average word count for dissertation

• 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.

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.

Finding a PhD has never been this easy – search for a PhD by keyword, location or academic area of interest.

Related Reading

Unfortunately, the completion of your thesis doesn’t mark the end of your degree just yet. Once you submit your thesis, it’s time to start preparing for your viva – the all-to-fun thesis defence interview! To help you prepare for this, we’ve produced a helpful guide which you can read here: The Complete Guide to PhD Vivas.

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• 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:

© [ year ] [ Author’s Name ] All rights reserved.

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 is a PhD dissertation? [Data by field]

The final piece of the PhD journey is the PhD dissertation. It takes many years to accumulate enough original and new data to fill out a dissertation to the satisfaction of experts in your 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, citations and references.

A PhD thesis contains different sections including an introduction, methods, results and discussion, conclusions, further work, and references. Each one of these different sections will vary in length depending on the field of study and your particular topic.

Ultimately, a PhD dissertation should contain as many pages and words as it takes to communicate the results of your multi-year investigation.

It is very rewarding to see your thesis come together as you are writing day after day. When I was writing my PhD dissertation I wrote the sections separately and my heart filled with joy when I finally put them all together and compile them into a single PDF document.

Counting the pages should not be the way to determine a PhD dissertation’s value but it certainly helps when your thesis is starting to look substantial in thickness.

How many pages should a PhD dissertation be?

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 PhD in your field.

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 Thesis or dissertation? [the data]

Can a PhD dissertation be too long?

A PhD thesis should contain enough evidence and discussion to report on the most significant findings of your PhD research.

A PhD dissertation should not contain everything that you have done during your PhD. It should only include the data and information required to convince your PhD examining body that wraps up and tells the full story of particular lines of investigation.

Including random results, thoughts, or superfluous explanation can result in a dissertation that is unfocused. I have heard of music PhD is being described as too verbose and physical sciences PhD dissertations as being unfocused.

Therefore, a PhD thesis can be too long if the information it contains does not form a full and cohesive story.

One of my colleagues during their PhD removed an entire chapter from the thesis after writing it as the supervisor said that it needed more experiments to be a full story. They did not want to spend the next six months gathering the data and simply removed the chapter altogether.

How short can PhD dissertation be?

The shortest PhD dissertations are typically found in mathematics.

George Bernard Danzig was an American mathematical scientist who made contributions to industrial engineering and many other mathematical-related fields. An interesting miscommunication led to 1 of the shortest PhD theses ever.

In 1939 his professor wrote two problems on the blackboard and Danzig thought they were homeless assignments. He stated that they were harder than usual but handed in solutions to the surprise of the professor.

They were, in fact, open mathematical problems in statistics.

His professor said to bind the solution to the two problems together and submit them as his thesis – the total thesis length = 14 pages.

Obviously, most PhD theses and dissertations will be so much longer than that!

My PhD dissertation was 256 pages long. It was full of schematics, diagrams, and tables to demonstrate and communicate my findings.

I would say that most people’s PhD thesis experience will be closer to mine than Prof George Bernard Danzig’s.

Why PhD dissertations are typically so long

PhD dissertations are often over 200 pages long.

One of the primary reasons they are so long is that it is a single document that summarises many years of hard work. Also, summarising the research field to date and making sure that all of your references and citations are included so you avoid plagiarism will bolster the word count of the thesis dramatically.

Here are all of the reasons PhD dissertations tend to be so long.

Many years of work

PhD theses or dissertations contain many years of research and analysis.

In many of my YouTube videos I recommend that a PhD student work towards their PhD thesis by doing at least three hours of focused work every work day.

This amount of work quickly adds up.

Of course, not every bit of work makes it into the PhD dissertation but a lot of it does. It can be difficult to work out what to include or leave out of your thesis.

As a PhD student, I perfected the art of turning one experiment into many different types of grafts and schematics to fully explore the limits of my data. The graphs can take up a lot of space in your PhD thesis and, therefore, bolster the page count significantly.

In depth literature review

One of the most substantial parts of a PhD dissertation is the literature review.

The literature review can take up a huge portion of the early part of your PhD dissertation depending on the amount of data and publications in your field.

Writing an in-depth literature review requires just as much meticulous data analysis and searching as the central part of your dissertation.

Figures and schematics

Some fields end up producing a lot of figures and schematics.

My thesis had many full-page figures of atomic force microscopy experiments with much more explanation on subsequent pages.

As they say, a picture paints a thousand words and a dissertation can really benefit from having many schematics to highlight the important aspects of your findings.

References and citations

The recommended PhD dissertation word count from an institution or university does not include citations, references, or other thesis parts such as summary of abbreviations, table of figures, et cetera.

However, these components of your dissertation can take up many pages and add to the overall thickness of your PhD dissertation.

University formatting rules

University formatting rules will also dictate how you many pages your words take up.

I often get roasted on my YouTube channel for having doublespaced lines and wide margins. Unfortunately, this layout was dictated by my university before printing.

PhD dissertations often end up going into long-term storage and therefore, need to adhere to archival and standardised formatting rules.

Deep in the depths of the University of Newcastle, there is a copy of my thesis on a shelf. The formatting and binding rules mean that my thesis looks like everyone else’s.

Universities will often have their own requirements for PhD dissertation cover colour, quality, and type of paper. Even the quality of the paper can change the thickness of the PhD dissertation significantly.

PhD by publication

It is becoming increasingly common to submit a number of peer-reviewed papers bound together with supplementary information in between instead of a PhD dissertation.

The benefits of this to the researcher and university are:

• More early career peer-reviewed journals for career advancement
• an easier review process – they have already been peer-reviewed
• an early focus on publishing means better research outcomes for the researcher, supervisor, and Department.
• No mad rush at the end to finish a thesis
• continually writing peer-reviewed papers throughout your PhD helps with timely analysis and communication of results

Even though this option has been available to PhD students for a number of years, I have only known a handful of students actually submit their PhD via publication.

Nonetheless, having this option will suit some research fields better than others and lead to a more productive PhD.

Wrapping up

This article has been through everything you need to know about the length of a PhD dissertation and the common lengths of PhD dissertations for various fields.

Ultimately, there is no predefined length of a PhD.

A PhD thesis is as long as it needs to be to convince your examiners that you have contributed significantly enough to an academic field to be awarded the title of Dr of philosophy.

Mathematical and analytical theses tend to be shorter and can be as short as 50 pages (with one of the shortest being only 14 pages long). At the other end of the spectrum, PhD students in anthropology and history tend to produce the longest dissertations.

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.

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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).

Pathetic fallacy and appeal to pathos sound similar but they refer to entirely different things.

• Pathetic fallacy is a figure of speech, at least in most contexts, and not a reasoning error. It refers to the attribution of human emotions to something non-human in novels or poems.
• Appeal to pathos , on the other hand, is a logical fallacy in which the speaker or author takes advantage of emotions, like fear or love for one’s family, to convince their audience instead of using rational arguments.

In other words, pathetic fallacy and appeal to pathos both relate to pathos or emotion but to a different end.

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 ‘.

Pathetic fallacy is not a logical fallacy . It is a literary device or figure of speech that often occurs in literature when a writer attributes human emotions to things that aren’t human, such as objects, the weather, or animals.

Pathetic fallacy is used to reflect a character’s emotions. For example, if a character has lost a loved one, they may hear “mournful” birdsong.

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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Yes, if your document is longer than 20,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback.

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You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently.

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Yes, you can upload your document in sections.

We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before.

However, we cannot guarantee that the same editor will be available. Your chances are higher if

• You send us your text as soon as possible and
• You can be flexible about the deadline.

Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.

If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the  Scribbr Improvement Model  and will deliver high-quality work.

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Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round.

If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening!

Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English.

Every Scribbr order comes with our award-winning Proofreading & Editing service , which combines two important stages of the revision process.

For a more comprehensive edit, you can add a Structure Check or Clarity Check to your order. With these building blocks, you can customize the kind of feedback you receive.

You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:

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When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area.

However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible.

This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.

Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine!

After your document has been edited, you will receive an email with a link to download the document.

The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one.

It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:

• You can learn a lot by looking at the mistakes you made.
• The editors don’t only change the text – they also place comments when sentences or sometimes even entire paragraphs are unclear. You should read through these comments and take into account your editor’s tips and suggestions.
• With a final read-through, you can make sure you’re 100% happy with your text before you submit!

You choose the turnaround time when ordering. We can return your dissertation within 24 hours , 3 days or 1 week . These timescales include weekends and holidays. As soon as you’ve paid, the deadline is set, and we guarantee to meet it! We’ll notify you by text and email when your editor has completed the job.

