] This is not always the case, however. Certain cognitive capacities (e.g., doing calculus) exist only in environments that support them; they do not say anything about innate cognitive capacities. Symbolism in the archaeological record therefore need not indicate the presence of qualitatively new innate cognitive capacities. Finally, other species in the genus homo must have possessed at least proto-linguistic communicative capacities that go beyond what we find in the great apes and other extant mammals. We can, of course, only conjecture, but these would have been bodily-gestural systems (akin to sign language) or spoken communicative systems that were more basic than our own linguistic systems. Either way, the point here is that these proto-linguistic communicative systems would not have been equivalent to the communicative systems that we find in, say, the great apes. Whatever they were like, it is much harder to draw a sharp divide between these proto-languages and human language.

Note that these considerations create problems for the second claim of the hard version of DKT—namely, that homo sapiens represents something ontologically new, indeed, something that might even require a special divine act in order to explain. But what exactly constitutes the ontological novelty, especially when we are viewing ourselves in relation to other species in our own genus? The boundaries between species is here highly porous. And for those who might want to resist a purely natural-evolutionary explanation of this breakthrough, why exactly does such an explanation not suffice in light of the clear continuities between ourselves and other species in the genus homo ?

We might be tempted at this stage to say that a claim about an ontological difference is a matter of metaphysics or theology alone. This is correct to some degree, but it is also not quite true to the spirit of the harder versions of DKT as represented by the likes of Aristotle and Scheler, both of whom hold themselves accountable to the empirical data. In Scheler’s case (and to his credit), he developed his account of Geist in close dialogue with the latest studies on animal behavior in the early twentieth century. In light of this material, Scheler was very generous with what he grants certain other animals (e.g., chimpanzees) in terms of their capacities—for instance, that they exhibit practical intelligence (i.e., do not just act out of innate instinct), have proto-concepts, and make genuine choices.

The new data on animal behavior indicate to Scheler that what is distinctively human is not to be located in the practical domain and the mode of means-ends practical reasoning ( Intelligenz ) that governs this domain. Intelligenz is a mode of reasoning that we share with other animals. Rather (and here he follows Aristotle closely), what is distinctively human is the capacity to suspend our practical orientation toward the world and, corresponding with this, the capacity to grasp universals and attain knowledge of essences (or “metaphysical knowledge”). For Scheler, this represents a cognitive revolution that, so to speak, lifts the human being above (but not totally out of) the domain of (animal) “life.”

There is much in such accounts that remains insightful. The problem here, however, is that Scheler (and of course Aristotle) does not focus on the other species within the genus homo . The hard version of DKT often feels more intuitively compelling because we are thinking of ourselves in relation to the other extant animals (normally the chimpanzee). But things become far more complicated when we take into consideration the extinct species within our genus. It is difficult to imagine a creature who is, say, controlling fire, making tools and shelters, living in intensely social groups with a proto-language, and burying its dead as having merely animal reason ( Intelligenz ) and, indeed, as not having some system of human-like morality and conception of God (or gods). It seems fair to say that the classical philosophical and theological accounts of human distinctiveness are not adequate enough when it comes to dealing with the other species within the genus homo . Doing so requires the development of new categories and more fine-grained conceptual distinctions than what we find in classical expressions of the hard version.

The Imago Dei and the Future of Theological Anthropology

Let us return at this point to the theological issue at stake here—namely, the capacities approach to the imago Dei . What importance do the above considerations have for the capacities approach that—historically at least—has rested upon a hard version of DKT? There are perhaps four possible tactics with which to deal with the issues that have been raised here:

1. First, double down on the hard version of DKT. It is indeed defensible, but—as suggested above—it requires more careful work than what we find in the classical theological and philosophical accounts of human distinctiveness. Alternatively, one justifies the hard version solely upon, say, scriptural revelation.

2. Second, drop the hard version and claim that a difference in degree in relation to other species in the genus homo is all that is needed in order to limit the imago Dei to the human being. Those who adopt this tactic could perhaps remain agnostic on the question of what exactly demarcates the different degrees. Otherwise, defending this position would require some nuanced work on what exactly demarcates the different degrees and why another species that, say, buries its dead, has culture, uses tools, and so forth has not crossed the threshold that is needed in order to be in the imago Dei .

3. Third, extend the imago Dei to certain (or all?) other species in the genus homo . There is nothing intrinsically contradictory here. However, putting aside the difficulty of reconciling it with both scripture and tradition, the thought of multiple species in the imago Dei going extinct is an interesting challenge.

4. Finally, dismiss the capacities approach to the imago Dei , thereby rendering the data of no real significance for theological anthropology. To do so would be to move in the direction of developments in theological anthropology in the past few decades. Rejecting the capacities approach, many contemporary accounts of the imago Dei focus instead on, for instance, relationality, function, or Christology. Furthermore, many theologians have proposed that a biblically based approach to the image of God does not require an account of distinctively human capacities. Genesis 2:7, for instance, does not say anything about human reason or will. The breath of life that God breathes into Adam is not a special human soul but the same breath breathed into all other living beings. The New Testament places its emphasis on Jesus as the image and likeness of God and our transformation into his image. Contemporary theologians have also argued that the capacities approach creates problems for people with, say, cognitive disabilities. Do they image God in a lesser manner? Are they closer to other animals? Finally, some theologians argue that evolutionary theory has rendered the notion of an “essence” highly problematic and that the capacities approach presumes that such neatly defined essences exist.

For these types of reasons, many contemporary theological accounts of the imago Dei have critiqued and moved away from the classical capacities approach. [19] This has led to an enrichment of theological anthropology in multiple ways. But let me conclude here with this final—albeit underdeveloped—thought: perhaps this shift has come with a certain loss—namely, a loss of the consideration of animals and their capacities as an intrinsic part of theological anthropology? As indicated earlier, the capacities approach is interspecies-comparative in methodology and so always has other animals in its purview. What theologians who follow the capacities approach say about other animals may, of course, be superficial or mistaken, but this is not always the case. Here I think, for instance, of Aquinas’s subtle treatment of the estimative power in (certain) other animals and its equivalent in the human soul, namely, the cogitative power. [20] Perhaps one way to resist the habit of anthropodenial and to retrieve attention to other animals as an intrinsic part of theological anthropology would, then, be to re-adopt and reform the capacities approach to the imago Dei ? [21]

[1] Raymond Corbey, The Metaphysics of Apes (Cambridge: Cambridge University Press, 2005), quoted on p. 49.

[2] Charles Darwin, The Descent of Man (Princeton: Princeton University Press, 1981), 105.

[3] An estimated 100 billion animals are eaten each year, a practice that presupposes that the suffering of certain animals is qualitatively different from our own. Influential humanist ethical and political theories are, moreover, built upon the assumption that there is a difference in kind between humans and all other animals who are not considered ethical or political agents.

[4] ST I, q93, a2.

[5] Frans de Waal, Are We Smart Enough to Know How Smart Animals Are? (New York: W.W. Norton & Company, 2016), 25.

[6] For a provocative theological argument along these lines, see Eric D. Meyer, Inner Animalities : Theology and the End of the Human (New York: Fordham University Press, 2018).

[7] For a helpful overview of other hominins, see Augustin Fuentes, Why We Believe? (New Haven, CT: Yale University Press, 2019). For other non-human animals, see Frans de Waal, Are We Smart Enough, op.cit. The question of animal culture is a contentious one. Those who maintain that animals have culture point to traditions in social animals that are specific to particular groups instead of the species as a whole. See Kevin N. Laland and Bennett G. Galef ed. The Question of Animal Culture (Cambridge: Harvard University Press, 2009).

