average length of dissertation

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How long is a dissertation, published by steve tippins on april 9, 2019 april 9, 2019.

Last Updated on: 2nd February 2024, 05:03 am

How long is a dissertation? This is a question that almost every doctoral student asks at some point. It is not a new question–in fact, it’s been asked every time a paper of some sort is assigned in any class.

The simple answer (for any paper) is, “long enough to answer the question.” Not a really helpful answer, but satisfying from a professor’s perspective.

The truth is, there is no one answer to how long a dissertation is. I can’t say 146 pages is what’s needed, as you may write to page 146 and stop without fully exploring your topic. 90 pages could adequately address your research question, or you could write 200 pages and still not fully answer what you set out to. Every topic is unique, as is each person’s writing style.

Some websites even give specific answers that are simply inaccurate. In my experience, dissertations vary too much to be pinned down like that.

However, there are some practical suggestions I can make about how long your dissertation should be, how to adequately address the requirements of each section, as well as how to expand or reduce the length of specific chapters according to your needs. I’ll explore these below.

But first, let’s try and at least give the beginning of an answer to the question “how long is a dissertation?”

Marcus Beck Sets Out to Answer “How Long Is A Dissertation?”

Any discussion of dissertation length must include the work done by Marcus Beck . As a way to distract himself from his own dissertation writing, Beck calculated the average length of dissertations in the University of Minnesota database.

infographic outlining the different Average Dissertation Lengths

You can see from looking at his data that average length varies by discipline. So the first answer to how long a dissertation is, is that it depends upon what area you are writing your dissertation. It appears that a dissertation in History will be much longer, on average, than one in Chemistry. He also calculated the average across all disciplines.

chart about the length of a dissertation paper

Don’t be intimidated by how long the average dissertation is.

Many people look at the average length of a dissertation and get intimidated by the high page count. But, as Marcus Beck says in his blog post, “The actual written portion may only account for less than 50% of the page length.”

I’ve found this to be true. References, appendixes, tables and figures, page breaks, and white space all contribute to the high page count. The actual number of words you need to write is likely considerably less than the page count initially implies.

How Long Should My Dissertation Be?

Even though there’s no single answer about how long a dissertation should be, there probably is an ideal range which your dissertation falls into. This depends on your topic of research, but also on other factors. I’ll discuss some of these below.

Institutional Guidelines

I know of schools that have policies such as “Chapter 2 must be at least 40 pages long and no more than 60 pages.” Why this type of requirement? In my mind, there are two reasons.

First, they want to give some sort of guideline for students that is helpful but does not overburden faculty (a 230-page lit review is daunting to read).

Second, credibility is important. An 8-page lit review does not reflect well upon the student or the institution.

Most schools now have a dissertation template with the headings that are needed for most sections. If you take the time to completely fill in the headings with all of the relevant information, you should come up with an adequate number of pages. Remember, in academic writing, we don’t leave much to chance, we tell the reader everything.

Committee Preferences

It is likely that you will get a committee member who will give you priceless advice such as, “more is needed here.” When you get this type of comment it can be frustrating as specific feedback can be much more helpful.

Usually, what a committee member means by comments like this is that you haven’t really convinced the reader that you have fully explored the area or demonstrated a strong understanding of the material. So, expand what you are saying. Don’t imply anything, state it directly. This lets your committee know that you really do get it.

Sometimes you will get committee members giving contradictory advice. One member may want more information and another may want less. My first piece of advice is to negotiate these types of requests through your Chairperson.

This is where your Chair’s experience and guidance can be very helpful. Second, if a member really wants material included but others do not think it is very helpful, then adding the material in an appendix may make everyone happy.

Practical Suggestions For Dissertation Length, Chapter-By-Chapter

close-up shot of a man in a blue checkered shirt typing on a laptop

If you adequately and succinctly address each required section, you should end up with the right length for each chapter (and therefore, a dissertation of the right length). I’ll also give some rough guidelines on average page length where appropriate.

This is the introduction to your study. It is important to lay out the agenda for your research. Be sure that your problem statement, title, and research questions are in alignment (all referring to the same idea).

Chapter 1 tends to average in the 15-25 page range. If you get beyond 25 pages, you are usually including material that is better presented elsewhere in the dissertation.

Chapter 2 should thoroughly explore the existing research on your topic. However, it shouldn’t go on and on.

If you are looking to beef up Chapter 2, it is always helpful to add research that supports the methodology that you are planning to use.
If the chapter is too long, try to reduce the references you cite to those that are the most relevant and recent.

Make sure that you tell the reader what you did and how you did it. What type of analysis did you use and why? How many respondents were involved and how did you find them? The idea is to make sure that readers understand what you did and could replicate it if they want to.

As this is a plan for your research, it seems to naturally fall in the 15 to 20 page range.

The results of your study are presented here. Include all material that will help the reader understand what you found. There is a tendency to inundate the reader with tables, charts, and graphs. If they don’t directly relate to what you found or are redundant they can be included in an appendix. You don’t want to lose your reader in an avalanche of tables and numbers.

In most dissertations, it is Chapter 5 where you get to explain what the results of your research mean and the implications. This is the only chapter where you have some freedom to really express your opinions. Go ahead and do so.

I am always surprised when someone has spent 15 months of their life working on a research topic and they submit a Chapter 5 that is 8 pages long. Spread your wings and really explore what your results mean.

How Long is a Dissertation? Summary

The is no doubt about it, a dissertation is a long document. It is, however, not written in one sitting. You work on it for many months, crafting paragraphs and coming to conclusions. Many people find that because the document can be written in pieces that when it’s all put together, it is longer than expected. Keep writing and adding your thoughts and you will make it.

Many students find it helps to have a supportive guide who’s both been through the dissertation writing process before and is experienced in helping students. If that would be useful to you, feel free to reach out to me about my dissertation coaching or dissertation editing services.

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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How Long Is a PhD Thesis?
Doing a PhD

It’s no secret that one of the most challenging aspects of a PhD degree is the volume of work that goes into writing your thesis . So this raises the question, exactly how long is a thesis?

Unfortunately, there’s no one size fits all answer to this question. However, from the analysis of over 100 PhD theses, the average thesis length is between 80,000 and 100,000 words. A further analysis of 1000 PhD thesis shows the average number of pages to be 204 . In reality, the actual word count for each PhD thesis will depend on the specific subject and the university it is being hosted by. This is because universities set their own word length requirements, with most found to be opting for around 100,000.

To find out more about how these word limits differ between universities, how the average word count from STEM thesis differ from non-STEM thesis and a more detailed breakdown from the analysis of over 1000 PhDs, carry on reading the below.

Word Count Differences Between Universities

For any PhD student writing a thesis, they will find that their document will be subject to a word limit set by their university. In nearly all cases, the limit only concerns the maximum number of words and doesn’t place any restrictions on the minimum word limit. The reason for this is that the student will be expected to write their thesis with the aim of clearly explaining their research, and so it is up to the student to determine what he deems appropriate.

Saying this, it is well accepted amongst PhD students and supervisors that the absence of a lower limit doesn’t suggest that a thesis can be ‘light’. Your thesis will focus on several years worth of original research and explore new ideas, theories or concepts. Besides this, your thesis will need to cover a wide range of topics such as your literature review, research methodology, results and conclusion. Therefore, your examiners will expect the length of your thesis to be proportional to convey all this information to a sufficient level.

Selecting a handful of universities at random, they state the following thesis word limits on their website:

University of Edinburgh: 100,000
University of Exeter: 100,000
University of Leister: 80,000
University of Bath: 80,000
University of Warwick: 70,000

The above universities set upper word limits that apply across the board, however, some universities, such as the University of Birmingham and the University of Sheffield, set different word limits for different departments. For example, the University of Sheffield adopts these limits:

Arts & Humanities: 75,000
Medicine, Dentistry & Health: 75,000
Science: 80,000
Social Sciences: 75,000-100,000

Although there’s a range of limit, it’s safe to say that the majority fall within the 80,000 to 100,000 bracket.

Word Count Based on Data from past Theses

A poll of 149 postdocs.

In mid-2019, Dr Eva Lantsoght, a published author, academic blogger and Structural Engineering Professor, conducted a poll which asked postgraduate doctoral students to share the length of their final thesis. 149 PostDoc students responded to the survey, with the majority reporting a length falling within the ‘80,000 – 120,000 words’ bracket as seen below.

DiscoverPhDs_How-long-is-a-PhD-Thesis_Poll

Analysis of 1000 PhD Theses

Over a three-year time period, Dr Ian Brailsford, a then Postgraduate Learning Adviser at the University of Auckland, analysed 1000 doctoral thesis submitted to his university’s library. The PhD theses which formed the basis of his analysis were produced between 2008 to 2017 and showed:

Average number of pages = 204
Median number of pages = 198
Average number of chapters = 7.6

We should note that the above metrics only cover the content falling within the main body of the thesis. This includes the introduction, literature review, methods section, results chapter, discussions and conclusions. All other sections, such as the title page, abstract, table of contents, acknowledgements, bibliography and appendices were omitted from the count.

Although it’s impossible to draw the exact word count from the number of pages alone, by using the universities recommended format of 12pt Times New Roman and 1.5 lines spacing, and assuming 10% of the main body are figures and footnotes, this equates to an average main body of 52,000 words.

STEM vs Non-STEM

As part of Dr Ian Brailsford’s analysis, he also compared the length of STEM doctorate theses to non-STEM theses. He found that STEM theses tended to be shorter. In fact, he found STEM theses to have a medium page length of 159 whilst non-STEM theses had a medium of around 223 pages. This is a 40% increase in average length!

Can You Exceed the Word Count?

Whilst most universities will allow you to go over the word count if you need to, it comes with the caveat that you must have a very strong reason for needing to do so. Besides this, your supervisor will also need to support your request. This is to acknowledge that they have reviewed your situation and agree that exceeding the word limit will be absolutely necessary to avoid detriment unnecessary detriment to your work.

This means that whilst it is possible to submit a thesis over 100,000 words or more, it’s unlikely that your research project will need to.

How Does This Compare to a Masters Dissertation?

The average Masters dissertation length is approximately 20,000 words whilst a thesis is 4 to 5 times this length at approximately 80,000 – 100,000.

The key reason for this difference is because of the level of knowledge they convey. A Master’s dissertation focuses on concluding from existing knowledge whilst a PhD thesis focuses on drawing a conclusion from new knowledge. As a result, the thesis is significantly longer as the new knowledge needs to be well documented so it can be verified, disseminated and used to shape future research.

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How Long is a Dissertation for a PhD?

How long is a dissertation? This is one of the most common questions asked by current or aspiring doctoral students.

A dissertation is an extensive research project that contributes fresh knowledge to the author’s field. Many doctoral programs require students to write, defend, and revise a dissertation to earn their degrees.

Editorial Listing ShortCode:

This article explores the average length of a dissertation and various factors that affect the scope of these papers.

How Long Is a Dissertation?

Man taking PhD, working on his dissertation

For most graduate students, the dissertation is the longest and most time-consuming paper they write throughout their education. This project aims to address a research question in the student’s field and build on existing bodies of scholarship.

Additionally, the dissertation showcases the student’s mastery of research methodologies. Because a dissertation must accomplish several goals, they tend to be ambitious and lengthy.

Most dissertations are 100 to 250 pages long. But many factors impact the length of a dissertation, including:

Academic field . Each discipline has different norms regarding dissertation content and length. For instance, literature dissertations often include extended close readings of texts, while science dissertations focus more on data analysis and research design. As a result, humanities dissertations are typically longer.
Institutional requirements . Most colleges and universities specify a minimum number of pages for the dissertation. You can ask your academic advisor or the graduate studies office about specific dissertation guidelines.
Topic complexity . Broad or complex research questions may require more data and analysis to answer, resulting in a longer dissertation.
Individual writing style . Some doctoral students enjoy writing elaborate and detailed sentences, while others prefer to get straight to the point.
Research methodologies . The approaches and methods used in the research can dramatically affect the length of the dissertation. For example, some dissertations include case studies, which may require lengthy explanations and analyses. Others use quantitative research methods and may include a number of charts and figures.
Committee feedback . Graduate students typically write their dissertations under the supervision of a faculty committee. These professors may recommend adding or cutting material during revision, impacting the dissertation’s final length.
Future goals . Some doctoral students plan to revise their dissertation and publish it as an academic monograph after graduation. Writing a longer dissertation gives them more material to adapt for their book.

Reading dissertations from your department and field can give you a better sense of the typical length.

How Long Are Dissertations by Discipline?

Woman doing research for her dissertation

The answer to “how long is a doctoral dissertation?” varies by field. In some disciplines, it’s common for graduate students to spend several years writing extensive manuscripts. Other fields produce shorter dissertations of approximately 100 pages.

Here are a few examples of dissertation norms for different disciplines:

Arts : Students studying art and creative writing may produce a creative dissertation. These projects typically consist of a theoretical introduction and a creative project, such as a poetry collection or a series of paintings. Creative dissertations may have fewer than 100 written pages, but the artistic elements can still be time-consuming.
Chemistry : These dissertations typically range from 100 to 150 pages. They typically describe experimental methodologies and draw conclusions from the author’s findings.
Computer science : Dissertations in this field are generally 50 to 150 pages long and often include quantitative data and algorithms.
English : PhD students in this field frequently write lengthy literature reviews and provide in-depth analyses of novels and other texts. English dissertations may range from 200 to 350 pages.

Faculty in your discipline can help you understand dissertation expectations.

Average Dissertation Length Per Chapter

Man working on a chapter of his dissertation

Dissertations typically have similar components and structures across disciplines. Here are six common chapters and their average lengths:

Introduction : The first chapter introduces the primary research topic and explains its relevance to the field. The introduction also provides a brief overview of the investigation method. Most introductions are 10 to 25 pages long.
Literature review : This chapter situates the project within the broader discipline. The author analyzes existing sources already written on their research topic and identifies biases or gaps that their own project remedies. Humanities dissertations tend to have 20 to 30 page literature reviews, while STEM dissertations devote 10 to 20 pages to this component.
Methodology : The third chapter typically focuses on the methods and techniques used to gather data. Ideally, this description should contain all the details that another researcher would need to duplicate the experiment and verify the results. The methodology chapter ranges from 15 to 25 pages, depending on the complexity of the research.
Findings : This section analyzes the collected data and discusses the results of the research project. It often includes charts, diagrams, and other data visualizations illustrating the findings. This chapter could be 20 to 50 pages, depending on the number of images and the amount of text needed to thoroughly examine the results.
Discussion : This chapter explains the significance of the results. The author may also compare their findings to previously published scholarship. The discussion is typically 15 to 25 pages.
Conclusion : The author summarizes their findings, acknowledges the limitations of their research, and suggests avenues for future studies. Most conclusions consist of 15 to 20 pages.

Some dissertations deviate from this format. For instance, English dissertations may devote several chapters to critical analyses of different genres or authors.

What Is a Dissertation?

students researching together for their dissertation

A dissertation is a lengthy written document that a doctoral student produces based on their original research. This project demonstrates the student’s expertise in their area of specialty and contributes to existing knowledge in the field.

Dissertations typically fall into one of two categories. Empirical dissertations require students to collect and analyze data. For instance, a psychology student may interview people about their mental health, while a marketing student could interpret sales data.

Theoretical dissertations focus on analyzing existing scholarship and secondary sources. For example, a literature student might research psychoanalysis and apply this approach to superhero comics.

How Many Pages Is a Dissertation?

Man reviewing some printouts of his dissertation

The length of a dissertation varies by discipline, institution, and research project. Some dissertations can be as short as 50 pages, while others may total 400 pages. But the average dissertation ranges from 100 pages to 250 pages.