Very large orders might not be possible to complete in 24 hours. On average, our editors can complete around 13,000 words in a day while maintaining our high quality standards. If your order is longer than this and urgent, contact us to discuss possibilities.

Always leave yourself enough time to check through the document and accept the changes before your submission deadline.

Scribbr is specialised in editing study related documents. We check:

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Our customer support team is here to find the solution that helps you the most, whether that’s a free new edit or a refund for the service.

Yes, in the order process you can indicate your preference for American, British, or Australian English .

If you don’t choose one, your editor will follow the style of English you currently use. If your editor has any questions about this, we will contact you.

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Word limits and requirements of your Degree Committee

Candidates should write as concisely as is possible, with clear and adequate exposition. Each Degree Committee has prescribed the limits of length and stylistic requirements as given below. On submission of the thesis you must include a statement of length confirming that it does not exceed the word limit for your Degree Committee.

These limits and requirements are strictly observed by the Postgraduate Committee and the Degree Committees and, unless approval to exceed the prescribed limit has been obtained beforehand (see: Extending the Word Limit below), a thesis that exceeds the limit may not be examined until its length complies with the prescribed limit.

Extending the Word Limit

Thesis word limits are set by Degree Committees. If candidates need to increase their word limits they will need to apply for permission.

Information on how to apply (via self-service account) is available on the ‘ Applying for a change in your student status’  page. If following your viva, you are required to make corrections to your thesis which will mean you need to increase your word-limit, you need to apply for permission in the same way.

Requirements of the Degree Committees

Archaeology and anthropology, architecture and history of art, asian and middle eastern studies, business and management, clinical medicine and clinical veterinary medicine, computer laboratory, earth sciences and geography, scott polar institute, engineering, history and philosophy of science, land economy, mathematics, modern and medieval languages and linguistics, physics and chemistry, politics and international studies, archaeology and social anthropology.

The thesis is not to exceed 80,000 words (approx. 350 pages) for the PhD degree and 60,000 words for the MSc or MLitt degree. These limits include all text, figures, tables and photographs, but exclude the bibliography, cited references and appendices. More detailed specifications should be obtained from the Division concerned. Permission to exceed these limits will be granted only after a special application to the Degree Committee. The application must explain in detail the reasons why an extension is being sought and the nature of the additional material, and must be supported by a reasoned case from the supervisor containing a recommendation that a candidate should be allowed to exceed the word limit by a specified number of words. Such permission will be granted only under exceptional circumstances. If candidates need to apply for permission to exceed the word limit, they should do so in good time before the date on which a candidate proposes to submit the thesis, by application made to the Graduate Committee.

Biological Anthropology:

Students may choose between two alternative thesis formats for their work:

either in the form of a thesis of not more than 80,000 words in length for the PhD degree and 60,000 words for the MSc or MLitt degree. The limits include all text, in-text citations, figures, tables, captions and footnotes but exclude bibliography and appendices; or

in the form of a collection of at least three research articles for the PhD degree and two research articles for the MSc or MLitt degree, formatted as an integrated piece of research, with a table of contents, one or more chapters that outline the scope and provide an in-depth review of the subject of study, a concluding chapter discussing the findings and contribution to the field, and a consolidated bibliography. The articles may be in preparation, submitted for publication or already published, and the combined work should not exceed 80,000 words in length for the PhD degree and 60,000 words for the MSc or MLitt degree. The word limits include all text, in-text citations, figures, tables, captions, and footnotes but exclude bibliography and appendices containing supplementary information associated with the articles. More information on the inclusion of material published, in press or in preparation in a PhD thesis may be found in the Department’s PhD submission guidelines.

Architecture:

The thesis is not to exceed 80,000 words for the PhD and 60,000 words for the MSc or MLitt degree. Footnotes, references and text within tables are to be counted within the word-limit, but captions, appendices and bibliographies are excluded. Appendices should be confined to such items as catalogues, original texts, translations of texts, transcriptions of interview, or tables.

History of Art:

The thesis is not to exceed 80,000 words for the PhD and 60,000 words for the MLitt degree. To include: footnotes, table of contents and list of illustrations, but excluding acknowledgements and the bibliography. Appendices (of no determined word length) may be permitted subject to the approval of the candidate's Supervisor (in consultation with the Degree Committee); for example, where a catalogue of works or the transcription of extensive primary source material is germane to the work. Permission to include such appendices must be requested from the candidate's Supervisor well in advance of the submission of the final thesis. NB: Permission for extensions to the word limit for most other purposes is likely to be refused.

The thesis is for the PhD degree not to exceed 80,000 words exclusive of footnotes, appendices and bibliography but subject to an overall word limit of 100,000 words exclusive of bibliography. For the MLitt degree not to exceed 60,000 words inclusive of footnotes but exclusive of bibliography and appendices.

The thesis for the PhD is not to exceed 60,000 words in length (80,000 by special permission), exclusive of tables, footnotes, bibliography, and appendices. Double-spaced or one-and-a-half spaced. Single or double-sided printing.

The thesis for the MPhil in Biological Science is not to exceed 20,000 words in length, exclusive of tables, footnotes, bibliography, and appendices. Double-spaced or one-and-a-half spaced. Single or double-sided printing.

For the PhD Degree the thesis is not to exceed 80,000 words, EXCLUDING bibliography, but including tables, tables of contents, footnotes and appendices. It is normally expected to exceed 40,000 words unless prior permission is obtained from the Degree Committee. Each page of statistical tables, charts or diagrams shall be regarded as equivalent to a page of text of the same size. The Degree Committee do not consider applications to extend this word limit.

For the Doctor of Business (BusD) the thesis will be approximately 200 pages (a maximum length of 80,000 words, EXCLUDING bibliography, but including tables, tables of contents, footnotes and appendices).

For the MSc Degree the thesis is not to exceed 40,000 words, EXCLUDING bibliography, but including tables, tables of contents, footnotes and appendices.

The thesis is not to exceed 80,000 words including footnotes, references, and appendices but excluding bibliography; a page of statistics shall be regarded as the equivalent of 150 words. Only under exceptional circumstances will permission be granted to exceed this limit. Candidates must submit with the thesis a signed statement giving the length of the thesis.

For the PhD degree, not to exceed 60,000 words (or 80,000 by special permission of the Degree Committee), and for the MSc degree, not to exceed 40,000 words. These limits exclude figures, photographs, tables, appendices and bibliography. Lines to be double or one-and-a-half spaced; pages to be double or single sided.

The thesis is not to exceed, without the prior permission of the Degree Committee, 60,000 words including tables, footnotes and equations, but excluding appendices, bibliography, photographs and diagrams. Any thesis which without prior permission of the Degree Committee exceeds the permitted limit will be referred back to the candidate before being forwarded to the examiners.

The thesis is not to exceed 80,000 words for the PhD degree and the MLitt degree, including footnotes, references and appendices but excluding bibliography. Candidates must submit with the thesis a signed statement giving the length of the thesis. Only under exceptional circumstances will permission be granted to exceed this limit for the inclusion of an appendix of a substantial quantity of text which is necessary for the understanding of the thesis (e.g. texts in translation, transcription of extensive primary source material). Permission must be sought at least three months before submission of the thesis and be supported by a letter from the supervisor certifying that such exemption from the prescribed limit of length is absolutely necessary.

The thesis is not to exceed, without the prior permission of the Degree Committee, 80,000 words for the PhD degree and 60,000 words for the MSc or MLitt degree, including the summary/abstract.  The table of contents, photographs, diagrams, figure captions, appendices, bibliography and acknowledgements to not count towards the word limit. Footnotes are not included in the word limit where they are a necessary part of the referencing system used.

Earth Sciences:

The thesis is not to exceed, without the prior permission of the Degree Committee, 275 numbered pages of which not more than 225 pages are text, appendices, illustrations and bibliography. A page of text is A4 one-and-a-half-spaced normal size type. The additional 50 pages may comprise tables of data and/or computer programmes reduced in size.

If a candidate's work falls within the social sciences, candidates are expected to observe the limit described in the Department of Geography above; if, however, a candidate's work falls within the natural sciences, a candidate should observe the limit described in the Department of Earth Sciences.

Applications for the limit of length of the thesis to be exceeded must be early — certainly no later than the time when the application for the appointment of examiners and the approval of the title of the thesis is made. Any thesis which, without the prior permission of the Degree Committee, exceeds the permitted limit of length will be referred back to the candidate before being forwarded to the examiners.

