difference between dissertation and publication

• February 8, 2024
• Academic Advice , Education Advice

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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Difference Between A Dissertation, Thesis, Paper, And Publication?

Scratching your brain to figure out the difference between Thesis Writing and a dissertation? If you are a final year student or working on scientific projects of a PhD, the degree will be required to write a thesis, research paper, or dissertation.

Let us Discuss Deeply The Distinctions Between A Thesis, A Dissertation, And A Research Paper.

 Dissertation: It is a paper that is written for a university degree or diploma. Individual researchers can also start writing a Ph.D. dissertation.

 Thesis Writing: A qualified scientific research document written by students in order to acquire an education degree or credentials during their university education.

 Research papers: It is a type of Academic Paper that is usually written as part of a class necessity. You must conduct objective research when conducting research.

After you’ve completed it, you’ll need to write a summary of your findings. Furthermore, you present your thoughts and arguments on the subject at hand.

The inner core of such papers is something that all three of these have in widespread. In each of them, you’ll find an initiation, review of literature, methodology of the study, results, conversation, and summary.

5 Notable Distinguishment On Dissertation Vs Thesis:

 Dissertation, on the other way, you must conduct your own research.

 A thesis is shorter than a dissertation and gets less time to accomplish. A dissertation, on the other hand, is longer and takes more time to complete.

 In order to uncover your decision in the dissertation, you must have a good understanding of new discoveries.

 However, a hypothesis based on your research work must be included in a thesis.

 Mostly in a particular instance of a thesis, you are eligible for a scholarship, whereas, in the case of a dissertation, you are not.

Significant Configuration Between Thesis And A Dissertation:

In order to write a thesis, you must first study a specific topic, analyze it, and ask a question on the findings. You must be sympathetic to the topic you are working on. When writing a thesis, you must think critically gather information in great detail. Individuals must choose a subject for their Thesis Writing that is most relevant to the expert specialized field they want to continue. A thesis should be at least pages long, if not more.

In a Thesis Writing Services , you must conduct all of your research on your own because your facilitator will only be able to provide limited guidance. Another option is to hire a published author to assist you with your thesis. In contrast to a thesis, a dissertation makes use of other people’s research for inspiration. It’s up to you to prove your own hypothesis, theory, or concept. When a thesis and a dissertation are compared, the latter is longer. A dissertation entails a significant amount of research data.

Fundamental Steps On Thesis Writing Format:

 Step 1: Choose a methodological approach that interests you.

 Step 2: Choose an endeavor that is special.

 Step 3: Related to your project, ask well-defined questions.

  Step 4 : Take a glance at educational initiatives that integrate employable skills.

 Step 5: Visualize your final Publications

Finally, Academic papers are distinguished by a number of factors. The most essential thing, regardless of the technique or intention of these papers, is that students demonstrate their textual abilities. Furthermore, Thesis writing help to improves your knowledge of various aspects of the subject.

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The Difference Between a Published & Unpublished Dissertation

How to Locate PhD Dissertations

A dissertation is the main element in completion of a Ph.D. The central element of a doctoral dissertation, and the quality that differentiates it from a master's thesis or an undergraduate thesis, is that it must make an original contribution to its field, usually using primary research. The structure and content of a completed doctoral dissertation is often very different from the structure required for articles or books that are based on it.

Unpublished Dissertations

When a Ph.D. candidate completes her dissertation, this usually results in three or four copies: one each for the candidate, the dissertation supervisor, the university library and sometimes an archive. Unless a dissertation is subsequently published, these are the only copies that are ever created. What this means in practical terms is that unpublished dissertations are almost never widely read. The vast majority of dissertations serve their purpose of gaining a Ph.D. for their author and then fade into obscurity. If you write a dissertation that you want to have an impact, you will need to revise it and publish it in some form.

One of the easiest options for getting your research into published form is to revise a single chapter into an article for a peer-reviewed journal in your field. The difference between this article and an unpublished dissertation is clear: The article is present in a journal that is printed in thousands of copies and distributed to influential academics around the world. In most cases, the editors of the journal will want the form of the dissertation chapter reworked to some extent to make it more accessible to readers who are probably not experts in that particular subject matter.

Motivated dissertation authors often seek to have their dissertations published in book form. As with journal articles, books that are based on dissertations need to be reworked. This usually takes the form of downplaying the methodology and literature-review sections, cutting down on the density of footnotes and references and generally making the text more readable to non-specialists. A published book can get your name out in your academic field and to the world in general. Having a book and some published articles in your field will be helpful to you in advancing your academic career. Within academia, an unpublished dissertation is really nothing more than a prerequisite.

Online Publishing

The Internet has opened up tremendous new opportunities for academic publishing. While having your work accessible online doesn't carry the same weight with hiring committees as an article in a peer-reviewed journal, or better yet a book, it is an effective way to make yourself and your work known, as long as you get it published in the right places. Making contacts through online publishing can be an effective stepping stone toward breaking into journals and book publishing. It's also a useful way to get feedback from other academics about your work.

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• University of California Berkeley/Graduate Division: Publishing Your Dissertation

Jagg Xaxx has been writing since 1983. His primary areas of writing include surrealism, Buddhist iconography and environmental issues. Xaxx worked as a cabinetmaker for 12 years, as well as building and renovating several houses. Xaxx holds a Doctor of Philosophy in art history from the University of Manchester in the U.K.

What’s the Difference Between a Dissertation vs. Thesis?

If you plan to pursue an advanced degree, you may be wondering about the differences between dissertation vs. thesis projects.

Most candidates in master’s or PhD programs are required to complete a final project in the form of a thesis or dissertation before graduating.

Editorial Listing ShortCode:

Because both a master’s thesis and a doctoral dissertation consist of in-depth research, analysis, and writing, some people use the terms interchangeably. They are separate assignments, though, that are completed at different stages in a student’s academic journey.

What’s the Difference Between a Dissertation vs. Thesis?

Students enrolled in masters degree programs typically write a thesis, whereas doctoral students complete a dissertation.

Although these projects are both research-based and are usually required for graduation, they have different lengths, requirements, and timeframes. Knowing how to distinguish between them is vital for anyone hoping to attend graduate school.

What Is a Thesis?

A thesis is a scholarly paper that many students write before completing their master’s programs. Most graduate schools require students to submit a thesis that reflects the breadth and depth of their knowledge of the field and demonstrates their advanced research skills.

To create a thesis, students begin by selecting a topic of interest and conducting extensive research in that area. That information serves as the basis for a lengthy paper that describes the student’s position on the subject and presents evidence proving their assertion. Although it’s not common, some undergraduate schools also require students to write a short thesis.

What Is a Dissertation?

A dissertation is a doctoral-level project that is a standard feature of PhD programs. When completing a dissertation, a student will conduct original or expanded research that can contribute to their field and solidify their status as a scholar.

So, what’s a dissertation and how long does it take to write one? Dissertations are often hundreds of pages in length and are completed over several years. They reflect that a student not only understands the current state of the field but also has a unique approach to the subject matter. After writing a dissertation, a student must defend it to a committee and faculty chair, who determine whether it is acceptable.

A number of universities now offer doctorate programs without dissertations but have a capstone project for completion. Common programs are DBA degrees or doctoral programs in education with no dissertation online or on campus.

Differences Between a Dissertation vs. Thesis

A dissertation and thesis are distinct in important ways, including:

• Academic level . A thesis is generally the final project required for a master’s degree. Students complete a dissertation during their doctoral programs.
• Research requirements . Writing a thesis involves reviewing and compiling information from reputable sources. To complete a dissertation, students must also present new information based on original research.
• Length . The difference in the length of a PhD dissertation vs. masters thesis is noteworthy. A thesis is typically between 50 and 100 pages in length and is written during the final year of a master’s degree program. A dissertation is generally 100 to 400 pages long and may involve several years of work.
• Completion process . Students submit their thesis projects to a group of two or more faculty mentors. Dissertation projects culminate with an oral defense, where the student answers questions from a committee of various faculty members.

The difference between dissertation and thesis projects depends on where you’re studying. For instance, in the United Kingdom, master’s students complete a dissertation, and PhD students write a doctoral thesis.

Similarities Between a Thesis vs. Dissertation

Much like the similarity between a dissertation vs. capstone , a thesis is usually accomplished as a partial requirement for an academic degree. A thesis and a dissertation usually share these common features:

• Purpose . Thesis and dissertation projects are designed to indicate whether a student is ready to graduate.
• Required skills . To successfully write a dissertation or thesis, students often have strong writing, analytical, research, and editing skills.
• Process . To complete either project, students select a focused research area, review existing sources, and write a long paper summarizing their findings and analysis.
• Expectations . Students cannot commit plagiarism or act unethically.
• Benefits . Completing a thesis or dissertation helps students gain a more thorough understanding of their field, narrow down their career goals, and demonstrate their achievements.

If you intend to complete a doctoral dissertation, you may have the opportunity to expand on the research from your master’s thesis or select a new area of interest.

Theses and Dissertations

There may be a difference between a thesis and a dissertation in terms of when students complete them, how long they are, and what type of research they involve, but they are both beneficial academically and professionally.

These projects provide students with an opportunity to gain expertise in their fields and prepare for their careers. Graduates also sometimes publish their work as journal articles or books. Having a record of publications may strengthen your candidacy for future positions. Writing a dissertation or thesis is challenging, but it can be a valuable experience.

You can take the next step in your educational journey today by researching accredited master’s and PhD programs.

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Dissertation vs thesis vs publishable papers, published by steve tippins on may 14, 2020 may 14, 2020.

Last Updated on: 3rd June 2022, 04:29 am

What is the difference between a dissertation and a thesis? Here is a simple answer:

A thesis is usually done as part of a master’s program. A dissertation is written as part of a doctoral program.

However, that’s where the simplicity ends. Because in truth, the use of those terms isn’t agreed on by everyone. It depends where in the world you’re located, and even which institution you’re in.

Some institutions call the paper that doctoral students write a “thesis.” 

Also, in the UK, it’s just the opposite: a thesis is the crowned jewel of doctoral research, whereas a dissertation is submitted as an intermediary part of the graduate process.

Need help with your dissertation? Our Coaching services can help you finish faster.

Confused yet?

Here’s one more difference between a dissertation vs. thesis: A dissertation is usually original research, whereas a thesis is often a synthesis of research . However, that is not always true. I know plenty of people who completed original research as part of their master’s thesis (in fact, I’m married to one of them). 

So, unfortunately, there’s no clear, cut-and-dried answer to the “dissertation vs thesis” question. It’s often a question of semantics, though there are some important differences between the papers written in master’s and PhD programs.

Dissertation vs Thesis Pros and Cons

For the purposes of this article, let’s define a dissertation as an independent research project completed as part of a doctoral program, and a thesis as a long paper, often synthesizing research, written as part of a master’s program.

Doing Original Research

Doing a research project gets you familiar with the skills so that you can replicate it and do original research. Whatever your degree is, if your final paper doesn’t ask you to do original research, you may struggle with doing research on your own or with jobs that ask that of you.

That said, doing original research does take an enormous amount of work. So if it’s not part of your foreseeable career trajectory, there’s something to be said for skipping that part.

Writing a dissertation usually takes longer than writing a master’s thesis. In general, master’s programs are shorter than PhD programs and have lower attrition rates. If you don’t have a good reason to pursue writing a dissertation as part of a PhD program, it may be much easier to go the thesis (master’s) route.

Generally speaking, those who have written dissertations and graduated from PhD programs are assumed to have a high level of expertise. Those who have completed master’s degrees which are not terminal degrees aren’t afforded quite the same level of assumed expertise. 

Dissertation vs Thesis Summary

I hope I’ve shed some light on the definitions that some people use. Hopefully, this knowledge will be enough to guide you to ask the right questions at the right time so that you understand exactly what somebody is talking about.

Dissertation vs Publishable Papers. 

Ten years ago or so, some schools recognized that it might be more valuable for their students to get out of school with actual publications. So instead of having students write a dissertation , they gave them the option to write two or three papers worthy of publication in an academic journal.

Pros of Writing Publishable Papers

If you intend to go with an academic career track, then coming out with several publications or papers to submit for publication can help you get a job. And once you have a job, these publications can help you get promotion and tenure. It’s a great way to kick-start your career and get valuable experience submitting to academic journals.

Cons of Writing Publishable Papers

The drawbacks are that you’re writing papers on different topics, so you may have to do multiple studies instead of one. You may have committee members who aren’t familiar with that and they might expect dissertation-quantity work instead of publishable paper quantity work.

If the requirement is published papers vs publishable papers, then you’re subject to the whims of journals in addition to your committee. 

It’s a relatively new option, so some employers may not be that familiar with it. Some people may be stuck in the, “well I did a whole dissertation” paradigm and not recognize that writing publishable papers is equally rigorous.

Dissertation vs Publishable Papers Summary

Neither path is easier than the other. If you’re going down the publishing track, be careful with the methodologies you have to use, because you may have to learn several different methodologies as opposed to one in a dissertation.

Steve Tippins

Steve Tippins, PhD, has thrived in academia for over thirty years. He continues to love teaching in addition to coaching recent PhD graduates as well as students writing their dissertations. Learn more about his dissertation coaching and career coaching services. Book a Free Consultation with Steve Tippins

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Frequently asked questions

How is a dissertation different from an essay.

The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).

Frequently asked questions: Knowledge Base

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.

Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.

A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.

The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.

In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’

A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.

MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.

Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.

Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12

In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.

When a source has two or three authors, name all of them in your MHRA references . When there are four or more, use only the first name, followed by ‘and others’:

Note that in the bibliography, only the author listed first has their name inverted. The names of additional authors and those of translators or editors are written normally.

A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.

In Vancouver style , you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.

In Vancouver style , when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.

In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.

The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:

• In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
• In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.

References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.

If you don’t acknowledge your sources, you can get in trouble for plagiarism .

Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:

• Harvard referencing , the most commonly used style in UK universities.
• MHRA , used in humanities subjects.
• APA , used in the social sciences.
• Vancouver , used in biomedicine.
• OSCOLA , used in law.

Your university may have its own referencing style guide.

If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.

To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.

You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.

In Harvard style , when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).

You can also include page numbers to point the reader towards a passage that you paraphrased . If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.

When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation , first mention the source you want to refer to, and then the source in which you found it. For example:

It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.