[8] Robin I.M. Dunbar, “Why Humans Aren’t Just Great Apes,” Issues in Ethnology and Anthropology 3. no. 3 (2008): 16.

[9] Cultural anthropologists in particular have therefore emphasized the limitations of attempts at uniting the social and other life sciences (e.g., biology and psychology) via some sort of Darwinian logic (as we see in sociobiology, for instance). For a classic expression, see Marshall Sahlins, The Use and Abuse of Biology (Ann Arbor: The University of Michigan Press, 1976).

[10] See especially Terrance Deacon, The Symbolic Species (New York: W. W. Norton & Company, 1997). See also Agustin Fuentes, Why We Believe .

[11] Michael Tomasello, A Natural History of Human Thinking (Cambridge: Harvard University Press, 2014).

[12] For a recent expression of this view, see Charles Taylor, The Language Animal (Cambridge, MA: The Belknap Press of Harvard University Press, 2016). For Taylor, what human language is and how it emerged cannot be explained if we think of it as just “more” of what we find in the communicative systems of great apes (and other social animals).

[13] Max Scheler, The Human Place in the Cosmos , trans. Manfred S. Frings (Evanston, IL: Northwestern University Press, 2009).

[14] This gave rise within the literature to the so-called “[ homo ] sapiens paradox”: genetically and anatomically speaking, our species ( homo sapiens ) emerges on the evolutionary scene roughly 200-150,000 years ago. Behaviorally speaking, however, it is only roughly 50,000 years ago that we find widespread archeological evidence of cultural practices that we recognize as “behaviorally modern” (i.e., without a doubt “human”). The paradox is: why the lag between anatomically modern humans and behaviorally modern humans? For a helpful discussion, Kim Sterelny, “From Hominins to Humans: How Sapiens Became Behaviorally Modern,” Philosophical Transactions: Biological Sciences 366, no. 1566 (2011): 811.

[15] Deacon, The Symbolic Species.

[16] Ernst Cassirer, An Essay on Man (New Haven: Yale University Press, 1944). The defender could perhaps go even further at this stage and assert that this breakthrough places human beings within a new genus that contains only one species—namely, us. This was a move made by Johann Friedrich Blumenbach in the 1770s. Blumenbach split the order of primates in two, placing human beings in a biological order of their own—namely, Bimanus (two-handed). It contains one genus ( Homo ) and one species ( homo sapiens ). See Corbey, The Metaphysics of Apes , 50. This move is not one we find in contemporary scientific literature.

[17] Mark Kissel and Agustin Fuentes. “Semiosis in the Pleistocene,” Cambridge Archaeological Journal 27, no. 3 (2017): 397–412.

[18] Sterelny, “From Hominins to Humans.”

[19] See Kathryn Tanner, Christ the Key (Cambridge: Cambridge University Press, 2010); Joshua M. Moritz, “Evolutionary Biology and Theological Anthropology,” in The Ashgate Research Companion to Theological Anthropology , ed. Joshua R. Farris and Charles Taliaferro (New York: Ashgate Publishing, 2015), 45-56. For a discussion of disability, see Lorraine Cuddeback-Gedeon, “Disability: Raising Challenges to Rationality and Embodiment in Theological Anthropology,” in T&T Handbook of Theological Anthropology , ed. Mary Ann Hinsdale, I.H.M. and Stephen Okey (New York: T&T Clark, 2021), 333-345. It seems to me, however, that whether or not they make it explicit, contemporary theological anthropologies that claim to move away from a capacities approach still often presume something distinctively human, however it may be defined (e.g., “plasticity” or “relationality/sociality”). The only other option here is to render to image a matter purely of God’s elective will, having nothing to do with any capacity.

[20] Aquinas, Summa Theologiae I, q78, a4. The estimative power is a power of judgment concerning the value of objects or situations in relation to the animal in question.

[21] For an example of the most interesting and creative work in this direction, see Celia Deane-Drummond, The Wisdom of the Liminal (Grand Rapids, William B. Eerdmans Publishing Company, 2014) and Theological Ethics through a Multispecies Lens , vol. 1 (Oxford: Oxford University Press, 2019).

Featured Image: Chimpanzee mosaic at Belgrade Zoo, photo by antomoro; Source: Wikimedia Commons, Free Art License 1.3 .

Dylan Belton

Dylan Belton received his PhD in systematic theology from the University of Notre Dame. He has taught at Villanova University and at Providence College. He currently lives in Berlin. 

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June 20, 2024 | Dylan Belton

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Using the difference-in-differences design with panel data in international business research: progress, potential issues, and practical suggestions

• Published: 02 September 2024

Cite this article

• Jiatao Li 1   na1 ,
• Han Jiang 2   na1 ,
• Jia Shen 3 ,
• Haoyuan Ding 4 &
• Rongjian Yu 5  

Avoid common mistakes on your manuscript.

Difference-in-differences (DID) is a popular design of causal inference in social science research. The rationale is to identify a particular event (the treatment) that influences some subjects (the treated group) but not others (the control group) and compare the differences in an outcome of interest between the treated and control groups before and after the treatment. This design constitutes a quasi-experiment that helps rule out confounding effects. As such, when applied with panel data (or, time-series cross-sectional data), DID design offers robust causal inference in terms of Granger causality between the time series of the treatment and that of the outcome of interest.

This rationale of DID design aligns well with the nature of international business (hereafter, IB) research. Firms’ IB practices are jointly shaped by location-specific factors in different countries and firm- and industry-specific factors in global value networks. Therefore, IB research often seeks to unveil the cause-and-effect implications (or, treatment effects) of critical events that shift these factors at the country, industry, or firm level for firms’ global strategies and international operations. Moreover, as the impacts of these critical events in the IB contexts often last over years and affect different firms at different time points, IB research commonly relies on panel data to capture such treatment effects. In this regard, DID design offers a superior empirical solution in line with such key features of IB research. Commensurately, ever more studies using DID design with panel data have been published over the past decade. For example, among the 131 empirical studies published in the Journal of International Business Studies between 2020 and 2022, a sizable portion (18 studies, 13.0%) applied DID design with panel data as at least part of their empirical methodologies.

Notably, recent research has begun to recognize that, in many circumstances, DID design with panel data could obtain biased casual estimates and thus ought to be applied with caution (e.g., Goodman-Bacon, 2021 ; Sun & Abraham, 2021 ). In light of such methodological progress, it is both pertinent and valuable for us to review the progress and potential issues of the application of DID design with panel data in the extant IB literature in the spirit of offering practical guidance to help enhance the rigorousness of future IB research.

DID design with panel data

In practice, the simplest DID design (or a “2 × 2” design) is often estimated as follows:

where \(Treated_{i} { }\) indicates whether subject i belongs to the treated or control group, and \(Post_{t}\) indicates whether an observation is from a time t before or after the treatment. The estimand is the averaged change in the outcome among the treated subjects before and after the treatment ( y i,t (pre) − y i,t (post) ), i.e., the average treatment effect on the treated (ATT). If the ATT is significantly different from the outcome change of the control subjects, we can infer a significant treatment effect.

Take the Sino–US trade conflict starting in 2018 as an example. One may reasonably wonder if this shock could drive the United States to replace China as source of imports with other countries geographically adjacent to China (e.g., Vietnam, Thailand, India, etc.). Such treatment effects of the trade war can be tested with a 2 × 2 DID design based on the US annual import data in 2017 and 2018. In this design, the annual imports of the US from each of China-adjacent countries serve as the treated group, and the imports from other countries (excluding China) as the control group. Should US imports from China-adjacent countries, on average, increase more in 2018 than those from other countries, we can infer a significant treatment effect of the trade war in terms of driving the US to relocate its imports from China to adjacent countries (rather than to the rest of the world).