Dissertations in the humanities and social sciences are typically the most extensive. These projects often include detailed analyses, case studies, and literature reviews. By contrast, dissertations in STEM fields like economics and mathematics are often 150 pages or less.

Students in these disciplines frequently use mathematical formulas and data visualizations to prove their findings, resulting in less text.

Why Are Dissertations So Long?

Dissertations are typically the most extended assignments that students complete in graduate school. Several factors contribute to their length, including:

Depth of research . Doctoral students can spend several years researching their topic and analyzing data. This extensive work often takes hundreds of pages to summarize and explain.
Literature review . Most fields have extensive bodies of scholarship, so students spend many pages analyzing sources and contextualizing their projects.
Bibliography . Dissertations often cite dozens or even hundreds of sources, resulting in lengthy bibliographies.

Also, colleges often require dissertations to include additional materials like abstracts and tables of content.

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

Depending on the type of graduate program you enroll in, you may be required to write a dissertation or thesis. Here are the main differences between a thesis vs. dissertation .

If you want to develop your own concepts or theories, a dissertation can help you accomplish this goal.

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

There are doctorate programs with no dissertation. Like some online doctoral programs in education without dissertation requirements, they have a capstone project instead. Here’s a comparison of a dissertation vs. capstone .

Your career goals can help determine if a capstone or dissertation is right for you.

How Long Is a Dissertation for a PhD Degree?

students taking PhD degree, working on their dissertation

People often wonder, “How long is a PhD dissertation?” These projects are typically 100 to 250 pages long, though dissertations on complex topics may total more than 400 pages.

Writing a dissertation allows you to develop advanced expertise on your chosen research topic. Many students also publish portions of their dissertations as peer-reviewed articles and share their findings at conferences. These activities enhance your CV and may make you more competitive for academic jobs.

You can kickstart your doctoral journey today by researching accredited online programs in your field.

Formatting Your Dissertation
Introduction

Harvard Griffin GSAS strives to provide students with timely, accurate, and clear information. If you need help understanding a specific policy, please contact the office that administers that policy.

Application for Degree
Credit for Completed Graduate Work
Ad Hoc Degree Programs
Acknowledging the Work of Others
Advanced Planning
Dissertation Submission Checklist
Publishing Options
Submitting Your Dissertation
English Language Proficiency
PhD Program Requirements
Secondary Fields
Year of Graduate Study (G-Year)
Master's Degrees
Grade and Examination Requirements
Conduct and Safety
Financial Aid
Non-Resident Students
Registration

On this page:

Language of the Dissertation

Page and text requirements, body of text, tables, figures, and captions, dissertation acceptance certificate, copyright statement.

Table of Contents

Front and Back Matter

Supplemental material, dissertations comprising previously published works, top ten formatting errors, further questions.

Related Contacts and Forms

When preparing the dissertation for submission, students must follow strict formatting requirements. Any deviation from these requirements may lead to rejection of the dissertation and delay in the conferral of the degree.

The language of the dissertation is ordinarily English, although some departments whose subject matter involves foreign languages may accept a dissertation written in a language other than English.

Most dissertations are 100 to 300 pages in length. All dissertations should be divided into appropriate sections, and long dissertations may need chapters, main divisions, and subdivisions.

8½ x 11 inches, unless a musical score is included
At least 1 inch for all margins
Body of text: double spacing
Block quotations, footnotes, and bibliographies: single spacing within each entry but double spacing between each entry
Table of contents, list of tables, list of figures or illustrations, and lengthy tables: single spacing may be used

Fonts and Point Size

Use 10-12 point size. Fonts must be embedded in the PDF file to ensure all characters display correctly.

Recommended Fonts

If you are unsure whether your chosen font will display correctly, use one of the following fonts:

If fonts are not embedded, non-English characters may not appear as intended. Fonts embedded improperly will be published to DASH as-is. It is the student’s responsibility to make sure that fonts are embedded properly prior to submission.

Instructions for Embedding Fonts

To embed your fonts in recent versions of Word, follow these instructions from Microsoft:

Click the File tab and then click Options .
In the left column, select the Save tab.
Clear the Do not embed common system fonts check box.

For reference, below are some instructions from ProQuest UMI for embedding fonts in older file formats:

To embed your fonts in Microsoft Word 2010:

In the File pull-down menu click on Options .
Choose Save on the left sidebar.
Check the box next to Embed fonts in the file.
Click the OK button.
Save the document.

Note that when saving as a PDF, make sure to go to “more options” and save as “PDF/A compliant”

To embed your fonts in Microsoft Word 2007:

Click the circular Office button in the upper left corner of Microsoft Word.
A new window will display. In the bottom right corner select Word Options .
Choose Save from the left sidebar.

Using Microsoft Word on a Mac:

Microsoft Word 2008 on a Mac OS X computer will automatically embed your fonts while converting your document to a PDF file.

If you are converting to PDF using Acrobat Professional (instructions courtesy of the Graduate Thesis Office at Iowa State University):

Open your document in Microsoft Word.
Click on the Adobe PDF tab at the top. Select "Change Conversion Settings."
Click on Advanced Settings.
Click on the Fonts folder on the left side of the new window. In the lower box on the right, delete any fonts that appear in the "Never Embed" box. Then click "OK."
If prompted to save these new settings, save them as "Embed all fonts."
Now the Change Conversion Settings window should show "embed all fonts" in the Conversion Settings drop-down list and it should be selected. Click "OK" again.
Click on the Adobe PDF link at the top again. This time select Convert to Adobe PDF. Depending on the size of your document and the speed of your computer, this process can take 1-15 minutes.
After your document is converted, select the "File" tab at the top of the page. Then select "Document Properties."
Click on the "Fonts" tab. Carefully check all of your fonts. They should all show "(Embedded Subset)" after the font name.
If you see "(Embedded Subset)" after all fonts, you have succeeded.

The font used in the body of the text must also be used in headers, page numbers, and footnotes. Exceptions are made only for tables and figures created with different software and inserted into the document.

Tables and figures must be placed as close as possible to their first mention in the text. They may be placed on a page with no text above or below, or they may be placed directly into the text. If a table or a figure is alone on a page (with no narrative), it should be centered within the margins on the page. Tables may take up more than one page as long as they obey all rules about margins. Tables and figures referred to in the text may not be placed at the end of the chapter or at the end of the dissertation.

Given the standards of the discipline, dissertations in the Department of History of Art and Architecture and the Department of Architecture, Landscape Architecture, and Urban Planning often place illustrations at the end of the dissertation.

Figure and table numbering must be continuous throughout the dissertation or by chapter (e.g., 1.1, 1.2, 2.1, 2.2, etc.). Two figures or tables cannot be designated with the same number. If you have repeating images that you need to cite more than once, label them with their number and A, B, etc.

Headings should be placed at the top of tables. While no specific rules for the format of table headings and figure captions are required, a consistent format must be used throughout the dissertation (contact your department for style manuals appropriate to the field).

Captions should appear at the bottom of any figures. If the figure takes up the entire page, the caption should be placed alone on the preceding page, centered vertically and horizontally within the margins.

Each page receives a separate page number. When a figure or table title is on a preceding page, the second and subsequent pages of the figure or table should say, for example, “Figure 5 (Continued).” In such an instance, the list of figures or tables will list the page number containing the title. The word “figure” should be written in full (not abbreviated), and the “F” should be capitalized (e.g., Figure 5). In instances where the caption continues on a second page, the “(Continued)” notation should appear on the second and any subsequent page. The figure/table and the caption are viewed as one entity and the numbering should show correlation between all pages. Each page must include a header.

Landscape orientation figures and tables must be positioned correctly and bound at the top so that the top of the figure or table will be at the left margin. Figure and table headings/captions are placed with the same orientation as the figure or table when on the same page. When on a separate page, headings/captions are always placed in portrait orientation, regardless of the orientation of the figure or table. Page numbers are always placed as if the figure were vertical on the page.

If a graphic artist does the figures, Harvard Griffin GSAS will accept lettering done by the artist only within the figure. Figures done with software are acceptable if the figures are clear and legible. Legends and titles done by the same process as the figures will be accepted if they too are clear, legible, and run at least 10 or 12 characters per inch. Otherwise, legends and captions should be printed with the same font used in the text.

Original illustrations, photographs, and fine arts prints may be scanned and included, centered between the margins on a page with no text above or below.

Use of Third-Party Content

In addition to the student's own writing, dissertations often contain third-party content or in-copyright content owned by parties other than you, the student who authored the dissertation. The Office for Scholarly Communication recommends consulting the information below about fair use, which allows individuals to use in-copyright content, on a limited basis and for specific purposes, without seeking permission from copyright holders.

Because your dissertation will be made available for online distribution through DASH , Harvard's open-access repository, it is important that any third-party content in it may be made available in this way.

Fair Use and Copyright

What is fair use?

Fair use is a provision in copyright law that allows the use of a certain amount of copyrighted material without seeking permission. Fair use is format- and media-agnostic. This means fair use may apply to images (including photographs, illustrations, and paintings), quoting at length from literature, videos, and music regardless of the format.

How do I determine whether my use of an image or other third-party content in my dissertation is fair use?

There are four factors you will need to consider when making a fair use claim.

1) For what purpose is your work going to be used?

Nonprofit, educational, scholarly, or research use favors fair use. Commercial, non-educational uses, often do not favor fair use.
A transformative use (repurposing or recontextualizing the in-copyright material) favors fair use. Examining, analyzing, and explicating the material in a meaningful way, so as to enhance a reader's understanding, strengthens your fair use argument. In other words, can you make the point in the thesis without using, for instance, an in-copyright image? Is that image necessary to your dissertation? If not, perhaps, for copyright reasons, you should not include the image.

2) What is the nature of the work to be used?

Published, fact-based content favors fair use and includes scholarly analysis in published academic venues.
Creative works, including artistic images, are afforded more protection under copyright, and depending on your use in light of the other factors, may be less likely to favor fair use; however, this does not preclude considerations of fair use for creative content altogether.

3) How much of the work is going to be used?

Small, or less significant, amounts favor fair use. A good rule of thumb is to use only as much of the in-copyright content as necessary to serve your purpose. Can you use a thumbnail rather than a full-resolution image? Can you use a black-and-white photo instead of color? Can you quote select passages instead of including several pages of the content? These simple changes bolster your fair use of the material.

4) What potential effect on the market for that work may your use have?

If there is a market for licensing this exact use or type of educational material, then this weighs against fair use. If however, there would likely be no effect on the potential commercial market, or if it is not possible to obtain permission to use the work, then this favors fair use.

For further assistance with fair use, consult the Office for Scholarly Communication's guide, Fair Use: Made for the Harvard Community and the Office of the General Counsel's Copyright and Fair Use: A Guide for the Harvard Community .

What are my options if I don’t have a strong fair use claim?

Consider the following options if you find you cannot reasonably make a fair use claim for the content you wish to incorporate:

Seek permission from the copyright holder.
Use openly licensed content as an alternative to the original third-party content you intended to use. Openly-licensed content grants permission up-front for reuse of in-copyright content, provided your use meets the terms of the open license.
Use content in the public domain, as this content is not in-copyright and is therefore free of all copyright restrictions. Whereas third-party content is owned by parties other than you, no one owns content in the public domain; everyone, therefore, has the right to use it.

For use of images in your dissertation, please consult this guide to Finding Public Domain & Creative Commons Media , which is a great resource for finding images without copyright restrictions.

Who can help me with questions about copyright and fair use?

Contact your Copyright First Responder . Please note, Copyright First Responders assist with questions concerning copyright and fair use, but do not assist with the process of obtaining permission from copyright holders.

Pages should be assigned a number except for the Dissertation Acceptance Certificate . Preliminary pages (abstract, table of contents, list of tables, graphs, illustrations, and preface) should use small Roman numerals (i, ii, iii, iv, v, etc.). All pages must contain text or images.

Count the title page as page i and the copyright page as page ii, but do not print page numbers on either page .

For the body of text, use Arabic numbers (1, 2, 3, 4, 5, etc.) starting with page 1 on the first page of text. Page numbers must be centered throughout the manuscript at the top or bottom. Every numbered page must be consecutively ordered, including tables, graphs, illustrations, and bibliography/index (if included); letter suffixes (such as 10a, 10b, etc.) are not allowed. It is customary not to have a page number on the page containing a chapter heading.

Check pagination carefully. Account for all pages.

A copy of the Dissertation Acceptance Certificate (DAC) should appear as the first page. This page should not be counted or numbered. The DAC will appear in the online version of the published dissertation. The author name and date on the DAC and title page should be the same.

The dissertation begins with the title page; the title should be as concise as possible and should provide an accurate description of the dissertation. The author name and date on the DAC and title page should be the same.

Do not print a page number on the title page. It is understood to be page i for counting purposes only.

A copyright notice should appear on a separate page immediately following the title page and include the copyright symbol ©, the year of first publication of the work, and the name of the author:

Alternatively, students may choose to license their work openly under a Creative Commons license. The author remains the copyright holder while at the same time granting up-front permission to others to read, share, and (depending on the license) adapt the work, so long as proper attribution is given. (By default, under copyright law, the author reserves all rights; under a Creative Commons license, the author reserves some rights.)

Do not print a page number on the copyright page. It is understood to be page ii for counting purposes only.

An abstract, numbered as page iii , should immediately follow the copyright page and should state the problem, describe the methods and procedures used, and give the main results or conclusions of the research. The abstract will appear in the online and bound versions of the dissertation and will be published by ProQuest. There is no maximum word count for the abstract.

double-spaced
left-justified
indented on the first line of each paragraph
The author’s name, right justified
The words “Dissertation Advisor:” followed by the advisor’s name, left-justified (a maximum of two advisors is allowed)
Title of the dissertation, centered, several lines below author and advisor

Dissertations divided into sections must contain a table of contents that lists, at minimum, the major headings in the following order:

Front Matter
Body of Text
Back Matter

Front matter includes (if applicable):

acknowledgements of help or encouragement from individuals or institutions
a dedication
a list of illustrations or tables
a glossary of terms
one or more epigraphs.

Back matter includes (if applicable):

bibliography
supplemental materials, including figures and tables
an index (in rare instances).

Supplemental figures and tables must be placed at the end of the dissertation in an appendix, not within or at the end of a chapter. If additional digital information (including audio, video, image, or datasets) will accompany the main body of the dissertation, it should be uploaded as a supplemental file through ProQuest ETD . Supplemental material will be available in DASH and ProQuest and preserved digitally in the Harvard University Archives.

As a matter of copyright, dissertations comprising the student's previously published works must be authorized for distribution from DASH. The guidelines in this section pertain to any previously published material that requires permission from publishers or other rightsholders before it may be distributed from DASH. Please note:

Authors whose publishing agreements grant the publisher exclusive rights to display, distribute, and create derivative works will need to seek the publisher's permission for nonexclusive use of the underlying works before the dissertation may be distributed from DASH.
Authors whose publishing agreements indicate the authors have retained the relevant nonexclusive rights to the original materials for display, distribution, and the creation of derivative works may distribute the dissertation as a whole from DASH without need for further permissions.

It is recommended that authors consult their publishing agreements directly to determine whether and to what extent they may have transferred exclusive rights under copyright. The Office for Scholarly Communication (OSC) is available to help the author determine whether she has retained the necessary rights or requires permission. Please note, however, the Office of Scholarly Communication is not able to assist with the permissions process itself.