In Harvard style referencing , to distinguish between two sources by the same author that were published in the same year, you add a different letter after the year for each source:

• (Smith, 2019a)
• (Smith, 2019b)

Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list .

To create a hanging indent for your bibliography or reference list :

• Highlight all the entries
• Click on the arrow in the bottom-right corner of the ‘Paragraph’ tab in the top menu.
• In the pop-up window, under ‘Special’ in the ‘Indentation’ section, use the drop-down menu to select ‘Hanging’.
• Then close the window with ‘OK’.

Though the terms are sometimes used interchangeably, there is a difference in meaning:

• A reference list only includes sources cited in the text – every entry corresponds to an in-text citation .
• A bibliography also includes other sources which were consulted during the research but not cited.

It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).

The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources . You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.

You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.

For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.

Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.

Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .

Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.

The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract .

In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.

You may acknowledge God in your thesis or dissertation acknowledgements , but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract   in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction.

An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

Your dissertation sometimes contains a list of abbreviations .

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.

Be sure to add each abbreviation in your list of abbreviations !

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, minimising confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.

Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.

These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.

Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.

Some synonyms and near synonyms of among include:

• In the company of
• In the middle of
• Surrounded by

Some synonyms and near synonyms of between  include:

• In the space separating
• In the time separating

In spite of   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s always used in a subordinate clause to contrast with the information given in the main clause of a sentence (e.g., ‘Amy continued to watch TV, in spite of the time’).

Despite   is a preposition used to mean ‘ regardless of ‘, ‘notwithstanding’, or ‘even though’.

It’s used in a subordinate clause to contrast with information given in the main clause of a sentence (e.g., ‘Despite the stress, Joe loves his job’).

‘Log in’ is a phrasal verb meaning ‘connect to an electronic device, system, or app’. The preposition ‘to’ is often used directly after the verb; ‘in’ and ‘to’ should be written as two separate words (e.g., ‘ log in to the app to update privacy settings’).

‘Log into’ is sometimes used instead of ‘log in to’, but this is generally considered incorrect (as is ‘login to’).

Some synonyms and near synonyms of ensure include:

• Make certain

Some synonyms and near synonyms of assure  include:

Rest assured is an expression meaning ‘you can be certain’ (e.g., ‘Rest assured, I will find your cat’). ‘Assured’ is the adjectival form of the verb assure , meaning ‘convince’ or ‘persuade’.

Some synonyms and near synonyms for council include:

There are numerous synonyms and near synonyms for the two meanings of counsel :

AI writing tools can be used to perform a variety of tasks.

Generative AI writing tools (like ChatGPT ) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.

These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool , summarising tool , and grammar checker , which are designed specifically for these purposes.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

The Scribbr Knowledge Base is a collection of free resources to help you succeed in academic research, writing, and citation. Every week, we publish helpful step-by-step guides, clear examples, simple templates, engaging videos, and more.

The Knowledge Base is for students at all levels. Whether you’re writing your first essay, working on your bachelor’s or master’s dissertation, or getting to grips with your PhD research, we’ve got you covered.

As well as the Knowledge Base, Scribbr provides many other tools and services to support you in academic writing and citation:

• Create your citations and manage your reference list with our free Reference Generators in APA and MLA style.
• Scan your paper for in-text citation errors and inconsistencies with our innovative APA Citation Checker .
• Avoid accidental plagiarism with our reliable Plagiarism Checker .
• Polish your writing and get feedback on structure and clarity with our Proofreading & Editing services .

Yes! We’re happy for educators to use our content, and we’ve even adapted some of our articles into ready-made lecture slides .

You are free to display, distribute, and adapt Scribbr materials in your classes or upload them in private learning environments like Blackboard. We only ask that you credit Scribbr for any content you use.

We’re always striving to improve the Knowledge Base. If you have an idea for a topic we should cover, or you notice a mistake in any of our articles, let us know by emailing [email protected] .

The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.

If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.

As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.

Paraphrasing without crediting the original author is a form of plagiarism , because you’re presenting someone else’s ideas as if they were your own.

However, paraphrasing is not plagiarism if you correctly reference the source . This means including an in-text referencing and a full reference , formatted according to your required citation style (e.g., Harvard , Vancouver ).

As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.

Accidental plagiarism is one of the most common examples of plagiarism . Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from, and aren’t totally sure if it’s original or not.

These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re citing your sources . Also consider running your work through a plagiarism checker tool prior to submission, which work by using advanced database software to scan for matches between your text and existing texts.

Scribbr’s Plagiarism Checker takes less than 10 minutes and can help you turn in your paper with confidence.

The accuracy depends on the plagiarism checker you use. Per our in-depth research , Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.

Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts. Their accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared).

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

Plagiarism can be detected by your professor or readers if the tone, formatting, or style of your text is different in different parts of your paper, or if they’re familiar with the plagiarised source.

Many universities also use   plagiarism detection software like Turnitin’s, which compares your text to a large database of other sources, flagging any similarities that come up.

It can be easier than you think to commit plagiarism by accident. Consider using a   plagiarism checker prior to submitting your essay to ensure you haven’t missed any citations.

Some examples of plagiarism include:

• Copying and pasting a Wikipedia article into the body of an assignment
• Quoting a source without including a citation
• Not paraphrasing a source properly (e.g. maintaining wording too close to the original)
• Forgetting to cite the source of an idea

The most surefire way to   avoid plagiarism is to always cite your sources . When in doubt, cite!

Global plagiarism means taking an entire work written by someone else and passing it off as your own. This can include getting someone else to write an essay or assignment for you, or submitting a text you found online as your own work.

Global plagiarism is one of the most serious types of plagiarism because it involves deliberately and directly lying about the authorship of a work. It can have severe consequences for students and professionals alike.

Verbatim plagiarism means copying text from a source and pasting it directly into your own document without giving proper credit.

If the structure and the majority of the words are the same as in the original source, then you are committing verbatim plagiarism. This is the case even if you delete a few words or replace them with synonyms.

If you want to use an author’s exact words, you need to quote the original source by putting the copied text in quotation marks and including an   in-text citation .

Patchwork plagiarism , also called mosaic plagiarism, means copying phrases, passages, or ideas from various existing sources and combining them to create a new text. This includes slightly rephrasing some of the content, while keeping many of the same words and the same structure as the original.

While this type of plagiarism is more insidious than simply copying and pasting directly from a source, plagiarism checkers like Turnitin’s can still easily detect it.

To avoid plagiarism in any form, remember to reference your sources .

Yes, reusing your own work without citation is considered self-plagiarism . This can range from resubmitting an entire assignment to reusing passages or data from something you’ve handed in previously.

Self-plagiarism often has the same consequences as other types of plagiarism . If you want to reuse content you wrote in the past, make sure to check your university’s policy or consult your professor.

If you are reusing content or data you used in a previous assignment, make sure to cite yourself. You can cite yourself the same way you would cite any other source: simply follow the directions for the citation style you are using.

Keep in mind that reusing prior content can be considered self-plagiarism , so make sure you ask your instructor or consult your university’s handbook prior to doing so.

Most institutions have an internal database of previously submitted student assignments. Turnitin can check for self-plagiarism by comparing your paper against this database. If you’ve reused parts of an assignment you already submitted, it will flag any similarities as potential plagiarism.

Online plagiarism checkers don’t have access to your institution’s database, so they can’t detect self-plagiarism of unpublished work. If you’re worried about accidentally self-plagiarising, you can use Scribbr’s Self-Plagiarism Checker to upload your unpublished documents and check them for similarities.

Plagiarism has serious consequences and can be illegal in certain scenarios.

While most of the time plagiarism in an undergraduate setting is not illegal, plagiarism or self-plagiarism in a professional academic setting can lead to legal action, including copyright infringement and fraud. Many scholarly journals do not allow you to submit the same work to more than one journal, and if you do not credit a coauthor, you could be legally defrauding them.

Even if you aren’t breaking the law, plagiarism can seriously impact your academic career. While the exact consequences of plagiarism vary by institution and severity, common consequences include a lower grade, automatically failing a course, academic suspension or probation, and even expulsion.

Self-plagiarism means recycling work that you’ve previously published or submitted as an assignment. It’s considered academic dishonesty to present something as brand new when you’ve already gotten credit and perhaps feedback for it in the past.

If you want to refer to ideas or data from previous work, be sure to cite yourself.

Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offences like plagiarism and contract cheating, which are examples of academic misconduct.

Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and it varies in severity.

It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism . It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.

Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.

Consequences of academic dishonesty depend on the severity of the offence and your institution’s policy. They can range from a warning for a first offence to a failing grade in a course to expulsion from your university.

For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.

Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbour’s answers on an exam.

You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.

Plagiarism means presenting someone else’s work as your own without giving proper credit to the original author. In academic writing, plagiarism involves using words, ideas, or information from a source without including a citation .

Plagiarism can have serious consequences , even when it’s done accidentally. To avoid plagiarism, it’s important to keep track of your sources and cite them correctly.

Common knowledge does not need to be cited. However, you should be extra careful when deciding what counts as common knowledge.

Common knowledge encompasses information that the average educated reader would accept as true without needing the extra validation of a source or citation.

Common knowledge should be widely known, undisputed, and easily verified. When in doubt, always cite your sources.

Most online plagiarism checkers only have access to public databases, whose software doesn’t allow you to compare two documents for plagiarism.

However, in addition to our Plagiarism Checker , Scribbr also offers an Self-Plagiarism Checker . This is an add-on tool that lets you compare your paper with unpublished or private documents. This way you can rest assured that you haven’t unintentionally plagiarised or self-plagiarised .

Compare two sources for plagiarism

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

The Scribbr Reference Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Reference Generator in our publicly accessible repository on Github .

To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:

• Reformulating the sentence (e.g., change active to passive , or start from a different point)
• Combining information from multiple sentences into one
• Leaving out information from the original that isn’t relevant to your point
• Using synonyms where they don’t distort the meaning

The main point is to ensure you don’t just copy the structure of the original text, but instead reformulate the idea in your own words.

Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas into your own words.

So when does paraphrasing count as plagiarism?

• Paraphrasing is plagiarism if you don’t properly credit the original author.
• Paraphrasing is plagiarism if your text is too close to the original wording (even if you cite the source). If you directly copy a sentence or phrase, you should quote it instead.
• Paraphrasing  is not plagiarism if you put the author’s ideas completely into your own words and properly reference the source .

To present information from other sources in academic writing , it’s best to paraphrase in most cases. This shows that you’ve understood the ideas you’re discussing and incorporates them into your text smoothly.

It’s appropriate to quote when:

• Changing the phrasing would distort the meaning of the original text
• You want to discuss the author’s language choices (e.g., in literary analysis )
• You’re presenting a precise definition
• You’re looking in depth at a specific claim

A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.

Every time you quote a source , you must include a correctly formatted in-text citation . This looks slightly different depending on the citation style .

For example, a direct quote in APA is cited like this: ‘This is a quote’ (Streefkerk, 2020, p. 5).

Every in-text citation should also correspond to a full reference at the end of your paper.

In scientific subjects, the information itself is more important than how it was expressed, so quoting should generally be kept to a minimum. In the arts and humanities, however, well-chosen quotes are often essential to a good paper.

In social sciences, it varies. If your research is mainly quantitative , you won’t include many quotes, but if it’s more qualitative , you may need to quote from the data you collected .

As a general guideline, quotes should take up no more than 5–10% of your paper. If in doubt, check with your instructor or supervisor how much quoting is appropriate in your field.

If you’re quoting from a text that paraphrases or summarises other sources and cites them in parentheses , APA  recommends retaining the citations as part of the quote:

• Smith states that ‘the literature on this topic (Jones, 2015; Sill, 2019; Paulson, 2020) shows no clear consensus’ (Smith, 2019, p. 4).

Footnote or endnote numbers that appear within quoted text should be omitted.

If you want to cite an indirect source (one you’ve only seen quoted in another source), either locate the original source or use the phrase ‘as cited in’ in your citation.

A block quote is a long quote formatted as a separate ‘block’ of text. Instead of using quotation marks , you place the quote on a new line, and indent the entire quote to mark it apart from your own words.

APA uses block quotes for quotes that are 40 words or longer.

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

Common examples of primary sources include interview transcripts , photographs, novels, paintings, films, historical documents, and official statistics.

Anything you directly analyze or use as first-hand evidence can be a primary source, including qualitative or quantitative data that you collected yourself.

Common examples of secondary sources include academic books, journal articles , reviews, essays , and textbooks.

Anything that summarizes, evaluates or interprets primary sources can be a secondary source. If a source gives you an overview of background information or presents another researcher’s ideas on your topic, it is probably a secondary source.

To determine if a source is primary or secondary, ask yourself:

• Was the source created by someone directly involved in the events you’re studying (primary), or by another researcher (secondary)?
• Does the source provide original information (primary), or does it summarize information from other sources (secondary)?
• Are you directly analyzing the source itself (primary), or only using it for background information (secondary)?

Some types of sources are nearly always primary: works of art and literature, raw statistical data, official documents and records, and personal communications (e.g. letters, interviews ). If you use one of these in your research, it is probably a primary source.

Primary sources are often considered the most credible in terms of providing evidence for your argument, as they give you direct evidence of what you are researching. However, it’s up to you to ensure the information they provide is reliable and accurate.

Always make sure to properly cite your sources to avoid plagiarism .

A fictional movie is usually a primary source. A documentary can be either primary or secondary depending on the context.

If you are directly analysing some aspect of the movie itself – for example, the cinematography, narrative techniques, or social context – the movie is a primary source.

If you use the movie for background information or analysis about your topic – for example, to learn about a historical event or a scientific discovery – the movie is a secondary source.

Whether it’s primary or secondary, always properly cite the movie in the citation style you are using. Learn how to create an MLA movie citation or an APA movie citation .

Articles in newspapers and magazines can be primary or secondary depending on the focus of your research.

In historical studies, old articles are used as primary sources that give direct evidence about the time period. In social and communication studies, articles are used as primary sources to analyse language and social relations (for example, by conducting content analysis or discourse analysis ).

If you are not analysing the article itself, but only using it for background information or facts about your topic, then the article is a secondary source.