The thesis is not to exceed, without the prior permission of the Degree Committee, 60,000 words including tables, footnotes, bibliography and appendices. The Degree Committee points out that some of the best thesis extend to only half this length. Each page of statistical tables, charts or diagrams shall be regarded as equivalent to a page of text of the same size.

The thesis is not to exceed 80,000 words for the PhD and EdD degrees and 60,000 words for the MSc and MLitt degrees, in all cases excluding appendices, footnotes, reference list or bibliography. Only in the most exceptional circumstances will permission be given to exceed the stated limits. In such cases, you must make an application to the Degree Committee as early as possible -and no later than three months before it is proposed to submit the thesis, having regard to the dates of the Degree Committee meetings. Your application should (a) explain in detail the reasons why you are seeking the extension and (b) be accompanied by a full supporting statement from your supervisor showing that the extension is absolutely necessary in the interests of the total presentation of the subject.

For the PhD degree, not to exceed, without prior permission of the Degree Committee, 65,000 words, including appendices, footnotes, tables and equations not to contain more than 150 figures, but excluding the bibliography. A candidate must submit with their thesis a statement signed by the candidate themself giving the length of the thesis and the number of figures. Any thesis which, without the prior permission of the Degree Committee, exceeds the permitted limit will be referred back to the candidate before being forwarded to the examiners.

The thesis is not to exceed 80,000 words or go below 60,000 words for the PhD degree and not to exceed 60,000 words or go below 45,000 words for the MLitt degree, both including all notes and appendices but excluding the bibliography. A candidate must add to the preface of the thesis the following signed statement: 'The thesis does not exceed the regulation length, including footnotes, references and appendices but excluding the bibliography.'

In exceptional cases (when, for example, a candidate's thesis largely consists of an edition of a text) the Degree Committee may grant permission to exceed these limits but in such instances (a) a candidate must apply to exceed the length at least three months before the date on which a candidate proposes to submit their thesis and (b) the application must be supported by a letter from a candidate's supervisor certifying that such exemption from the prescribed limit of length is absolutely necessary.

It is a requirement of the Degree Committee for the Faculty of English that thesis must conform to either the MHRA Style Book or the MLA Handbook for the Writers of Research papers, available from major bookshops. There is one proviso, however, to the use of these manuals: the Faculty does not normally recommend that students use the author/date form of citation and recommends that footnotes rather than endnotes be used. Bibliographies and references in thesis presented by candidates in ASNaC should conform with either of the above or to the practice specified in Cambridge Studies in Anglo-Saxon England.

Thesis presented by candidates in the Research Centre for English and Applied Linguistics must follow as closely as possible the printed style of the journal Applied Linguistics and referencing and spelling conventions should be consistent.

A signed declaration of the style-sheet used (and the edition, if relevant) must be made in the preliminary pages of the thesis.

PhD theses MUST NOT exceed 80,000 words, and will normally be near that length.

A minimum word length exists for PhD theses: 70,000 words (50,000 for MLitt theses)

The word limit includes appendices and the contents page but excludes the abstract, acknowledgments, footnotes, references, notes on transliteration, bibliography, abbreviations and glossary.  The Contents Page should be included in the word limit. Statistical tables should be counted as 150 words per table. Maps, illustrations and other pictorial images count as 0 words. Graphs, if they are the only representation of the data being presented, are to be counted as 150 words. However, if graphs are used as an illustration of statistical data that is also presented elsewhere within the thesis (as a table for instance), then the graphs count as 0 words.

Only under exceptional circumstances will permission be granted to exceed this limit. Applications for permission are made via CamSIS self-service pages. Applications must be made at least four months before the thesis is bound. Exceptions are granted when a compelling intellectual case is made.

The thesis is not to exceed 80,000 words for the PhD degree and 60,000 words for the MLitt degree, in all cases including footnotes and appendices but excluding bibliography. Permission to submit a thesis falling outside these limits, or to submit an appendix which does not count towards the word limit, must be obtained in advance from the Degree Committee.

The thesis is not to exceed 80,000 for the PhD degree and 60,000 words for the MSc or MLitt degree, both including footnotes, references and appendices but excluding bibliographies. One A4 page consisting largely of statistics, symbols or figures shall be regarded as the equivalent of 250 words. A candidate must add to the preface of their thesis the following signed statement: 'This thesis does not exceed the regulation length, including footnotes, references and appendices.'

For the PhD degree the thesis is not to exceed 80,000 words (exclusive of footnotes, appendices and bibliography) but subject to an overall word limit of 100,000 words (exclusive of bibliography, table of contents and any other preliminary matter). Figures, tables, images etc should be counted as the equivalent of 200 words for each A4 page, or part of an A4 page, that they occupy. For the MLitt degree the thesis is not to exceed 60,000 words inclusive of footnotes but exclusive of bibliography, appendices, table of contents and any other preliminary matter. Figures, tables, images etc should be counted as the equivalent of 200 words for each A4 page, or part of an A4 page, that they occupy.

Criminology:

For the PhD degree submission of a thesis between 55,000 and 80,000 words (exclusive of footnotes, appendices and bibliography) but subject to an overall word limit of 100,000 words (exclusive of bibliography, table of contents and any other preliminary matter). Figures, tables, images etc should be counted as the equivalent of 200 words for each A4 page, or part of an A4 page, that they occupy. For the MLitt degree the thesis is not to exceed 60,000 words inclusive of footnotes but exclusive of bibliography, appendices, table of contents and any other preliminary matter. Figures, tables, images etc should be counted as the equivalent of 200 words for each A4 page, or part of an A4 page, that they occupy.

There is no standard format for the thesis in Mathematics.  Candidates should discuss the format appropriate to their topic with their supervisor.

The thesis is not to exceed 80,000 words for the PhD degree and 60,000 words for the MLitt degree, including footnotes and appendices but excluding the abstract, any acknowledgements, contents page(s), abbreviations, notes on transliteration, figures, tables and bibliography. Brief labels accompanying illustrations, figures and tables are also excluded from the word count. The Degree Committee point out that some very successful doctoral theses have been submitted which extend to no more than three-quarters of the maximum permitted length.

In linguistics, where examples are cited in a language other than Modern English, only the examples themselves will be taken into account for the purposes of the word limit. Any English translations and associated linguistic glosses will be excluded from the word count.

In theses written under the aegis of any of the language sections, all sources in the language(s) of the primary area(s) of research of the thesis will normally be in the original language. An English translation should be provided only where reading the original language is likely to fall outside the expertise of the examiners. Where such an English translation is given it will not be included in the word count. In fields where the normal practice is to quote in English in the main text, candidates should follow that practice. If the original text needs to be supplied, it should be placed in a footnote. These fields include, but are not limited to, general linguistics and film and screen studies.

Since appendices are included in the word limit, in some fields it may be necessary to apply to exceed the limit in order to include primary data or other materials which should be available to the examiners. Only under the most exceptional circumstances will permission be granted to exceed the limit in other cases. In all cases (a) a candidate must apply to exceed the prescribed maximum length at least three months before the date on which a candidate proposes to submit their thesis and (b) the application must be accompanied by a full supporting statement from the candidate's supervisor showing that such exemption from the prescribed limit of length is absolutely necessary.

It is a requirement within all language sections of MMLL, and also for Film, that dissertations must conform with the advice concerning abbreviations, quotations, footnotes, references etc published in the Style Book of the Modern Humanities Research Association (Notes for Authors and Editors). For linguistics, dissertations must conform with one of the widely accepted style formats in their field of research, for example the style format of the Journal of Linguistics (Linguistic Association of Great Britain), or of Language Linguistic Society of America) or the APA format (American Psychology Association). If in doubt, linguistics students should discuss this with their supervisor and the PhD Coordinator.

The thesis is not to exceed 80,000 words for the PhD degree and 60,000 words for the MLitt degree, both excluding notes, appendices, and bibliographies, musical transcriptions and examples, unless a candidate make a special case for greater length to the satisfaction of the Degree Committee. Candidates whose work is practice-based may include as part of the doctoral submission either a portfolio of substantial musical compositions, or one or more recordings of their own musical performance(s).

PhD (MLitt) theses in Philosophy must not be more than 80,000 (60,000) words, including appendices and footnotes but excluding bibliography.

Institute of Astronomy, Department of Materials Science & Metallurgy, Department of Physics:

The thesis is not to exceed, without prior permission of the Degree Committee, 60,000 words, including summary/abstract, tables, footnotes and appendices, but excluding table of contents, photographs, diagrams, figure captions, list of figures/diagrams, list of abbreviations/acronyms, bibliography and acknowledgements.