Notably, the validity of this 2 × 2 DID design rests on a key assumption: The assignment of treatment is random and exogenous to any pre-existing difference between the treated group and the control group. In the above example, it is apparent that whether a country was adjacent to China (which determined whether it was in the treated or control group) was not related to the breakout of Sino–US trade war in 2018. As such, the assumption of random and exogenous treatment is met.

Compared with the basic 2 × 2 DID design, DID design with panel data allows the outcome of interest for a treated subject to be observed in multiple time periods before and after receiving the treatment, thus allowing unobserved subject- and time-specific heterogeneities to confound with the treatment effects. In this regard, researchers often perform DID estimations with panel data using two-way fixed effects (TWFE) regressions to control for subject- and time-fixed effects. If all treated subjects receive the treatment at the same time, the TWFE DID estimation manifests as follows:

where \(\gamma_{{i{ }}}\) indicates subject-fixed effects, and \(\delta_{{t{ }}}\) indicate time-fixed effects. Note that the main effects of \(Treated_{i}\) and \(Post_{t}\) are fully subsumed into the subject-fixed and the time-fixed effects. The ATT is calculated as the variance-weighted average change in the outcome over multiple periods.

Along with the fixed-time treatment above, DID design with panel data also allows different subjects to receive the treatment in different time periods (i.e., time-varying treatments). In that case, the treatment group in each time period consists of subject-time observations of those receiving the treatment in that period. In the meanwhile, the control group for that period may consist of three scenarios: (1) the observations for subjects that never received treatment throughout the entire observation window (i.e., never-treated subjects ), (2) those for subjects that received the treatment after the given period (i.e., to-be-treated subjects ), and (3) those for subjects that received the treatment prior to the given period (i.e., already-treated subjects ). In such a manner, the ATT estimand of such time-varying treatment is the variance-weighted average of a series of time-specific 2 × 2 DIDs from each time period in the observation window. With the TWFE estimation, it is estimated as follows:

where \(\gamma_{{i{ }}}\) is the subject-fixed effects, and \(\delta_{{t{ }}}\) is the time-fixed effects. In this model specification, the post-treatment dummy \(Post_{i,t}\) is equivalent to the interaction between \(Treated_{i}\) and \(Post_{t}\) in a fixed-time treatment design, the main effects of which are subsumed by the two fixed effects.

We can also illustrate the above DID design with panel data using the example of the Sino–US trade war. By extending the observation window to cover US annual imports before 2017 and after 2018, we can build a panel dataset consisting of sourcing country-year observations to test the treatment effects of the trade war on the relocation of US imports. In this design, all China-adjacent countries serve as the treated group, and all other countries (excluding China) as the control group. 2018 and subsequent years serve as the post-treatment period. If US imports from China-adjacent countries increased more quickly than those from other countries after 2018 after controlling for country- and year-fixed effects, we can infer a significant Granger causality through which the trade war stimulated the growing time-series trend of US import relocation to China-adjacent countries.

It is also worth noting that, in practice, the Sino–US trade war did not affect all industries all at once in 2018 as an overarching policy shock. Instead, the US government gradually expanded the list of entities and products to enforce punitive tariffs, thus involving more industries and firms in the trade war over time. As such, the impacts of the trade war on US imports can be tested at the industry level in a time-varying manner, with different industries receiving the treatment at different time points. Such industry-level treatment effects can be tested with a panel dataset consisting of industry-sourcing country-year observations. In each year, the treatment group consists of the annual imports of the US from each of the China-adjacent countries in each industry newly added to the punitive tariff list in this year. The control group includes annual imports from all non-China-adjacent countries in all industries, those from China-adjacent countries in industries that were not yet added to the punitive tariff list in the given year, and those from China-adjacent countries in industries that were already on the list prior to this given year. If US imports from China-adjacent countries, after controlling for industry-, country-, and year-fixed effects, increased more quickly over time than those from other countries after an industry was added to the punitive tariff list, we can infer the industry-level treatment effects of the trade war on the import relocation of the US.

Like the 2 ×2 DID design, the validity of the above Granger causalities inferred from TWFE DID estimations also hinges on the randomness and exogeneity of the treatment assignment. In DID design with panel data, this assumption requires the treated and control subjects share the comparable or parallel time-series trends in the outcome of interest prior to the occurrence of the treatment (i.e., the “ parallel trend assumption ”). To meet this assumption, whether and when a subject receives the treatment should be random and free of time-varying confounders, and it cannot be related to the subject’s past outcome of interest. In the second sample design above, this assumption requires that whether and when an industry was added to the punitive tariff list was not determined by the US imports in this industry from China-adjacent countries prior to the trade war. If this is the case, we can observe no systematic deviation in the time-series patterns of industry-level imports of the US from China-adjacent countries and other countries before an industry was added to the list.

Moreover, since the treated subjects in DID design with panel data can have multiple post-treatment observations, this design also requires the stability of post-treatment trends. Such stability is hinged on two assumptions that rule out systematic differences among never-treated, not-yet-treated, and already-treated observations in the control group from different time periods. First, the treatment occurring in a given time is assumed to only affect the treated subjects’ outcomes in that period without lingering impact afterward (or, carryover effect). This is termed the static treatment assumption . Second, since different subjects may be treated at different times, the treatment effects are assumed to remain constant over time no matter when a subject receives the treatment. This is termed the constant treatment assumption . Violating either of the post-treatment trend assumptions would confound the already-treated observations and contaminate the control group, thus biasing TWFE DID estimations with panel data. In such a manner, for the second sample design above to generate unbiased estimations, it requires the trade war to drive the US to relocate imports only in the year of its breakout in a given industry, and to affect all industries equally over time.

DID design with panel data in IB research: Progress and issues

Notably, recent econometric studies (e.g., Baker, Larcker, & Wang, 2022 ) brought to our attention that, in prior studies using DID design with panel data, the violations of the three assumptions above are rather common. In research contexts filled with major events that can affect broad and diverse subjects at multiple levels and over long-time spans (like the IB contexts), the violations could be even more pronounced (Liu et al. 2021 ). To investigate whether DID design with panel data has been used in the IB literature as effective as it should be, we conduct a comprehensive literature search to systematically comb through all IB studies published on major academic journals between 2012 and 2022. This search identifies 59 studies that examined IB-related topics using DID design with panel data as at least part of their empirical methodologies (hereafter, IB-DID studies). Online Appendix 1 highlights the method of this literature search and briefly summarizes the 59 studies.

Among the 59 studies, 27 (45.76%) were published in and after 2020, including 18 studies published in JIBS between 2020 and 2022. This indicates the fast-growing application of DID design in IB research in recent years. This trend is also witnessed by the organization and strategy research. For example, 11% (26 studies, including 3 IB-DID studies) of the 232 empirical studies published in SMJ over the past 3 years adopted DID design with panel data. Considering this emerging trend, it is both timely and pertinent to grasp the state of the arts for this method to be applied in the IB literature and diagnose the issues thereof to enhance the rigorousness of future research.

A key difference across the DID design in the 59 IB-DID studies is the level of treatments. Specifically, 21 studies (35.6%) used a key event that occurred on the firm level as the treatment. Most of those firm-level treatments were based on a firm’s first internationalization (its first overseas investment, the beginning to export, cross-listing in U.S., directors’ first foreign experience, etc.) or a major change in the firm’s ownership structure (e.g., an IJV becoming a wholly owned subsidiary, a local firm being acquired by MNEs, a government acquisition, an IPO, etc.). Notably, as those firm-specific events inherently occurred at different times for different firms, all 21 studies based on firm-level treatments adopted the DID design with time-varying treatments (as in Eq.  3 above).