In academic writing , there are three main situations where quoting is the best choice:

• To analyse the author’s language (e.g., in a literary analysis essay )
• To give evidence from primary sources
• To accurately present a precise definition or argument

Don’t overuse quotes; your own voice should be dominant. If you just want to provide information from a source, it’s usually better to paraphrase or summarise .

Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.

Lists of figures and tables are often not required, and they aren’t particularly common. They specifically aren’t required for APA Style, though you should be careful to follow their other guidelines for figures and tables .

If you have many figures and tables in your thesis or dissertation, include one may help you stay organised. Your educational institution may require them, so be sure to check their guidelines.

Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article , look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.

If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.

A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.

APA doesn’t require you to include a list of tables or a list of figures . However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures .

A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

However, if you’d prefer to cite your sources , you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

Usually, no title page is needed in an MLA paper . A header is generally included at the top of the first page instead. The exceptions are when:

• Your instructor requires one, or
• Your paper is a group project

In those cases, you should use a title page instead of a header, listing the same information but on a separate page.

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 organise 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.)

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page, after the title page and acknowledgements but before the table of contents .

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

A noun is a word that represents a person, thing, concept, or place (e.g., ‘John’, ‘house’, ‘affinity’, ‘river’). Most sentences contain at least one noun or pronoun .

Nouns are often, but not always, preceded by an article (‘the’, ‘a’, or ‘an’) and/or another determiner such as an adjective.

There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.

Some of the main types of nouns are:

• Common nouns and proper nouns
• Countable and uncountable nouns
• Concrete and abstract nouns
• Collective nouns
• Possessive nouns
• Attributive nouns
• Appositive nouns
• Generic nouns

Pronouns are words like ‘I’, ‘she’, and ‘they’ that are used in a similar way to nouns . They stand in for a noun that has already been mentioned or refer to yourself and other people.

Pronouns can function just like nouns as the head of a noun phrase and as the subject or object of a verb. However, pronouns change their forms (e.g., from ‘I’ to ‘me’) depending on the grammatical context they’re used in, whereas nouns usually don’t.

Common nouns are words for types of things, people, and places, such as ‘dog’, ‘professor’, and ‘city’. They are not capitalised and are typically used in combination with articles and other determiners.

Proper nouns are words for specific things, people, and places, such as ‘Max’, ‘Dr Prakash’, and ‘London’. They are always capitalised and usually aren’t combined with articles and other determiners.

A proper adjective is an adjective that was derived from a proper noun and is therefore capitalised .

Proper adjectives include words for nationalities, languages, and ethnicities (e.g., ‘Japanese’, ‘Inuit’, ‘French’) and words derived from people’s names (e.g., ‘Bayesian’, ‘Orwellian’).

The names of seasons (e.g., ‘spring’) are treated as common nouns in English and therefore not capitalised . People often assume they are proper nouns, but this is an error.

The names of days and months, however, are capitalised since they’re treated as proper nouns in English (e.g., ‘Wednesday’, ‘January’).

No, as a general rule, academic concepts, disciplines, theories, models, etc. are treated as common nouns , not proper nouns , and therefore not capitalised . For example, ‘five-factor model of personality’ or ‘analytic philosophy’.

However, proper nouns that appear within the name of an academic concept (such as the name of the inventor) are capitalised as usual. For example, ‘Darwin’s theory of evolution’ or ‘ Student’s t table ‘.

Collective nouns are most commonly treated as singular (e.g., ‘the herd is grazing’), but usage differs between US and UK English :

• In US English, it’s standard to treat all collective nouns as singular, even when they are plural in appearance (e.g., ‘The Rolling Stones is …’). Using the plural form is usually seen as incorrect.
• In UK English, collective nouns can be treated as singular or plural depending on context. It’s quite common to use the plural form, especially when the noun looks plural (e.g., ‘The Rolling Stones are …’).

The plural of “crisis” is “crises”. It’s a loanword from Latin and retains its original Latin plural noun form (similar to “analyses” and “bases”). It’s wrong to write “crisises”.

For example, you might write “Several crises destabilized the regime.”

Normally, the plural of “fish” is the same as the singular: “fish”. It’s one of a group of irregular plural nouns in English that are identical to the corresponding singular nouns (e.g., “moose”, “sheep”). For example, you might write “The fish scatter as the shark approaches.”

If you’re referring to several species of fish, though, the regular plural “fishes” is often used instead. For example, “The aquarium contains many different fishes , including trout and carp.”

The correct plural of “octopus” is “octopuses”.

People often write “octopi” instead because they assume that the plural noun is formed in the same way as Latin loanwords such as “fungus/fungi”. But “octopus” actually comes from Greek, where its original plural is “octopodes”. In English, it instead has the regular plural form “octopuses”.

For example, you might write “There are four octopuses in the aquarium.”

The plural of “moose” is the same as the singular: “moose”. It’s one of a group of plural nouns in English that are identical to the corresponding singular nouns. So it’s wrong to write “mooses”.

For example, you might write “There are several moose in the forest.”

Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.

Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behaviour and external factors (difficult circumstances) to justify the same behaviour in themselves.

Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews . These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.

Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen either because people are not willing or not able to participate.

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can bias your research findings.

Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.

These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity . You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.

You can control demand characteristics by taking a few precautions in your research design and materials.

Use these measures:

• Deception: Hide the purpose of the study from participants
• Between-groups design : Give each participant only one independent variable treatment
• Double-blind design : Conceal the assignment of groups from participants and yourself
• Implicit measures: Use indirect or hidden measurements for your variables

Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic research bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity .

To avoid attrition bias , applying some of these measures can help you reduce participant dropout (attrition) by making it easy and appealing for participants to stay.

• Provide compensation (e.g., cash or gift cards) for attending every session
• Minimise the number of follow-ups as much as possible
• Make all follow-ups brief, flexible, and convenient for participants
• Send participants routine reminders to schedule follow-ups
• Recruit more participants than you need for your sample (oversample)
• Maintain detailed contact information so you can get in touch with participants even if they move

If you have a small amount of attrition bias , you can use a few statistical methods to try to make up for this research bias .

Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.

Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.

The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.

Although there is no definite answer to what causes the placebo effect , researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.

Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.

Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.

Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.

Positivity bias is phenomenon that occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias , or the e xpectation that things will work out well, even if rationality suggests that problems are inevitable in life.

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

There are many ways to categorize adjectives into various types. An adjective can fall into one or more of these categories depending on how it is used.

Some of the main types of adjectives are:

• Attributive adjectives
• Predicative adjectives
• Comparative adjectives
• Superlative adjectives
• Coordinate adjectives
• Appositive adjectives
• Compound adjectives
• Participial adjectives
• Proper adjectives
• Denominal adjectives
• Nominal adjectives

Cardinal numbers (e.g., one, two, three) can be placed before a noun to indicate quantity (e.g., one apple). While these are sometimes referred to as ‘numeral adjectives ‘, they are more accurately categorised as determiners or quantifiers.

Proper adjectives are adjectives formed from a proper noun (i.e., the name of a specific person, place, or thing) that are used to indicate origin. Like proper nouns, proper adjectives are always capitalised (e.g., Newtonian, Marxian, African).

The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.

For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as £0.01 per word, but in many cases, your text will also require some level of editing , which costs slightly more.

It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.

Then and than are two commonly confused words . In the context of ‘better than’, you use ‘than’ with an ‘a’.

• Julie is better than Jesse.
• I’d rather spend my time with you than with him.
• I understand Eoghan’s point of view better than Claudia’s.

Use to and used to are commonly confused words . In the case of ‘used to do’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to do laundry once a week.
• They used to do each other’s hair.
• We used to do the dishes every day .

There are numerous synonyms and near synonyms for the various meanings of “ favour ”:

There are numerous synonyms and near synonyms for the two meanings of “ favoured ”:

No one (two words) is an indefinite pronoun meaning ‘nobody’. People sometimes mistakenly write ‘noone’, but this is incorrect and should be avoided. ‘No-one’, with a hyphen, is also acceptable in UK English .

Nobody and no one are both indefinite pronouns meaning ‘no person’. They can be used interchangeably (e.g., ‘nobody is home’ means the same as ‘no one is home’).

Some synonyms and near synonyms of  every time include:

• Without exception

‘Everytime’ is sometimes used to mean ‘each time’ or ‘whenever’. However, this is incorrect and should be avoided. The correct phrase is every time   (two words).

Yes, the conjunction because is a compound word , but one with a long history. It originates in Middle English from the preposition “bi” (“by”) and the noun “cause”. Over time, the open compound “bi cause” became the closed compound “because”, which we use today.

Though it’s spelled this way now, the verb “be” is not one of the words that makes up “because”.

Yes, today is a compound word , but a very old one. It wasn’t originally formed from the preposition “to” and the noun “day”; rather, it originates from their Old English equivalents, “tō” and “dæġe”.

In the past, it was sometimes written as a hyphenated compound: “to-day”. But the hyphen is no longer included; it’s always “today” now (“to day” is also wrong).

IEEE citation format is defined by the Institute of Electrical and Electronics Engineers and used in their publications.

It’s also a widely used citation style for students in technical fields like electrical and electronic engineering, computer science, telecommunications, and computer engineering.

An IEEE in-text citation consists of a number in brackets at the relevant point in the text, which points the reader to the right entry in the numbered reference list at the end of the paper. For example, ‘Smith [1] states that …’

A location marker such as a page number is also included within the brackets when needed: ‘Smith [1, p. 13] argues …’

The IEEE reference page consists of a list of references numbered in the order they were cited in the text. The title ‘References’ appears in bold at the top, either left-aligned or centered.

The numbers appear in square brackets on the left-hand side of the page. The reference entries are indented consistently to separate them from the numbers. Entries are single-spaced, with a normal paragraph break between them.

If you cite the same source more than once in your writing, use the same number for all of the IEEE in-text citations for that source, and only include it on the IEEE reference page once. The source is numbered based on the first time you cite it.

For example, the fourth source you cite in your paper is numbered [4]. If you cite it again later, you still cite it as [4]. You can cite different parts of the source each time by adding page numbers [4, p. 15].

A verb is a word that indicates a physical action (e.g., ‘drive’), a mental action (e.g., ‘think’) or a state of being (e.g., ‘exist’). Every sentence contains a verb.

Verbs are almost always used along with a noun or pronoun to describe what the noun or pronoun is doing.

There are many ways to categorize verbs into various types. A verb can fall into one or more of these categories depending on how it is used.

Some of the main types of verbs are:

• Regular verbs
• Irregular verbs
• Transitive verbs
• Intransitive verbs
• Dynamic verbs
• Stative verbs
• Linking verbs
• Auxiliary verbs
• Modal verbs
• Phrasal verbs

Regular verbs are verbs whose simple past and past participle are formed by adding the suffix ‘-ed’ (e.g., ‘walked’).

Irregular verbs are verbs that form their simple past and past participles in some way other than by adding the suffix ‘-ed’ (e.g., ‘sat’).

The indefinite articles a and an are used to refer to a general or unspecified version of a noun (e.g., a house). Which indefinite article you use depends on the pronunciation of the word that follows it.

• A is used for words that begin with a consonant sound (e.g., a bear).
• An is used for words that begin with a vowel sound (e.g., an eagle).

Indefinite articles can only be used with singular countable nouns . Like definite articles, they are a type of determiner .

Editing and proofreading are different steps in the process of revising a text.

Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice ).

Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization ). Proofreaders often also check for formatting issues, especially in print publishing.

Whether you’re publishing a blog, submitting a research paper , or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:

• Take a break : Set your work aside for at least a few hours so that you can look at it with fresh eyes.
• Proofread a printout : Staring at a screen for too long can cause fatigue – sit down with a pen and paper to check the final version.
• Use digital shortcuts : Take note of any recurring mistakes (for example, misspelling a particular word, switching between US and UK English , or inconsistently capitalizing a term), and use Find and Replace to fix it throughout the document.

If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.

There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.

For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.

To learn practical proofreading skills, you can choose to take a course with a professional organisation such as the Society for Editors and Proofreaders . Alternatively, you can apply to companies that offer specialised on-the-job training programmes, such as the Scribbr Academy .

Though they’re pronounced the same, there’s a big difference in meaning between its and it’s .

• ‘The cat ate its food’.
• ‘It’s almost Christmas’.

Its and it’s are often confused, but its (without apostrophe) is the possessive form of ‘it’ (e.g., its tail, its argument, its wing). You use ‘its’ instead of ‘his’ and ‘her’ for neuter, inanimate nouns.

Then and than are two commonly confused words with different meanings and grammatical roles.

• Then (pronounced with a short ‘e’ sound) refers to time. It’s often an adverb , but it can also be used as a noun meaning ‘that time’ and as an adjective referring to a previous status.
• Than (pronounced with a short ‘a’ sound) is used for comparisons. Grammatically, it usually functions as a conjunction , but sometimes it’s a preposition .

Use to and used to are commonly confused words . In the case of ‘used to be’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.

• I used to be the new coworker.
• There used to be 4 cookies left.
• We used to walk to school every day .

A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes , and problems with sentence structure . You can check out our analysis of the best free grammar checkers to learn more.

A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.

Some tools available online combine both functions. Others, such as QuillBot , have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.

Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing . Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.

If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation , word choice, and sentence structure that more basic tools would miss.

Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.

Our research indicates that the best free grammar checker available online is the QuillBot grammar checker .

We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.

It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.

A teacher’s aide is a person who assists in teaching classes but is not a qualified teacher. Aide is a noun meaning ‘assistant’, so it will always refer to a person.

‘Teacher’s aid’ is incorrect.

A visual aid is an instructional device (e.g., a photo, a chart) that appeals to vision to help you understand written or spoken information. Aid is often placed after an attributive noun or adjective (like ‘visual’) that describes the type of help provided.

‘Visual aide’ is incorrect.

A job aid is an instructional tool (e.g., a checklist, a cheat sheet) that helps you work efficiently. Aid is a noun meaning ‘assistance’. It’s often placed after an adjective or attributive noun (like ‘job’) that describes the specific type of help provided.

‘Job aide’ is incorrect.

There are numerous synonyms for the various meanings of truly :

Yours truly is a phrase used at the end of a formal letter or email. It can also be used (typically in a humorous way) as a pronoun to refer to oneself (e.g., ‘The dinner was cooked by yours truly ‘). The latter usage should be avoided in formal writing.

It’s formed by combining the second-person possessive pronoun ‘yours’ with the adverb ‘ truly ‘.