Department of Chemistry:

The thesis is not to exceed, without prior permission of the Degree Committee, 60,000 words, including summary/abstract, tables, and footnotes, but excluding table of contents, photographs, diagrams, figure captions, list of figures/diagrams, list of abbreviations/acronyms, bibliography, appendices and acknowledgements. Appendices are relevant to the material contained within the thesis but do not form part of the connected argument. Specifically, they may include derivations, code and spectra, as well as experimental information (compound name, structure, method of formation and data) for non-key molecules made during the PhD studies.

Applicable to the PhDs in Politics & International Studies, Latin American Studies, Multi-disciplinary Studies and Development Studies for all submissions from candidates admitted prior to and including October 2017.

A PhD thesis must not exceed 80,000 words, and will normally be near that length. The word limit includes appendices but excludes footnotes, references and bibliography. Footnotes should not exceed 20% of the thesis. Discursive footnotes are generally discouraged, and under no circumstances should footnotes be used to include material that would normally be in the main text, and thus to circumvent the word limits. Statistical tables should be counted as 150 words per table. Only under exceptional circumstances, and after prior application, will the Degree Committee allow a student to exceed these limits. A candidate must submit, with the thesis, a statement signed by her or himself attesting to the length of the thesis. Any thesis that exceeds the limit will be referred back to candidate for revision before being forwarded to the examiners.

Applicable to the PhDs in Politics & International Studies, Latin American Studies, Multi-disciplinary Studies and Development Studies for all submissions from candidates admitted after October 2017.

A PhD thesis must not exceed 80,000 words, including footnotes. The word limit includes appendices but excludes the bibliography. Discursive footnotes are generally discouraged, and under no circumstances should footnotes be used to include material that would normally be in the main text. Statistical tables should be counted as 150 words per table. Only under exceptional circumstances, and after prior application, will the Degree Committee allow a student to exceed these limits. A candidate must submit, with the thesis, a statement signed by her or himself attesting to the length of the thesis. Any thesis that exceeds the limit will be referred back to candidate for revision before being forwarded to the examiners.

Only applicable to students registered for the degree prior to 1 August 2012; all other students should consult the guidance of the Faculty of Biological Sciences.

Applicable to the PhD in Psychology (former SDP students only) for all submissions made before 30 November 2013

A PhD thesis must not exceed 80,000 words, and will normally be near that length. The word limit includes appendices but excludes footnotes, references and bibliography. Footnotes should not exceed 20% of the thesis. Discursive footnotes are generally discouraged, and under no circumstances should footnotes be used to include material that would normally be in the main text, and thus to circumvent the word limits. Statistical tables should be counted as 150 words per table. Only under exceptional circumstances, and after prior application, will the Degree Committee allow a student to exceed these limits. A candidate must submit, with the thesis, a statement signed by her or himself attesting to the length of the thesis. Any thesis that exceeds the limit will be referred back to candidate for revision before being forwarded to the examiners.

Applicable to the PhD in Psychology (former SDP students only) for all submissions from 30 November 2013

A PhD thesis must not exceed 80,000 words, and will normally be near that length. The word limit includes appendices but excludes footnotes, references and bibliography. Footnotes should not exceed 20% of the thesis. Discursive footnotes are generally discouraged, and under no circumstances should footnotes be used to include material that would normally be in the main text, and thus to circumvent the word limits. Statistical tables should be counted as 150 words per table. Only under exceptional circumstances, and after prior application, will the Degree Committee allow a student to exceed these limits. Applications should be made in good time before the date on which a candidate proposes to submit the thesis, made to the Graduate Committee. A candidate must submit, with the thesis, a statement signed by her or himself attesting to the length of the thesis. Any thesis that exceeds the limit will be referred back to candidate for revision before being forwarded to the examiners.

A PhD thesis must not exceed 80,000 words, and will normally be over 60,000 words. This word limit includes footnotes and endnotes, but excludes appendices and reference list / bibliography. Figures, tables, images etc should be counted as the equivalent of 150 words for each page, or part of a page, that they occupy. Other media may form part of the thesis by prior arrangement with the Degree Committee. Students may apply to the Degree Committee for permission to exceed the word limit, but such applications are granted only rarely. Candidates must submit, with the thesis, a signed statement attesting to the length of the thesis.

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Library Guides

Dissertations 1: getting started: starting your dissertation.

• Starting Your Dissertation
• Choosing A Topic and Researching
• Devising An Approach/Method
• Thinking Of A Title
• Writing A Proposal

What is a Dissertation?

A dissertation is a research project completed as part of an undergraduate or postgraduate degree. Typically, a dissertation will enable you to present your findings in response to a question that you propose yourself. It is probably the longest piece of academic work you will produce. At undergraduate level, word count requirements can range anywhere from 5,000 to 8,000 words while a Masters level dissertation can be 10,000 to 15,000 words long! 

Why are you required to write a dissertation? 

A dissertation is a core requirement of most university degrees. 

The dissertation will enhance your employability. For instance, you will develop transferable skills in inter-personal communication, data collection and analysis, report writing and effective time-management.  

While it is demanding, writing a dissertation is your chance to explore, in depth, a topic that interests you. Therefore, ensuring you choose a topic you are passionate about will make your experience more rewarding and even enjoyable! 

Supervision Advice

A supervisor will be assigned to you to assist with guidance on how to prepare, produce and improve your dissertation.  

The supervisor’s role is to: 

Assist in the organisation of the project in the early stages of preparation 

Advise you on the feasibility of what you plan to do 

Advise on methods and ethics of your research  

The supervisor is not expected to: 

Proofread your work 

Provide you with a topic or research question 

Direct the research  

Ensure that a dissertation is of sufficient quality to pass: this is your responsibility 

To get the best out of your time with your supervisor, you should: 

Check formal requirements early 

Check arrangements for supervisions and how your supervisor likes to work 

Organise regular supervision meetings and prepare work for each one 

Let your supervisor know how you work best 

Using Dissertation Marking Criteria

Your dissertation, like your previous assessments, will be marked against a set of assessment criteria which is published in your module or course handbook and posted on Blackboard.  

Assessment criteria are intended to: 

Ensure you meet the learning outcomes. 

Help you understand how your work is assessed. 

Allow tutors to focus their feedback. They will let you know what you are doing well and what needs improvement. 

Dissertation assessment criteria usually specifies what the tutor expects in terms of: 

Clarity: have you expressed your ideas clearly? 

Relevance: does your work fit into/fill a gap in existing research/literature on similar topics? 

Originality: does it offer a fresh perspective on a topic? 

Meeting course requirements: does it meet the word count / deadlines, for example? 

Before starting your dissertation, it is essential that you check what is expected of you and how your work will be graded. It is also useful to regularly check what you have written every few weeks and after you have finished to see if you are on track to meet the assessment criteria.  

First Steps

Ready to get started but uncertain how to begin? These are normally the first steps of dissertation writing:  

Choose a topic 

Conduct a literature search 

Devise research question(s) / hypotheses 

Devise your approach (e.g. if undertaking primary research, you will need to devise your methodology, methods, etc.) 

Think of a title 

Plan your time 

Write a proposal (if requested)

These steps are addressed in the tabs of this guide.

• Next: Choosing A Topic and Researching >>
• Last Updated: Aug 1, 2023 2:36 PM
• URL: https://libguides.westminster.ac.uk/starting-your-dissertation

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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.

• Revisiting data science, the career
• Data comic shows an average American day
• Not so likely life of The Simpsons

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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How To Reduce Word Count In Your Dissertation, Thesis Or Academics Assignments

(without losing those precious marks).

If you follow some of the advice on this blog, chances are one of your biggest challenges is keeping your academic writing projects within the specified word count limits. It’s a good problem to have (at least compared to having not enough to say), and in this post, I’ll discuss 4 steps to reduce word count without risking losing marks.

First things first – write to think.

Before I get started, it’s worth making an important point regarding writing in general. There are essentially two ways to think about the process of writing :

• Writing as the outcome of thinking – in other words, you think deeply first, construct your argument, and then simply transfer it to paper by way of writing. You do little revising.
• Writing as a form of thinking – in other words, writing helps you flesh out your thinking and develop your arguments. Writing is an iterative process, wherein you might revise numerous times and even rewrite altogether, but this all contributes to a better quality of thinking.

Which side of the fence do you sit on? I’m an avid advocate of the latter perspective and approach – and I’m not alone. Numerous books and journal articles have covered the topic of “writing as thinking”. If the idea interests you, have a look at Henning’s “Finding your way in academic writing.”