The remaining 38 studies (64.4%) adopted treatments on the country level (32 studies), the regional level (two studies), or the industry level (four studies). Those country-level treatments were based on major socioeconomic events that occurred on the national or supranational level (e.g., the 1998 Asian financial crisis, a natural disaster, elections, etc.), or the enactment of critical laws or policies in a given country. At regional level, both studies adopted treatments based on major political changes. Lastly, all four industry-level studies focused on tariff reductions in a given industry as the treatments.

Although these IB-DID studies offer valuable insights about firms’ global strategies and international operations, our review also reveals alarming issues in their DID estimations with panel data in these studies, especially in terms of potential violations of the three critical DID assumptions.

Potential violation of treatment randomness and exogeneity

The first issue revealed by our review is that the treatments adopted in a sizable portion of the 59 studies may not be random or exogenous by nature. This issue was particularly prominent in IB-DID studies using firm-level treatments based on representative events in a firm’s history (e.g., internationalization, ownership changes, etc.). These firm-specific events are inherently correlated with the treated firms’ strategies and performance, which also affect the firms’ outcomes of interest both before and after the treatments. The assignment of these treatments can thus be endogenous, such that whether a firm received the treatments is determined by the historical status of its outcomes of interest (i.e., the “feedback effect”). For example, several studies used firms’ first overseas investment as a treatment, which may be endogenous in a firm’s strategies and performance. Likewise, another treatment, local firms being acquired by foreign MNEs, can also be endogenous in the local firms’ performance and quality. In both cases, the treated firms can be systematically different from control firms in terms of their operations, and outcomes, thus violating the randomness of treatments.

On a related note, 12 of the 21 studies using firm-level treatments (57.1%) did not report any form of parallel trend test at all. Moreover, only 30 studies (50.8%) reported tests or illustrations demonstrating parallel trends between the treated and control groups. Such lack of robust tests for parallel trend further exacerbates the above concern about non-random and endogenous treatments.

Potential violations of the static treatment assumption

Our review also shows that the treatments adopted in many of the 59 IB-DID studies can be subjected to carryover effects over time. These treatments may not only have contemporaneous impacts in the time period when the treated subjects received the treatments but can also continue to affect these subjects’ outcomes in future periods (i.e., already-treated observations). Such carryover effects were particularly prevalent in studies adopting grand events like 9/11 or China’s WTO entry as the treatments. The impacts of those grand events often take multiple years to be incorporated by firms. Moreover, there can often be a series of consequent changes and shocks following those events. As such, those treatments not only have lingering impacts, but can also vary over time in terms of range and magnitude. In the meanwhile, these grand events can often affect various facets of treated subjects’ operations and performance, which may interact with the outcome of interest over time. Such heterogeneous treatment effects would bias the estimand of TWFE DID design with panel data (Liu et al. 2021 ).

Potential violations of the constant treatment assumption

It also comes to our attention that some of the IB-DID studies used treatments that may not necessarily have constant influences across different subjects and/or time periods. This issue was particularly prominent in studies using samples from multiple countries. For example, several studies constructed DID design based on the enactment of M&A laws in a given country. Such laws or regulations often have different terms and stipulations in different countries and may thus have different implications for firms in those countries. Likewise, another treatment widely adopted in prior IB-DID studies, tariff reductions in the U.S., can often vary across industries in terms of magnitude, length, and range, thus leading to the heterogeneous treatment effects of such policy shocks on firms in different industries. In both cases, the treatment effects are characterized by heterogeneities across treated subjects, which, as discussed, can contaminate the already-treated observations and thus bias the DID estimations.

Notably, only 22 (37.3%) of the 59 IB-DID studies strictly followed the standard model specification of TWFE DID estimation and accounted for both subject- and time-fixed effects, including nine studies using fixed-time treatments and 13 using time-varying treatments. Nine of the 59 studies (15.3%) did not control for any fixed effect at all in their DID analyses. Twenty-eight (47.5%, five with fixed-time treatments and 23 with time-varying treatments) controlled for either subject- or time-fixed effects but not both. As both the carryover and heterogeneities in the treatment effects are partially nested in subject- and time-fixed effects, failing to appropriately control for both may exacerbate the bias caused by violation of the static treatment or the constant treatment assumption.

Illustrations on potential bias of DID design with panel data

Following prior studies (e.g., Baker et al., 2022 ; Bertrand , Duflo, & Mullainathan , 2004 ), we perform two Monte Carlo simulations of TWFE DID estimations using hypothetical treatments that violate the three key assumptions. Note that these simulations are not designed to replicate prior studies, but to illustrate the estimation biases that could be caused by the above issues in the DID design with panel data we detect in prior IB-DID studies. In line with this mission, without further specification, the parameters we adopt in the simulations hereafter are set in the spirit of increasing the clarity of illustrations. In both illustrations, the panel datasets are generated using data on U.S. public firms extracted from Compustat . The codes used to perform the simulations are available upon request.

Illustration 1: Import tariff reduction and R&D intensity

The first illustration focuses on an industry-level treatment used in several IB-DID studies, i.e., tariff reductions. We simulate the impacts of a tariff reduction on R&D spending by firms in an industry. Echoing the insights and findings of prior IB studies, we posit that the tariff reductions decrease treated firms’ R&D intensity in our simulations. That is, the intensified foreign competition brought about by tariff cut can not only undermine domestic firms’ sales and profitability, but also drive them to focus on the immediate competitive threats on the market and thus become more short-term oriented in their strategic decisions. As such, domestic firms often cut back the resources they deploy to innovation in response to tariff reduction, thus reducing R&D activities.

In practice, a tariff reduction is unlikely to be directly shaped by the operations and decisions of any single firm, thus qualifying as a random and exogenous treatment. However, as it often takes time for foreign competitors to gradually enter the domestic market after the tariff barriers are lowered, the influences of tariff reductions in an industry, as noted by prior studies, take multiple years to manifest. Moreover, it is also noted that different industries are not equally susceptible to the impacts of tariff reductions. As such, the treatment effects of tariff reductions can be inherently dynamic and non-constant across treated firms and time periods. In such a manner, tariff reductions offer a realistic IB setting to illustrate the estimation biases that can be caused by the violations of the static and constant treatment assumptions even when the treatment is random and exogenous.

Using the annual records of all U.S. publicly listed firms from 1979 to 2019 from Compustat , we construct an unbalanced panel dataset including 102,835 firm-year observations. The outcome of interest, R&D intensity (RDI for short), is measured as a ratio of a firm’s R&D spending over its total assets. We decompose RDI into a firm-fixed effect, a year-fixed effect, and a residual. The firm-year observations are sorted into 90 categories based on a firm’s two-digit SIC code. We randomly designate an industry as experiencing a tariff reduction in a given year (or not) to ensure that the treatments of tariff reductions are randomized and exogenous to pre-existing heterogeneities. Based on this setup, we compose six hypothetical treatment effects of tariff reductions, each of which illustrate the bias caused by heterogeneous treatment effect, carryover effect, and the combination thereof. Using each hypothetical treatment, we use Monte Carlo simulations to create 500 simulated panel datasets. We use TWFE DID regressions to estimate the ATT of tariff reductions on treated firms’ RDI for each simulated dataset, with tariff reductions measured as staggered shock dummies set to 1 for all treated and already-treated firm-year observations in a given year, and 0 otherwise.