Missing Dissertation Acceptance Certificate. The first page of the PDF dissertation file should be a scanned copy of the Dissertation Acceptance Certificate (DAC). This page should not be counted or numbered as a part of the dissertation pagination.
Conflicts Between the DAC and the Title Page. The DAC and the dissertation title page must match exactly, meaning that the author name and the title on the title page must match that on the DAC. If you use your full middle name or just an initial on one document, it must be the same on the other document.
Abstract Formatting Errors. The advisor name should be left-justified, and the author's name should be right-justified. Up to two advisor names are allowed. The Abstract should be double spaced and include the page title “Abstract,” as well as the page number “iii.” There is no maximum word count for the abstract.
The front matter should be numbered using Roman numerals (iii, iv, v, …). The title page and the copyright page should be counted but not numbered. The first printed page number should appear on the Abstract page (iii).
The body of the dissertation should be numbered using Arabic numbers (1, 2, 3, …). The first page of the body of the text should begin with page 1. Pagination may not continue from the front matter.
All page numbers should be centered either at the top or the bottom of the page.
Figures and tables Figures and tables must be placed within the text, as close to their first mention as possible. Figures and tables that span more than one page must be labeled on each page. Any second and subsequent page of the figure/table must include the “(Continued)” notation. This applies to figure captions as well as images. Each page of a figure/table must be accounted for and appropriately labeled. All figures/tables must have a unique number. They may not repeat within the dissertation.
Any figures/tables placed in a horizontal orientation must be placed with the top of the figure/ table on the left-hand side. The top of the figure/table should be aligned with the spine of the dissertation when it is bound.
Page numbers must be placed in the same location on all pages of the dissertation, centered, at the bottom or top of the page. Page numbers may not appear under the table/ figure.
Supplemental Figures and Tables. Supplemental figures and tables must be placed at the back of the dissertation in an appendix. They should not be placed at the back of the chapter.
Permission Letters Copyright. permission letters must be uploaded as a supplemental file, titled ‘do_not_publish_permission_letters,” within the dissertation submission tool.
DAC Attachment. The signed Dissertation Acceptance Certificate must additionally be uploaded as a document in the "Administrative Documents" section when submitting in Proquest ETD . Dissertation submission is not complete until all documents have been received and accepted.
Overall Formatting. The entire document should be checked after all revisions, and before submitting online, to spot any inconsistencies or PDF conversion glitches.
You can view dissertations successfully published from your department in DASH . This is a great place to check for specific formatting and area-specific conventions.
Contact the Office of Student Affairs with further questions.

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How Long Should Be PhD Dissertation? Unlocking The Mystery of PhD Thesis Length

Embarking on the journey of a PhD is a scholarly endeavour that demands not only intellectual prowess but also a deep commitment to contributing valuable insights to the academic world. At the heart of this rigorous pursuit lies the dissertation, a magnum opus that serves as the pinnacle of one’s academic achievement. Yet, as aspiring scholars delve into the realms of research and knowledge creation, a pivotal question looms large: How long should a Ph.D. dissertation be?

The optimal length of a Ph.D. dissertation is a strategic equilibrium, determined by the intricate interplay of research complexity, disciplinary norms, and institutional guidelines, emphasizing quality over mere quantity.

In the intricate tapestry of academia, the length of a dissertation is a nuanced consideration, influenced by a myriad of factors ranging from disciplinary norms to the intricacies of research design. In this exploration, we embark on a journey to unravel the complexities surrounding dissertation length, understanding the guiding principles, and offering insights into how aspiring doctoral candidates can strike the delicate balance between depth and brevity.

Join us as we navigate the academic landscape, demystifying the expectations, uncovering the variances across disciplines, and providing practical tips for crafting a dissertation that stands as a testament to scholarly excellence. Whether you’re at the threshold of your doctoral journey or guiding others through its twists and turns, this discourse aims to shed light on the intricacies of dissertation length, empowering you to embark on this intellectual odyssey with confidence and purpose.

Introduction

A. defining the purpose.

B. The Dissertation's Role in Academic Knowledge

C. Emphasizing Thorough Research and Original Contributions

A. exploring institutional guidelines, b. specific requirements and length expectations, c. the importance of adherence, a. acknowledging disciplinary differences, b. examples of varied expectations, c. emphasizing disciplinary norms, a. research complexity, b. data collection and analysis, c. literature review, d. methodology, a. emphasizing the importance of balance, b. strategies for maintaining focus and relevance, c. encouraging quality over quantity, a. effective time management, b. techniques for concise writing, c. value of feedback from advisors and peers.

Embarking on a PhD journey is akin to setting sail into uncharted waters of knowledge, with the dissertation standing tall as the crowning achievement. It’s not just a document; it’s a testament to years of intellectual toil, research finesse, and a contribution to the vast expanse of human understanding. But as scholars immerse themselves in the sea of research, a question inevitably surfaces: How long should a Ph.D. dissertation be?

Significance Unveiled

A Ph.D. dissertation is more than a lengthy document; it’s a scholarly masterpiece that not only encapsulates an individual’s intellectual prowess but adds a unique brushstroke to the grand canvas of academia. It’s a beacon of knowledge, guiding future researchers and shaping the trajectory of scholarly discourse. Understanding its significance is the first step toward unlocking the secrets of its length.

Navigating Length: A Delicate Balancing Act

Determining the appropriate length for a Ph.D. dissertation is no mere formality. It’s a delicate dance between providing comprehensive insights and maintaining reader engagement. Striking this balance is crucial, as the length not only influences how the research is perceived but also reflects the researcher’s ability to weave a compelling narrative without unnecessary verbosity.

Factors in the Equation

The length of a dissertation is a dynamic variable influenced by a multitude of factors. From the complexity of the research question to the intricacies of data collection, each element plays a role. It’s a symphony where the depth of the literature review, the intricacy of the methodology, and the nature of the chosen discipline all contribute to the final crescendo of the dissertation length.

Purpose of a PhD Dissertation

At the heart of the Ph.D. journey lies the dissertation, a formidable endeavour that goes beyond being a mere academic requirement. It serves as the cornerstone of a scholar’s intellectual legacy, encapsulating years of research, critical thinking, and a commitment to advancing knowledge in a specific field. The purpose of a Ph.D. dissertation extends far beyond a graduation requirement; it is a scholarly rite of passage that marks one’s entry into the ranks of contributing intellectuals.

B. The Dissertation’s Role in Academic Knowledge

The dissertation, in essence, is a torchbearer of academic knowledge. It doesn’t merely regurgitate existing information but actively contributes to the ongoing conversation within a field. Picture it as a puzzle piece that, when seamlessly integrated, enriches the larger mosaic of human understanding. Its role is not just to summarize what is known but to illuminate uncharted territories, challenging established paradigms and paving the way for new insights.

Example: In the field of environmental science, a Ph.D. dissertation might delve into the impact of climate change on a specific ecosystem, offering novel findings that reshape our understanding of ecological resilience. It becomes a pivotal contribution that informs future research and policy decisions.

Thorough research is the bedrock upon which a meaningful dissertation stands. It involves meticulously exploring existing literature, methodologies, and gaps in knowledge. This isn’t a cursory glance but a deep dive into the scholarly ocean, where each wave of information contributes to the construction of a comprehensive understanding.

Original contributions are the soul of a Ph.D. dissertation. It’s not just about rehashing what’s already known but about introducing something new and transformative. This could be a novel research methodology, a groundbreaking theory, or empirical findings that challenge existing theories.

Example: In the realm of psychology, a Ph.D. dissertation might involve the development of a new therapeutic approach for a specific mental health condition, backed by both a comprehensive review of existing literature and empirical evidence from original studies. This not only adds to the academic discourse but also has tangible implications for clinical practice.

University Guidelines PhD Dissertation Length: Navigating the Academic Framework

In academia, each university sets the stage with its own set of guidelines governing the composition and expectations of a Ph.D. dissertation. These guidelines serve as the rulebook, providing a roadmap for aspiring scholars to traverse the challenging terrain of research and writing. Understanding these guidelines is akin to deciphering the code that unlocks the door to doctoral success.

Dive into the particulars, and you’ll find that universities often outline specific requirements and expectations regarding the length of a dissertation. These may range from prescribed word counts for each section to broader expectations for the overall document. Some institutions might emphasize brevity, while others encourage a more expansive exploration. Unravelling these expectations is not just a bureaucratic formality; it’s a strategic move that ensures your work aligns with the academic standards set by your institution.

Example: Imagine a university that places a strong emphasis on concise and focused dissertations. Here, the guidelines may state a maximum word count for each chapter, encouraging a streamlined and impactful presentation of research findings. Adhering to these specifics ensures that your dissertation not only meets the academic standards but also resonates with the evaluators who appreciate clarity and precision.

While the allure of academic freedom may tempt scholars to chart their own course, adherence to institutional guidelines is paramount. It’s not just a matter of compliance; it’s a strategic decision that aligns your work with the expectations of the academic community. Universities set guidelines with a purpose – to maintain standards, ensure consistency, and facilitate fair evaluation. Ignoring these guidelines can inadvertently hinder the reception of your dissertation, potentially overshadowing the brilliance of your research with concerns about adherence to academic norms.

Disciplinary Variances: Navigating the Length Spectrum

In the vast landscape of academia, one size certainly does not fit all, especially when it comes to the length of Ph.D. dissertations. It’s crucial to acknowledge and appreciate the dynamic nature of disciplinary differences, where each field has its own set of expectations, traditions, and scholarly norms. Understanding this diversity is the key to crafting a dissertation that resonates within the specific academic community to which it belongs.

Let’s take a stroll through different academic landscapes to grasp the breadth of expectations. In the sciences, precision and brevity often reign supreme. A molecular biology dissertation, for instance, may prioritize concise methodology and results chapters, with an emphasis on data interpretation. Contrast this with a dissertation in the humanities, where the richness of language and the depth of literary analysis might lead to a more extensive exploration of concepts.

Example: In engineering, a Ph.D. dissertation might lean towards a succinct presentation of methodologies, experimental results, and their implications for the field. Meanwhile, in the realm of philosophy, a dissertation could be characterized by a more expansive engagement with existing literature and a thorough philosophical exploration of the research question.

The beauty of academia lies in its diversity, but this very diversity requires scholars to be attuned to the norms of their specific discipline. What might be considered a concise and impactful dissertation in one field could be seen as lacking depth in another. Recognizing these norms is not just a matter of fitting in; it’s a strategic decision that ensures your work aligns with the expectations of your academic peers.

Understanding disciplinary norms is like speaking the language of your scholarly community. It’s about knowing when to be succinct and when to elaborate, when to prioritize methodology and when to delve deep into theoretical frameworks.

Factors Influencing PhD Dissertation Length

1. Impact of Research Question Complexity

The complexity of your research question is like the compass guiding the depth and breadth of your dissertation. Intricate inquiries often demand more comprehensive exploration, delving into multiple facets and dimensions. For instance, a research question investigating the genetic determinants of a rare disease may necessitate an extensive review of existing literature, detailed methodologies, and intricate analyses to uncover meaningful insights.

2. Examples of Research Topics

Consider the following examples to illustrate the point:

Less Complex: An analysis of consumer behavior in response to a specific marketing strategy might require a detailed but more straightforward exploration.
More Complex: On the other hand, a study examining the intersection of artificial intelligence and ethical considerations in healthcare may demand a multifaceted investigation into both technological and ethical dimensions, significantly impacting the length of the dissertation.

1. Influence of Data Nature and Quantity

The nature and amount of data collected cast a profound shadow on dissertation length. A project relying on extensive datasets, intricate statistical analyses, or comprehensive case studies inherently demands a more extended exploration. In contrast, qualitative research might be more concise but equally impactful in unraveling complex phenomena.

2. Role of Statistical Analysis, Case Studies, or Qualitative Research

Consider the following scenarios:

Statistical Analysis: A dissertation delving into the economic impact of climate change policies might involve sophisticated statistical models and require an in-depth presentation of results and their implications.
Qualitative Research: Conversely, a dissertation employing qualitative interviews to explore the lived experiences of individuals facing a specific social challenge may present findings in a more narrative form.

1. Significance of a Comprehensive Literature Review

A robust literature review acts as the scaffolding for your dissertation, providing the theoretical foundation and context for your research. The broader and more complex the field, the more extensive the literature review. For instance, exploring a niche area within a rapidly evolving field, like emerging technologies, may demand a more thorough literature review to capture the latest developments and debates.

2. Contribution to Overall Length

Consider this:

A dissertation in environmental science, investigating the impact of urbanization on biodiversity, might necessitate a detailed exploration of existing literature on ecology, urban planning, and biodiversity conservation.

1. Impact of Detailed Research Methodology

The methodology section is the blueprint of your research, and its level of detail significantly influences the length of your dissertation. A dissertation with a meticulous methodology section is like a well-constructed building, providing a clear roadmap for readers to understand the research process.

2. Need for Clarity and Precision in Research Design

For example:

A dissertation in public health aiming to assess the effectiveness of a health intervention might require a detailed explanation of the study design, participant recruitment strategies, and data collection methods to ensure the study’s validity and reliability.

Balancing Depth and Brevity: Crafting a Dissertation Masterpiece

In the symphony of scholarly writing, achieving harmony between depth and brevity is a skill that distinguishes a stellar dissertation. While delving deep into the nuances of your research is essential, presenting it with conciseness ensures that your audience remains engaged. The challenge lies in striking the right balance, where the richness of content is not sacrificed on the altar of brevity.

Clear Research Objectives: Begin with well-defined research objectives that serve as the North Star for your dissertation. This clarity guides your writing, preventing unnecessary tangents and ensuring each section contributes directly to your overarching goals. Example: In a dissertation exploring the impact of mindfulness-based interventions on stress reduction, clear objectives would center around understanding the effectiveness of specific mindfulness techniques and their implications for stress management.
Thematic Structure: Organize your dissertation thematically, ensuring each chapter has a clear purpose and contributes to building a cohesive narrative. This not only aids readability but also enhances the overall impact of your research. Example: In a literature review, grouping studies thematically—such as by intervention type or outcome measures—provides a structured and focused presentation of existing research, avoiding a scattered and disjointed narrative.
Rigorous Editing: Approach your writing with a discerning eye during the editing process. Trim unnecessary words, sentences, or sections that do not directly contribute to the core message of your dissertation. Be ruthless in maintaining relevance. Example: In the methodology section, focus on explaining key decisions and processes, omitting redundant details that do not influence the study’s validity or replicability.
Depth of Analysis: Instead of inundating your dissertation with an abundance of superficial analyses, delve deep into a few key points. Thoroughly explore the significance and implications of your findings, providing a nuanced understanding of your research. Example: In a discussion section, rather than covering multiple tangential points, focus on the most critical aspects of your results, analyzing their theoretical and practical implications in detail.
Precision in Language: Choose words judiciously to convey your ideas with precision. Aim for clarity without unnecessary embellishments, ensuring that each sentence adds value to your argument. Example: In the introduction, use concise language to clearly articulate the research gap, the significance of the study, and the specific research questions without unnecessary elaboration.
Selectivity in Citations: While a comprehensive literature review is vital, selectively cite studies that directly contribute to your research context. Avoid an exhaustive list that overwhelms readers with unnecessary details. Example: In a literature review on educational interventions for students with learning disabilities, selectively cite studies that showcase diverse approaches and methodologies, providing a nuanced understanding of the existing landscape.

As we navigate the delicate equilibrium between depth and brevity, remember that a well-crafted dissertation is not measured by its length but by the impact of its scholarly contributions. Join us as we explore the art of balancing substance with succinctness, ensuring your dissertation stands as a masterpiece in the annals of academic inquiry.