A pathetic fallacy can be a short phrase or a whole sentence and is often used in novels and poetry. Pathetic fallacies serve multiple purposes, such as:

• Conveying the emotional state of the characters or the narrator
• Creating an atmosphere or set the mood of a scene
• Foreshadowing events to come
• Giving texture and vividness to a piece of writing
• Communicating emotion to the reader in a subtle way, by describing the external world.
• Bringing inanimate objects to life so that they seem more relatable.

AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.

You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.

An AMA in-text citation consists of the number of the relevant reference on your AMA reference page , written in superscript 1 at the point in the text where the source is used.

It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).

An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.

References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.

An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page , written in superscript at the point in the text where the source is referred to.

You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.

On your AMA reference page , author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.

There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas , and the whole list ends in a period, e.g., ‘Andreessen F, Smith PW, Gonzalez E’.

The names of up to six authors should be listed for each source on your AMA reference page , separated by commas . For a source with seven or more authors, you should list the first three followed by ‘ et al’ : ‘Isidore, Gilbert, Gunvor, et al’.

In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations ). If you do mention them, though, you should use the first author’s name followed by ‘et al’ when there are three or more : ‘Isidore et al argue that …’

Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after ‘et al’ unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.

Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.

When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., ‘Published June 19, 2021. Accessed August 29, 2022.’

Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation ).

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyse data in more ways. The higher the level of measurement, the more precise your data is.

The level at which you measure a variable determines how you can analyse your data.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorised.
• Ordinal : the data can be categorised and ranked.
• Interval : the data can be categorised and ranked, and evenly spaced.
• Ratio : the data can be categorised, ranked, evenly spaced and has a natural zero.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic , meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution .

When there are only one or two degrees of freedom , the chi-square distribution is shaped like a backwards ‘J’. When there are three or more degrees of freedom, the distribution is shaped like a right-skewed hump. As the degrees of freedom increase, the hump becomes less right-skewed and the peak of the hump moves to the right. The distribution becomes more and more similar to a normal distribution .

‘Looking forward in hearing from you’ is an incorrect version of the phrase looking forward to hearing from you . The phrasal verb ‘looking forward to’ always needs the preposition ‘to’, not ‘in’.

• I am looking forward in hearing from you.
• I am looking forward to hearing from you.

Some synonyms and near synonyms for the expression looking forward to hearing from you include:

• Eagerly awaiting your response
• Hoping to hear from you soon
• It would be great to hear back from you
• Thanks in advance for your reply

People sometimes mistakenly write ‘looking forward to hear from you’, but this is incorrect. The correct phrase is looking forward to hearing from you .

The phrasal verb ‘look forward to’ is always followed by a direct object, the thing you’re looking forward to. As the direct object has to be a noun phrase , it should be the gerund ‘hearing’, not the verb ‘hear’.

• I’m looking forward to hear from you soon.
• I’m looking forward to hearing from you soon.

Traditionally, the sign-off Yours sincerely is used in an email message or letter when you are writing to someone you have interacted with before, not a complete stranger.

Yours faithfully is used instead when you are writing to someone you have had no previous correspondence with, especially if you greeted them as ‘ Dear Sir or Madam ’.

Just checking in   is a standard phrase used to start an email (or other message) that’s intended to ask someone for a response or follow-up action in a friendly, informal way. However, it’s a cliché opening that can come across as passive-aggressive, so we recommend avoiding it in favor of a more direct opening like “We previously discussed …”

In a more personal context, you might encounter “just checking in” as part of a longer phrase such as “I’m just checking in to see how you’re doing”. In this case, it’s not asking the other person to do anything but rather asking about their well-being (emotional or physical) in a friendly way.

“Earliest convenience” is part of the phrase at your earliest convenience , meaning “as soon as you can”. 

It’s typically used to end an email in a formal context by asking the recipient to do something when it’s convenient for them to do so.

ASAP is an abbreviation of the phrase “as soon as possible”. 

It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).

“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience .

Some synonyms and near synonyms of the verb   compose   (meaning “to make up”) are:

People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing . “Comprise” traditionally means “to be made up of,” not “to make up.”

Some synonyms and near synonyms of the verb comprise are:

• Be composed of
• Be made up of

People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing .

A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:

• Correlation/causation fallacy: Claiming that two events that occur together have a cause-and-effect relationship even though this can’t be proven
• Ecological fallacy : Making inferences about the nature of individuals based on aggregate data for the group
• The sunk cost fallacy : Following through on a project or decision because we have already invested time, effort, or money into it, even if the current costs outweigh the benefits
• The base-rate fallacy : Ignoring base-rate or statistically significant information, such as sample size or the relative frequency of an event, in favor of  less relevant information e.g., pertaining to a single case, or a small number of cases
• The planning fallacy : Underestimating the time needed to complete a future task, even when we know that similar tasks in the past have taken longer than planned

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.

Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:

• A red herring fallacy refers to an attempt to change the subject and divert attention from the original issue. In other words, a seemingly solid but ultimately irrelevant argument is introduced into the discussion, either on purpose or by mistake.
• A straw man argument involves the deliberate distortion of another person’s argument. By oversimplifying or exaggerating it, the other party creates an easy-to-refute argument and then attacks it.

The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.

Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.

The sunk cost fallacy and escalation of commitment (or commitment bias ) are two closely related terms. However, there is a slight difference between them:

• Escalation of commitment (aka commitment bias ) is the tendency to be consistent with what we have already done or said we will do in the past, especially if we did so in public. In other words, it is an attempt to save face and appear consistent.
• Sunk cost fallacy is the tendency to stick with a decision or a plan even when it’s failing. Because we have already invested valuable time, money, or energy, quitting feels like these resources were wasted.

In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).

When you are faced with a straw man argument , the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.

The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.

A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).

This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy .

A slippery slope argument is not always a fallacy.

• When someone claims adopting a certain policy or taking a certain action will automatically lead to a series of other policies or actions also being taken, this is a slippery slope argument.
• If they don’t show a causal connection between the advocated policy and the consequent policies, then they commit a slippery slope fallacy .

There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:

• Slippery slope arguments take advantage of the gray area between an initial action or decision and the possible next steps that might lead to the undesirable outcome. You can point out these missing steps and ask your partner to indicate what evidence exists to support the claimed relationship between two or more events.
• Ask yourself if each link in the chain of events or action is valid. Every proposition has to be true for the overall argument to work, so even if one link is irrational or not supported by evidence, then the argument collapses.
• Sometimes people commit a slippery slope fallacy unintentionally. In these instances, use an example that demonstrates the problem with slippery slope arguments in general (e.g., by using statements to reach a conclusion that is not necessarily relevant to the initial statement). By attacking the concept of slippery slope arguments you can show that they are often fallacious.

People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:

• Cognitive bias is the tendency to make decisions or take action in an illogical way because of our values, memory, socialization, and other personal attributes. In other words, it refers to a fixed pattern of thinking rooted in the way our brain works.
• Logical fallacies relate to how we make claims and construct our arguments in the moment. They are statements that sound convincing at first but can be disproven through logical reasoning.

In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.

An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy .

It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.

For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy .

An ad hominem (Latin for “to the person”) is a type of informal logical fallacy . Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.

This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.

Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).

Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.

In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy .

Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”

Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence .

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics , if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population . We can achieve that by using a probability sampling method , like simple random sampling or stratified sampling .
• In academic writing , use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking” ) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.

Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:

• Begging the question fallacy occurs when you assume that an argument is true in order to justify a conclusion. If something begs the question, what you are actually asking is, “Is the premise of that argument actually true?” For example, the statement “Snakes make great pets. That’s why we should get a snake” begs the question “are snakes really great pets?”
• Circular reasoning fallacy on the other hand, occurs when the evidence used to support a claim is just a repetition of the claim itself.  For example, “People have free will because they can choose what to do.”

In other words, we could say begging the question is a form of circular reasoning.

Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.

An example of a non sequitur is the following statement:

“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”

Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy .

The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.

In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.

An example of post hoc fallacy is the following line of reasoning:

“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”

Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.

Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:

• Post hoc fallacy is assuming a cause and effect relationship between two events, simply because one happened after the other.
• Hasty generalisation fallacy is drawing a general conclusion from a small sample or little evidence.

In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.

The fallacy of composition is similar to and can be confused with the hasty generalization fallacy . However, there is a difference between the two:

• The fallacy of composition involves drawing an inference about the characteristics of a whole or group based on the characteristics of its individual members.
• The hasty generalization fallacy involves drawing an inference about a population or class of things on the basis of few atypical instances or a small sample of that population or thing.

In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.

The opposite of the fallacy of composition is the fallacy of division . In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”

Base rate fallacy can be avoided by following these steps:

• Avoid making an important decision in haste. When we are under pressure, we are more likely to resort to cognitive shortcuts like the availability heuristic and the representativeness heuristic . Due to this, we are more likely to factor in only current and vivid information, and ignore the actual probability of something happening (i.e., base rate).
• Take a long-term view on the decision or question at hand. Look for relevant statistical data, which can reveal long-term trends and give you the full picture.
• Talk to experts like professionals. They are more aware of probabilities related to specific decisions.

Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.

When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy .

Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy .

In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.

Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.

The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.

Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.

For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.

The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:

• In the ad hominem fallacy , the goal is to discredit the argument by discrediting the person currently making the argument.
• In the genetic fallacy , the goal is to discredit the argument by discrediting the history or origin (i.e., genesis) of an argument.

False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.

The false dilemma fallacy works in two ways:

• By presenting only two options as if these were the only ones available
• By presenting two options as mutually exclusive (i.e., only one option can be selected or can be true at a time)

In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).

The best way to avoid a false dilemma fallacy is to pause and reflect on two points:

• Are the options presented truly the only ones available ? It could be that another option has been deliberately omitted.
• Are the options mentioned mutually exclusive ? Perhaps all of the available options can be selected (or be true) at the same time, which shows that they aren’t mutually exclusive. Proving this is called “escaping between the horns of the dilemma.”

Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.

For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”

The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:

• A complex question fallacy occurs when someone asks a question that presupposes the answer to another question that has not been established or accepted by the other person. For example, asking someone “Have you stopped cheating on tests?”, unless it has previously been established that the person is indeed cheating on tests, is a fallacy.
• Begging the question fallacy occurs when we assume the very thing as a premise that we’re trying to prove in our conclusion. In other words, the conclusion is used to support the premises, and the premises prove the validity of the conclusion. For example: “God exists because the Bible says so, and the Bible is true because it is the word of God.”

In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.

“ No true Scotsman ” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.

For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.

No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.

The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.

To identify an appeal to authority fallacy , you can ask yourself the following questions:

• Is the authority cited really a qualified expert in this particular area under discussion? For example, someone who has formal education or years of experience can be an expert.
• Do experts disagree on this particular subject? If that is the case, then for almost any claim supported by one expert there will be a counterclaim that is supported by another expert. If there is no consensus, an appeal to authority is fallacious.
• Is the authority in question biased? If you suspect that an expert’s prejudice and bias could have influenced their views, then the expert is not reliable and an argument citing this expert will be fallacious.To identify an appeal to authority fallacy, you ask yourself whether the authority cited is a qualified expert in the particular area under discussion.

Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.

Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.

The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”

This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.

The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.

Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:

• An ad populum fallacy tries to persuade others by claiming that something is true or right because a lot of people think so.
• An appeal to authority fallacy tries to persuade by claiming a group of experts believe something is true or right, therefore it must be so.

To identify a false cause fallacy , you need to carefully analyse the argument:

• When someone claims that one event directly causes another, ask if there is sufficient evidence to establish a cause-and-effect relationship. 
• Ask if the claim is based merely on the chronological order or co-occurrence of the two events. 
• Consider alternative possible explanations (are there other factors at play that could influence the outcome?).

By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.

False cause fallacy examples include: 

• Believing that wearing your lucky jersey will help your team win 
• Thinking that everytime you wash your car, it rains
• Claiming that playing video games causes violent behavior 

In each of these examples, we falsely assume that one event causes another without any proof.

The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:

• The planning fallacy describes our inability to correctly estimate how long a future task will take, mainly due to optimism bias and a strong focus on the best-case scenario.
• Procrastination refers to postponing a task, usually by focusing on less urgent or more enjoyable activities. This is due to psychological reasons, like fear of failure.

In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.

A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.

Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.

The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.

An example of appeal to pity fallacy is the following appeal by a student to their professor:

“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”

While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.

While both the appeal to pity fallacy and   red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:

• Appeal to pity fallacy attempts to evoke feelings of sympathy, pity, or guilt in an audience, so that they accept the speaker’s conclusion as truthful.
• Red herring fallacy attempts to introduce an irrelevant piece of information that diverts the audience’s attention to a different topic.

Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.

Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy . It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.

Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.

For example, the sentence “ To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to  many”, but there’s no particular reason to do so. Both versions are fine.

Some people argue that you shouldn’t end a sentence with a preposition , but that “rule” can also be ignored, since it’s not supported by serious language authorities.

Yes, it’s fine to end a sentence with a preposition . The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.

Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about ” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

GPT  stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.

The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.

ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.

OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.

ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.

In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content , and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.

Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.

ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.

ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.

Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations : because its answers are based on probabilities, they’re not always trustworthy .

OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.

Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.

The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.

ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.

However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.

Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.

Try using a prompt like this one:

“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”

Yes, there are a variety of ways to use ChatGPT for language learning , including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.

AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT ) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors , we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.

Because of how AI detectors work , they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.

Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.

But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.

You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.

Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.

While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.

Try the QuillBot summarizer for free

Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.

Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.

A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):

• Gives the tool a role to explain what type of answer you expect from it
• Is precisely formulated and gives enough context
• Is free from bias
• Has been tested and improved by experimenting with the tool

ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.

ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

According to OpenAI’s terms of use, users have the right to reproduce text generated by ChatGPT during conversations.

However, publishing ChatGPT outputs may have legal implications , such as copyright infringement.

Users should be aware of such issues and use ChatGPT outputs as a source of inspiration instead.

According to OpenAI’s terms of use, users have the right to use outputs from their own ChatGPT conversations for any purpose (including commercial publication).

However, users should be aware of the potential legal implications of publishing ChatGPT outputs. ChatGPT responses are not always unique: different users may receive the same response.

Furthermore, ChatGPT outputs may contain copyrighted material. Users may be liable if they reproduce such material.