In short, putting pen to paper as early as possible (i.e. before you feel “ready”) and then revising as your thoughts develop (as a result of writing) is an excellent way to improve the overall quality of your arguments and academic work. To do this, you cannot constantly fret over word count (at least not while you’re writing). Instead, you need to let the words flow onto paper, and then sort the wheat from the chaff at a later stage. Sure, you need some constraints, but forcing yourself to apply X model within 350 words is going to stifle your flow and limit your depth. Rather let your thoughts flow onto paper, and then trim them down once your thinking is fully fleshed out.

What does this have to do with reducing your word count? It means that word count reduction (particularly, the techniques I’ll cover below) is something you do once you’ve wrapped up your writing, not while you write . Accordingly, all the steps I’ll propose here are to be applied after the fact.

Right, let’s get into it. Follow these 4 steps (in this order) to strategically reduce your word count without losing the “meat” of your assignment/dissertation.

Step 1: Audit for purely descriptive content.

Broadly speaking, content can fall into one of two categories – descriptive or analytical.  Simply put, descriptive content eludes to the “what”, whereas analytical content describes the impact and consequence of the event/factor/situation – in other words, the “so what”. The table below highlights some of the differences between the two:

Ideally, you should try to keep your discussion analytical, rather than descriptive ( read more about this here ). There’s always be a need for some descriptive content, but ideally, this should be limited to only that content which forms the foundation for analytical content. Therefore, the first step of word count reduction is to audit for descriptive content which does not lead to analytical content . In other words, content which is purely descriptive, and is not required to get to the “so what?” content.

Read through your dissertation/thesis/assignment and trim out all content that doesn’t make the analytical cut , or doesn’t form a foundation for analysis. This is your first target – be aggressive with your trimming. Descriptive writing is pure fat and will not earn you marks – kill it!

Step 2: Audit for content which does not contribute towards answering your research question(s).

One of the reasons that it’s so important to set unambiguous research questions in your introduction is that this practice allows you to ringfence the focus of your work. In other words, it helps you to narrow the discussion to only that which is most relevant.

That said, as you write, you will invariably produce a fair deal of content that does not contribute towards your research questions . You’ll naturally digress into an interesting but irrelevant discussion about A, B and C – this might be very intellectually satisfying, but it doesn’t contribute to answering your research question. Therefore, this sort of content is your next target. Re-read your document from start to finish through the lens of your research questions or objectives. That which does not in some way contribute to answering the research question(s) or achieving the objective(s) must go .

Step 3: Audit for overly-detailed section summaries.

A good piece of academic writing should always feature summary paragraphs that link the end of one section/chapter to the beginning of the next. They should do this by summarising the key points of the former to the direction and purpose of the latter. For example:

“In this section, the analysis revealed that the key contributors to the issue included A, B and C. Accordingly, these factors will be analysed in the next chapter.”

By stating this link very clearly, you help the reader (marker) to understand your argument (which is, after all, completely new to them), which in turn helps you earn marks. Therefore, these summary sections are important. However, they can become wordy and repetitive, and you should, therefore, audit them.

Make sure that they are summarising only the absolute highlights of your argument and providing a clear, well-justified link to the next section. Don’t restate your entire chapter. The example above is what you should aim for, namely:

• Key observations/insights/highlights – followed by
• Logical link to next section

If you are extremely over word count, you may even consider removing these sections altogether. After all, it is better to remove summary content than core content. This should, however, be an absolute last resort as doing so can seriously reduce the overall flow of your document and blur the “golden thread” of your argument(s).

Step 4: Audit for wordy, bloated discussion.

This is the easiest of the four steps, and typically what most students look for when trying to reduce word count – but it usually has a comparatively minor impact. Therefore, I’m positioning it as the last step.

Naturally, your dissertation, thesis or assignment document will contain sections which are just plain wordy. This is a result of “writing as thinking” (whether you agree with the approach or not!). Therefore, the last step is to audit for sentences and paragraphs which are just plain wordy and rewrite them more concisely.

Some common trimming opportunities:

• Adjectives and adverbs – although these are sometimes necessary when developing your arguments, they are often just bloat contributors. Additionally, they can create an emotive, subjective tone, which is typically not encouraged in academic writing (where objectivity is essential).
• The word “that” – oftentimes, a sentence can communicate the same point without the inclusion of the word “that”. Use Word’s find function (Ctrl+F) to search for “that” and check where it can be omitted.
• Spaces around mathematical operators – if you’re copying numbers from Excel, chances are there are spaces between mathematical operators which can be removed. For example, p < 0.05 (3 words) can be reduced to p<0.05 (1 word).
• Abbreviate/acronymise repetitive phrases/names – if you’re repeatedly referring to a person(s) or organisation(s) that have multi-word names, create acronyms for them and replace all instances with the acronymised version. For example, “Blue Basket Enterprises” (3 words) can be replaced with “BBE” (1 word). Make sure you introduce the acronym early in the document and consider presenting a list of abbreviations. A word of warning – don’t overuse this tactic, as too many acronyms can make it difficult for the reader to understand what’s going on!

Wrapping up.

There you have it – four steps to reduce your word count without losing your core arguments. To recap, you need to:

• Audit for descriptive (rather than analytical) content.
• Audit for content which doesn’t link to the research question(s)/aim(s).
• Audit for overly detailed section summaries.
• Audit for general wordiness and bloat.

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• Knowledge Base
• How long is an essay? Guidelines for different types of essay

How Long is an Essay? Guidelines for Different Types of Essay

Published on January 28, 2019 by Shona McCombes . Revised on July 23, 2023.

The length of an academic essay varies depending on your level and subject of study, departmental guidelines, and specific course requirements. In general, an essay is a shorter piece of writing than a research paper  or thesis .

In most cases, your assignment will include clear guidelines on the number of words or pages you are expected to write. Often this will be a range rather than an exact number (for example, 2500–3000 words, or 10–12 pages). If you’re not sure, always check with your instructor.

In this article you’ll find some general guidelines for the length of different types of essay. But keep in mind that quality is more important than quantity – focus on making a strong argument or analysis, not on hitting a specific word count.

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Table of contents

Essay length guidelines, how long is each part of an essay, using length as a guide to topic and complexity, can i go under the suggested length, can i go over the suggested length, other interesting articles, prevent plagiarism. run a free check..

In an academic essay, the main body should always take up the most space. This is where you make your arguments, give your evidence, and develop your ideas.

The introduction should be proportional to the essay’s length. In an essay under 3000 words, the introduction is usually just one paragraph. In longer and more complex essays, you might need to lay out the background and introduce your argument over two or three paragraphs.

The conclusion of an essay is often a single paragraph, even in longer essays. It doesn’t have to summarize every step of your essay, but should tie together your main points in a concise, convincing way.

The suggested word count doesn’t only tell you how long your essay should be – it also helps you work out how much information and complexity you can fit into the given space. This should guide the development of your thesis statement , which identifies the main topic of your essay and sets the boundaries of your overall argument.

A short essay will need a focused, specific topic and a clear, straightforward line of argument. A longer essay should still be focused, but it might call for a broader approach to the topic or a more complex, ambitious argument.

As you make an outline of your essay , make sure you have a clear idea of how much evidence, detail and argumentation will be needed to support your thesis. If you find that you don’t have enough ideas to fill out the word count, or that you need more space to make a convincing case, then consider revising your thesis to be more general or more specific.

The length of the essay also influences how much time you will need to spend on editing and proofreading .

You should always aim to meet the minimum length given in your assignment. If you are struggling to reach the word count:

• Add more evidence and examples to each paragraph to clarify or strengthen your points.
• Make sure you have fully explained or analyzed each example, and try to develop your points in more detail.
• Address a different aspect of your topic in a new paragraph. This might involve revising your thesis statement to make a more ambitious argument.
• Don’t use filler. Adding unnecessary words or complicated sentences will make your essay weaker and your argument less clear.
• Don’t fixate on an exact number. Your marker probably won’t care about 50 or 100 words – it’s more important that your argument is convincing and adequately developed for an essay of the suggested length.

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In some cases, you are allowed to exceed the upper word limit by 10% – so for an assignment of 2500–3000 words, you could write an absolute maximum of 3300 words. However, the rules depend on your course and institution, so always check with your instructor if you’re unsure.