Tips for Managing PhD Dissertation Length: Navigating the Dissertation Writing Process

Setting Realistic Milestones: Break down the dissertation-writing process into manageable milestones. Establish realistic timelines for each section, considering the complexity and time required for research, writing, and revisions. Example: Allocate a specific time frame for conducting literature reviews, data analysis, and drafting each chapter. This ensures steady progress and prevents last-minute rushes.
Regular Progress Checks: Monitor your progress regularly and adjust your schedule as needed. If a particular section is taking longer than anticipated, evaluate the reasons and recalibrate your timeline accordingly. Example: If data analysis is proving more time-consuming than expected, revisit your research plan and, if necessary, adjust your writing schedule for subsequent chapters.
Balancing Research and Writing: Strive for a balance between conducting research and writing. While research is crucial, allocate dedicated time for translating findings into written content to avoid a backlog of information. Example: If you’re conducting experiments, allocate specific writing sessions to summarize and interpret the results, ensuring a continuous flow of progress.

Visit my article on ” How to Manage Research Time” for managing PhD time.

Clarity in Expression: Aim for clarity in your writing. Express complex ideas in straightforward language, avoiding unnecessary jargon or convoluted sentences that can inflate the word count without adding substance. Example: Instead of using complex terminology in a theoretical framework, opt for clear and precise language that conveys the theoretical concepts without unnecessary embellishments.
Economical Word Choices: Choose words judiciously. Opt for strong, impactful words that convey your message succinctly. Trim redundant phrases and eliminate words that don’t contribute directly to your argument. Example: Instead of saying “due to the fact that,” use the more concise “because” to convey the same meaning with fewer words.
Strategic Use of Figures and Tables: Incorporate visuals strategically to convey information efficiently. Figures and tables can often replace lengthy textual explanations, providing a visual representation of data or concepts. Example: Instead of describing a complex set of results in paragraphs, present key findings in a well-designed table, allowing readers to grasp the information at a glance.
Early and Regular Feedback: Share drafts of your work with advisors and peers early in the writing process. Their feedback can identify potential issues and guide revisions, preventing the need for extensive rewrites later. Example: Submit a draft of your literature review to your advisor before completing the entire chapter. Early feedback can help refine your approach and ensure you’re on the right track.
Objective External Perspectives: Advisors and peers offer valuable external perspectives. They can identify areas where your explanation may be unclear or where additional details may be necessary. Example: If your research methodology is intricate, seek feedback from a peer who is not intimately familiar with your topic. Their questions and comments can reveal where additional clarification is needed for a broader audience.
Critical Review for Redundancy: Advisors and peers can help identify redundant sections or unnecessary details. A fresh set of eyes can pinpoint areas where content can be streamlined without compromising the depth of your argument. Example: If two sections of your dissertation cover similar ground, feedback from others can highlight the need to merge or eliminate redundant content, improving the overall flow.

Visit my articles related to PhD , Exciting Careers after PhD .

The journey through the complexities of determining the length of a Ph.D. dissertation reveals a delicate interplay of factors crucial to its scholarly impact. From understanding the significance of this academic endeavour to navigating institutional guidelines and disciplinary variances, the pursuit of balance between depth and brevity emerges as a paramount challenge.

As researchers, we must navigate the intricate landscapes of research complexity, data analysis, literature review, and methodology while maintaining a steadfast commitment to quality over quantity. The tips offered for effective time management, concise writing, and the judicious seeking of feedback underscore the strategic nature of dissertation crafting.

Ultimately, the dissertation is not just an academic requirement but a scholarly legacy—a testament to our intellectual contributions and a beacon guiding future inquiry. In this conclusion, let us recognize that the true measure of a dissertation’s success lies not solely in its length but in its enduring impact on the trajectory of knowledge within our respective fields.

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What Is The Average Dissertation Length?

Published by Alvin Nicolas at March 4th, 2024 , Revised On March 5, 2024

There is no one-size-fits-all answer when it comes to answering the question, “How long should my dissertation be?”. However, the standard is as follows.

Shorter Dissertations

Some fields or programs may have shorter dissertation requirements, particularly in disciplines where the research tends to be more focused or where a shorter document suffices to convey the research findings. In such cases, dissertations might range from 50 to 100 pages or even shorter.

Average-Length Dissertations

For many doctoral programs, particularly in the humanities, social sciences, and some STEM fields, the average dissertation length falls between 150 and 300 pages . This length allows for a thorough exploration of the research topic , a review of literature , methodology , data analysis , and a discussion of findings .

Longer Dissertations

In certain scientific or technical fields and interdisciplinary studies, dissertations can be longer, ranging from 300 to 500 pages or more. These longer dissertations often involve extensive data collection, complex analyses, multiple experiments or case studies, and detailed discussion of results and implications.

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Factors Affecting Dissertation Length

There are a number of factors that contribute to the length, including

Field of Study

Your dissertation depends on your discipline. Humanities and social sciences usually have a lower average word count compared to sciences and engineering because the latter requires extensive data analysis and experimentation.

University Requirements

Most universities have certain specific guidelines influenced by department and faculty expectations. It is important to go through them before you start writing.

Research Methodology

The complexity of the research design and methodology can impact the length. Dissertations having comprehensive data collection might need additional space for detailed justification.

Literature Review

Depending on how novel your research topic is, the length of your literature review varies. If there is a lot of research done on your variables, your dissertation will be longer.

Can a dissertation exceed recommended length?

Yes, but ensure all content is relevant and contributes meaningfully to the research.

How can I manage dissertation length effectively?

Plan meticulously, prioritize content, and seek feedback to ensure conciseness without sacrificing depth.

How Long is a Dissertation

How Long is a Dissertation? Concrete Answers

An undergraduate dissertation usually falls within the range of 8,000 to 15,000 words, while a master's dissertation typically spans from 12,000 to 50,000 words. In contrast, a PhD thesis is typically of book length, ranging from 70,000 to 100,000 words.

Let’s unravel the mystery of how long should a dissertation be. If you’ve ever wondered about this, look no further. Our comprehensive guide delves into the nitty-gritty of dissertation lengths across diverse academic realms. Whether you're a budding grad student, an academic advisor, or just curious, we've got you covered.

From Master's to PhD programs, we decode the variations in length requirements and shed light on disciplinary disparities. In general, dissertations are 150 to 300 words. But factors influence the length of these daunting scholarly requirements! But fear not as we break it down for you.

We’ll unveil the secrets behind dissertation writing, from how they reflect the depth and breadth of research to offering invaluable tips for planning and writing. So, if you're ready to demystify the daunting dissertation journey, hop on board! Let's navigate the labyrinth of academia together and empower you to conquer your scholarly aspirations.

Institutional guidelines on dissertation length

You can think of institutional guidelines as purveyors of academic excellence. Ever wondered why schools impose specific requirements like "Chapter 1: The Introduction must be at least 35 pages long and no more than 50 pages"?

It's not just about arbitrary rules! However, it's about striking the perfect balance between guidance and practicality. These guidelines serve as guardrails, steering students like you towards scholarly success without overwhelming faculty with endless pages to peruse.

Moreover, credibility is key here! A mere 8-page literature review won't cut it in the realm of academia. But fear not, for most institutions provide dissertation templates, complete with essential headings to streamline the process.

And for those seeking a helping hand, a dissertation writing service like ours stands ready to assist, ensuring your masterpiece meets the lofty standards of academic rigor. So, embrace the guidelines, weave your narrative, and let your dissertation shine with scholarly prowess.

Variations in dissertation length across academic disciplines

Dissertation length varies significantly across academic disciplines due to differences in research methods, data presentation, and writing conventions. Here's a general overview of how dissertation length can differ by discipline:

Humanities and Social Sciences: Dissertations in these fields tend to be longer because they often involve comprehensive literature reviews, detailed theoretical analyses, and extensive qualitative data. It's not uncommon for dissertations in history, literature, or sociology to exceed 200 pages.
Sciences and Engineering: Dissertations in the sciences and engineering might be shorter in terms of page count but are dense with technical details, data, charts, and appendices. They often range between 100 to 200 pages. However, the length can vary significantly depending on the complexity of the work and the requirements of the specific program.
Arts and Design: In creative disciplines, the dissertation might include a practical component (like a portfolio, exhibition, or performance) alongside a written thesis. The written component might be shorter, focusing on the conceptual and contextual analysis of the creative work, usually ranging from 40 to 80 pages.
Professional Fields (Business, Education, etc.): Dissertations in professional fields such as business or education often focus on case studies, practical applications, and policy analysis. These dissertations can vary widely in length but often fall in the range of 100 to 200 pages.

Dissertations vary in length due to many factors, which shows the diverse nature of academic research. Disciplinary differences are significant, as each field may have distinct expectations regarding the depth and scope of the study.

The type of analysis conducted, whether qualitative, quantitative, or a combination of both, also impacts the length.

For instance, qualitative studies may involve extensive textual analysis, resulting in longer manuscripts, while quantitative studies may require detailed statistical analyses. Additionally, the specific area of research within a discipline can also affect the length, as certain topics may necessitate more:

extensive literature reviews
data collection (e.g., fieldwork, surveys, interviews, lab work)
discussion sections

While the average length typically falls within the range of 150-300 pages, it's essential to recognize the nuanced factors contributing to variations in dissertation length. You must remain informed about the variables shaping your document's overall size and structure to deliver exemplary results.

Factors influencing the length of doctoral dissertations

Various factors determine the length of a dissertation, such as the specific guidelines set by universities, the type of research conducted, the extent of analysis required, and the presence of supplementary materials.

Several factors come into play when determining the ideal length of a dissertation. University guidelines set the tone, with institutions offering word count ranges typically between 8,000 to 15,000 words for undergraduates and masters and 75,000 to 100,000 words for PhD.

Yet, beyond these guidelines, the nature of your research holds sway.

Disciplines vary, with humanities favoring extensive literature reviews and scientific fields emphasizing methodological intricacies. Depth of analysis matters, too; a thorough exploration demands more space.

Balancing these elements ensures a well-rounded dissertation. So, as you embark on your scholarly journey, consider these factors carefully. By understanding them, you'll craft a dissertation that not only meets academic standards but also showcases your analytical prowess and depth of intelligence.

Length, components, and scholarly dedication

Many aspiring scholars think, "How long is a doctoral dissertation?" However, the answer isn't straightforward. Yes, length varies, but let's not forget to factor in a crucial element: time. And we know because many students have instructed us to “ write my dissertation !”

Remember, a dissertation isn't penned in one sitting. Rather, it often evolves from smaller academic chapters. This gradual process allows students to explore diverse topics before committing to a book-length project they're passionate about.

Beyond the central argument lie various components that contribute to the overall length. Take the literature review, for example—an essential segment that contextualizes the research by analyzing existing scholarship. Then there's the myriad of ancillary elements like the title page, acknowledgments, abstract, and appendix, each adding to the dissertation's page count.

It’s the accumulation of these parts that determines the length. So, while the answer may not be a precise number, it's crucial to acknowledge many elements that make up a doctoral dissertation. And for those embarking on this scholarly journey, we can help you conquer this challenge.

How Long is a Dissertation Chapter? Uncover the Mystery

When it comes to dissertation length, most grad students fret over how long each chapter should be. While there's no one-size-fits-all answer, there is a golden rule–chapters should be long enough to address the research question comprehensively.

Think quality over quantity! Ask any dissertation adviser, and they’ll say aiming to fill a predetermined number of pages shouldn’t be the goal. Rather, you must thoroughly explore your topic, conduct extensive research, and present your findings effectively.

Your writing style and the unique nature of your research also play pivotal roles. So, whether your chapter spans 50 pages or 150, ensure it's packed with substantive content that advances your study. Ultimately, it's not about hitting a page count but about delivering a high-quality scholarly contribution.

Writing an Excellent PhD Dissertation: Strategies and Tips

After you’re done pondering on how many pages should a dissertation be, you can move on with production. Wondering how to write a dissertation , here are some tips:

Start with a significant research topic that inspires you and formulate a clear research question.
Thoroughly review existing literature to contextualize your study.
Develop a robust methodology and collect comprehensive data.
Analyze findings meticulously and synthesize them effectively.
Ensure logical flow throughout your writing, striving for clarity and coherence.
Engage with other scholars, both peers and mentors, to refine your work.
Maintain consistency in formatting and adhere to academic standards.

Remember, with meticulous planning and dedication, you'll produce a dissertation that makes you and your mentors proud.

How long is a PhD dissertation?: The Conundrum

Do you belong to the list of students who feel bewildered about PhD dissertation length? Many wonder because of the length’s variability across disciplines and institutions. The general ballpark figure for a completed doctoral dissertation is typically between 150 to 300 pages. Yet, this can vary widely depending on factors such as:

field of study
research methodology used
individual institutional requirements
guidelines of mentors

Although there's no one-size-fits-all answer, understanding these variables can help you navigate the ambiguity surrounding dissertation length. And with proper planning, you can create an impressive output.

Frequently asked questions

How to properly plan and prepare for a long dissertation .

Thinking about how long is a dissertation for PhD stops students on their track. It can indeed be overwhelming when you think of the amount of work involved. But with proper planning, you can crush your goals. Here are some helpful tips:

Break down your work into manageable steps.
Define your research question clearly and set realistic milestones.
Create an outline to help you write. `
Have a schedule for research, writing, and revisions.
Stay organized with notes, citations, and references.
Seek feedback from advisors and peers throughout the process.

Remember, embrace the challenges you face as opportunities for growth!

Are supporting materials counted in the dissertation word count?

Worried about how long is a dissertation paper and if yours will make the cut? Remember, appendices, tables, and figures, while essential, aren't factored into the word count. So, you can incorporate these supplementary elements without concerns about exceeding word limits.

If you’re pressed for time, you can buy dissertation online . Just ensure to give appropriate instructions so the final output adheres to your institution's formatting guidelines. With these supporting materials appropriately included, your dissertation will be comprehensive.

Are there different types of dissertations?

When asking how long are dissertations, one of the first things to consider is the field of study. Various types of dissertations exist, often shaped by research methodology. It can be quantitative to qualitative studies or triangulation (a blend of both).

Instead of worrying about the length, determine your research approach—whether it's primary or secondary, qualitative or quantitative. This decision significantly impacts the depth and breadth of your investigation, ultimately influencing the expected length of your dissertation. By aligning your research methods with your academic goals, you'll gain clarity on the scope of your writing project.

Another aspect of the length of the entire document is the type of thesis - be it an undergraduate thesis, masters thesis, or thesis for an advanced degree, most dissertations for academic programs are lengthy. The more advanced the degree, the longer the thesis usually is.

Are dissertations just for PhDs?

How many pages in a dissertation is something most students worry about. But is a dissertation just for doctoral candidates? In some countries, dissertations are exclusive to PhDs. However, for other countries, the term “dissertation” is interchangeable with "thesis." Why so?

Because both are research projects completed for undergraduate or postgraduate degrees. Keep in mind that whether you’re pursuing a bachelor's, an MA, or a doctorate, dissertation writing demonstrates your research skills and academic proficiency.

Your doctoral degree, just like your graduate degree from a graduate school, shows you can successfully navigate the research process, theoretical framework, and dissertation defense. Sure, the scope of research was less focused while you were a graduate student with a master's thesis. Nonetheless, it shows consistent work and dedication.

How many chapters in a dissertation?

Still mulling over how long does a dissertation have to be and how many chapters you must write? Dissertations usually consist of five to seven chapters. These typically cover the following:

introduction
literature review
methodology

However, the structure can vary depending on your field of study and specific institutional guidelines. Each chapter plays a vital role, leading readers through your research journey, from laying the groundwork to presenting findings and drawing conclusions.

How do I find a reputable dissertation writer to help me?

Worried about how long are PhD dissertations? No need to worry. You can opt for professional help, and there’s no shame in that! Research for online platforms that specialize in academic writing services like our Studyfy team.

You can take a peek at our positive reviews and testimonials, showing our track record of delivering high-quality work. Choose a writer who possesses expertise in your field of study and can meet your specific requirements. Prioritize the following:

clear communication
appropriate instructions (from word count to deadlines)
transparency regarding pricing
upfront about revision policies.