ChatGPT can sometimes reproduce biases from its training data , since it draws on the text it has “seen” to create plausible responses to your prompts.

For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.

The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

Information extraction  refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP) .

For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity 1 ,celebrity 2 ,date) .

The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.

Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.

An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.

A related concept is information extraction , concerned with how to get structured information from unstructured sources.

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.

However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).

However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well. Alternatively, you can get advice using Scribbr’s essay editing service .

No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.

Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.

However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).

ChatGPT and other AI writing tools can have unethical uses. These include:

• Reproducing biases and false information
• Using ChatGPT to cheat in academic contexts
• Violating the privacy of others by inputting personal information

However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:

• Following your institution’s guidelines
• Critically evaluating outputs
• Being transparent about how you used the tool

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Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7.

Yes, if your document is longer than 20,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback.

How does the sample edit work?

You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently.

Read more about how the sample edit works

Yes, you can upload your document in sections.

We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before.

However, we cannot guarantee that the same editor will be available. Your chances are higher if

• You send us your text as soon as possible and
• You can be flexible about the deadline.

Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.

If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the  Scribbr Improvement Model  and will deliver high-quality work.

Yes, our editors also work during the weekends and holidays.

Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round.

If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening!

Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English.

Every Scribbr order comes with our award-winning Proofreading & Editing service , which combines two important stages of the revision process.

For a more comprehensive edit, you can add a Structure Check or Clarity Check to your order. With these building blocks, you can customize the kind of feedback you receive.

You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:

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When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area.

However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible.

This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.

Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine!

After your document has been edited, you will receive an email with a link to download the document.

The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one.

It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:

• You can learn a lot by looking at the mistakes you made.
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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

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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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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: Your 2024 Guide

If you’ve been thinking about going to graduate school, you may be familiar with the application requirements, rigorous academic schedule, and thesis or dissertation you’ll be expected to complete. So, what exactly is the difference between a thesis and a dissertation? While there are similarities, there’s a clear difference between the two. In our guide, we compare dissertation vs thesis. Discover more about both – and what you can expect during your graduate program. Let’s get started!

• Table of Contents

What Is a Thesis?

A thesis is an academic paper or project that’s completed towards the end of a master’s degree program . It is typically completed as the capstone project , meaning it’s the final project required for a student to graduate.

Students need to select a narrow, specific topic within – or relating to – their field of study. Once they’ve selected a topic, students must conduct an in-depth review of existing research on their chosen subjects. The next step is to formulate an academic argument, an assertion they’ll need to support or prove with said research.

Therefore, a thesis is akin to an in-depth research paper. It’s comprised of research that essentially proves what a student has learned during their program.

What Is a Typical Thesis Structure?

A thesis generally follows a rigid structure that’s decided by the program, department, or university. Here is an example of a thesis structure:

• The Title Page
• Summary of Thesis Abstract
• Table of Maps and Figures
• The Thesis Body (Sometimes divided into chapters)
• The Results or Conclusion

Who Needs to Complete a Thesis?

Most master’s degree programs require students to complete a thesis. While some undergraduate programs may also require a thesis, these are generally shorter and narrower in scope.

Some programs will also require a master’s student to defend their thesis in front of a panel or committee.

What Is a Dissertation?

What is “the PhD paper” called? Some people refer to it as a PhD thesis, but it’s most commonly known as a dissertation in the US. Dissertations are the capstone project required at the tail end of a PhD program . It is almost always required, except for a select few one-year PhD programs .

Much like a thesis, dissertations are also academic papers that aim to prove a student’s expertise – while adding to the current body of knowledge – in their field. Thus, a student must look at existing research and conduct their own research .

Basically, it’s the magnum opus of a doctoral journey in the United States. A dissertation isn’t just a long research paper; it’s a beast of a project. It demands extensive research, originality, and the ability to make a meaningful contribution to your chosen field. Think of it as a research odyssey guided by a seasoned mentor. Once you’ve conquered this scholarly quest and defended your findings, you’ll proudly emerge with your hard-earned doctoral degree, a testament to your dedication and scholarly prowess.

A dissertation typically comes after a PhD student completes their required courses and passes their qualifying exams. In some programs, the dissertation process is embedded into the coursework. In such cases, students receive a jump start on their work, allowing them to potentially finish their program earlier.

What Does a Dissertation Do?

PhD candidates must present a new theory or hypothesis. Alternatively, they must present their research to question (or disprove) the existing accepted theory on their chosen subject. Students may choose to tackle their topic from a new angle or take their research in a different direction.

Most programs will require students to defend their dissertations. During the defense, candidates must be able to justify the methodology of their research and the results and interpretation of their findings. Defenses are typically oral presentations in front of a dissertation committee , where the students are asked questions or presented with challenges.

Although the defense may seem daunting, PhD students work closely with their advisors to prepare for their dissertations. Students receive feedback and advice to guide their dissertations in their chosen direction.

What Is the Typical Dissertation Structure? 

Dissertations follow a rigid structure typically set by the program, department, or university. Here is an example format:

• The Acknowledgments Page
• The Abstract
• Introduction
• The Literature Review & Theoretical Framework
• The Methodology
• Findings/Results
• Discussions of the Findings, including analysis, interpretation, and applications
• The Conclusion
• List of References
• Any Appendices

What Is a Doctoral Thesis?

A doctoral thesis is a substantial piece of scholarly work that marks the pinnacle of a doctoral degree program, such as a PhD. Think of it as the academic grand finale. Its primary mission? To showcase the candidate’s mastery in their chosen field and their knack for delving deep into research.

In a nutshell, a doctoral thesis is a mammoth project that calls for originality. You’ve got to dig, investigate, gather data, crunch numbers, and present real data-supported findings. All this hard work usually happens under the watchful eye of a knowledgeable mentor. Once you’ve conquered this scholarly mountain and defended your thesis successfully, you’ll be proudly awarded your well-deserved doctoral degree. It’s the hallmark of your expertise and contribution to your field.

And how does a doctoral thesis differ from a dissertation? That’s mainly a geographic explanation. While they’re largely similar in scope and purpose, when comparing a doctoral thesis vs. a dissertation:

• A dissertation is the PhD capstone requirement in the US .
• A doctoral thesis is the PhD capstone requirement in Europe .

Related Reading: The Easiest PhDs

Dissertation vs. Thesis: The Similarities

In the master’s thesis vs dissertation discussion, there are plenty of similarities. Both are lengthy academic papers that require intense research and original writing. They’re also capstone projects which are completed at the tail end of their respective programs.

Students must work closely with their respective committees (e.g., faculty members, advisors, professionals) who provide feedback and guidance on their research, writing, and academic arguments. Both thesis and dissertation committees have a committee chair with whom the students work closely.

In some ways, the requirements for theses and dissertations are quite similar. They require a skillful defense of a student’s academic arguments. What’s more, both papers require critical thinking and good analytical reasoning, as well as in-depth expertise in the chosen field of study.

Students must also invest a significant amount of time into both projects while also being able to accept and action feedback on their work.

Dissertation vs. Thesis: The Differences

What are the differences between a PhD dissertation vs. thesis? The first and most distinct difference is the degree program requiring a PhD dissertation or thesis. A dissertation is typically the capstone project for a doctorate, while a thesis is the capstone project for a master’s degree program (or undergraduate program).

Candidates will have to defend their dissertation during an oral presentation in front of their committee. Only some master’s theses require this.

During a thesis, students typically conduct research by reviewing existing literature and knowledge on their chosen subject. During a dissertation, students must do their own research and prove their theory, concept, or hypothesis. They should also expect to develop a unique concept and defend it based on the practical and theoretical results achieved from their rigorous research.

Theses are also typically shorter (around 40 to 80 pages). Dissertations, however, are much longer (between 100 and 300 pages). Of course, the actual length of the paper may depend on the topic, program, department, or university.

Related Reading : PhD Candidate vs Student: What’s the Difference? 

Dissertations and Theses: US vs. Europe

Whether you’re in the US or Europe, dissertations and theses are similar. However, European requirements and conventions differ slightly:

Doctoral Thesis

To ensure your PhD graduation, a dissertation is generally required. Doctoral theses in Europe are much like a PhD dissertation in the US : You must complete your own research and add to the existing body of knowledge in your field.

Master’s Dissertation

It may seem odd to require a dissertation for master’s degree programs, but in Europe, this is exactly what you’ll need. A master’s dissertation is a broader post-graduate program research project , though it’s most typically required for master’s programs.

Frequently Asked Questions

Here are a few of the most common questions we hear about the meaning of thesis vs. dissertation.

Is a Thesis and a Dissertation the Same?

Yes and no. In some ways, a dissertation and a thesis are the same. For example, both require original writing, critical skills, analytical thinking, plenty of research, and lots of academic effort. However, a thesis is more commonly reserved for master’s – and some undergraduate – programs. Dissertations are generally required by PhD programs in the United States.

Additionally, a thesis typically calls for heavy research and compilation of existing knowledge and literature on a subject. A dissertation requires candidates to conduct their own research to prove their own theory, concept, or hypothesis – adding to the existing body of knowledge in their chosen field of study.

How Long Is a Thesis vs. a Dissertation?

One of the primary differences between thesis and dissertation papers is their length. While a thesis might be anywhere from 40 to 80 pages long, a dissertation can easily run from 100 to 300. It’s important to note that these numbers depend on the specific program and university.

Does a PhD Require a Thesis or a Dissertation?

It all depends on where you are! While a US-based PhD requires you to complete a dissertation, a thesis (or “doctoral thesis”) is more commonly required for PhD candidates in Europe. In the US, a thesis is more commonly reserved for master’s degree programs and occasionally undergraduate programs. In Europe, a “master’s dissertation” is typically required for the completion of a master’s degree.

So, there you have it: an in-depth comparison of the dissertation vs. thesis academic requirements. Now that you know the primary similarities and differences between the two, it might become easier to decide your academic path. Just remember, you may be able to find a master’s program without a thesis or a doctorate without a dissertation requirement if you prefer. Good luck!

Are you ready to jump into your doctorate? Find out if you need a master’s degree to get a PhD .

Chriselle Sy

Chriselle has been a passionate professional content writer for over 10 years. She writes educational content for The Grad Cafe, Productivity Spot, The College Monk, and other digital publications.  When she isn't busy writing, she spends her time streaming video games and learning new skills.

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Dissertation vs Thesis: The Differences that Matter

Updated: January 24, 2023

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!

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"Are you gonna publish that?" Peer-reviewed publication outcomes of doctoral dissertations in psychology

Spencer c. evans.

1 Clinical Child Psychology Program, University of Kansas, Lawrence, KS, United States of America

2 Department of Psychology, Harvard University, Cambridge, MA, United States of America

Christina M. Amaro

Robyn herbert.

3 Department of Psychology, Washington State University, Pullman, WA, United States of America

Jennifer B. Blossom

4 Department of Psychiatry & Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States of America

Michael C. Roberts

Associated data.

Data are publicly available from a variety of third party sources. A complete list of data sources has been included as a Supporting Information file, ' S1 File '.

If a doctoral dissertation represents an original investigation that makes a contribution to one’s field, then dissertation research could, and arguably should, be disseminated into the scientific literature. However, the extent and nature of dissertation publication remains largely unknown within psychology. The present study investigated the peer-reviewed publication outcomes of psychology dissertation research in the United States. Additionally, we examined publication lag, scientific impact, and variations across subfields. To investigate these questions, we first drew a stratified random cohort sample of 910 psychology Ph.D. dissertations from ProQuest Dissertations & Theses. Next, we conducted comprehensive literature searches for peer-reviewed journal articles derived from these dissertations published 0–7 years thereafter. Published dissertation articles were coded for their bibliographic details, citation rates, and journal impact metrics. Results showed that only one-quarter (25.6% [95% CI: 23.0, 28.4]) of dissertations were ultimately published in peer-reviewed journals, with significant variations across subfields (range: 10.1 to 59.4%). Rates of dissertation publication were lower in professional/applied subfields (e.g., clinical, counseling) compared to research/academic subfields (e.g., experimental, cognitive). When dissertations were published, however, they often appeared in influential journals (e.g., Thomson Reuters Impact Factor M = 2.84 [2.45, 3.23], 5-year Impact Factor M = 3.49 [3.07, 3.90]) and were cited numerous times (Web of Science citations per year M = 3.65 [2.88, 4.42]). Publication typically occurred within 2–3 years after the dissertation year. Overall, these results indicate that the large majority of Ph.D. dissertation research in psychology does not get disseminated into the peer-reviewed literature. The non-publication of dissertation research appears to be a systemic problem affecting both research and training in psychology. Efforts to improve the quality and “publishability” of doctoral dissertation research could benefit psychological science on multiple fronts.

Introduction

The doctoral dissertation—a defining component of the Doctor of Philosophy (Ph.D.) degree—is an original research study that meets the scientific, professional, and ethical standards of its discipline and advances a body of knowledge [ 1 ]. From this definition it follows that most dissertations could, and arguably should, be published in the peer-reviewed scientific literature [ 1 – 2 ]. For example, research participants typically volunteer their time and effort for the purposes of generating new knowledge of potential benefit; therefore, to breach this contract by not attempting to disseminate one’s findings is to violate the ethical standards of psychology [ 3 ] and human subjects research [ 2 , 4 ]. The nonpublication of dissertation research can also be detrimental to the advancement of scientific knowledge in other ways. Researchers may unwittingly and unnecessarily duplicate efforts from doctoral research when conducting empirical studies, or draw biased conclusions in meta-analytic and systematic reviews that often deliberately exclude dissertations. Many dissertations go unpublished due to nonsignificant and complicated results, exacerbating the “file drawer” problem [ 5 – 6 ]. Indeed, unpublished dissertations are rarely if ever cited [ 7 – 8 ].