Only exceed the word count if it’s really necessary to complete your argument. Longer essays take longer to grade, so avoid annoying your marker with extra work! If you are struggling to edit down:

• Check that every paragraph is relevant to your argument, and cut out irrelevant or out-of-place information.
• Make sure each paragraph focuses on one point and doesn’t meander.
• Cut out filler words and make sure each sentence is clear, concise, and related to the paragraph’s point.
• Don’t cut anything that is necessary to the logic of your argument. If you remove a paragraph, make sure to revise your transitions and fit all your points together.
• Don’t sacrifice the introduction or conclusion . These paragraphs are crucial to an effective essay –make sure you leave enough space to thoroughly introduce your topic and decisively wrap up your argument.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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Word Limits in Master’s Dissertations

Word Limits in Master’s Dissertations. A frequent question from Master’s students when doing their project dissertations is how many words am I aiming for? What is the word count? This blog will explore Word Count limits for dissertations, and also includes some tips for using word count in WORD. This will also be useful for bachelor’s final year projects when students are asking “How long is the final year project required to be?”

Here is a short video clip on the subject.

Bachelors Final Year Project Word Count

This is often the first time a student has to set their own question for an assignment, and a typical bachelors final year project may be 6,000 – 10,000 words. Many of the following tips may also apply at bachelor’s level.

Masters Final Year Project Word Count

How long is a Master’s dissertation? Essentially the project is again a student setting their own question, but this time at a Master’s level, so it requires more critical evaluation, and a demonstration of mastery in their research area. Therefore, the word limit may be up to 20,000 words, but do check with your institution for exact limits.

Approach the course/module coordinator for the dissertation rather than your project supervisor, as although the project supervisor may be the technical expert in your subject, they may not however understand the module/project requirements.

How is the Word Count Calculated?

Usually from the start of chapter 1 to the end of the Conclusion/Further Work/Recommendations chapter. This means it excludes the abstract, table of contents, list of figures, acknowledgements, declaration of originality at the beginning, and also excludes the references, bibliography and appendices at the end to of the document, but please do check with your institution.

MBA, MSc, or MA qualification?

I wrote a separate blog on the differences between these qualifications here .

MBA students are encouraged to deliver short, concise business documents. Therefore the limit for an MBA might be just 12,000 words in total (including everything in some cases – check with your institution). It is often very difficult to cover everything is so few words.

An MSc dissertation should be displaying the student as a technical expert in a deep and technical area. The dissertation may be solving a problem. A minimum of 12,000 words might be required, and an advised maximum of 16,000 words. I find it easy for some students to write too much, and remind them to refer back to their objectives – is what they are writing contributing to meeting the research aims and objectives? If not, take it out!

An MA in Social Science, Education, History or English, may include much more debate and written in an essay writing style. Therefore, the word count may be higher, perhaps 20,000 – 25,000 words.

Check with your institution and find out if there are penalties for being more than 10% over, or under word count.

Tips for Word Count in Word

These tips are for the PC application of MS WORD.

• Firstly, ‘turn it on’ if it isn’t there, by right clicking in status bar.
• Select just a portion of text to count just the selection.
• Use the “Review” Tab and click “Word Count” as an alternative method.
• Look for the details on characters, lines, paragraphs, and pages, by right clicking on the word count in the status bar.
• Use the navigation pane to select sections automatically (by right-clicking), and then count the words in each section. This is useful for excluding or subtracting sections from the total word count.

With submission of paper copies, nobody is really going to check your word count by counting each word. However, there may be a more subjective comment such as – “It’s only 55 pages” or “there were over 200 pages with few diagrams, and no real structure”.

With the recent move to the submission of electronic copies, your word count is there for everybody to see, so pay attention to the limits and penalties that your institution operates.

Check with your institution regarding exact limits, tolerance bands, and penalties. My view is as long as it is easy to read, well structured, has good headings, plenty of diagrams, tables, and bullet points, then I’m not too worried about long dissertations. However short Master’s MSc dissertations are always an area for concern!

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Thesis word count and format

Three months ago you considered whether you required a restriction to the access of your thesis, and you submitted your ‘Approval of Research Degree Thesis Title’ form. You’ve now finished writing up your thesis and it’s time to submit. We require your thesis to be presented and formatted in a certain way, so it’s important you read through the requirements below, before submitting your thesis. Find out more about thesis submission policy  (.pdf)

The completed thesis should be saved in PDF format. Once saved, please review the file to ensure all pages are displayed correctly.

Page layout

• Double line spacing should be used for everything except quotations, footnotes, captions to plates etc.
• It is desirable to leave 2.5cm margins at the top and bottom of the page.
• The best position for the page number is at the top right 1.3cm below the top edge.
• The fonts of Arial or Times New Roman should be used throughout the main body of the thesis, in the size of no less than 12 and no greater than 14

Illustrations (Graphs, diagrams, plates, computer printout etc.)

Illustrations embedded within the thesis should be formatted, numbered and titled accordingly:

a) Illustration upright - Caption at the bottom, Illustration number immediately above the

Illustration.

b) Illustration sideways - Caption at right-hand side with Illustration number above it.

Numbers for graphs, diagrams and maps are best located in the bottom right hand corner.

For further advice, please consult your supervisor.

Word counts

The following word counts are the maximum permitted for each level of award*:

What's excluded from the word count

*In all cases above, the word count includes quotations but excludes appendices, tables (including tables of contents), figures, abstract, references, acknowledgements, bibliography and footnotes (as long as the latter do not contain substantive argument). Please note these are word limits, not targets.

Specific requirements

For degrees which involve Practice as Research (PaR), no less than 50% of the research output should be the written thesis. The written thesis for PaR degrees may be comprised of a range of written elements including, but not limited to, a critical review, a portfolio, and/or a statement on theoretical discourse or methodology.

**In cases of practice-based PhD’s or MPhil’s these suggested word counts may be different. It is normally expected that the written component would comprise no less than 50% of the overall output.

Each copy of the thesis should contain a summary or abstract not exceeding 300 words.

As an example, see how the  layout of your title page (.pdf) should be.

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Average word count of a dissertation

• November 15, 2018
• evalantsoght_uw8lmy
• Uncategorized

For some reason, I always thought a doctoral thesis is about 100,000 words in length (and I’ve taken that number as a reference for my book as well). However, I wanted to test this assumption, so I ran a poll about the topic. From the comments, I learned that word count limits are common in the UK (mostly), and that they differ across disciplines.

You can find the poll and its wake here:

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Trapped in dissertation revisions?

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.

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.

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

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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• GETTING STARTED
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• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

CONSIDERATION ONE

Word count issues.

Most students run out of words when writing up. At the start of the process, especially if you're an undergraduate doing a dissertation for the first time, 10,000, 12,000, or 15,000 words (and up) sound like a lot, but they soon get eaten up. Worst still, they get eaten up in the wrong places, so you have a lop-sided dissertation, with some chapters receiving more focus than they should, whilst others are relatively neglected. Your dissertation guidelines might provide some instructions or recommendations on word count per chapter, but this is not always the case. Since you're likely to run out of words at some point during the writing up process, we'd recommend the following:

Always leave extra words for your Results chapter. This chapter can be concisely written, especially when you know how to summarize data well and make good use of Appendices . However, more often than not, too much is included and it becomes excessively large. The problem is that you can suddenly find the Results chapter becoming 1,000 to 2,000 words too long (sometimes more), and it's very hard to either shorten the chapter or reduce the word count in other chapters. Leaving a little extra in terms of word count for this chapter is advisable, but when it comes down to it, knowing how to write up the Results chapter properly is important and will help you get this right first time.

Don't waste words on peripheral sections within chapters. Every chapter has a number of sections that are useful, and often have to be included to some extent, but (a) can eat into your word count and (b) won't give you lots of extra marks by themselves. Obvious examples include the Chapter Summaries section within the Introduction chapter, as well as necessary components such as Acknowledgements . In the case of Acknowledgements , this is sometimes even included in your word count, despite having no influence on the mark you are awarded, even though you would be expected to include it.

Don't waste words (a) waffling or (b) going off-point in your Literature Review , Research Strategy and Results chapters. Now there is a difference between waffling and going off-point:

Going off-point When writing a dissertation as a student, as opposed to a conference paper or journal as an academic, you have to provide a lot more explanation of possible choices you could have made, rather than simply justifying the choices you made. For example, in the Research Strategy chapter, you'll often be expected to explain the differences between research designs, research methods or sampling strategies that could have been used. This is sometimes the result of a marker needing to know that you have read up about the available options and can demonstrate this by briefly summarising these different components of research strategy. This is what we mean by going off-point , and it can be a real word hog, eating into your available word count. You need to try and avoid this by keeping these sections short, but also focusing on justifications (i.e., why you are using one research method or sampling strategy over another), which when written well, demonstrate your understanding of different components of research strategy, without having to waste words explaining each component in turn.