In the first two simulations, we manipulate this treatment of tariff reductions by designating all firms in 45 randomly selected industries as experiencing a tariff reduction in 1999. In Simulation 1-1, the treatment effect of the tariff reduction is assumed to be a static shock that decreases the RDI of all treated firms by 50% of the standard deviation of RDI \((\sigma_{{{\text{RDI}}}} )\) in 1999. This fixed-time treatment meets all three assumptions of DID with panel data: random and exogenous, static, and constant. In contrast, the fixed-time tariff reduction in Simulation 1-2 is assumed to decrease all treated firms’ RDI by a constant extent of 5% of \(\sigma_{{{\text{RDI}}}}\) for 1999 and each year afterward. The second treatment thus violates the static treatment assumption, but not the constant treatment assumption.

We then conduct four simulations in which the tariff reductions are set to take place over three time periods—1989, 1999, and 2009. For each period, we designate the firms in 30 random industries as experiencing a tariff reduction. In Simulation 1-3, we assume the tariff reduction to decrease the RDI of all treated firms by a constant extent of 50% of \(\sigma_{{{\text{RDI}}}}\) in the shock year but not afterward. This treatment thus meets all three assumptions of DID design with panel data.

The treatment effects of tariff reductions in Simulation 1-4 are also assumed to only decrease a treated firm’s RDI in the year it experienced the reduction. However, such static treatment effects are set as varying across the three treatment periods at the level of 50% of \(\sigma_{{{\text{RDI}}}}\) for 1989, 30% of \(\sigma_{{{\text{RDI}}}}\) for 1999, and 10% of \(\sigma_{{{\text{RDI}}}}\) for 2009. As such, it violates the constant treatment assumption.

The treatment effects in Simulation 1-5 are assumed to be constant across industries treated at different time periods. However, such constant treatments are set as continuously decreasing the RDI of treated firms every year after the tariff reductions at the level of 3% of \(\sigma_{{{\text{RDI}}}}\) . In this regard, it has a significant carryover effect and thus violates the static treatment assumption.

Lastly, the treatment effect in Simulation 1-6 is assumed to both change over the three treatment periods and have significant carryover effect. For the firms receiving the treatment in 1989, their RDI is assumed to increase by 5% of \(\sigma_{{{\text{RDI}}}}\) in each year afterward. The change is instead taken as 3% of \(\sigma_{{{\text{RDI}}}}\) for firms treated in 1999, and 1% of \(\sigma_{{{\text{RDI}}}}\) for those treated in 2009. The last hypothetical treatment thus violates both the assumptions of constant and static treatment effect.

Figures 1 and 2 depict the distributions of the 500 estimated ATTs using TWFE DID regressions for the six hypothetical treatments, respectively. In each simulation, the “true” ATT can be calculated as the observation-average ATT , which is the equally weighted average of the ATTs across all treated firm-year observations, and the firm-average ATT , which first calculates each treated firm’s equally weighted average ATT, and average those ATTs across all treated firms. Both approaches can be deemed unbiased in practice. We depict the two “true” ATTs in each figure with dotted lines.

Import tariff reduction and R&D intensity: fixed-time treatment

Import tariff reduction and R&D intensity: time-varying treatment

Figure 1 presents the estimations for Simulations 1-1 and 1-2. As depicted in Fig.  1 a, the TWFE DID regressions obtain unbiased estimations in Simulation 1-1, which adopts the fixed-time treatment that is both static and constant. The 500 estimated ATTs average to − 0.176, which equals both the observation-average ATT and the firm-average ATT. In contrast, Fig.  1 b shows that the estimated ATTs are seriously biased in Simulation 1-2, which adopts the fixed-time treatment that is constant but not static. The mean of the 500 estimated ATTs from TWFE DID regressions is − 0.133, with a standard deviation (SD) of 0.005. This distribution significantly deviates from the observation-average ATT (− 0.060) and the firm-average ATT (− 0.058), indicating that the estimations of TWFE DID regressions are substantially biased by the presence of carryover effects.

Figure 2 presents the estimations for Simulations 1-3 through 1-6, which all adopt the time-varying hypothetical treatments of tariff reductions. As depicted in Fig.  2 a, the estimated ATTs of the 500 TWFE DID regressions average to − 0.176, which equals both the observation- and firm-average ATTs. This result indicates that TWFE DID regression can obtain unbiased estimations for the time-varying tariff treatment that meets all three assumptions for DID design with panel data.

In contrast, the estimated ATTs of the 500 TWFE DID regressions are severely biased in Simulation 1-4, in which the treatments are random, exogenous, static, but not constant across all treated firms. As depicted in Fig.  2 b, the estimated ATTs average to − 0.102 (SD = 0.004), which significantly deviates from both the observation-average ATT (− 0.138) and the firm-average ATT (− 0.137). This result indicates that TWFE DID regressions will obtain biased estimations even when the time-varying treatments only violate the assumption of constant treatment effects.

Likewise, as shown in Fig.  2 c, TWFE DID regressions fail to obtain unbiased estimations in Simulation 1-5, in which the treatments are random, exogenous, and constant, but have carryover effects over time. The estimation bias in Simulation 1-5 is even more severe than 1-4: In contrary to the significant and negative “true” ATT set in Simulation 1-5 (observation-average ATT = − 0.037; firm-average ATT = − 0.037), the mean of the 500 estimated ATTs is around − 0.001 (SD = 0.004) and statistically insignificant. This result indicates that compared with the violation of the constant treatment assumption, the violation of the static treatment assumption can be more problematic.

Lastly, Fig.  2 d shows that in Simulation 1-6, in which both constant and static treatment assumptions are violated, the estimated ATTs are again significantly biased. In particular, TWFE DID regressions estimate significant and positive ATTs, with the 500 estimated ATTs averaging to 0.031 (SD = 0.004). However, as stipulated by the simulation setting, the observation-average ATT (− 0.049) and the firm-average ATT (− 0.048) are both significant and negative.

In sum, Illustration 1 cautions that DID design with panel data will obtain biased estimations when the treatments are not static or constant. Even though those treatments can be random and exogenous, using TWFE DID regressions to estimate their treatment effects is still problematic.

Illustration 2: Firms’ internationalization and performance

The second illustration uses a firm-level treatment adopted in several IB-DID studies, i.e., the beginning of a firm’s internationalization. We simulate the impacts of this treatment on firms’ financial performance (ROA). The cause-and-effect link between internationalization and firm performance remains a fundamental debate in the extant IB literature. Using DID design with panel data, recent studies recognize that international operations not only expand the firms’ accessible markets and resources, but also allow them to learn from foreign stakeholders to improve their operations and productivity, thus enhancing the focal firms’ performance. Echoing such findings, we propose internationalization, as a treatment in our simulations, to improve firm performance.

However, a firm’s internationalization is a self-selected decision inherently endogenous to its past strategies and operations. Such decision can be systematically correlated with the firm’s past financial performance: Superior performance from the past can encourage a firm to engage in international expansion and affect the likelihood for the firm to receive the treatment (i.e., the “feedback effect”), thus violating the assumption of random and exogenous treatment. On top of that, it also stands to reason that the impacts of internationalization, as a fundamental strategic initiative, often manifest over a rather long period (non-static) and might vary across different firms (non-constant). Taken together, the treatment effects of internationalization on firms’ financial performance offer a pertinent and realistic instance in the IB contexts to show the estimation biases that can be caused by potential violations of all three assumptions of DID design with panel data.