By vetting potential writers and choosing a reputable service, you can secure the assistance of a reliable professional to guide you through the dissertation writing process.

How long is a Thesis or dissertation? [the data]

Writing a thesis for your undergraduate, master’s, or PhD can be a very daunting task. Especially when you consider how long a thesis can get. However, not all theses are the same length and the expected submission length is dependent on the level of study that you are currently enrolled in and the field in which you are studying.

An undergraduate thesis is likely to be about 20 to 50 pages long. A Master’s thesis is likely to be between 30 and 100 pages in length and a PhD dissertation is likely to be between 50 and 450 pages long.

In the table below I highlight the typical length of an undergraduate, master’s, and PhD.

It is important to note that this is highly dependent on the field of study and the expectations of your university, field, and research group.

If you want to know more about how long a Masters’s thesis and PhD dissertation is you can check out my other articles:

How Long is a Masters Thesis? [Your writing guide]
How long is a PhD dissertation? [Data by field]
How to write a masters thesis in 2 months [Easy steps to start writing]

These articles go into a lot more detail and specifics of each level of study.

Let’s take a more detailed look at the length of a thesis or dissertation. We’ll start from the very basics including what a thesis or dissertation really is.

What exactly is a thesis or a dissertation?

A thesis or a dissertation is a research project that is typically required of students in order to gain an advanced degree.

A dissertation is usually much longer and more detailed than a thesis, but they both involve extensive research and provide an in-depth analysis of their given subject.

Many people use the term interchangeably but quite often a Masters level research project results in a thesis. While a PhD research project results in a much longer dissertation.

Thesis work is usually completed over the course of several months and can require multiple drafts and revisions before being accepted. These will be looked over by your supervisor to ensure that you are meeting the expectations and standards of your research field.

PhD Dissertations are typically even more involved, taking years to complete. My PhD took me three years to complete but it is usual for them to take more than five years.

Both a thesis and a dissertation involve researching a particular topic, formulating an argument based on evidence gathered from the research, and presenting the findings in written form for review by peers or faculty members.

My Master’s thesis was reviewed by the chemistry Department whilst my PhD thesis was sent to experts in the field around the world.

Ultimately, these experts provide a commentary on whether or not you have reached the standards required of the University for admittance into the degree and the final decision will be made upon reviewing these comments by your universities graduation committee.

There are several outcomes including:

accepted without changes – this is where you must make no changes to your thesis and is accepted as is.
accepted with minor changes this is where your thesis will require some minor changes before being admitted to the degree. Usually, it is not sent back to the reviewers.
Major changes – this is where the committee has decided that you need to rework a number of major themes in your thesis and will likely want to see it at a later stage.
Rejected – this is where the thesis is rejected and the recommendation to downgrade your degree is made.

What is the typical length of a thesis or dissertation?

The length of a thesis or dissertation varies significantly according to the field of study and institution.

Generally, an undergraduate thesis is between 20-50 pages long while a PhD dissertation can range from 90-500 pages in length.

However, longer is not necessarily better as a highly mathematical PhD thesis with proofs may only be 50 pages long.

It also depends on the complexity of the topic being studied and the amount of research required to complete it.

A PhD dissertation should contain as many pages and words as it takes to outline the current state of your field and provide adequate background information, present your results, and provide confidence in your conclusions. A PhD dissertation will also contain figures, graphs, schematics, and other large pictorial items that can easily inflate the page count.

Here is a boxplot summary of many different fields of study and the number of pages of a typical PhD dissertation in the field. It has been created by Marcus Beck from all of the dissertations at the University of Minnesota.

Typically, the mathematical sciences, economics, and biostatistics theses and dissertations tend to be shorter because they rely on mathematical formulas to provide proof of their results rather than diagrams and long explanations.

On the other end of the scale, English, communication studies, political science, history and anthropology are often the largest theses in terms of pages and word count because of the number of words it takes to provide proof and depth of their results.

At the end of the day, it is important that your thesis gets signed off by your review committee and other experts in the field. Your supervisor will be the main judge of whether or not your dissertation is capable of satisfying the requirements of a master’s or doctoral degree in your field.

How Many Pages Should a Master or PhD Thesis Have? Length of a thesis?

The length of a master’s thesis can vary greatly depending on the subject and format.

Generally, a masters thesis is expected to be around 100 pages long and should include:

a title page,
table of contents,
introduction,
literature review,
main test and body of work,
discussions and citations,
conclusion,
bibliography
and (sometimes) appendix.

Your supervisor should provide you with a specific format that you are expected to follow.

Depending on your field of study and the word count specified by your supervisor, these guidelines may change. The student must ask their advisor for examples of past student thesis and doctoral dissertations.

For example, if there is a limited number of words allowed in the thesis then it may not be possible to have 100 pages or more for the thesis.

Additionally, if you are including a lot of technical information such as diagrams or tables in the appendix then this could increase the page count as well. For example, my PhD thesis contained a page like the one below. This page only contains images from atomic force microscopy. Because my PhD was very visual many pages like this exist.

Ultimately it is important to consult with your supervisor and determine how many pages your master’s thesis or PhD dissertation will be expected to have.

How long does it take to write a graduate thesis? Write your thesis quickly

Writing a graduate thesis can be a daunting task.

It is typically expected to take anywhere from one to three years, depending on the subject and scope of the project.

However, this is not just writing. A typical thesis or dissertation will require you to:

formulate a research question
do a literature review
create research methodology
perform original research
collect and analyse results
write peer-reviewed research papers
write a PhD/masters thesis
submit thesis and respond to examiners comments.

The actual writing component of a thesis or dissertation can take anywhere from one month to 6 months depending on how focused the graduate student is.

The amount of time it takes to write a thesis or dissertation can vary based on many factors, such as the type of research required, the length of the project, and other commitments that may interfere with progress.

Some students may have difficulty focusing or understanding their topic which can also add to the amount of time it takes to complete the project.

Regardless, writing a thesis is an important part of obtaining a graduate degree and should not be taken lightly.

It requires dedication and determination in order for one to successfully complete a thesis or dissertation within an appropriate timeframe.

In my YouTube video, below, I talk about how to finish your thesis or dissertation quickly:

it is full of a load of secrets including owning your day, managing your supervisor relationship, setting many goals, progress over perfection, and working with your own body clock to maximise productivity.

Wrapping up

This article has been through everything you need to know about the typical length of a thesis.

The answer to this question is highly dependent on your field of study and the expectations of your supervisor and university.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

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How Long Is a Dissertation? Here’s the Average Length

How long is a dissertation? This is a question that you’re likely to ask if you’ve just enrolled for a doctoral program. But, this question is not new because other students have asked it too. Perhaps, the simplest answer to this question is that a dissertation should be sufficiently long to answer the stated question satisfactorily.

How Long Is a Dissertation?

There is no single answer to the question of a doctoral dissertation length. But most dissertations are on average 100-200 pages long. Somebody can say it should be 146 pages while somebody else can say 90 pages. Another person can say it should be 200 pages. Essentially, the length of this important academic document should depend on the topic, writing style, and goals of the writer.

Universities require students to write dissertations when pursuing masters and PhD degree programs. This paper forms an invaluable part of these academic programs in reputable learning institutions. As such, most undergraduate students are worried about the length of this document because it forms an important aspect of their academic life.

There are generally practical suggestions about how long should a dissertation be. For instance, the areas that your dissertation project focuses on should influence its length. For instance, a dissertation on a history topic will most likely be longer than an average dissertation on a chemistry topic. That means your academic discipline will influence the length of your dissertation.

How Long Is a PhD Dissertation?

Most academicians say that a PhD dissertation length should not exceed 80, 000 words . Thus, your text should be near this length. This word count includes the appendices while excluding references, footnotes, and bibliography. The footnotes of a dissertation shouldn’t exceed 20% of the text.

Students are discouraged from including discursive footnotes. What’s more, they should not use footnotes to include materials that should be in their main text. Doing so would circumvent the average length of the dissertation.

The statistical tables of a dissertation are counted as around 150 words each . But, the Degree Committee can allow learners to exceed the limits after application and under exceptional situations. Therefore, students should apply for this exception early before the date they propose to submit their thesis. The Graduate Committee evaluates the application and decides whether to allow the student to alter the average dissertation length.

The committee considers the dissertation proposal, as well as, a signed statement attesting to their proposed dissertation length. If they don’t and their dissertation exceeds the average length, it will be referred back for revision before it is forwarded to the institution’s examiners.

Average Dissertation Length

Many factors should generally be considered when answering the question of how long is a PhD dissertation. However, students should limit their writing between 80,000 and 100,000 words . On average, this document should have around 204 pages. But, the average length of a PhD thesis also depends on the university and the specific subject.

Essentially, a university can set the requirements of a dissertation, including the length. Nevertheless, most institutions opt for around 100,000 words. Research has shown that STEM dissertations have a medium length of 159 pages. Non-STEM dissertations, on the other hand, have a medium length of about 223 pages. Thus, there is a significant difference in length between STEM and non-STEM dissertations.

How Long Is a Dissertation Chapter?

Another question that might be lingering in your mind is how long is a dissertation chapter? Well, this varies because each chapter is a separate section that should address something specific. If you include the right information succinctly and adequately, your final write-up should have the right length. So, how long is a doctoral dissertation in every section? Here is a rough estimate.

Introduction

This section of a dissertation should tell readers why the topic is important. It should also mention relevant facts about the topic. Essentially, the introduction should tell readers why you have chosen the topic or issue and what makes it relevant. On average, the introduction should be between 10 and 15 pages . This is a perfect size because it allows you to mention everything concisely. Focus on intriguing the audience without disclosing or revealing anything in detail. Thus, your dissertation introduction should be more of a teaser.

Literature review

This is the section where you analyze relevant sources that you have chosen for your paper. It should include only sources that can be used to write the dissertation. In this section, you analyze and point out the strengths and weaknesses of your chosen sources. The literature review should be between 20 and 25 pages long.

Methodology

The length of this part of a dissertation ranges from 10 to 15 pages . It focuses on the methods used to conduct the research. Here, you discuss the specific method and data-selection processes used. This section also includes ways of contacting the sample population, as well as, the gathered data. What’s more, the encountered challenges can be included.

This section of a dissertation should not exceed 10 pages . It features the list and clarification of the obtained findings. Research findings are mentioned alongside the hypotheses explicitly. For instance, you should state whether they were refuted or confirmed. This section should also include a conclusion for everything done as far as your investigation is concerned.

This is also called the discussion section. Its length should range between 15 and 20 pages . This is the section where you explain the implications of your research. Provide a current and bigger perspective regarding your research. What’s more, diversify the current evidence base regarding the topic. Also, provide recommendations while acknowledging limitations in this section.

So, how long is a dissertation chapter? Well, it boils down to factors of the topic, learning institution, and goals of the study. Each section varies in length based on the nature of the research conducted and gathered data.

But, exactly how long does a dissertation have to be? There is no clear answer to this question. However, psychology, history, communication, nursing, and management dissertation can be up to 300 pages long. Education, Environmental Health, and Political Science dissertations can be around 100 pages. Overall, this length is influenced by the nature of the conducted research and the data that must be included in the document.

How Long Should a Dissertation Proposal Be?

You’re no longer asking how long is a dissertation word count because this question has been answered. But, you probably want to know the length of its proposal. Well, there is no specific length for this document. However, it should be considered as a long, formal treatise.

Well, most faculties recommend between 15 and 20 pages . However, the length of a proposal should depend on factors like the problem to be pursued, reasons to address the problem, where the answers will come from, and why you will go for certain sources.

This document proposes the work to be done. The focus of this text should be on convincing the thesis committee of your faculty that you have a research question that’s worth pursuing. Thus, you must present a convincing document for your faculty to allow you to proceed with the dissertation writing project. So, how long should it be?

How Long Should a Dissertation Abstract Be?

Lastly, you may want to know how long the abstract section of your dissertation should be. This is a separate page of the entire document. It is a summary of the dissertation. It reports the outcomes and aims of the research. Essentially, readers should know what your document is all about by reading the abstract. As such, this section should be written at the end. It should state the research objectives, problem, methods, key results, and the conclusion.

The dissertation abstract length ranges between 150 and 300 words . But just like with the main sections of a dissertation, there is a strict limit on the number of words that can be included. Therefore, check with the journal or university for which you are writing the dissertation.

Dissertation Length Consulting: Expert Guidance

When it comes to the length of your dissertation, our dissertation consulting service is here to provide expert guidance. Wondering about the ideal length for your dissertation? Look no further! Our experienced team offers personalized assistance to help you navigate this aspect of your research. With our dissertation consulting services, you can ensure that your dissertation meets the required length while maintaining its academic rigor. Contact us today for valuable insights and support throughout your dissertation journey.

The Bottom Line

There is no specific number of words or pages that determine the length of any dissertation. Therefore, check with your university, faculty, or journal to know the specific requirements for your dissertation in terms of length.

after completing the dissertation. However, you should have a rough draft or use the proposal’s introduction as a template because it has most of the elements that you need.

Frequently Asked Questions – FAQ

How long is a dissertation should be.

The dissertation length, on average, is between 100 to 200 pages long. Nevertheless, a dissertation can be 146 pages or 96 pages long. The length depends on the goals of the writer, topic, and writing style.

What is the average length of the dissertation?

The length of the dissertations is from 100 to 200 pages. On average the dissertation should be at least 80 000 words . However, this depends on the scope, expertise, and knowledge in the field of study. Longer dissertation papers have more words with a relatively higher number of pages.

How long does it take to write a dissertation?

Writing a dissertation will depend on several considerations. The subject of study, such as chemistry, may take longer because of the logistics involved. On the other hand, a simple sociology dissertation may take one month.

How long is a Ph.D. dissertation have to be?

Ph.D. dissertation length does not exceed more than 80 000 words . Therefore, your paper should be within these limits. Note that these words do not include references, footnotes, and bibliography.

Richard Ginger is a dissertation writer and freelance columnist with a wealth of knowledge and expertise in the writing industry. He handles every project he works on with precision while keeping attention to details and ensuring that every work he does is unique.

Succeed With A Perfect Dissertation

What Is The Average Dissertation Length?

There is no one-size-fits-all answer when it comes to answering the question, “How long should my dissertation be?”. However, the standard is as follows.

Table of Contents

Shorter Dissertations

Average-Length Dissertations

For many doctoral programs, particularly in the humanities, social sciences, and some STEM fields, the average dissertation length falls between 150 and 300 pages . This length allows for a thorough exploration of the research top ic , a review of literature , methodology, data analysis, and a discussion of findings.

Longer Dissertations

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Factors Affecting Dissertation Length

There are a number of factors that contribute to the length, including

Field of Study

University Requirements

Most universities have certain specific guidelines influenced by department and faculty expectations. It is important to go through them before you start writing.

Research Methodology

The complexity of the research design and methodology can impact the length. Dissertations having comprehensive data collection might need additional space for detailed justification.

Literature Review

Depending on how novel your research topic is, the length of your literature review varies. If there is a lot of research done on your variables, your dissertation will be longer.

Frequently Asked Questions

Can a dissertation exceed recommended length.

Yes, but ensure all content is relevant and contributes meaningfully to the research.

How can I manage dissertation length effectively?

Plan meticulously, prioritize content, and seek feedback to ensure conciseness without sacrificing depth.

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Frequently asked questions

How long is a dissertation.

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.

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.

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).

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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How Long Is A Dissertation? Here Is The Average Length

A dissertation represents the pinnacle of a graduate student’s academic journey. This extensive project requires years of study, research, writing, and editing to complete. But given its multifaceted nature, how long should a dissertation be?