Waffling Ignoring waffling that comes from laziness - we know this happens! - waffling is often a problem of the Literature Review and Results chapters. Waffling is simply similar to dumping everything you know on the page, which can happen when (a) you don't know the material very well or (b) you're struggling to gauge which content is important and which can be left out, something that is a real challenge for the first-time dissertation student. As a result, you add too much content. This happens a lot in the Literature Review chapter because it is hard to be selective and critical, and in the Results chapter when you don't know (a) what analysis should be included, (b) what can be omitted entirely, and (c) what can be removed and put into the Appendix . In these chapters within the Route #1: Chapter-by-Chapter section of Lærd Dissertation, we help you to avoid this kind of waffling, which not only saves words, but makes your argument much more coherent.

Finally, there can be an obsession with word count when doing marked work. You're doing an essay of 1,500 words or 3,000 words, and you try to use every single word available. This can make sense when you have a small word count and lots of worthwhile things to say in such a small space. However, when taking on a much larger document (i.e., 10,000 words or more), it is not only important what is being said, but also what you leave out. Rather than thinking too hard about word count, focus on making sure that everything being said is worthwhile. All chapters, but especially your Literature Review and Results chapter can lose a lot of quality simply because of three or four unnecessary paragraphs that disrupt the flow and logic of your arguments and results. Despite the added word count of dissertations compared with essays, less can be more.

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A General Guide to PhD Dissertation Word Count And Structure

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Home » Blog » Dissertation » Structure » Dissertation Word Count Breakdown Structure With Calculator

Dissertation Word Count Breakdown Structure With Calculator

By Adam Oct 15, 2023 in Dissertation , Structure | No Comments

Dissertation writing is no simple feat. It’s a lengthy process that usually lasts for months. To understand how to structure a dissertation is one of the most important questions. If you know, let’s say, a 10000 word dissertation breakdown into its chapters, you’ve got a major problem solved.

Dissertation writing in UK is no simple feat. It’s a lengthy process that usually lasts for months and novice students have a lot of trouble adjusting to its many needs, some of which are unexpected. In that manner, understanding how to structure a dissertation and dissertation word count breakdown becomes one of the most important questions. If you know, let’s say, a 10000-word dissertation breakdown into its chapters, you’ve got the first problem solved.

Yes, you have. You see? Knowing your dissertation word count breakdown structure sorts out so many problems for you. Once you have that sorted, you can move on to focusing on what to write in each of the chapters and structure them properly without having to worry about crossing your word limit because now you know how many words go per section in the dissertation.

And that’s what we’re here to do: to calculate your dissertation breakdown .

If you are still finding it difficult to allocate the word count or are unsure about the writing of your dissertation, you can click here to hire a dissertation writer to write your dissertation and help you claim your degree with flying colors. However, it's not just the writer's job to do all the efforts. Your involvement in the process is key to success.

Knowing Dissertation Word Count Breakdown In Chapters

Let’s begin with what the usual number of chapters is in a dissertation. Before you read the rest of this section, though, let me tell you the most important thing here is to follow your university’s/college’s guidelines. If they have given you a particular structure to follow, do exactly that.

In case you’ve been thrown in the arena to battle with the dissertation dragon (that sounds so corny) without much help, you need to figure out how to overcome the beast.

Out of the usual options for dissertation breakdown, a 5-chapter structure is more convenient for this discussion.

I just noticed you looking suspiciously at the word convenient .

Well, I didn’t mean we were going to miss anything. Of course, we can’t leave anything important undiscussed. I just don’t want to make this discussion complicated for you.

So, have faith.

This is the chapters of dissertation word count breakdown I want you to consider:

A simple 5-chapter dissertation structure.

The chapters are listed in the pyramid in order of sequence. Do not think the size of a chunk is connected to the chapter’s word count percentage.

Important Reminder: Your 10000 Word Dissertation is a 10000 Word Essay

Another thing you should remember about your dissertation.

It’s an essay.

Or a book of interconnected essays, since every chapter is itself an essay as well. With quite a few sub-sections.

So, when you’re working on your dissertation structure, remember to write it like an essay.

How Many Words Go Per Section in My Dissertation?

It’s all about the ratio. Each chapter of the dissertation is expected to have a particular chunk of the report in terms of the number of words allowed.

Let me give you the percentage ratio I use for calculating each chapter’s word count for our customers at Dissertation Sage. I’ll be using a 10000 word dissertation word count breakdown for simpler calculations.

NOTE: This is a tentative dissertation breakdown. Some university guidelines will ask for a different number of words for a chapter and some other places will ask for a different set of chapter titles. So, again, stick to your university’s given guidelines. What I have done here is a convenient breakdown for you to understand how much of your dissertation’s word count should go into its main sections.

Final Year Dissertation Breakdown Calculator

Now that you know what percentage of words goes into which section of your dissertation, you can easily calculate your dissertation breakdown on your own.

But we’ll make it even easier. You don’t have to leave this page without getting the actual numbers for your dissertation chapters, and it doesn’t matter whether it’s a 10000-word dissertation or a 12000-word dissertation word count breakdown (or more) because it’s the ratio that matters.

Here’s your dissertation word count breakdown calculator. Just put in your total word count and the calculator will give you the numbers in a tick.

There you go. Let us know if you have any comments or suggestions for our topics related blog posts for the future or looking to get help with dissertation writing , send us an email at [email protected] .

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How Many Words is a Dissertation?

Date published July 11 2020 by Barbara Neil

During my last year of Master’s program, I heard from my professors and seniors’ dissertation is the most challenging assignment and risk if done wrong. After listening to these comments about the dissertation, I got too scared.

Something as simple as having 15000 words of dissertation wrong is the worst nightmare which can put your career and years of hard work in vain. So, I probably do not want this to happen. After hearing a lot about the dissertation, I finally settle down with a pen and paper.

Table of Contents

How “Dissertation Proposal” Can Help You!

Our top dissertation writing experts are waiting 24/7 to assist you with your university project, from critical literature reviews to a complete masters dissertation.

Got myself work up with the internet, laptop and my fingers (for typing), dig all the stuff that can make up a perfect dissertation writing and can instantly impress my supervisor. Dealing with dissertation myself, I got to know many tips and tricks about how to complete full-length research writing.

The common question most of the undergraduate ask about dissertation is how many words is a dissertation.

I would say probably 12000-15000 words but the figure might change as per your University or supervisor is a concern.

So, How Many Words Is A Dissertation….

Why do you need to know the word count of a dissertation? When I first started off my dissertation I wasn’t aware of how much to write in each heading and this mistake led me without words.

Each chapter has its own specific word count and it needs to be followed religiously so that you may not run out of words again.

Therefore, let’s divide each word count according to the chapters and their headings.

Start With 1000 words of introduction

The first chapter starts with an introduction to your dissertation writing. The introductory section is subdivided into the scope of the research, research question etc. Though the 1000 words carried for an introduction may vary according to the title and how much essential background you have gathered.

Next Comes The 3000 words literature review

A literature review is the heart of a dissertation, and it contains 30% of the weightage. The word count calculated might vary from 3000 words to 4000 words, you will have to address the conceptual framework and have to give a critical perspective. The literature review helps to support the existing knowledge relevant to the topic ideas you choose from.

Adding On 2000 words of methodology

Chapter 3 consist of the method at which all your research question and problem statement will get an answer. You must tell how the data will be gathered and what method is necessary to adopt while collecting answers to our research question. The word count may vary to 1500 to 2000 words or more if possible. There are multiple questions you must answer when writing your methodology, such as what alternative you could have taken, how you will select the samples etc.

Only 4000 words of finding and results

Well, the finding and result section do not contain a lot of words and the weightage of the section is approximately 5% in the average dissertation words. The reason is, the findings and results cover half of the charts and graphs showing the summarized data. However, the findings and results are mostly divided into separate sections. Hence, the weightage of results IS 30% of the dissertation. The word count of the results section is calculated around 3000, you will have to write a complete overview of the summary of results conducted.

Lastly, 2000 words of Conclusion and Recommendation

In some cases, the conclusion and recommendation may carry 1000-word however, it is most probably 2000 words each. In those words, you need to sum up all the important points and summarize the main idea. The conclusion should answer, does your research covers the problem stated in the first chapter and how did you overcome the problem.

How Long is the Dissertation?

When we ask about how much is the average dissertation length nobody knows the exact answer. Even when I was crafting my dissertation I did not know how long is the dissertation going to be, probably 149 pages or 150 or only 100. So, the answer to this question is, is that it depends upon what area you are writing your dissertation, for instance, the domain you choose appears to be different when writing the dissertation. Sometimes, what you see is not exactly what it looks like in a dissertation. It may look longer at first but when you write and construct fully you get to know the more area or pages are covered by References, appendix, tables of content, figures, page breaks or white spacing these all contribute to higher page length.