We construct a firm-year panel dataset using Compustat data from 2010 to 2019. The ROA of each firm-year observation is decomposed into a firm-fixed effect, a year-fixed effect, and a residual. The treatment assignment for internationalization is manipulated as follows: For each firm-year observation in year t , we first calculate the firm’s ROA in year t  − 1, along with the mean and SD of the lagged ROAs. The observations in year t are then grouped up based on sample firms’ ROA in year t  − 1, with each group of firms assigned a different likelihood of internationalization. Firms with a lagged ROA two SDs above the mean or higher are assigned a 90% likelihood of internationalization in year t . Such likelihood is set as 70% for firms with lagged ROA between one and two SDs above the mean, 50% for firms with a lagged ROA within one SD of the mean, 30% for firms with lagged ROA between one and two SDs below the mean, and 10% for all the other firms. The assignment of this hypothetical shock is non-random and endogenous, as the likelihood for a firm to embark on internationalization in a given year is set to be systematically correlated with its previous ROA.

Based on this setup, we compose four hypothetical treatments of internationalization, each of which illustrates the estimation biases caused by non-random and endogenous treatments, along with heterogeneous treatment effect, carryover effect, and the combination thereof. Like Illustration 1, we create 500 simulated panel datasets for each hypothetical treatment and estimate the ATT thereof with TWFE DID regressions. Internationalization is measured as a staggered shock dummy valued as 1 for all treated and already-treated firm-year observations in a given year, and 0 otherwise.

The treatment effect of internationalization in Simulation 2-1 is assumed to increase the ROA of all treated firms by a constant level of 50% of the SD of ROA of all firms \((\sigma_{{{\text{ROA}}}} )\) in the year in which they started internationalization (i.e., treatment year). Also, the simulated treatment is defined as statically affecting only the treatment year with no carryover effect afterward.

In contrary to the constant and static treatment above, Simulation 2-2 sets the treatment effects of internationalization to be static but varying over time in terms of magnitude. Specifically, for treated firms engaging in internationalization in 2011, we designate their ROA to increase by 90% of \(\sigma_{{{\text{ROA}}}}\) . Such effect is assumed to grow weaker by 10% of \(\sigma_{{{\text{ROA}}}}\) for firms treated in every year afterward (i.e., 80% of \(\sigma_{{{\text{ROA}}}}\) for firms treated in 2012, 70% of \(\sigma_{{{\text{ROA}}}}\) in 2013, and so forth). This setting echoes the non-random and endogenous nature of the hypothetical treatment assignment. That is, as internationalization is designated as a self-selected decision based on firms’ performance concerns, it would stand to reason that firms’ decision of later internationalization might be driven by the lesser benefits they could derive from such strategic initiative than early treated peers.

The treatment effect in Simulation 2-3 is designated as constant across all treated firms in terms of increasing their ROA by 5% of \(\sigma_{{{\text{ROA}}}}\) in the treatment year. On top of that, upon receiving the treatment, each already-internationalized firm is assumed to experience an increase in their ROA at the level of 5% of \(\sigma_{{{\text{ROA}}}}\) every year after the treatment year. With this setting, internationalization is designated to be constant but characterized with substantial carryover effects. Such non-static treatment effects also reflect the reality that it can take multiple years for firms to fully incorporate the benefits derived from their international expansion into their operations and performance.

Lastly, the treatment in Simulation 2-4 is defined as both non-static and non-constant. We designate the ROA of firms treated in 2011 to increase by 20% of \(\sigma_{{{\text{ROA}}}}\) upon being treated. Such treatment effect is set to grow weaker by 2% of \(\sigma_{{{\text{ROA}}}}\) for firms treated in every year afterward (18% of \(\sigma_{{{\text{ROA}}}}\) for firms treated in 2012, 16% of \(\sigma_{{{\text{ROA}}}}\) in 2013, and so forth). We designate this regressive treatment effect to be applied every year after a firm received the original treatment. By doing so, this hypothetical treatment in Simulation 2-4 violates all three DID assumptions.

Figure 3 depicts the distributions of the estimated ATTs of TWFE DID regressions based on the 500 Monte Carlo simulated datasets for each of the four hypothetical treatments. Again, the observation- and firm-averaged ATTs are plotted with dotted lines in each figure. As shown in Fig.  3 a, in Simulation 3-1, the 500 estimated ATTs average to 0.173 (SD = 0.006), which largely deviates (2.8 SDs away) from both the observation-average ATT (0.190) and the firm-average ATT (0.190). Such significant bias indicates that TWFE DID regressions are unlikely to obtain unbiased estimations for an endogenous treatment, even when this treatment is static and constant.

Firms’ internationalization and financial performance

Figure 3 b through 3d further show that the biases caused by endogenous treatments will be exacerbated by the violations of the constant and static treatment assumptions. As shown in Fig.  3 b, in Simulation 2-2 in which the endogenous treatment is set to be static but not constant, the estimated ATTs from TWFE DID regressions are subjected to more pronounced biases. That is, the 500 estimated ATTs average to 0.214 (SD = 0.006). The observation-average ATT (0.283) and the firm-average ATT (0.273) are both 10 SDs aways from the mean of the 500 estimated ATTs.

Moreover, in Simulation 2-3 in which the endogenous treatment is set to be constant but not static, the estimation biases of DID design with panel data are even more striking. As shown in Fig.  3 c, TWFE DID regressions estimate the ATT of internationalization to be significant and negative (mean = − 0.019, SD = 0.006). However, in our setting, the observation-average ATT (0.062) and the firm-average ATT (0.059) are both positive. Such biases echo the findings of recent research (e.g., Baker et al., 2022 ), which note that DID design with panel data could obtain ATT estimations with opposite signs of the true ATTs with the presence of carryover effects in the treatment.

Lastly, Fig.  3 d shows that in Simulation 2-4, in which the treatment is set to violate all three DID assumptions, the ATT estimations of TWFE DID regressions are again significant and of opposite sign. The estimated ATTs are significant and negative (mean = − 0.108, SD = 0.006), while the true ATTs are set to be positive (observation-average ATT = 0.219; firm-average ATT = 0.20).

In sum, Illustration 2 shows that when the treatment is endogenous, DID design with panel data will always obtain biased estimations. Such biases can be further intensified by the violation of the other two assumptions of DID designs, especially the carryover effects in the treatments.

Practical recommendations

Research design: choose ib treatments carefully, firm-level treatments.

As we highlighted above, many IB-DID studies adopted major events in firm histories as the treatments in DID design with panel data. However, this practice should be treated with great caution. Because these firm-level events are commonly endogenous to a firm’s historical paths of operations and performance, they are often correlated with various firm-specific factors (especially those steering the firms’ IB practices) and thus subjected to potential feedback effects with the outcomes of interest over time. As a result, DID design based on these events often violates the assumption of random and exogenous treatment. Moreover, firm-level treatments can vary across firms and time periods, thus violating the constant treatment assumption. In addition, because a major event in a firm’s history often has lingering impact on numerous facets of the firm’s operations and outcomes, it often has significant carryover effects and therefore violates the static treatment assumption. As demonstrated in Illustration 2, such potential issues of firm-level treatments can render the ATT estimations of DID design with panel data fundamentally biased.

In fact, in light of the severity of the above issues, DID design with panel data might not be the optimal empirical design for firm-level treatments. Instead, other identification strategies (e.g., two-stage regression with instruments, regression discontinuity design (RDD), etc.) may allow scholars to obtain more rigorous estimations for the cause-and-effect links of firm-level events in the IB contexts. Take another popular treatment in prior studies, i.e., local firms being acquired by MNEs, as an instance. Instead of using these events as DID treatments, scholars may only examine the “treated” firms acquired by MNEs, focusing on the strategic implications of key features of such cross-border acquisitions. For example, scholars may examine how the acquired firms’ performance is affected by their foreign ownership, which can be instrumented by the industry average thereof (excluding the focal firms). Scholars may also use 50% of foreign ownership as the discontinuity to perform RDD for the impacts of foreign ownership on local firms’ post-acquisition performance.