The problem of dissertation non-publication is of critical importance in psychology. Some evidence [ 9 ] suggests that unpublished dissertations can play a key role in alleviating file drawer bias and reproducibility concerns in psychological science [ 10 ]. More broadly, the field of psychology—given its unique strengths, breadth, and diversity—poses a useful case study for examining dissertation nonpublication in the social, behavioral, and health sciences. Like other scientific disciplines, many Ph.D. graduates in psychology may be motivated to revise and submit their dissertations for publication for the usual reasons offered by academic and research careers. However, other new psychologists might not pursue this goal for a variety of reasons. Those in professional and applied subfields may commit most or all of their working time to non-research activities (e.g., professional practice, clinical training) and have little incentive to seek publication. Even those in more research-oriented subfields increasingly take non-research positions (e.g., industry, consultation, teaching, policy work) or other career paths which do not incentivize publications. Negative graduate school experiences, alternative career pursuits, and personal or family matters can all be additional factors that may decrease the likelihood of publication. Moreover, it is typically a challenging and time-consuming task to revise a lengthy document for submission as one or more journal articles. Still, all individuals holding a Ph.D. in psychology have (in theory) produced an original research study of scientific value, which should (again, in theory) be shared with the scientific community. Thus, for scientific, ethical, and training reasons, it is important to understand the frequency and quality of dissertation publication in psychology.

There is an abundance of literature relevant to this topic, including student or faculty perspectives (e.g., [ 11 – 13 ]) and studies of general research productivity during doctoral training and early career periods (e.g., [ 14 – 19 ]). However, evidence specifically regarding dissertation publication is remarkably sparse and inconsistent [ 8 , 20 – 24 ]. This literature is limited by non-representative samples, biased response patterns, and disciplinary scopes that are either too narrow or too broad to offer insights that are useful and generalizable for psychological science. For example, in the only psychology-specific study to our knowledge, Porter and Wolfle [ 23 ] mailed surveys to a random sample of individuals who earned their psychology doctorates. Of 128 respondents, 59% reported that their dissertation research had led to at least one published article. Unfortunately, this study [ 23 ] and others (e.g., [ 8 ]) are now over 40 years old, offering little relevance to the present state of training and research in psychology. A much more recent and rigorous example comes from the field of social work. Using a literature searching methodology and a random sample of 593 doctoral dissertations in social work, Maynard et al [ 22 ] found that 28.8% had led to peer-reviewed publications. However, this estimate likely does not generalize to psychology and its myriad subfields. Thus, there is a need for more comprehensive, rigorous, and recent data to better understand dissertation publication in psychology.

Accordingly, the present study investigated the extent and nature of dissertation publication in psychology, specifically examining the following questions: (a) How many dissertations in psychology are eventually published in peer-reviewed journals? (b) How long does it take from dissertation approval to article publication? (c) What is the scientific impact of published dissertations (PDs)? and (d) Are there differences across subfields of psychology? Based on the literature and our own observations, we hypothesized that (a) a majority of dissertations in psychology would go unpublished; (b) dissertation publication would occur primarily during the first few years after Ph.D. approval, diminishing thereafter; (c) PDs would show evidence of at least moderate scientific influence via citation rates and journal metrics; and (d) professional/applied subfields (clinical, counseling, school/educational, industrial-organizational, behavioral) would yield fewer PDs than research-oriented subfields (social/personality, experimental, cognitive, neuroscience, developmental, quantitative).

Materials and methods

The dataset of psychology dissertations was obtained directly from ProQuest UMI’s Dissertations and Theses Database (PQDT), which is characterized as “the world’s most comprehensive collection of dissertations and theses. . . [including] full text for most of the dissertations added since 1997. . . . More than 70,000 new full text dissertations and theses are added to the database each year through dissertations publishing partnerships with 700 leading academic institutions worldwide” [ 25 ]. While international coverage varies across countries, PQDT’s repository is estimated to include approximately 97% of all U.S. doctoral dissertations [ 26 ], across all disciplines, institutions, and training models.

Upon request, PQDT provided a database of all dissertations indexed with the term “psychology” in the subject field during the year 2007. This resulted in a total population of 6,580 dissertations, which were then screened and sampled according to pre-defined criteria. The number of dissertations included at each stage in the sampling process is summarized in a PRISMA-style [ 27 ] flow diagram for the overall sample in Fig 1 , and broken down by subfield in Table 1 . Dissertations were excluded if written in a language other than English, for any degree other than Ph.D. (e.g., Psy.D., Ed.D.), or in any country other than U.S. The remaining dissertations were recoded for subfields based on the subject term classification in PQDT, with a few modifications (e.g., combining “neuroscience” and “biological psychology”). This left a remaining sample of 3,866 relevant dissertations, representing our population. This figure is approximately in line with the U.S. National Science Foundation’s Survey of Earned Doctorates [ 28 ] estimate that 3,276 research doctorates in psychology were granted during the year 2007, suggesting that PQDT could be slightly broader or more comprehensive in scope.

Note. PQDT = ProQuest Dissertations and Theses. a Categories of excluded dissertations are mutually exclusive, summing to 100%. b PQDT exclusion criteria were applied sequentially in the order presented; thus, the number associated with each exclusion criterion reflects how many were excluded from the sample that remained after the previous criterion was applied. Adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [ 27 ].

Note. “Relevant dissertations” refers to all PQDT dissertations that satisfied screening criteria for inclusion. Dissertations were excluded in the eligibility stage based on the date of approval in the full text (see Fig 1 ). Rates of exclusion were not significantly different across subfields. Sampling weights for each subfield were calculated as the proportion in the population (relevant dissertations) divided by proportion in the full sample, after adjusting for the proportions within each subfield that were from excluded for ineligibility.

From this relevant population of 3,866, we drew a stratified random sample of 1,000 dissertations. This number was selected because it represented over 25% of the population and offered sufficient power to obtain 95% CIs less than ±3% for the overall proportion estimates (i.e., the primary research question). As shown in Table 1 , the sampling procedure was stratified by subfield using a formula that sought to balance (a) power for between-group comparisons, aiming to include ≥50 dissertations from each subfield; and (b) representativeness to the population, aiming to include ≥10% of the dissertations from each subfield. This resulted in subfield sample sizes ranging from 59 for general/miscellaneous (75.6% of relevant subfield population) to 179 for clinical (12.5% of relevant subfield population). Ninety (9.0%) dissertations were later found to be ineligible during the full-text review because the approval date was before or after the year 2007. This incongruence was partly explained by copyright or graduation dates differing from the dissertation year, and was not significantly different across subfields. The resulting final sample consisted of 910 dissertations, with subfield samples ranging from 52 (general/miscellaneous) to 159 (clinical). Because this study did not meet the definition of human subjects research, institutional review board approval was not required.

Search timeframe

We aimed to conduct prospective follow-up searches for PDs within a timeframe that was both (a) long enough to capture nearly 100% of PDs and (b) short enough for results to retain their relevancy to the current state of psychological science. Because the literature does not offer dissertation-publication “lag time” statistics for reference, we used the “half-life” of knowledge—i.e., the average time it takes for half of a body of knowledge to become disproven or obsolete [ 29 – 30 ]. Across methodologies, the half-life of knowledge in psychology has been estimated at 7–9 years [ 31 – 33 ]. Accordingly, we selected a prospective search window allowing 0–7 years for dissertations to be published. Because the doctoral dissertations were sampled from the year 2007, follow-up searches were restricted to articles published between 2007 and 2014. We elected to exclude candidate publications from years prior to 2007 for several reasons. First, most U.S. psychology Ph.D. programs follow a more traditional dissertation model (and this would have been even more ubiquitous in 2007), where the dissertation would have to be completed before it could be published in a peer-reviewed journal. Second, even for the minority of programs that might follow less conventional models such as dissertation-by-publication [ 34 ], the lag-time to publication would likely still result in at least one PD appearing in print concurrently with or after the dissertation, and would therefore be captured by our search strategy. Finally, any potential benefits of searching retrospectively were outweighed by the potential risks of introducing unreliability into the data, such as identifying false positives from student publications, master’s theses, pilot studies, or other analyses from the same sample. On the other end of our search window, candidate publications that appeared in print during or after 2015 were also not considered. Post hoc analyses (see Results ) suggested that this 0–7 year timeframe was adequate.

Publication search and coding procedures

Searches for PDs were conducted in two rounds, utilizing scholarly databases in a manner consistent with the evidence regarding their specificity, sensitivity, and quality. Specifically, searches were conducted first in PsycINFO, which has high specificity for psychological, social, and health sciences [ 35 – 36 ]; and second, in Google Scholar, casting a much broader net but still searching for peer-reviewed scholarly journal articles [ 35 , 37 – 40 ]. The objective of these searches was to locate the PDs or to determine that the dissertation had not been published in the indexed peer-reviewed journals. Although it is never possible to definitively ascertain a thing’s non-existence, we added additional steps and redundancies to ensure that our searches were as exhaustive as possible. First, when no PD was found in either scholarly databases, as a final step we conducted Google searches for the dissertation author and title, then reviewed the search results (e.g., CVs posted online, faculty web pages) for possible PDs. Second, all searching/coding procedures were performed at least twice by trained research assistants. If two coders disagreed on whether a PD was found, which article it was, or if either coder was uncertain, these dissertations were then coded by consensus among three or more members of the research team, including master’s-level researchers (SCE and CMA).

In all literature searches, the following queries were entered for each dissertation: (a) title of dissertation, without punctuation or logical operand terms; (b) author/ student’s name; and (c) chair/ advisor’s name. Search results were assessed for characteristics of authorship (student and chair names), content (title, abstract, acknowledgments, methods), and publication type (specifically targeting peer-reviewed journal articles) by which a PD could be positively identified. Determination of PD status was made and later validated based on global judgments of these criteria. Identified PDs were then coded for their bibliographic characteristics. Results were excluded if published in a non-English journal, outside of the 0–7 year (2007–2014) window, or in a non-refereed or non-journal outlet (e.g., book chapters). Because dissertations can contain multiple studies and be published as multiple articles, searches aimed to identify a single article that was most representative of the dissertation, based on the criteria outlined above and by consensus agreement among coders. All searches were conducted and coding was completed between January 2015 and May 2017.

Dissertation, publication, and year

Although the structure and content of doctoral dissertations varies across institutions, countries, and disciplines, the common unifying factor is that the dissertation represents an original research document produced by the student, approved by faculty, and for which a degree is conferred. Accordingly, in using PQDT as our population of U.S. Ph.D. psychology dissertations, we adhere to this broad but essential definition of a dissertation. This definition includes all different models of dissertations (e.g., ranging from traditional monographs to more recent models, such as briefer publication-ready dissertations and dissertation by publication [ 34 ]), but does not differentiate among them.

In this paper and in common scientific usage, “publication” refers to the dissemination of a written work to a broad audience, typically through a journal article. Accordingly, we do not consider indexing in digital databases for theses and dissertations as a publication such as in PQDT, even though it may be called “publishing” by the company. Rather, we define “dissertation publication” as the dissemination of at least part of one’s Ph.D. dissertation research in the form of an article published in a peer-reviewed journal. The peer-reviewed status of the journal was included among the variables that were coded twice with discrepancies resolved by consensus. Lastly, year of publication (2007, 2008, 2009 … 2014) and years since approval (0, 1, 2 … 7) were coded from when the print/final version of the article appeared, given that advance online access varies and is not available in all journals.

The PQDT subject terms were used as a proxy indicator of the subfield of psychology from which the dissertation was generated. As described above, twelve categories were derived ( Table 1 ). We considered five categories as professional/ applied subfields (clinical, counseling, educational/school, industrial-organizational, and behavioral), given that graduates in these fields are trained for careers that often include professional licensure or applied activities (e.g., consultation, program evaluation). In contrast, seven categories were considered research/ academic subfields (cognitive, developmental, experimental, neuroscience, quantitative, and social/personality), given that these subfields train primarily in a substantive or methodological research area. Note that Ph.D. programs in all of these subfields train their students to conduct research; when professional/ applied training components are present, they are there in addition to, not instead of, research training.

Article citations

The influence of PDs was estimated using article- and journal-level variables. At the article level, we used Web of Science to code the number of citations to the PD occurring each year since publication, tracking from 2007 up through year 2016. Importantly, Web of Science has been found to exhibit the lowest citation counts, but the citations which are included are drawn from a more rigorously controlled and higher quality collection of scholarly publications compared to others like Google Scholar, PubMed, and Scopus [ 35 , 37 – 38 , 40 – 41 ]. Citations were coded and analyzed primarily as the mean number of citations per year in order to account for time since publication. Total citations and citations each year were also calculated.

Journal-level metrics

The following journal impact metrics were recorded for the year in which the PD was published: (a) Impact Factor (IF) and (b) 5-Year IF [ 42 ]; (c) Article Influence Score (AIS) [ 43 ]; (d) Source Normalized Impact (SNIP) [ 44 ]; and (e) SCImago Journal Rank indicator (SJR) [ 45 ]. Each of these indices shares different similarities and distinctions from the others and provides different information about how researchers cite articles in a given journal. While each has its limitations, these five indicators together offer a broad overall characterization of a journal’s influence, without over-relying on any single metric. As a frame of reference, the population-level descriptive statistics for each of these journal metrics (2007–2014) are as follows: IF ( M = 1.8, SD = 2.9), 5-year IF ( M = 2.2, SD = 3.0), SNIP ( M = 0.9, SD = 1.0), SJR ( M = 0.6, SD = 1.1), and AIS ( M = 0.8, SD = 1.4).

As described above, all of the dissertation, literature searching, and outcome data used in the present study were obtained from a variety of online sources available freely or by institutional subscription. Links to these sources can be found in the supplementary materials ( S1 File ).

Analytic plan

Overall descriptive analyses were conducted to examine the univariate and bivariate characteristics of the data, including the frequency and temporal distribution of PDs in psychology. Similar descriptive statistics were provided to characterize the nature of and scholarly influence of the PD via article citations and journal impact metrics. Group-based analyses were conducted using chi-square and ANOVAs to assess whether dissertation publication rates and scientific influence differed across subfields of psychology. The 95% CIs surrounding the total weighted estimate were used as an index of whether subfield estimates were significantly above or below average.

Time-to-publication analyses were conducted in three different ways. First, we used weighted Cox regression and Kaplan-Meier survival analyses to model dissertation publication as a time-to-event outcome, both for the overall sample and separately by subfields. Second, because the large majority of dissertations “survived” the publication outcome past our observation window (i.e., most cases were right-censored), we also conducted between-group comparisons regarding subfield publication times for only those whose dissertations were published. Finally, in order to ensure the adequacy of our 0–7 year search window, we fit a distribution to our observed data and projected this trend several years into the future.