While individual requirements vary, gaining insight into typical length expectations provides helpful guidance for students undertaking this monumental effort. Let’s closely examine common dissertation lengths and chapter word counts.

Average Dissertation Length

While specific length requirements vary, the average dissertation length falls between 80,000-100,000 words . Some succinct dissertations certainly come in under that range, but others may extend to or even surpass 100,000 words when including all core chapters, supplementary sections, back matter, citations, and references.

So realistically, most dissertation students should expect to produce a project spanning 200-300 pages by the time final revisions are complete. However, it is wise to focus more on developing robust scholarly content rather than rigidly adhering to page counts. The depth of knowledge and analysis takes priority over length.

How Long Is a PHD Dissertation?

For a doctoral dissertation, length specifications differ based on the university and academic discipline. However, many institutions set an upper limit stipulating that dissertations should remain under 80,000 words or around 250 pages. This aims to restrain projects from becoming overly long or unmanageable.

Yet even at the maximum of 80,000 words, a PhD dissertation still greatly exceeds most other forms of academic writing. This substantial length reflects the advanced rigor and breadth of research expected for a doctoral-level project. Students must put in an immense amount of time and effort to thoroughly investigate their specialized research problem and present sophisticated analysis across multiple chapters.

Why Write a Thesis or Dissertation?

Why do graduate students undertake this intensive process? In disciplines like the humanities and social sciences, completing a dissertation is mandatory to earn a PhD. The dissertation represents an original substantive contribution to knowledge in the student’s specialized field.

This distinction arises because the student must exhibit mastery of advanced research methodologies to investigate their complex chosen research problem systematically. The project involves comprehensively reviewing the literature to establish context, identifying a knowledge gap or area needing further analysis, conducting rigorous primary research, critically analyzing findings, and drawing informed conclusions.

The final dissertation formats this long academic journey into a formal document. It involves the student’s expertise regarding disciplinary concepts, theories, models, and investigative techniques developed through their intensive program of study. The dissertation proves the student’s readiness to receive their doctoral degree based on the new knowledge their diligent efforts produced. Hence, its extensive length captures the breadth of scholarship undertaken.

How Long Is a Dissertation Chapter?

Let’s examine typical length expectations for each chapter within a complete dissertation:

Introduction

This short opening chapter ranges between 10 and 15 pages . It identifies the specific research problem or gap being addressed, the purpose or objectives of the study, the research questions and/or hypotheses proposed, and the overall significance of the topic. It also provides the necessary background context to orient readers.

Literature Review

This lengthy chapter ranges between 20 and 25 pages . It comprehensively synthesizes the scholarly literature related to the research focus. It should demonstrate command of the academic landscape surrounding the topic by organizing themes, comparing theories, revealing gaps or contradictions, and providing context for the study’s aims and methodology.

Methodology

Here, the student outlines and precisely justifies the chosen research design, data sources or collection procedures, variables examined, instruments utilized, data analysis methods applied, methodology limitations, ethical considerations, and other details needed to facilitate replication. Ranging between 10 to 15 pages , a rigorous methodology underpins valid results.

This section should not exceed 10 pages . Succinctly report key quantitative or qualitative findings derived from the research without interpretation. Use data visualizations to summarize significant patterns. Reserve extensive analysis for the discussion chapter.

In the dissertation discussion section which ranges between 15 to 20 pages , deeply analyze the implications and importance of the results within the context of prior literature and established theories in the field. Identify how the findings expand knowledge related to the specific research problem by revealing new insights, angles, or relationships.

This section ranges between 15 to 20 pages . Synthesize the overall significance of the research and the new contribution it makes to the academic discipline. Reflect on limitations and make logically justified suggestions for future work building on the study. Close with final thoughts regarding lessons learned through the intensive process.

How Long Should a Dissertation Proposal Be?

A dissertation proposal introduces and justifies the proposed research focus for faculty approval before launching the full project. These persuasive proposals generally span 15-20 pages including a condensed introduction, targeted literature review, and thorough outline of the intended methodology. The proposal demonstrates the originality, significance, and feasibility of the impending study.

How Long Should a Dissertation Abstract Be?

The abstract serves as a one-page overview summarizing the entire dissertation research process and findings. It highlights the core focus, methods, results, and conclusions in a condensed form, typically ranging from 150-300 words . This snapshot helps readers quickly gauge the essence of this massive project.

How Long Is a Dissertation?

Dissertation writing represents substantial scholarly undertakings that reflect the pinnacle of graduate study in academia. While individual specifications vary, they commonly exceed 80,000 words and 100 pages across several core chapters. Gaining insight into the purpose and typical length of each section helps prepare students for this enormous endeavor essential for earning a PhD in their field of study.

Undoubtedly, completing a dissertation with its expansive research and writing demands requires immense focus, perseverance, and time management skills. Yet the extensive length simply indicates the wealth of specialized knowledge a successful dissertation contributes when completed. The final work represents genuine advancement within the academic discipline.

So embrace the process and don’t become overwhelmed by worrying about length or page counts early on. Maintain focus on developing solid content demonstrating your subject matter expertise. With dedication and support, you can craft a dissertation that makes a meaningful impact, regardless of its exact length. The journey leads to enormous personal and professional growth.

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How Long Is a Dissertation?

When thinking about the realities of graduate school, one topic that looms large in the mind of every doctoral student is the dissertation. A dissertation is a complex document that elicits many burning questions, both from doctoral candidates and those outside of academia:

How long does my dissertation need to be?
How much research is involved?
Can I choose my own topic?
What does academic research actually look like, and am I capable of it?
What will I do with my dissertation?
How much time will I spend writing my dissertation?

Like many aspects of academia, the length of a dissertation is a bit complicated, and it varies by field of study. It’s determined by multiple factors, including: discipline, type of analysis (qualitative or quantitative), and the area of research. Generally speaking, the average length of a dissertation is between 150-300 pages. However, it’s important to consider a number of contributing factors. It also pays to be well-informed about variables that can influence the length of the document.

Why Write a Dissertation?

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In addition to considering the length of a dissertation, also think about what is contained within its pages, and what purpose it serves. As a key metric used to evaluate scholarly preparedness and expertise, few documents incite as much fear and anxiety as the dissertation.

Since it represents the culmination of graduate education, the dissertation is the pinnacle of a graduate student’s research. A dissertation is an expression of years’ worth of academic training and study. In other words, it’s a pretty big deal.

The dissertation also serves as an indicator of the student’s long-term potential as a scholar. A strong dissertation can be mined for the writer’s first few academic publications–maybe even a monograph, if the stars align.

It generally sets the tone for the doctoral candidate’s experience on the job market and transition into their professional career, as well as within their scholarly community. The dissertation is also most students’ first opportunity to find a gap in the body of knowledge and stake their claim as experts on a narrow topic. For most researchers, it’s their first foray into primary investigation and a major project of their own.

Dissertation Length By Discipline

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Types of Dissertations

When asking how long a dissertation is, one of the first things to consider is the field of study. Most dissertations are the product of either quantitative or qualitative research, or a hybrid of both. When thinking about length, first consider the research methods that will be used. Is your research primary or secondary? Qualitative or quantitative? The answer will offer insight into how much you can expect to write.

How Long Are Quantitative Dissertations?

Doctoral candidates in academic disciplines that rely on quantitative research, like scientific studies and analyses of human behavior, tend to produce shorter dissertations. Fields like chemistry, biology, and some social sciences are driven by statistical analysis of primary research.

This means they adhere to methods-based practices and studies that follow a clear, linear trajectory. Likewise, researchers in fields like economics, business, psychology, and health sciences may defend dissertations that are fewer than 100 pages long.

How Long Are Qualitative Dissertations?

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In academic disciplines that use qualitative research to add to the body knowledge, it’s a very different story. Fields like history, which have a notoriously lengthy process for completing the dissertation and earning the Ph.D., are known for producing dissertations that are well over 300 pages, often stretching to 400 pages or more. These dissertations are a compilation of secondary and tertiary materials, documents, images, and hundreds of references acknowledging thousands of pages read and consulted during the research process.

Sounds exhausting, right? It is, but there are benefits to writing a longer dissertation. The good news is that dissertations of this length and scope often produce enough research for multiple scholarly journal articles and conference papers. Rather than having just one study to publish, writers that complete dissertations in fields like anthropology, theology, art history, and literature will have several articles and conference presentations ready to go. With such a competitive job market, particularly with post-docs and tenure track positions , the more prepared scholars are to start publishing, the better.

Creative Dissertations

When thinking about how long a dissertation is, another, lesser-known variant to consider is the creative dissertation. Though still comparatively small, a growing number of programs are offering this option to students in creative fields.

In what is best-described as a hybrid model of original creative work presented within the theoretical framework of a critical introduction, creative dissertations have been embraced by poets, fiction writers, and essayists who have made a place for themselves within academia. These dissertations are book-length manuscripts that typically clock in around 200 pages.

Deconstructing the Dissertation

So how long is a dissertation? Length varies, as we’ve discovered, but you should also think about how long you have to write it. There’s a common assumption that a dissertation is a singular document, written all at once.

Not true! Dissertation chapters , especially in qualitative research, often get their start as final papers in academic seminars. This gives students an opportunity to dip their toes into a variety of topics before diving into one they love and committing to a book-length project.

When thinking about how long a dissertation is, also consider all the parts of the project that are not the writer’s central argument. Many dissertations will include a literature review. This is an overview of existing publications used to illustrate a gap in peer-reviewed scholarship that will ideally be filled by the dissertation. There is also a lot of minutiae that adds to the page count of a dissertation, including:

Acknowledgements
Introduction

Long story short, it all adds up.

How Long Is a Dissertation? Summary

Keep in mind that dissertation lengths vary by field. Here’s what to expect:

Quantitative Dissertations (math, science, psychology, physics, etc.)–100-150 pages.
Quantitative Dissertations (history, art, linguistics, literature, etc.)–150-300+ pages.
Creative Dissertations (fiction, poetry, memoir, translation, etc.)–150-200 pages.

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Courtney Watson, Ph.D.

Courtney Watson, Ph.D. is an Associate Professor of English at Radford University Carilion, in Roanoke, Virginia. Her areas of expertise include undergraduate and graduate curriculum development for writing courses in the health sciences and American literature with a focus on literary travel, tourism, and heritage economies. Her writing and academic scholarship has been widely published in places that include Studies in American Culture , Dialogue , and The Virginia Quarterly Review . Her research on the integration of humanities into STEM education will be published by Routledge in an upcoming collection. Dr. Watson has also been nominated by the State Council for Higher Education of Virginia’s Outstanding Faculty Rising Star Award, and she is a past winner of the National Society of Arts & Letters Regional Short Story Prize, as well as institutional awards for scholarly research and excellence in teaching. Throughout her career in higher education, Dr. Watson has served in faculty governance and administration as a frequent committee chair and program chair. As a higher education consultant, she has served as a subject matter expert, an evaluator, and a contributor to white papers exploring program development, enrollment research, and educational mergers and acquisitions.

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Finding the Answer to How Long is a Dissertation – Read On

If you are asking the question, “how long is a dissertation,” you are probably getting ready to attack the final hurdle toward that Master’s or Doctorate. Make no mistake – you are in for a lot of work. But worrying about the length should not be “on your plate” of issues. This guide will show you how to answer this question.

The Average Dissertation Length

Overall, dissertations may run between 100 and 200 pages, but this number could vary. Dissertation lengths for a Master’s are at the lower end of this page number, while Ph.D.’s are at the higher end.

Length variations depend on your institutional requirements, your department requirements, your committee requirements, and the research field of a thesis itself.

So, let’s unpack some factors that determine the average length of dissertation projects.

University Guidelines

Universities do provide some guidelines for Master’s and doctoral candidates. Usually, these guidelines serve three purposes:

To give students general ideas of overall length
To protect faculty from being overburdened by theses that are too lengthy
To establish credibility for a candidate and university.

Department Guidelines

While universities have general guidelines for the average length of dissertation projects, academic departments have their own. These can vary. Universities give departments latitude, as they want departments to be autonomous in their criteria. Departments establish length parameters for all parts of a thesis, and these vary. A thesis in social sciences, for example, is longer than one in hard science, such as chemistry, because book reviews and research methods vary so much.

Committee Guidelines/Advice

A dissertation proposal passes through an advisor but goes to a committee for approval. If you are not certain how to write a dissertation proposal , get some professional help, and/or read through proposals of colleagues. Individual committee members make requests and provide advice that either expand or contract your project parameters – literature to be reviewed, methodologies, the data to be collected, etc. All of this impacts the average length of a dissertation.

NOTE: there are times when committee members disagree. One might want a piece of literature included while another could think it unnecessary. You may be able to satisfy both by adding it to an appendix with appropriate acknowledgments.

A Look at Dissertation Length by Chapters

The length of a dissertation is a sum of its parts. And each part will have length parameters. Let’s unpack each chapter of a thesis, look at what it contains, and then consider how long should a dissertation be.

Chapter 1 – Introduction

Just like any academic piece, the beginning is the introduction. In a research paper, you introduce your topic and your thesis. In a thesis introduction, you introduce your research question, explaining the importance of your research to your academic field, and briefly describing your research methodology. In terms of how long is a doctoral dissertation introduction, a general rule is between 12-25 pages. The temptation is to write more than you should. If you find your introduction running more than 25 pages, get some dissertation help online from a service that has Ph.D. consultants in your research field. They can whittle that introduction down, removing content that should be reserved for elsewhere.

Chapter 2 – Literature Review

How long is the average dissertation literature review? This chapter will vary significantly, based upon a discipline. For example, a narrow thesis topic in the field of chemistry could yield a relatively small number of literature to review. On the other hand, a broader research area, such as psychology or sociology, will yield a much larger amount of literature and might have to be scaled down.

If your literature review is less than 10 pages, you should scale up, perhaps adding literature that relates to research methodology you are using. If your review exceeds 25 pages, you will need to scale down, focusing on those that most directly relate to your research question and are most current. If that review is too long, your committee may request revision when you arrive for your oral defense.

The literature review is much like writing a research paper. If you struggle with it, contact a custom research paper writing service that has a Ph.D. in your field to help.

Chapter 3 - Methodology

Just how long is a doctoral dissertation methodology chapter? This vary somewhat but not as much as the literature review chapter. This chapter must provide a detailed description of the research methodology, whether quantitative or qualitative. Your methodology could involve experimental and control groups; it might involve qualitative research with human respondents. The key is to describe in detail exactly what you did, and why you did it. As well, it must explain how you intend to analyze results, once the methodology had been implemented.

The “rule of thumb” here is to provide enough detail so that another researcher can replicate your methodology exactly. This means the length of this chapter can be as much as 20-25 pages, standing upon the necessary detail. It would be a good idea to present this chapter to a “ do my dissertation ” writing service and let a Ph.D. in your field review it to ensure that it provides the necessary information.

Chapter 4 – Results

This chapter provides a full picture of results of your research. It may be lengthy, not because of word count, but because of the need for charts and graphs that depict your results. A single chart, for example, might consume an entire page. This chapter can be as long as 25 pages or more, depending upon graphics involved. The key here is not how long is a phd dissertation results chapter. The key is, have you shown all results, in both graphic and verbal format?

Chapter 5 – Discussion

Now it’s time to put the meaning into the data that you presented in the previous chapter. This, of course, requires statistical analysis, and this is where math-challenged degree candidates seek help from professionals. There is no shame in this. You have gathered the right data through your research, but statistical results should be perfectly analyzed and presented to show that your research shows a statistical significance. Exactly how long is a dissertation paper discussion chapter depends upon the amount of data to be analyzed and, again, graphics and verbal explanations that are necessary. And again, it’s not a matter of word count. There must be a clear and understandable presentation and explanation of the data’s importance. This chapter could run anywhere from 15 to 25 pages, it depends on the analytical complexity.