Industry- and country-level treatments

Compared with firm-level treatments, major events in industrial institutions or socioeconomic contexts at the national or supranational level in the IB contexts may offer more suitable settings in which DID design with panel data can be appropriately applied, as these industry- and country-level events are generally exogenous to any single firm’s decisions or performance. That said, scholars still need to remain cautious about the randomness and exogeneity of such industry- and country-level treatments, especially those based on major changes in business policies or institutions. As highlighted by prior studies, governments and policy makers often make such changes based on certain systematic patterns in the operations or outcomes of firms in a particular industry or region. For example, governments commonly adjust the tariffs based on the historical import records and domestic firms’ performance in an industry. Likewise, the enactment of new cross-border M&A regulations in a country can be driven by foreign acquirers’ activities in this country from the past. In these cases, there might exist heterogeneities between the treated and control groups that may render the pre-treatment trends unparallel. In such a manner, it is critical for scholars to carefully choose the outcomes of interest in their IB-DID design based on industry- or country-level treatments in the spirit of avoiding potential feedback effects.

What calls for greater caution when using these treatments is the potential violation of the static treatment assumption. As we highlighted above, as these macro events commonly have long-lasting influence on all firms in the treated industries, regions, or countries, their treatment effects are intrinsically subjected to carryover effects. Such carryover effects can be particularly prominent for grand national and supranational shocks in the IB contexts. For example, it is widely noted that the influence of a country’s entry into and exit from major supranational organizations (e.g., China’s WTO entry and Brexit) would generally affect the firms in this country for decades. Likewise, recent studies recognize that the global supply chains and value networks nowadays are still incorporating the impacts of the Sino–US trade war starting in 2018. As shown above, using non-static treatments with prominent carryover effects in DID design with panel data could be particularly problematic.

In addition, these industry- or country-level treatments may also risk violating the constant treatment assumption, as they often unfold distinctly in different industries, regions, or economies in terms of manifestations and magnitudes. Such potential violation can be particularly prominent in IB studies using samples from multiple countries. For example, it is noted that the impacts of a country joining the European Union on its firms’ foreign market entry largely vary across countries joining EU at different times. Likewise, the recent global pandemic would affect the global supply chains of firms from different countries and different industries in majorly different ways. These issues not only bias the DID estimand but may also loom in a study’s theorization as alternative explanations. In this regard, scholars need to closely delineate the sources of carryover effects and heterogeneities of their non-firm-specific treatments, both in theoretical development and in empirical estimations.

Analytical strategies: Adopting rigorous protocols

Perform appropriate parallel trend tests.

The prominence and prevalence of the above issues urge scholars to diligently adopt rigorous protocols when using DID design with panel data in the IB contexts. First and foremost, it is critical for scholars to conduct appropriate parallel trend tests to gauge the randomness and exogeneity of the treatments they adopt. In such tests, the treated group and the control group must show no systematic difference in the outcome of interest prior to receiving the treatment. Notably, parallel trend tests are needed in both fixed-time and time-varying treatment designs. For fixed-time treatment designs, the common practice is to depict and compare the pre-treatment trend for both the treated and the control groups. For time-varying designs, Bertrand et al. ( 2004 ) propose the following parallel trend test:

where \(Post_{i,t - n}\) is the pre-trend dummy valued as 1 for a treated subject n years prior to its first receiving the treatment and 0 otherwise. \(Post_{i,t + n}\) is the post-trend dummy valued as 1 for a treated subject n years after its receiving the treatment and 0 otherwise. Note that the number of such pre- and post-trend dummies can be adjusted based on the empirical setting of a given study. These pre-trend dummies contrast the n -year pre-treatment trend between the treated group and the control group. Should these dummies have no significant impact on the outcome of interest, it indicates that the treated sample’s pretreatment trend of the outcome of interest is not significantly different from that of the control group. The random and exogenous treatment assumption is therefore satisfied.

Thoroughly control for fixed effects

Another protocol that was not always followed in prior IB-DID studies, as recognized by our review, is to account for fixed effects at all levels (especially subject- and time-fixed effects). These fixed effects at least partially subsume the carryover effects and heterogeneities in treatments, which are enrooted in the unique and non-varying heterogeneities of firms, years, industries, or countries. As such, failing to control all relevant fixed effects can exacerbate the violations of the constant and static treatment assumptions. This is particularly crucial for time-varying designs, in which the post-treatment dummy takes the place of the DID interactor.

In fact, it is widely noted that firms’ international operations and global strategies are jointly determined by firm-, industry-, and location-specific factors, as well as temporal dynamics. In line with the nature of IB practices, IB scholars may try to account for the interactive fixed effects as a more robust approach to capture the interplay across unobserved specificities at different levels. Doing so can help better control for the potential non-static and non-constant treatments in DID design with panel data. For example, it is recognized that global supply chain resilience is shaped by firms’ industrial conditions, as well as the locational specificities in both their home countries and sourcing countries. In this regard, to examine the impacts of the global pandemic on the survival of global supply chains of MNEs, scholars can control for the interactive fixed effects across industries, home countries, and sourcing countries, along with the regular firm- and year-fixed effects.

Use matched samples

A common practice in prior studies using DID design with panel data is to construct matched control samples in the spirit of alleviating potentially non-random treatment assignments. This approach is also adopted by several IB-DID studies in our review. The rationale of this approach is to match each treated subject with one or several control subjects that share comparable likelihood of being treated or similar key features with the treated subject (e.g., firm fundamentals, geographic locations, industrial affiliations, etc.). The matched samples are often constructed using propensity score matching (PSM) or coarsened exact matching (CEM). This approach can indeed help mitigate the estimation biases caused by the potential violation of random and exogenous treatment assumption, given that the matching criteria can accurately capture the major causes for non-random and endogenous treatment assignments. For firm-level treatments, the historical records of the outcomes of interest should always be incorporated in the matching criteria to control for feedback effects. For example, in Illustration 2, each treated firm in each period should be matched with a group of never- or not-yet internationalized firms with similar historical ROA, which was the key cause for the non-random treatment assignment in the setting.

However, due to the complexities of the IB contexts and firms’ global strategies and cross-border operations, it is hard for researchers to thoroughly identify and account for all sources of endogeneities in treatment assignments in the matching process in practice, especially for firm-level treatments. More importantly, using matched sample as the control group cannot address the biases caused by non-constant or non-static treatments, which only affect the treated subjects. In such a manner, while the matched sample approach offers a valuable protocol to improve the rigorousness of DID design with panel data, IB scholars should trust its effectiveness with caution. Scholars need to diligently identify and adopt appropriate criteria to construct the matched samples.

Consider alternative estimators: CSDID and stacked regression estimators

Along with the above protocols, scholars have also recently developed alternative estimators to remediate the violations of DID assumptions and enhance the robustness of DID design with panel data. In particular, Callaway and Sant’Anna ( 2021 ) developed an estimator that is later known as the “CSDID estimator”. It was originally designed to cope with heterogeneities of time-varying treatments in DID design with panel data. The CSDID estimator first estimates a “cohort treatment effect” for each cohort of subjects treated in the same time period. That boils down the subject-time observations into a series of time-specific cohorts, each of which provides a simple 2 × 2 DID design with clean controls consisting of never-treated or not-yet-treated observations. All cohort-time-specific treatment effects are then aggregated into a cohort-average ATT as the estimand of interest.