Full-sample analyses were conducted using the complex samples option in SPSS Version 24, which yields weighted estimates that are less biased by sample proportions and more generalizable to the population. Distribution model-fitting and projections were estimated in R. For analyses related to dissertation publication outcomes, there were no missing data because all values could be coded based on obtained dissertations. Data availability for journal- and article-level variables are reported in those results tables.

Frequency of and time to publication

The overall weighted estimate showed that 25.6% (95% CI: 23.0, 28.4) of psychology dissertations were published in peer-reviewed journals within the period of 0–7 years following their completion. The unweighted estimate was similar (27.5% [24.6, 30.4]), but reflected sampling bias due to differences between subfields. Thus, weighted estimates are used in all subsequent results. Significant variations were found across subfields (Rao-Scott adjusted χ 2 ( df = 9.65) = 65.28, F (9.65, 8869.62) = 8.28, p < .001). As shown in Table 2 , greater proportions of PDs were found in neuroscience (59.4% [47.8, 70.1]), experimental (50.0% [37.7, 62.3]), and cognitive (41.0% [31.1, 51.8]), whereas much lower rates were found for industrial-organizational (10.1% [5.0, 19.5]) and general/miscellaneous (13.5% [6.5, 25.7]). All other subfields fell between 19.0 and 29.0%. Quantitative and social/personality fell within the 95% CIs for the weighted total, suggesting no difference; however, most other subfields fell above or below this average. Of note, three core professional subfields (clinical, counseling, and school/educational) were all between 19.0 and 20.8%—below average and not different from one another.

a Greater than weighted total

b Less than weighted total.

The overall time-to-publication results are presented in Table 3 and the left panel of Fig 2 . As shown, over half (56.0% of those ultimately published; 14.3% of the total sample) of PDs appeared in print within 2 years following the year of completion, with the large majority (89.7% of ultimately published; 23.0% of total) being published within 5 years. Among those dissertations that were ultimately published, the time to publication averaged about 2–3 years ( M = 2.58 [2.34, 2.83]), with a median of 2 years and a mode of 1 year. Omnibus comparisons from the Kaplan-Meier survival model revealed significant variations across subfields, χ 2 ( df = 1) = 4.24, p = .039), as plotted in the right panel of Fig 2 . These results generally mirrored the same pattern found for overall binary publication outcomes across subfields. Among only those dissertations that were published, the subfield differences in time-to-publication were marginal overall, F (11, 238) = 5.99, p = .064, but still shed some additional light beyond the binary publication outcomes. Specifically, neuroscience ( M = 1.61, [1.09, 2.13]), counseling ( M = 1.92, [1.04, 2.79]), and experimental ( M = 1.93 [1.45, 2.41]) averaged less than two years to publication, shorter than the weighted average. In contrast, clinical ( M = 2.88, [2.20, 3.56]), social/personality ( M = 2.90, [1.92, 3.89]), school/educational ( M = 2.94, [1.78, 4.1]), industrial-organizational ( M = 3.00, [0.73, 5.27]), and quantitative ( M = 3.06, [2.35, 3.78]) all took longer, approximately three years.

Overall estimates and 95% confidence intervals (left panel) are derived from the weighted Cox regression model (see Table 3 ). Subfield estimates (right panel) are derived from the unweighted Kaplan-Meier regression model. In both plots, cumulative publication estimate = one minus survival function.

Note. Cumulative estimates and 95% confidence intervals are derived from the weighted Cox regression model, where cumulative publication estimate = one minus survival function. All estimates based on the full sample (denominator n = 910 for all percentages). See Fig 2 , left panel, for a visual presentation of these results.

Lastly, as a methodological check, we modeled our time-to-publication data and projected this trend into the future to estimate what percentage of PDs we might have missed by stopping after 7 years. More specifically, these models used the weighted estimates of how many dissertations were published each year as the outcome and time (years 0 to 7) as the predictor. A Poisson model containing quadratic and linear effects for time fit the data best. When projected into the future, this model estimated that an additional 7 dissertations would be published at 8–10 years post-dissertation (4, 2, and 1 PDs, respectively). From 11 years onward, estimates asymptotically approached and rounded down to zero, even cumulatively. Thus, our sampling frame appears to have captured virtually all (97.3%) of the dissertations that ultimately would be published. In other words, had the study been implemented for as long as necessary to capture all PDs, the data suggest that our primary result, the estimated percentage of dissertations published, would increase only modestly from 25.6% to 26.4%.

Scientific impact

As shown in Table 4 , PDs were cited an average of 3.65 times per year since publication, totaling 15.95 citations on average during the years captured by the study. There were significant variations by subfield in terms of both total and per-year citations. Specifically, PDs in cognitive ( M = 5.08 [1.33, 8.83]) and industrial-organizational ( M = 5.18 [0.80, 9.56]) were more highly cited, with over 5 citations/year. On the other end, fields that exhibited relatively lower (but still nontrivial) rates of citations/year included quantitative ( M = 1.42, [0.87, 1.97]), general/miscellaneous ( M = 1.46 [0.15, 2.78]), counseling ( M = 1.64 [0.63, 2.64]), developmental ( M = 2.82 [1.31, 4.32]), and social/personality ( M = 2.86 [2.05, 3.67]).

Note. Estimates are calculated from available years from the publication year (inclusive) through 2016. This sampling range naturally truncates the number of follow-up years available for dissertations published in later years (e.g., 7-year outcomes are available only for dissertations published in 2007 to 2009, n = 126).

The 250 PDs in our sample appeared in 186 different peer-reviewed outlets, including top-tier journals in general (e.g., Nature , Science ) and psychological (e.g., Psychological Science , Journal of Consulting and Clinical Psychology ) science. Notably, several PDs appeared in journals predominately representing professions or disciplines outside psychology (e.g., Public Health Nursing , Endocrinology ). The most common journal titles were all in relatively specialized areas of psychology (e.g., Applied Psychological Measurement , Brain Research ), tending to draw from experimental, social/personality, neuroscience, behavioral, and cognitive. Overall, however, dissertations were disseminated broadly, with no single journal “catching” more than five (2.0%) dissertations from our overall sample, and most journals publishing only one (0.4%).

As shown in Table 5 , PDs appeared in journals of moderate-to-high influence according to all five metrics used. Subfield differences were found for the IF, SNIP, and SJR ( p s < .01, but not in the 5-year IF or the AIS ( p s > .09). Specifically, neuroscience and cognitive PDs appeared in higher-IF journals ( M s = 4.47 [3.17, 5.78] and 3.86 [1.87, 5.86], respectively), while most others fell in the below-average IF, including those still within the 2+ range (clinical, social/personality, general/miscellaneous, developmental, and behavioral; M s = 2.14 to 2.45) and those in the 1–2 range (quantitative, school/educational, counseling, and industrial-organizational; M s = 1.27 to 1.71). Similarly, neuroscience ( M = 2.17 [1.68, 2.66]), cognitive ( M = 1.97 [1.22, 2.72]), and social/personality ( M = 1.65 [1.09, 2.21]) PDs appeared in higher-SJR journals, whereas behavioral, clinical, general/miscellaneous, quantitative, school/educational, industrial-organizational, and counseling PDs had lower SJRs ( M s = 0.51–1.21). Lastly, cognitive ( M = 1.61 [1.17, 2.05]) and social/personality ( M = 1.55 [1.18, 1.92]) were published in higher-SNIP journals, while clinical ( M = 1.28 [1.08, 1.47]), general/miscellaneous ( M = 1.19 [0.23, 2.15]), and counseling ( M = 0.57 [0.26, 0.89]) PDs appeared in journals with lower SNIPs.

The primary finding of this study was that only about one in four psychology Ph.D. dissertations in the U.S. was published in a peer-reviewed journal. Typically this occurred within 2–3 years after completing the dissertation. Despite variations across subfields, dissertation publication appears to be the exception not the rule. When dissertations were published, however, they were often highly cited and appeared in influential journals. The relatively high impact of published dissertations may reflect a gatekeeping effect, whereby only the highest quality or most significant contributions get published; or a refining effect, whereby the dissertation development and committee review process helps strengthen the contribution [ 1 , 46 ], increasing the likelihood and impact of publication. In other words, the dissertation process may add some value to doctoral research, and some doctoral research appears to add value to psychological science. A larger and more important question is why the vast majority of psychology dissertation research does not contribute to the peer-reviewed literature.

Our estimated rate of dissertation publication in psychology (25.6%) is similar to or slightly below a corresponding estimate for social work (28.8%) [ 22 ], the only field in which a similarly rigorous and comparable design had been used. To our knowledge, the present study is the first to offer a reliable estimate of publication rates specific to the dissertation and specific to psychology. Further, the present study advances the literature by demonstrating the impact that these published dissertations have on the scientific literature. Although it was only minority of cases, published dissertations in psychology were disseminated in moderate- to high-impact journals across a wide spectrum of disciplines and specialty interests. Whereas published dissertation articles were cited several times per year, anecdotally we saw very few citations to the actual dissertation documents in PQDT. These observations are consistent with evidence showing that the impact of dissertations themselves has declined markedly [ 7 – 8 ] in recent decades. In contrast, peer-reviewed journal articles are much more likely to be read, cited, and included in systematic and meta-analytic reviews.

Subfield differences were broadly consistent with hypotheses. Dissertations from professional/applied fields were less often published, whereas the more research/academic-oriented subfields published at rates much higher than average. These findings likely reflect differences in the nature of training and motivation in professional and scientific subfields, and also align with evidence about student research productivity in professional/applied subfields. For example, annual results from the Association of Psychology Postdoctoral and Internship Centers applicant survey indicate that only about 50% of advanced doctoral students in professional psychology have authored or co-authored any peer-reviewed publications, while only 10% have published 5 or more [ 18 ]. Given this relatively low baseline rate of productivity during graduate school for this population, the average likelihood of post-graduation publication seems low. On the other hand, individuals in more research-oriented subfields are often training specifically for an academic position which incentivizes publication. Further, lab-based dissertations often include multiple experiments, which may create more publishable units (this also may explain the relatively higher rate of publication in behavioral psychology). The low publication of dissertations in industrial-organizational (10%) is also interesting, and may reflect an applied focus, organizational propriety of data, greater non-academic incentives (e.g., higher salaries in industry), or limited generalizability as market or consultative research. When these and other types of applied/professional dissertations were published, however, they were often cited several times per year.

The time from dissertation completion to publication appears to be a critical consideration. From our main results and longitudinal projections, we can generalize that by two calendar-years post-dissertation, over 50% of ultimately-published dissertations will appear in print. After five years, this number increases to nearly 90% (10% probability of publication). After 7–10 years, the dissertation findings are likely to become outdated, irrelevant, or overturned [ 30 – 32 ], and the probability of publication approaches 0%. Thus, if students wish to publish their dissertation, it is recommended that they proactively develop a plan for adapting the full document into a manuscript (or multiple manuscripts) for publication [ 1 ]. As one example of this, we are aware of some universities that have begun requiring that approved dissertations be accompanied by a form that outlines an agreed-upon plan for publication and authorship.

The present findings raise questions about the reasons for nonpublication. Possible explanations include the burden of revising and submitting a lengthy document, or limited career incentives for pursuing publications in non-academic careers. Alternatively, unpublished dissertations may lack methodological rigor, including “fatal flaws,” or fail to make a novel and substantive contribution. Thus, unpublished dissertations might not pass the bar of peer review. The present results only illustrate how many dissertations were actually published, and cannot speak to how many students attempted to publish their dissertations, or how many dissertations might have been publishable quality. Similarly, these results do not provide direct evidence of the mechanisms underlying publication vs. nonpublication, but the apparently high quality of the published dissertation articles is consistent with the file drawer hypothesis. Interestingly, one recent study in management research found that in the path from dissertation to publication, studies appear to get “beautified,” for example, such that the ratio of supported to unsupported hypotheses more than doubles in at least one discipline [ 47 ]. Such questionable research practices may provide one explanation for how dissertations selectively get published, but this is clearly not an appropriate solution. Whatever the underlying explanations may be, the widespread non-publication of dissertation research is a problem in psychology. To the extent that this non-publication continues, it exacerbates the file drawer problem [ 5 – 6 , 9 ], biases systematic reviews and meta-analyses, and contributes to the replication problem in psychology [ 10 ]. It also amounts to inefficient use of time and resources, raising ethical questions about violating agreements with participants and funding agencies, and about the consequences of not disseminating research findings [ 2 , 4 ].

The present study was designed so that results could be generalized to the population of dissertations produced in U.S. psychology Ph.D. programs. However, some limitations should be noted. First, our stratified random sample was drawn from an archival data source (PQDT), which is an approximation of the population of dissertations in psychology (although a very comprehensive one) and a proxy of the boundaries delineating subfields in psychology. Our outcome variables were likewise drawn from various databases (e.g., PsycINFO, Google Scholar, Web of Science, Thomson Reuters) which are necessarily restricted in different ways. As noted in the Methods section, these databases were selected as the most comprehensive and appropriate sources available for the purposes for which they were used, and their strengths and weaknesses were considered in developing the study protocol.

A second constraint lies in the selection of a single cohort year (2007) and 7-year follow-up period, raising the possibilities of missed cases and of cohort/historical effects. The changing landscape of doctoral training in psychology (e.g., more competitive admissions, increasing emphasis on research productivity, nontraditional dissertation requirements) may limit generalizability to past and future populations. Of note, these results may not generalize broadly to other countries (e.g., Northern/Western Europe, Australia, and New Zealand) and fields (e.g., biomedical, natural, and physical sciences) that are increasingly using a dissertation-by-publication model [ 34 ]. From our read of the literature and our assessment of the present sample, this model has not been widely adopted in U.S. psychology, where more traditional dissertation documents are still the norm. Accordingly, our sampling and search strategies were designed to work reasonably well for all U.S. psychology dissertations, including a suspected minority of nontraditional models; however, we could not differentiate types of dissertations. For all of these reasons, periodic replication of these results would be useful. Nonetheless, the large sample size, stratified sampling method, comprehensive dataset, and thorough multi-stage search protocol help mitigate bias. Narrow 95% confidence intervals support the precision of the overall estimate, and post hoc analyses suggested that the results are unlikely to change substantially given a longer sampling frame.