Chapter 6 – Conclusion

Many institutions and departments combine the discussion and conclusion chapters, so be certain you know whether you need to do so or not. Your conclusion must cover these key elements:

Re-state the research question and how your specific research answered that question
Point out any nuisance factors in the research and how they were addressed
Avoid presenting any new information that was not contained in the other chapters
Show how your research supports or contradicts prior studies
Point future researchers in new directions.

This chapter may be the shortest of all, but it is no less important. Instead of worrying about how many words is a dissertation conclusion, be certain that you have covered these five elements. Do not scrimp on this chapter. Here, you could express your opinions, and it might take up to 15 or so pages to do so.

Are you finally finished? Not quite. Your thesis must be perfectly written, and you should not be the one to review and edit the work. Find an essay proofreading service with a Ph.D. in your research field to do this.

How Long are Dissertations by Discipline?

So, how long does a dissertation have to be? The answer is not definite. Most institutions have length guidelines, but these can and often are superseded by disciplinary departments.

In general, theses are, on average, between 100-200 pages long, and this also includes references, footnotes, and bibliography, which, by the way, should never exceed 20% of the length. And be certain you understand how to cite a master’s thesis APA or a dissertation in the same format unless another format is required.

But here is where the length could become varied, according to the academic discipline. A sociology thesis, for example, is closer to the 200-page mark, while one in chemistry is much closer to the 100-page mark. Much of this disparity is result of the literature reviews and methodologies. STEM research is quantitative with fewer pieces of literature, while a thesis in humanities could very well have a significantly longer literature review section and may be based upon qualitative research which can be more involved.

The other major disparity depends upon whether the thesis is for a Master’s or a Ph.D. degree program. In general, a Master’s thesis is much shorter because it does not involve original research.

Some University Requirement Examples

Here is a table that shows just a few examples of U.S. and UK university word count guidelines for theses.

Note: These word counts do not include the proposal or the abstract.

Use This Guide as a Start Point

So, how long are dissertations? In short, as long as they need to be to satisfy university, department, and committee guidelines/demands. This guide prepares you for what you are facing in a general sense, along with the structure of a dissertation . As such, you should find it very helpful as you plan what will be a lengthy project.

How Long is a Thesis or Dissertation: College, Grad or PhD

How long is a thesis

As a graduate student, you may have heard that you must complete a certain comprehensive project, either a thesis or a dissertation. In this guide, we will explore how long a thesis should be, the best length for a dissertation, and the optimal length for each part of the two.

If you read on to the end, we will also explore their differences to understand how it informs each length.

Both terms have distinct meanings, although they are sometimes used interchangeably and frequently confused.

Structure-wise, both papers have an introduction, a literature review, a body, a conclusion, a bibliography, and an appendix. That aside, both papers have some differences, as we shall see later on in this article.

How Long Should a Thesis be

Before discussing how long a thesis is, it’s critical to understand what it is. A thesis is a paper that marks the end of a study program.

Mostly, there is the undergraduate thesis, a project that marks the end of a bachelor’s degree, and a master’s thesis that marks the end of a master’s program.

A thesis should be around 50 pages long for a bachelor’s degree and 60-100 pages for a Master’s degree. However, the optimal length of a thesis project depends on the faculty’s instructions and the supervising professor’s expectations . The length also depends on the topic’s technicalities and the extent of research done.

A master’s thesis project is longer because it is a compilation of all your knowledge obtained in your master’s degree.

It basically allows you to demonstrate your abilities in your chosen field.

Often, graduate schools require students pursuing research-oriented degrees to write a thesis.

This is to demonstrate their practical skills before completing their degrees.

In contrast to undergraduate thesis, which are shorter in length and coverage area, usually less than 60 pages. A master’s theses are lengthy scholarly work allowing you to research a topic deeply.

Then you are required to write, expand the topic, and demonstrate what you have learned throughout the program. This is part of why you must write a thesis for some undergrad in some of the courses.

A Master’s thesis necessitates a large amount of research, which may include conducting interviews, surveys, and gathering information ( both primary and secondary) depending on the subject and field of study.

For this reason, the master’s thesis has between 60 and 100 pages, without including the bibliography. Mostly, the topic and research approach determine the length of the paper.

This means that there is no definite number of pages required. However, your thesis should be long enough to clearly and concisely present all important information.

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How long should a dissertation be.

A dissertation is a complex, in-depth research paper usually written by Ph.D. students. When writing the dissertation, Ph.D. Students are required to create their research, formulate a hypothesis, and conduct the study.

On average, a dissertation should be at least 90 pages at the minimum and 200 pages at the maximum , depending on the guidelines of the faculty and the professor. The optimal length for a dissertation also depends on the depth of the research done, the components of the file, and the level of study.

Most Ph.D. dissertations papers are between 120 to 200 pages on average.

However, as we said earlier, it all depends on factors like the field of study, and methods of data collection, among others.

Unlike a master’s thesis, which is about 100 pages, a dissertation is at least twice this length.

This is because you must develop a completely new concept, study it, research it, and defend it.

In your Ph.D. program, a dissertation allows you the opportunity to bring new knowledge, theories, or practices to your field of study.

The Lengths of Each Part of a Thesis and Dissertation

Factors determining the length of thesis or dissertation.

As we have seen, there is no definite length of a thesis and dissertation. Most of these two important academic documents average 100 to 400 pages. However, several factors determine their length.

Universities- we all know universities are independent bodies. Also, it’s important to know that each university is different from the other. As a result, the thesis and dissertation length varies depending on the set rules in a certain college or school.

Field of study- some fields of study have rich information, while others have limited information.

For example, you may have much to write about or discover when it comes to science compared to history.

As such, if you are to write a thesis or a dissertation in both fields, one will definitely be longer than the other. Check the time it takes to write a thesis or a dissertation to get more points.

Other factors that affect the length of a thesis or a dissertation include your writing style and the instructor’s specifications. These factors also come into play when it comes to the time taken to defend a thesis or your dissertation.

Tips for the Optimal Length for a Thesis or Dissertation

Instead of writing for length, write for brevity. The goal is to write the smallest feasible document with all of the material needed to describe the study and back up the interpretation. Ensure to avoid irrelevant tangents and excessive repetitions at all costs.

The only repetition required is the main theme. The working hypothesis seeks to be elaborated and proved in your paper.

The theme is developed in the introduction, expanded in the body, and mentioned in the abstract and conclusion.

Here are some tips for writing the right length of thesis and dissertation:

Remove any interpretation portion which is only tangentially linked to your new findings.
Use tables to keep track of information that is repeated.
Include enough background information for the reader to understand the point of view.
Make good use of figure captions.
Let the table stand on its own. I.e., do not describe the contents of the figures and/or tables one by one in the text. Instead, highlight the most important patterns, objects, or trends in the figures and tables in the text.
Leave out any observations or results in the text that you haven’t provided data.
Do not include conclusions that aren’t backed up by your findings.
Remove all inconclusive interpretation and discussion portions.
Avoid unnecessary adjectives, prepositional phrases, and adverbs.
Make your sentences shorter – avoid nesting clauses or phrases.
Avoid idioms and instead use words whose meaning can be looked up in a dictionary.

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Difference between a Thesis and a Dissertation

The most basic distinction between a thesis and a dissertation is when they are written.

While a thesis is a project completed after a master’s program, a dissertation is completed at the end of doctorate studies.

In a thesis, you present the results of your research to demonstrate that you have a thorough understanding of what you have studied during your master’s program.

On the other hand, a dissertation is your chance to add new knowledge, theories, or practices to your field while pursuing a doctorate. The goal here is to come up with a completely new concept, develop it, and defend it.

A master’s thesis is similar to the types of research papers you’re used to writing in your bachelor’s studies. It involves conducting research on a topic, analyzing it, and then commenting on your findings and how it applies to your research topic.

The thesis aims to demonstrate your capacity to think critically about and explain a topic in depth.

Furthermore, with a thesis, you typically use this time to elaborate on a topic that is most relevant to your professional area of specialization that you intend to pursue.

In a dissertation, on the other hand, you use other people’s research as an inspiration to help you come up with and prove your own hypothesis, idea, or concept. The majority of the data in a dissertation is credited to you.

Last but not least, these two major works differ greatly in length. The average length of a master’s thesis is at least 100 pages.

On the other hand, a doctoral dissertation should be substantially longer because it includes a lot of history and research information, as well as every element of your research, while explaining how you arrived at the information.

It is a complex piece of scholarly work, and it is likely to be twice or thrice the length of a thesis. To know the difference, check the best length for a thesis paper and see more about it.

Here is a Recap of the Differences

While the thesis is completed at the end of your master’s degree program, a dissertation is written at the end of your doctoral degree program.
Both documents also vary in length. A thesis should have at least 100 pages, while a doctoral dissertation is longer (over 200 pages)
In the thesis, you conduct original research; in the dissertation, you use existing research to help you develop your discovery.
For a thesis, you have to add analysis to the existing work, while a dissertation is part of the analysis of the existing work.
In comparison to a thesis, a dissertation requires a more thorough study to expand your research in a certain topic.
The statements in a thesis and a dissertation are distinct. While a thesis statement explains to readers how you will prove an argument in your research, a dissertation hypothesis defines and clarifies the outcomes you expect from your study. Here, you apply a theory to explore a certain topic.
A dissertation allows you to contribute new knowledge to your field of study, while a thesis makes sure you understand what you have studied in your program and how it applies.

A thesis or a dissertation is a difficult document to compile. However, you should not be worried since your school assigns you a dissertation advisor who is a faculty member.

These advisors or supervisors help you find resources and ensure that your proposal is on the right track when you get stuck.

Check out my guide on the differences between a research paper, proposal, and thesis to understand more about these issues.

Josh Jasen or JJ as we fondly call him, is a senior academic editor at Grade Bees in charge of the writing department. When not managing complex essays and academic writing tasks, Josh is busy advising students on how to pass assignments. In his spare time, he loves playing football or walking with his dog around the park.

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When launching academic research, one question often looms large in the minds of many: “How long is a proper dissertation?” This seemingly simple query holds within its layers of complexity, much like the dissertation itself. The question is not about the number of pages – it’s about the depth of analysis, the strength of the argument, and the breadth of the literature review.

Key Takeaways:

The length of a dissertation is influenced by plenty of factors, including the type of research, the academic discipline, and specific institutional guidelines.
A dissertation comprises various sections, from the abstract to the references. Understanding the typical length and depth of each component can clarify the dissertation’s overall length and structure.
The time required to write a dissertation can range from a few months for master’s students to several years for doctoral candidates.
While understanding average lengths and durations is helpful, the emphasis should always be on the quality of research and writing.

In the kingdom of academic writing, a dissertation proudly wears the crown. It is the queen – the most important, the most intricate, and, of course, the most time-consuming written work. From the abstract to the conclusion, from the glossary to the acknowledgments, every chapter and heading demands painstaking attention. With such a significant requirement, it’s natural to wonder how many pages a future Ph.D. holder should be prepared to write. How does the institution or the supervisor influence the formatting and length? What role does the research process play in determining the body of the work? And can the paper be turned down if it’s shorter than some existing standard?

Fear not, for this article is here to guide you through the complexities of dissertation length, offering insights, references, and clarity on this pivotal aspect of your academic journey. Whether you’re just starting out or are deep into your thesis, understanding the length and structure will undoubtedly help in your academic success. For additional support, students should take a look at the top 10 dissertation writing services to make writing a dissertation a breeze.

Types of Dissertations

An infographic with data that gives an information on average dissertation length

Much like the vast fields of research they encompass, dissertations come in various shapes and sizes. The length of a dissertation can vary based on several factors, including the type of research, the discipline, the institution’s requirements, and the depth of analysis. Let’s explore the different types of dissertations and their average lengths.

Length of Dissertations Based on Research

Empirical dissertations.

These involve collecting data from the real world. This could be through observations, surveys, or experiments. Due to the need for detailed data presentation and analysis, empirical dissertations tend to be longer.

A psychology researcher decides to study the impact of mindfulness meditation on stress levels among college students. They design an experiment where one group of students practices mindfulness meditation for 20 minutes daily, while a control group does not. Over a semester, the researcher collects data on stress indicators, such as cortisol levels and self-reported stress scales.

Average Page Count: Given the need to detail the methodology, present raw data, and provide a comprehensive analysis, empirical dissertations tend to be lengthier. They often range from 120 to 300 pages, depending on the depth and breadth of the data collected.

Non-Empirical Dissertations

Non-empirical dissertations are based on existing data or literature. Instead of collecting new data, researchers analyze what’s already available to make a new argument or perspective.

A literature scholar chooses to analyze the representation of nature in the works of William Wordsworth. Instead of collecting new data, the researcher explores Wordsworth’s poems, existing critiques, and historical contexts to build a nuanced argument about the poet’s unique perspective on nature.

Average Page Count: Since non-empirical dissertations rely heavily on existing literature and critical analysis, they may be shorter than empirical ones but still comprehensive. They typically range from 100 to 250 pages.

Theoretical Dissertations

These focus on developing new theories or analyzing existing ones. Their length can vary widely based on the complexity of the theory, but they may reach up to 270 pages.

A philosophy student is intrigued by the concept of free will. They decide to develop a new theoretical framework that reconciles free will with determinism. The dissertation delves into existing theories, identifies gaps or contradictions, and proposes a new model of understanding free will.

Average Page Count: Theoretical dissertations can vary widely based on the complexity of the theory and the depth of exploration. However, given the emphasis on conceptual exploration and model building, they generally range from 110 to 270 pages.

Case Study Dissertations

Case study dissertations focus on an in-depth analysis of a specific case, event, individual, or context. They provide detailed insights into real-world situations.

A business student is interested in understanding the success of a local startup that has recently gained significant market share. They conduct in-depth interviews with the company’s founders, employees, and customers, analyze its marketing and financial strategies, and compare it with competitors to draw insights.

Average Page Count: Due to their detailed nature, case study dissertations can be quite extensive. They often range from 130 to 300 pages, depending on the depth of the case and the number of data sources analyzed.

How Long Should a Dissertation Be?

Diverse factors influence the length of dissertations. While the type of work—be it empirical, theoretical, or case study—plays an essential role in determining its size, another equally significant determinant is the academic discipline itself. Each field of knowledge has its unique traditions, methodologies, and expectations. These difficulties often dictate the depth of research, the paper’s structure, and its length. As we explore the various disciplines and their respective characteristics, let’s examine how average dissertation length varies.

A bar chart representing an average length of dissertation based on the most popular majors

Humanities and Social Sciences

In disciplines like history, philosophy, sociology, and literature, the dissertation often involves a deep investigation of theoretical frameworks and extensive literature reviews. Given the need for comprehensive analysis and the inclusion of various perspectives, dissertations in these fields can be quite lengthy. They often range from 200 to 400 pages, with some even exceeding this range for exceptionally in-depth studies.

Sciences and Engineering

Dissertations in the fields of physics, chemistry, engineering, and biology are often data-heavy, requiring detailed explanations of methodologies and results. However, they tend to be more concise in their literature reviews and theoretical discussions. As a result, the average length for dissertations in these disciplines is generally between 100 to 300 pages.

Business and Management

In business and management, dissertations can vary widely in length due to the diverse nature of research topics, which can range from case studies of specific companies to broad surveys of business trends. Empirical research is common, and the need for practical application often shapes the structure and length. Typically, these dissertations fall between 150 to 250 pages.

Arts and Design

Dissertations in arts and design often combine practical and theoretical research. For instance, a dissertation might include both a written component and a portfolio of artworks or designs. The written portion can range from 100 to 300 pages, and when supplementary material like portfolios or exhibits is considered, the overall “length” can be pretty substantial.