Another popular estimator is called “stacked DID” (Cengiz , Dube, Lindner, & Zipperer , 2019 ). This estimator first generates a period-specific panel data for each treatment period that includes both the observations treated in this period and the clean control observations that are untreated both before and shortly afterward. This excludes all not-yet-treated and already-treated observations from the period-specific panel dataset, thus constructing a clean fixed-time DID design for each treatment period. All the period-specific panel datasets are then stacked together into a new panel data to perform TWFE DID estimation. Notably, this TWFE DID estimation no longer controls for firm-fixed effects, but accounts for period-specific firm-fixed effects (or firm-by-period fixed effects) instead.

To gauge the rigorousness of these two alternative estimators when DID assumptions are violated, we replicate our two illustrations by using these two estimators to replace regular TWFE DID estimations. Figures 4 and 5 depict the replication results for the two illustrations, respectively (see also Online Appendix 2). As shown in Fig.  4 , in Illustration 1, both estimators constantly obtain more rigorous estimations than TWFE DID regressions. For all six simulations, both the observation- and the firm-average ATT of the treatment effects of tariff reduction fall within one SD of the 500 estimated ATTs obtained with both CSDID and stacked DID. These results together show that when the treatment assignment is random and exogenous, both CSDID and stacked DID can effectively remediate the violations of the constant and static treatment assumptions.

Alternative estimators of tariff reduction and firm innovation

Alternative estimators of firms’ internationalization and performance

However, when the treatment assignment is non-random and endogenous, CSDID may no longer be rigorous. As shown in Fig.  5 , the “true” ATTs of the endogenous treatment of internationalization in Illustration 2 constantly fall beyond 8 SDs away from the average of the 500 estimated ATTs obtained with CSDID, indicating that CSDID is unlikely to obtain unbiased estimations whether the other two assumptions are violated or not. In contrast, for this endogenous treatment, stacked DID obtains much more robust estimations than both TWFE DID regressions and CSDID. Both the observation-average ATT and the firm-average ATT still constantly fall within 1 SD from the average of the 500 estimated ATTs obtained with the stacked DID estimator.

In light of the above findings, it can be valuable for future IB scholars to consider those alternative estimators (when applicable) in their DID design with panel data at least as robustness checks. Especially, for DID design based on firm-level treatments, using stacked DID estimator in place of regular TWFE DID regressions may effectively alleviate the potential estimation biases.

DID design with panel data has become increasingly popular in IB research over the past decade. However, our review recognizes that in the extant IB-DID studies, the assumptions of random and exogeneous treatment, static treatment, and constant treatment are often violated, which can cause drastic estimation biases. We offer a series of practical recommendations to help future IB researchers enhance the rigorousness of DID design with panel data.

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Acknowledgements

We thank the JIBS EIC, Prof. Rosalie Tung, and two anonymous reviewers for their insightful comments. This editorial was supported in part by the Research Grants Council of Hong Kong (HKUST# 16506622) and by the National Natural Science Foundation of China (No. 71973129).

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Jiatao Li and Han Jiang contributed equally to this study.

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Center for Business Strategy and Innovation, School of Business and Management, Hong Kong University of Science and Technology, Hong Kong, China

School of Management and Economics, Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen, China

Jindal School of Management, University of Texas at Dallas, Richardson, USA

College of Business, Shanghai University of Finance and Economics, Shanghai, China

Haoyuan Ding

College of Business, Zhejiang Gongshang University, Hangzhou, China

Rongjian Yu

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Li, J., Jiang, H., Shen, J. et al. Using the difference-in-differences design with panel data in international business research: progress, potential issues, and practical suggestions. J Int Bus Stud (2024). https://doi.org/10.1057/s41267-024-00725-3

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Published : 02 September 2024

DOI : https://doi.org/10.1057/s41267-024-00725-3

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New Study Reports High Rates of Anxiety and Depression in 11- to 13-Year-Olds During the COVID-19 Pandemic

Rates of depression in 11- to 13-year-olds increased significantly between the early and middle stages of the COVID-19 pandemic, and rates of anxiety and suicidal ideation stayed consistently high during the same period, according to a new study conducted in three U.S. states. The study, supported by the National Center for Complementary and Integrative Health with co-funding from the National Institute on Drug Abuse, the Office of Disease Prevention, and the Office of Behavioral and Social Sciences Research, and published in the Journal of Adolescence, also showed differences among population subgroups, with the greatest concerns about girls and Hispanic/Latinx youth in the early pandemic and among girls and Medicaid-insured youth at mid-pandemic.

Research conducted before the COVID-19 pandemic showed rates of anxiety, depression, and suicidal ideation among U.S. adolescents ranging from about 4 to 12 percent. Studies conducted during the early stage of the COVID-19 pandemic showed similar or slightly higher rates. This study extended the findings of previous research by analyzing data collected from a group of early adolescents, aged 11 to 13 years, during two time periods within the pandemic: March to September 2020 (early pandemic) and September 2020 to May 2021 (mid-pandemic). 

The 623 participants were recruited from pediatric primary care practices in California, Colorado, and Michigan for a pragmatic trial study testing the feasibility and effectiveness of implementing Guiding Good Choices, a family-focused substance use prevention program for caregivers of younger adolescents in health care systems. They completed a baseline behavioral health survey between March and September 2020, and then, because of a pandemic-related delay in the start of the study, they completed the survey again between September 2020 and May 2021. The survey included measures of anxiety (generalized anxiety disorder scale-7 [GAD-7]) and depression (patient health questionnaire-9 [PHQ-9]). The PHQ-9 item on “thoughts that you would be better off dead or of hurting yourself in some way” was used to assess suicidal ideation. 

During the early stage of the pandemic, 10.5 percent of the youth reported moderate-to-severe depression, with the lowest rate in boys (3.6 percent) and the highest rates in Hispanic/Latinx youth (16.7 percent) and girls (16.0 percent). In the overall sample, the rate of moderate-to-severe depression increased significantly from early to mid-pandemic, from 10.5 to 15.1 percent. The largest increases were seen in boys, black youth, and Medicaid-insured youth. Hispanic/Latinx youth showed a nonsignificant decrease in depression (from 16.7 to 13.9 percent). 

In the early stage of the pandemic, 12.0 percent of the youth reported moderate-to-severe anxiety, with the lowest rates in boys (4.6 percent) and Black youth (7.1 percent), and the highest rates in girls (17.7 percent) and Hispanic/Latinx youth (15.3 percent). The overall sample showed a nonsignificant increase in the prevalence of anxiety between early and mid-pandemic. In contrast, a decrease in the prevalence of anxiety was seen among Hispanic/Latinx youth at mid-pandemic.  

Early in the pandemic, 9.3 percent of the youth reported suicidal ideation, with the lowest rate among boys (4.8 percent) and the highest rates among girls (13.1 percent) and Hispanic/Latinx youth (12.2 percent). Nonsignificant increases were observed in the overall sample and some subgroups from early to mid-pandemic, and a nonsignificant decrease was seen among Hispanic/Latinx youth.

The researchers said that the COVID-19 pandemic may have had a persistent negative impact on mental health in early adolescents, as symptoms did not improve despite reductions in restrictions and closures in response to COVID-19 between early and mid-pandemic. The findings underscore the need for continued support for youth who experienced pandemic-related stressors. The researchers suggested that the possible improvements seen among Hispanic/Latinx youth from early to mid-pandemic might reflect the larger prevalence of multigenerational households in this group, which could contribute to a greater sense of community, perceived support, and resiliency. Additionally, most of the Hispanic/Latinx youth lived in Northern California or Colorado, where cities created outdoor space to promote outdoor activities during the pandemic, and these efforts were shown to significantly lower anxiety and depression. 

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• Danzo S, Kuklinski MR, Sterling SA, et al. Anxiety, depression, and suicidal ideation among early adolescents during the COVID-19 pandemic . Journal of Adolescence. 2024;96(6):1379-1387.

Publication Date: April 28, 2024

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