Finally, we note that this study should not be interpreted as any sort of evaluation of students, advisors, or programs for dissertations that were or were not published. Nor are we advocating that all dissertations be published, regardless of quality. Rather, these findings shed light on what appears to be a systemic problem affecting research and training in all areas of psychology. Efforts aimed at increasing the quality and “publishability” of doctoral dissertation research may have broad benefits for both training and research in psychology. On the training side, these efforts may benefit students and graduates in terms of providing a higher standard of scientific training, more research/publishing experience, and greater early-career productivity. On the research side, such efforts can help promote a higher level of rigor in doctoral research and increase the likelihood that the findings will be shared with the scientific community.

Supporting information

Acknowledgments.

A portion of this research was presented at the 28th Annual Convention of the Association for Psychological Science, Chicago, IL, May 2016, where it received the APS Student Research Award. We thank Austin McLean and the staff of ProQuest Dissertations and Theses for providing the population dataset of dissertations, Patrick Edmonds for offering statistical consultation, Oliver Blossom for reviewing the manuscript, and the following members of our research team for their extensive coding efforts: Maggie Biberstein, Jamie Eschrich, Andrea Garcia, Mackenzie Klaver, Alexa Mallow, Alexandra Monzon, and Emma Rogers.

Funding Statement

The journal's publication fees were covered by an award from the One University Open Access Fund at the University of Kansas. The authors received no other specific funding for this work.

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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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Thesis vs. Dissertation: Understanding the Differences

A thesis and a dissertation are often used interchangeably, causing confusion among students and academics alike. While they share some similarities, they are distinct in purpose, scope, and requirements. Understanding these differences is crucial for graduate students embarking on their academic journey. In this article, we delve into the world of thesis and dissertation, shedding light on what sets them apart and helping you navigate these essential components of advanced academic research.

Thesis: The Master's Magnum Opus

In the realm of academia, a thesis is often referred to as the master's magnum opus, representing the pinnacle of a student's academic journey at the master's level. It is a significant undertaking that requires dedication, research acumen, and a commitment to scholarly excellence. Here, we explore the key characteristics and elements that define a thesis:

Academic Level: A thesis is a hallmark of master's degree programs. It serves as the culmination of a student's graduate studies, demonstrating their ability to engage critically with their field of study and contribute meaningfully to it. Unlike undergraduate projects, a thesis delves deeper into the subject matter and requires a higher level of analysis and synthesis.

Purpose: The primary purpose of a thesis is to contribute to the existing body of knowledge within a specific field or discipline. It goes beyond mere regurgitation of facts and concepts; it involves original research or a novel approach to addressing a research question or hypothesis. In essence, a thesis is a scholarly endeavor that aims to add value to the academic conversation.

Scope: Theses typically have a narrower scope compared to their doctoral counterparts, dissertations. While they explore complex topics, they often focus on a specific aspect or dimension within a broader field. This focused approach allows students to delve deeply into their chosen subject matter and demonstrate expertise in that area.

Length: Theses vary in length depending on the institution, program, and field of study. However, they are generally shorter than dissertations. A typical master's thesis can range from 50 to 100 pages. This concise format necessitates precision and clarity in presenting research findings and arguments.

Committee Evaluation: A key aspect of the thesis process is the evaluation by a committee of professors or advisors. This committee plays a pivotal role in guiding the student's research, offering feedback, and assessing the final product. Their expertise ensures that the thesis meets academic standards and contributes meaningfully to the field.

Research and Methodology: Research is at the heart of any thesis. Students engage in systematic inquiry, which may involve data collection, experimentation, surveys, literature analysis, or a combination of research methods. The methodology chosen should align with the research question and contribute to the overall quality of the thesis.

Scholarly Writing: A thesis demands scholarly writing of the highest caliber. Students are expected to adhere to academic writing conventions, including proper citation, referencing, and adherence to a specific style guide (e.g., APA, MLA). Clarity, coherence, and a formal tone are essential elements of thesis writing.

Originality: Perhaps the most crucial aspect of a thesis is its contribution to the field's body of knowledge. It must offer fresh insights, perspectives, or solutions to existing challenges. Originality is the hallmark of a well-executed thesis, distinguishing it from undergraduate assignments.

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Dissertation: the doctoral opus.

In the realm of academia, a dissertation is the pinnacle of scholarly achievement within the context of doctoral programs. It represents the culmination of extensive academic preparation and research and is often referred to as the doctoral opus. Here, we explore the defining characteristics and elements that set a dissertation apart in the academic world:

Academic Level: A dissertation is synonymous with doctoral programs, such as a Doctor of Philosophy (Ph.D.) or other terminal degrees. It marks the highest level of academic achievement and signifies a student's readiness to make a significant contribution to their field.

Purpose: The primary purpose of a dissertation is to make a substantial and original contribution to the existing body of knowledge within a specific field or discipline. Unlike a thesis, which often focuses on a more specific aspect, a dissertation tackles complex research questions that have broader implications for the field.

Scope: Dissertations are renowned for their expansive scope. They typically explore multifaceted research questions that encompass multiple variables, perspectives, and dimensions. Doctoral candidates are expected to demonstrate a comprehensive understanding of their field and engage in in-depth analysis.

Length: Dissertations are considerably longer than theses. The length can vary depending on the field of study and the nature of the research, but they often exceed 100 pages and can extend to several hundred pages. This extended format allows for a thorough exploration of the research topic.

Committee Evaluation: A dissertation is rigorously evaluated by a committee of experts in the field. This committee plays a crucial role in guiding the research, offering feedback, and ultimately assessing the quality and significance of the dissertation. Their expertise ensures that the dissertation meets the highest academic standards.

Research and Methodology: Research in a dissertation is not only extensive but also often involves original data collection, experimentation, surveys, case studies, or longitudinal studies. The choice of research methodology is critical and should align with the research questions and objectives.

Scholarly Writing: As with theses, scholarly writing is paramount in dissertations. Doctoral candidates are expected to adhere to the most stringent academic writing conventions. This includes precise citation and referencing, adherence to a specific style guide, and the use of formal and rigorous language.

Originality: The hallmark of a successful dissertation is its originality. It must offer new insights, perspectives, or solutions to complex problems within the field. Doctoral candidates are expected to demonstrate their ability to contribute significantly to the advancement of knowledge.

Defending the Dissertation: A distinctive feature of the dissertation process is the formal defense. Candidates present their research findings and defend their methodology, conclusions, and contributions before their committee and often a public audience. This oral defense is a culmination of the rigorous research journey.

Navigating the Process

Embarking on the journey of crafting a thesis or dissertation is a significant undertaking that requires careful planning, dedication, and resilience. Navigating this process effectively is key to producing a scholarly work that reflects your expertise and contributes meaningfully to your field. Here, we provide an overview of the essential steps and strategies to guide you through this academic odyssey.

1. Define Your Research Question: The first and foremost step in both thesis and dissertation projects is defining a clear and research-worthy question or problem. Your question should be specific, relevant, and capable of generating new insights or solutions. Take the time to conduct a thorough literature review to understand the existing body of knowledge in your chosen area.

2. Assemble Your Committee: Your academic committee will play a vital role in guiding your research and evaluating your final work. Choose committee members who are experts in your field and who can provide valuable insights and feedback throughout the process. Effective communication with your committee is essential.

3. Develop a Research Proposal: Crafting a well-structured research proposal is crucial. It should outline the scope of your project, research methodology, timeline, and expected outcomes. Your proposal will serve as a roadmap for your research journey and will require approval from your committee.

4. Research and Data Collection: The heart of your thesis or dissertation lies in the research phase. Depending on your field and research question, this may involve conducting experiments, surveys, interviews, or extensive data analysis. Ensure that your data collection methods align with your research objectives.

5. Writing and Organization: Writing your thesis or dissertation is an ongoing process that requires discipline and organization. Create a detailed outline or structure to guide your writing. Pay careful attention to proper citation and referencing to avoid plagiarism. Seek feedback from your committee at various stages of writing.

6. Revision and Proofreading: Revising and proofreading are integral steps in producing a polished document. Review your work for clarity, coherence, and logical flow. Check for grammatical errors and ensure that your writing adheres to academic conventions.

7. Formal Defense: Doctoral candidates will typically undergo a formal defense of their work. This involves presenting your research findings, methodology, and conclusions to your committee and often a public audience. Prepare thoroughly for this oral defense, as it is a culmination of your research journey.

8. Submission and Publication: Once your thesis or dissertation is complete and approved by your committee, it's time to submit it to your institution's academic office. Depending on your field and aspirations, you may also consider publishing your work in academic journals to contribute to your field's body of knowledge.

9. Celebrate Your Achievement: Completing a thesis or dissertation is a monumental achievement. Take the time to celebrate your hard work and dedication. Share your research findings with peers and colleagues, and consider how your work can continue to impact your field.

10. Continuous Learning: The process of research and scholarship is ongoing. Continue to engage with your field, attend conferences, and seek opportunities for further research and collaboration. Your thesis or dissertation is a significant step in your academic journey, but it is not the end; it is a foundation upon which you can build your future contributions to your field.

Navigating the thesis or dissertation process is a transformative experience that equips you with valuable research, writing, and critical-thinking skills. Approach it with curiosity, dedication, and a commitment to advancing knowledge in your field, and you will emerge from this academic endeavor not only with a well-crafted document but also as a more accomplished scholar.

In the realm of academia, the terms "thesis" and "dissertation" represent distinct milestones in a student's educational journey. Understanding the differences between these two research projects is essential for selecting the appropriate path in your graduate studies. Whether you're aiming for a master's or doctoral degree, both theses and dissertations are opportunities to engage in meaningful research, contribute to your field, and demonstrate your expertise as a scholar.

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Know the Difference: PhD Dissertation vs. Thesis

One of the biggest turning points of any PhD student’s journey is the submission of a research writing project in the form of either a PhD thesis or a PhD dissertation. From an academic perspective, the thesis/dissertation is in many ways a major indicator of the abilities and expertise that you have gained as a doctoral candidate.

The mere task of understanding the requirements and compiling a PhD dissertation/thesis is in itself huge. However, what may be confusing to understand, especially if you are just embarking upon your doctoral journey, is the difference between thesis and dissertation.

Table of Contents

Similarities in phd dissertation vs. thesis, 1. understanding differences in the meaning of the two terms, 2. difference between thesis and dissertation based upon geographical location, 3. understanding a difference in content for a phd thesis v/s phd dissertation.

These two terms are often used interchangeably when referring to doctoral studies as there are a number of similarities between them:

• The very first commonality between thesis and dissertation is that the submission of both is considered to be an official culmination of the doctoral work of the candidate.
• Both the thesis and the dissertation demonstrate the ability of a doctoral candidate to effectively communicate their process of resolving a problem statement.
• The thesis and dissertation both test a candidate’s ability toward analytical reasoning and critical thinking , while showcasing his/her expertise in a particular subject area.
• Both the thesis and the dissertation are evaluated by an official review committee consisting of internal as well as external examiners who are experts in the specific subject area being explored in the doctoral study.
• Based upon the reviews of the committee members, both of these documents are then subject to changes and re-submission as required .
• Lastly, both a thesis and a dissertation can be treated as official publications that may be available as resources in the university library .

Owing to the above-mentioned similarities, the confusion between the correct usage of dissertation vs. thesis is quite understandable. In order to ensure the proper usage of these two terms, it’s crucial to understand the differences in a PhD thesis v/s PhD dissertation. Here are some quick pointers that may be useful.

Differences between thesis and dissertation

Since most academic institutions will continue to use these terms interchangeably, it is imperative that you confirm the intricate details regarding the expected structure of a PhD thesis/dissertation with your respective institution. However, for now, we hope that the above article helps in clarifying some of the major doubts that you may have had regarding a PhD thesis v/s dissertation.

In order to better understand the meaning of thesis vs dissertation, let us go back to the origin of the terms. The term ‘thesis’ originates from the Greek word ‘tithenai’, which means ‘to place a proposition’, while the term ‘dissertation’ has a Latin origin, which essentially means ‘disserere’/’dissertare’, i.e., ‘to (continue to) examine and discuss’. 1 To simplify further, a thesis by itself may simply represent an argument that you put forth and describe in depth, while a dissertation may represent a written summary/discussion of a particular work. 2

In countries/institutions that follow the British education system, it is common to term the final doctoral research writing project as a PhD thesis, while the countries/institutions that follow the American education system prefer to call it a PhD dissertation. In case you are unsure which education system is followed by your institution, it may be a good idea to verify this with the respective personnel, so that you can plan your doctoral journey effectively.

While the above two points may be useful to understand the differences between thesis and dissertation on a surface level, as a PhD student it is crucial for you to understand the deeper differences in the content and the type of work that goes into each of them. Let us do this by revisiting the differences in the origin of the two terms: ‘dissertare’ or to discuss (dissertation) v/s ‘to place a proposition’ (thesis). In my experience, the content of a PhD dissertation often comprises peer-reviewed publications that are published by the doctoral candidate during their doctoral work, along with supplementary chapters.

On the other hand, while compiling a PhD thesis, a doctoral candidate may need to describe the doctoral work in detail with the help of distinct chapters comprising: abstract, introduction, literature review, methodology, results, discussion, conclusion and bibliography/references. Thus, the main difference between thesis and dissertation, lies in the way the written document is being presented although the doctoral work done by the candidate will mostly remain the same .

References:

1.         What is the Difference Between a Dissertation and a Thesis? | Postgrad.com. https://www.postgrad.com/advice/exams/dissertation-and-theses/difference-between-a-dissertation-and-a-thesis/

2.         The PhD Thesis | FindAPhD.com. www.FindAPhD.com https://www.findaphd.com/guides/phd-thesis.

The time it takes to write a PhD thesis vs. dissertation can vary depending on several factors, including the research topic, the scope of the project, the research methodology, and the specific requirements of the academic program. However, in general, a dissertation is typically longer and more comprehensive than a thesis, and therefore may take longer to complete.

A dissertation for doctoral programs is typically required after the completion of required coursework, passing comprehensive exams, and hitting any other specific milestones outlined by the program. This means PhD students usually devote years in developing their dissertation, which demonstrates your ability to conduct independent research and make original contributions to the field

A thesis statement is used in academic writing to provide a concise summary of the main argument or point being made in an essay or paper. It should be used at the introduction at the beginning of the paper to guide the reader and provide focus for the writing. A thesis statement can be used in academic essays, research papers, analytical papers and literature reviews and presents readers with a clear roadmap of the research being conducted.

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