Based on a review of standard dissertations across various key fields, the average page count for a dissertation is approximately 225 pages.

Medicine and Health Sciences

Medical and health sciences dissertations often involve empirical research, such as clinical trials or epidemiological studies. These require detailed methodologies, data presentation, and analysis, making them quite extensive. The average length is often between 150 to 350 pages, not including appendices like data tables or supplementary material.

Legal dissertations often involve a mix of case law analysis, theoretical discussion, and policy implications. They require a deep understanding of legal principles and precedents, making them comprehensive and detailed. The average length for law dissertations is usually between 150 to 300 pages.

Dissertations in education often focus on empirical research, analyzing educational methods, policies, or trends. They require a balance of literature review, data collection, and analysis, making them moderately extensive. The typical length ranges from 150 to 250 pages.

While these figures offer a general guideline, it’s important to remember that the quality of your research and argument is more important than hitting a specific page count. It is strongly recommended to consult your supervisor and adhere to your institution’s guidelines when determining the appropriate length for your dissertation.

Breaking Down the Components of a Dissertation

Now that we know the differences between the sizes of various types and subjects of dissertations, it is also essential to get an idea of the components of the dissertation and how each contributes to the overall length. From the abstract to the reference list, each section has its own set of requirements and formatting guidelines, which can influence the total number of pages.

Abstract: The abstract is a concise summary of your dissertation, usually not exceeding 300 words. While it’s a small part of the dissertation, it’s critical, providing readers with an overview of what to expect.
Introduction:The introduction sets the stage for your research, outlining the problem statement, objectives, and research questions. Depending on the complexity of your topic and the need for background information, the introduction can range from 10 to 30 pages.
Literature Review: This is often one of the lengthier sections, especially for humanities and social sciences dissertations. A thorough literature review can easily span 40 to 100 pages, depending on the breadth of existing research and the number of sources reviewed.
Methodology: The methodology outlines the research design and, data collection and analysis methods. For empirical dissertations, this section can be quite detailed and lengthy, often ranging from 20 to 50 pages.
Data Analysis: The heart of any empirical dissertation, this section presents your findings. The length can vary widely based on the volume and complexity of the data, but it’s not uncommon for this section to be 50 to 100 pages.

A diagram representing an average length of dissertation components, in pages

Discussion: Here, you interpret your findings, linking them to your research questions and the existing literature. This section can range from 20 to 40 pages, depending on the depth of your analysis and the complexity of your argument.
Conclusion: The conclusion summarizes your research and its implications. It’s usually shorter than other sections, often around 10 to 20 pages.
References: The reference list can be extensive, especially for dissertations that require a comprehensive literature review. The length can range from 10 to 30 pages.
Appendices, Glossary, and Other Sections: These are optional and depend on the specific requirements of your discipline or institution. They can include data tables, supplementary analyses, a glossary of terms, or a list of abbreviations. These can add anywhere from 10 to 50 pages to your dissertation.
Acknowledgments and Preface: These are usually brief, often not exceeding a few pages, but they are an important part of the dissertation, offering a space to thank those who have supported your research journey.

How Long Does It Take to Write a Dissertation?

Writing a dissertation is like setting out on a long expedition. The destination is clear, but the duration of the journey can be uncertain. Just as we’ve explored the length of a dissertation in terms of pages, another relevant question comes up: How much time does it take to pen down this magnum opus of academic achievement? The time taken to write a dissertation is as varied as the topics they cover. While some scholars might write their work for a couple of years, others, such as professional custom thesis writers , may complete the project in just a few months. Based on general academic standards and practices, we can provide a rough estimate:

Master’s Dissertation: Typically, students take between 6 months to a year to complete a master’s dissertation. This timeframe includes the initial research proposal stage, data collection (if applicable), writing, and revisions.

PhD Dissertation : It is a more extensive piece of work and generally takes 2 to 4 years to complete. This duration encompasses the entire process, from topic selection, extensive literature review, methodology design, data collection, analysis, writing, and multiple rounds of revisions. Some complex empirical studies or projects in disciplines that require extensive fieldwork might even take longer.

Multiple factors influence this variability, each playing a crucial role in determining the pace of progress.

While understanding the factors that influence the pace can help in planning, it’s essential to remember that each dissertation has its own rhythm. Embrace the process, seek support when needed, and celebrate each milestone, knowing that the journey, though long, is a significant part of the academic adventure.

How long is a dissertation for PhD?

A PhD dissertation typically ranges from 100 to 400 pages, depending on the discipline, research type, and institutional guidelines. However, the average is often between 200 to 300 pages. The focus should be on the depth and quality of research rather than just the page count.

Can I write my dissertation in 2 months?

While it’s theoretically possible to write a dissertation in 2 months, it’s highly challenging and might not yield the best quality work . This timeframe requires intense dedication, a clear plan, and prior research. It’s also essential to consider factors like feedback and revisions, which can add to the timeline. For a master’s dissertation, this might be more feasible, but for a PhD, it’s a significant stretch.

How many pages is a 10000-word dissertation?

The page count for a 10,000-word dissertation can vary based on factors like formatting, font size, and spacing. However, with standard academic formatting (e.g., Times New Roman, 12-point font, double-spaced), a 10,000-word dissertation is around 40 to 50 pages.

What is the shortest PhD dissertation?

The length of a Ph.D. dissertation can vary widely across disciplines and topics. While there’s no universally recognized “shortest” dissertation, there are instances of notably concise dissertations in certain fields. For example, dissertations can sometimes be as short as 50 pages in mathematics and some hard sciences if they present a novel and significant result. However, such cases are exceptions rather than the norm.

How difficult is a dissertation?

A dissertation is often considered one of the most challenging academic writings. It requires extensive research, critical thinking, and the ability to sustain a coherent argument over a lengthy document. The difficulty also stems from the need for originality and the pressure of it being the culmination of one’s academic journey at that level. However, with proper planning, guidance, and dedication, it’s a manageable and rewarding process.

How many hours do you study for a dissertation?

The number of hours needed to study or work on a dissertation varies based on the individual, the topic, and the stage of the dissertation process. On average, a student might spend 15 to 30 hours a week over the course of several months or years. This includes reading, researching, writing, and revising. Some weeks might require more intense work, especially during the writing and revision stages, while others might be lighter.

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Length of the average dissertation

On R is My Friend, as a way to procrastinate on his own dissertation, beckmw took a look at dissertation length via the digital archives at the University of Minnesota.

I’ve selected the top fifty majors with the highest number of dissertations and created boxplots to show relative distributions. Not many differences are observed among the majors, although some exceptions are apparent. Economics, mathematics, and biostatistics had the lowest median page lengths, whereas anthropology, history, and political science had the highest median page lengths. This distinction makes sense given the nature of the disciplines.

I was on the long end of the statistics distribution, around 180 pages. Probably because I had a lot of pictures.

As I was working on my dissertation, people often asked me how many pages I had written and how many pages I had left to write. I never had a good answer, because there’s no page limit or required page count. It’s just whenever you (and your adviser) feel like there’s enough to get a point across. Sometimes that takes 50 pages. Other times it takes 200.

So for those who get that dreaded page-count question, you can wave your finger at this chart and tell people you’re somewhere in the distribution.

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13 Comments

As a student at Minnesota, I’m not sure our digital archive is representative, as it is optional, and and certain fields are more or less likely (either due to subject matter, or internal politics) to include their works within the archive. Other than that, this is neat!

what do the colors mean? at first glance I thought the colors had to do with subject area, but on closer examination that are same colored major that are very different (i.e. anthropology/aerospace engineering,

The colors look like they are reverse alphabetical.

Kyle, thanks – I figured I was missing something super simple – its been a long day. So the colors are meaningless then?

Yes, the colors are useless. They convey no information that is not already transparently conveyed.

For that matter, the box plot itself is thoroughly obsolete. Read Edward Tufte’s books, specifically chapter 6 of “The Visual Display of Quantitative Information” for the description of the successor to the box plot.

As for the topic of the post, my math dissertation was 88 pages. My advisor’s was (not a typo) 23 pages, double-spaced, including front matter and references.

And Presburger’s was 19 pages or so. But his supervisor, Tarski, would not give him his PhD. He thought the thesis was too short. So there is a lower bound.

would be interesting to see if they have been getting longer over time and by how much by subject

The chart would be more useful if the majors were ordered by median or average dissertatin length. But it is interesting to note that the more mathematical and objective the mayor, the less pages needed. Llongest dissertations: sociology and anthropology. Shortest: biostatistics.

Out of curiosity, Nathan, what was your dissertation on? (Not that I’d understand it anyway!)

Then, there are the departments’ or universities’ format standards. Single spaced? Double spaced? That, of course doubles the plage length. Better, what was the word count in the dissertations?

So with the current trend of dissertation chapters being prepared/formatted as manuscript submissions for journals up front (which have limits to the manuscript length), I would expect to see overall dissertation lengths get shorter over time (to present). Mine started out much longer, but as I started conforming to target journal format standards, I would say the overall length was reduced by a third or so. Is this true outside of ecology as well?

Oh, dear, my dissertation wouldn’t even appear on this chart, but would be off the right edge. And yes, I’m in one of the ‘runs long’ disciplines. Then again, my opus would count as short for, say, a German Habilitation or a French These d’Etat in my field, so it all depends!

This is very interesting. One variable that isn’t accounted for, but varies greatly between institutions and individual authors, is the number of words per page – determined by spacing, margins, font size, and so on. A more helpful plot would count theses by words, instead of pages, as a measure of content.

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COMMENTS

How Long Is a Dissertation?
Don't be intimidated by how long the average dissertation is. Many people look at the average length of a dissertation and get intimidated by the high page count. But, as Marcus Beck says in his blog post, "The actual written portion may only account for less than 50% of the page length."
How Long Is a PhD Thesis?
The average Masters dissertation length is approximately 20,000 words whilst a thesis is 4 to 5 times this length at approximately 80,000 - 100,000. The key reason for this difference is because of the level of knowledge they convey. A Master's dissertation focuses on concluding from existing knowledge whilst a PhD thesis focuses on drawing ...
How long is a PhD dissertation? [Data by field]
Interestingly, the PhD dissertation length and content vary significantly based on the field you are studying and the publishing conventions. A PhD can be anywhere from 50 pages to over 450 pages long. This equates to between about 20,000 words to 100,000 words. Most PhD theses are between 60,000 and 80,000 words long excluding contents ...
How long is a dissertation?
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 ...
How Long is a Dissertation for a PhD?
The length of a dissertation varies by discipline, institution, and research project. Some dissertations can be as short as 50 pages, while others may total 400 pages. But the average dissertation ranges from 100 pages to 250 pages. Dissertations in the humanities and social sciences are typically the most extensive.
Formatting Your Dissertation
Length. Most dissertations are 100 to 300 pages in length. All dissertations should be divided into appropriate sections, and long dissertations may need chapters, main divisions, and subdivisions. Page and Text Requirements Page Size. 8½ x 11 inches, unless a musical score is included; Margins. At least 1 inch for all margins; Spacing
How long Should be PhD Dissertation? Stepwise Guide for 2024
The optimal length of a Ph.D. dissertation is a strategic equilibrium, determined by the intricate interplay of research complexity, disciplinary norms, and institutional guidelines, emphasizing quality over mere quantity. In the intricate tapestry of academia, the length of a dissertation is a nuanced consideration, influenced by a myriad of ...
What Is The Average Dissertation Length?
Average-Length Dissertations. For many doctoral programs, particularly in the humanities, social sciences, and some STEM fields, the average dissertation length falls between 150 and 300 pages. This length allows for a thorough exploration of the research topic, a review of literature, methodology, data analysis, and a discussion of findings.
How Long is a Dissertation
An undergraduate dissertation usually falls within the range of 8,000 to 15,000 words, while a master's dissertation typically spans from 12,000 to 50,000 words. In contrast, a PhD thesis is typically of book length, ranging from 70,000 to 100,000 words. Let's unravel the mystery of how long should a dissertation be.
How long is a Thesis or dissertation? [the data]
An undergraduate thesis is likely to be about 20 to 50 pages long. A Master's thesis is likely to be between 30 and 100 pages in length and a PhD dissertation is likely to be between 50 and 450 pages long. In the table below I highlight the typical length of an undergraduate, master's, and PhD. Level of study. Pages. Words.
How Long Is a Dissertation?
The dissertation length, on average, is between 100 to 200 pages long. Nevertheless, a dissertation can be 146 pages or 96 pages long. The length depends on the goals of the writer, topic, and writing style.
The Dissertation
A typical dissertation runs between 250 and 300 pages, divided into four or five chapters, often with a short conclusion following the final full-scale chapter.There is no set minimum or maximum length, but anything below about 225 pages will likely look insubstantial in comparison to others, while anything over 350 pages may suggest a lack of proportion and control of the topic, and would ...
How Long is a Dissertation? Find the Answer in Our Guide
Average dissertation length For many students, it may be challenging to answer how long a Ph.D. dissertation has to be, as its length depends on various factors. Learners generally should restrict their writing to 80 and 100 thousand words, which usually translates to approximately 204 pages. The average size may also differ depending on the ...
What Is The Average Dissertation Length?
Average-Length Dissertations. For many doctoral programs, particularly in the humanities, social sciences, and some STEM fields, the average dissertation length falls between 150 and 300 pages. This length allows for a thorough exploration of the research top ic, a review of literature, methodology, data analysis, and a discussion of findings.
How long is a dissertation?
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 ...
How Long Is A Dissertation? Here Is The Average Length
Average Dissertation Length While specific length requirements vary, the average dissertation length falls between 80,000-100,000 words . Some succinct dissertations certainly come in under that range, but others may extend to or even surpass 100,000 words when including all core chapters, supplementary sections, back matter, citations, and ...
How Long Is a Dissertation?
Like many aspects of academia, the length of a dissertation is a bit complicated, and it varies by field of study. It's determined by multiple factors, including: discipline, type of analysis (qualitative or quantitative), and the area of research. Generally speaking, the average length of a dissertation is between 150-300 pages.
How Long is a Dissertation
The Average Dissertation Length. Overall, dissertations may run between 100 and 200 pages, but this number could vary. Dissertation lengths for a Master's are at the lower end of this page number, while Ph.D.'s are at the higher end. Length variations depend on your institutional requirements, your department requirements, your committee ...
How Long is a Thesis or Dissertation: College, Grad or PhD
A thesis should be around 50 pages long for a bachelor's degree and 60-100 pages for a Master's degree. However, the optimal length of a thesis project depends on the faculty's instructions and the supervising professor's expectations. The length also depends on the topic's technicalities and the extent of research done.
How Long Is a Dissertation: Average Legnth and Time Required
The average length for law dissertations is usually between 150 to 300 pages. Education. Dissertations in education often focus on empirical research, analyzing educational methods, policies, or trends. They require a balance of literature review, data collection, and analysis, making them moderately extensive. The typical length ranges from ...
Length of the average dissertation
Length of the average dissertation . May 8, 2013. Topic ... The chart would be more useful if the majors were ordered by median or average dissertatin length. But it is interesting to note that the more mathematical and objective the mayor, the less pages needed. Llongest dissertations: sociology and anthropology.
Average length of PhD dissertations by major : r/dataisbeautiful
Many dissertations are as long as a standard book. 900 pages is larger than most books. Reply reply. 3.2K votes, 760 comments. 21M subscribers in the dataisbeautiful community. DataIsBeautiful is for visualizations that effectively convey….

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How Long Should Be PhD Dissertation? Unlocking The Mystery of PhD Thesis Length

Introduction

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University Guidelines PhD Dissertation Length: Navigating the Academic Framework

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