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How A Postgraduate Student Chooses An Average Dissertation Length?

As described above the ideal range of a dissertation depends upon the topic of research but also some other factors that contribute to the average dissertation length.

An average length of the thesis should be 15 to 25 pages, if you go beyond 25 that is considered a better as the material that will be presented should be appropriate but just remember credibility is important.

An average length of the dissertation in chapter 2 should be almost 8 to 10 pages long. The chapter 2 explores the existing research and previous studies to help support the methodology section in the next chapter.

The planning of your research should be 15 to 20 pages long, this is because you need to give an idea to the readers what you did and how you did it.

The readers in chapter 4 will understand and will get to know what you find out and all the results of your study is presented here. So, the chapter length should be approximately 6-7 pages long.

This chapter include all the summary of what you have already done for the past 15 months. And the it is submitted in only 6 to 8 pages after working hard.

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Final Verdict

Dissertation writing is not written in one sitting; you need to work for many months just to form a piece of research, crafting paragraph and coming to the conclusion. Just be calm, be patient and stay confident in your work. Only then you can achieve a successful dissertation despite its word limit.

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Dissertation Word Count Breakdown

Dissertation Word Count and Breakdown– How To Follow The Assigned Word Limit Easily? Underwriting or overwriting; are two of the most common errors that students make while composing their dissertations. That is why it is important to know from the beginning how much the dissertation word count of each of the chapters should be. After […]

Table of Contents

Dissertation Word Count and Breakdown– How To Follow The Assigned Word Limit Easily?

Underwriting or overwriting; are two of the most common errors that students make while composing their dissertations..

That is why it is important to know from the beginning how much the dissertation word count of each of the chapters should be. After you have the details of the word count of each of the sections, you can then design your schedule accordingly.

The dissertation word limit is allotted by the university where you study and the Master’s Dissertation word count may vary from the undergraduate dissertation word count or the Ph.D. dissertation word count.

Mostly the dissertation word length is between 10,000 words to 15,000 words but some may even go up to the level of 30,000 words.

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But despite the total dissertation word count allotted, the main problem begins when students have to break down that word ratio into the headings of the dissertation . Here they make the common error of writing more words for a section that can be explained in less count and less count for a section that deserves more words.

So what is the solution to that problem?

Well, there are many online dissertation word count breakdown calculator websites available that can help you in that aspect. One advantage that they have is that they give an option to select degree level and word count is given accordingly.

However, a disadvantage that they serve is that not all of those calculators may be accurate.

So what to do now?

Navigating the complexity of dissertation word count.

If you’re wrestling with the intricacies of managing your dissertation word count, you’re not alone. Online tools and calculators, such as our dissertation word count breakdown calculator, aim to simplify this process. However, the accuracy of these tools can vary, underscoring the importance of choosing a reliable platform.

Choosing Accuracy for Your Dissertation Word Count

When exploring solutions for your dissertation word count, it’s essential to prioritize accuracy. Our online calculator stands out by allowing you to select your degree level, offering a tailored and more precise estimation of your word count.

Understanding the Dissertation Word Count Breakdown

For a more detailed breakdown based on a 10,000-word limit, let’s delve deeper into the word count allocation for each section of your dissertation:

Introduction (10% – 1000 words):

The introduction lays the groundwork for your dissertation, addressing the ‘whys’ behind your research. Dedicate 10% of your word count, equivalent to 1000 words, to this crucial section.

Literature Review (25% – 2500 words):

Analyzing past issues, the literature review is a substantial component, constituting 25% of your total word count, translating to 2500 words.

Methodology (15-20% – 1500 to 2000 words):

Answering the ‘how’ of your research, the methodology section encompasses 15-20% of your word count, ranging from 1500 to 2000 words.

Data Presentation (15% – 1500 words):

Presenting collected data, this section occupies 15% of your word count, totaling 1500 words.

Discussion, Analysis, And Data Interpretation (15-20% – 1500 to 2000 words):

Offering insights into your findings, this segment covers 15-20% of your word count, spanning from 1500 to 2000 words.

Summary, Conclusion, And Recommendations (15% – 1500 words):

Concluding your dissertation, this final section constitutes 15% of your entire dissertation or 1500 words.

Strategizing Your Dissertation Word Count

By adhering to the prescribed word count percentages for each section, you can efficiently manage your dissertation. This structured approach ensures that you allocate the appropriate word count to each vital component, facilitating a well-organized and coherent dissertation.

Ready to Take the Next Step?

If you’re ready to streamline your dissertation word count management, consider using our dissertation word count breakdown calculator. For personalized assistance, feel free to reach out to our expert team . We’re here to help you navigate the challenges of dissertation writing and ensure your success. Don’t let the word count complexities hold you back—empower your dissertation journey today!

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Posted on Oct 18, 2017

Mastering Chapter Length: How Long Should Your Chapters Be?

Stop us if you’ve experienced this before: the clock strikes 8pm and you realize that you really need to pick up your drycleaning. But you’re right in the middle of your book! Well, you’ll put the book down when you get to the end of this chapter, you reason.

30 minutes afterward, you can’t resist taking a quick peek to see where you are. That’s when you discover: you’re only a quarter of the way through.

If you’ve ever felt this sinking feeling before, you’ve already got a sense of why sectioning word count should be so darn important to writers. Lengthy chapters can make readers want to give up on reading a story.

So, what’s the sweet spot for an average chapter’s word count — and why should it matter to you? Let’s find out.

If you'd like to download our free cheat sheet on word counts, skip ahead to the appropriate section below! Otherwise, read on. 

Why does the word count of a chapter matter?

Chapter breaks aren’t blocks of space that the writer just arbitrarily decided to insert. Instead, they:

• help keep your readers in the story;
• help reset the story so that there’s no information overload. (Imagine watching all of The Lord of the Rings at once!);
• help you adjust the pacing of the story and create suspense .

Knowing this, why does chapter length matter? Imagine that you’re a conductor for a moment. If your story is a melody, its sections make up the underlying beat or rhythm. So the length of these affects the pacing of your story , and the way the reader experiences it. Do you want the pace to be brisk to the reader and skip in a staccato? Or do you want to keep your readers suspended on a whole note that carries on for some time?

A break also tells the reader to mentally prepare for some sort of shift. If your chapters are too short, that might make for an erratic experience. You might not be able to build up the forward momentum that your story needs to click for the reader.

Long chapters, on the opposite end of the spectrum, risk dropping the reader out of the story forever. That’s because a break between sections allows readers to take a quick breather in between words. If we made this one block of text go on for twenty pages or thirty pages on end, for instance, our best guess is that your eyes would glaze over and your skin would fall away and your fingernails would rot while you turn into The Skeleton With a Burning Cigarette , forcing yourself to continue reading a slab of text that just never seemed to stop . Or — worse still — you’d give up and put the text aside altogether.

Therefore, the word count of a novel is crucial if you want your book to be fit for publication. But the word count of a chapter matters if you want your story to flow right. Of course, when you're writing the first draft of your story, you want to just get your words out on paper. But chapter word count is something that you want to keep in mind for future edits.

So, with all of this in mind, is there actually an average word count you should aim for?

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How long should a chapter be?

To determine how long should a chapter be, we examined books from a wide variety of genres. Based on this dataset of novels, we can establish some guidelines for chapter length: the average word count of a chapter generally ranges between 1,500 words to 5,000 words, with 3,000-4,000 words being the most common sweet spot. 

Does this mean that every chapter must end up somewhere in this range? Heck no. Books with much shorter sections become bestsellers all the time. (Kurt Vonnegut or Dan Brown, for instance.) And are there books with chapters that consistently score above 5,000 words? Of course! May we introduce you to J.R.R. Tolkien?

But it's safe to say that 1,500-5,000 is the normal range for most books. In any case, to see how authors use this tactic to set their pacing, we also mapped out the word count of every chapter in four famous books:

And as you can see, it varies, depending on the story. The average chapter word count of these novels falls within the 2,000-5,000 range. As Persuasion progresses, Jane Austen seems to elongate hers, building them up on a crescendo. George R.R. Martin and Harper Lee keep things more evenly paced in A Game of Thrones and To Kill A Mockingbird , respectively. The chapters in The Fault In Our Stars , meanwhile, grow shorter just as Hazel's time with Augustus shortens. Ultimately, the way you section your book depends on the effect that you want to achieve at any given point in your novel.

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CHEATSHEET: What Should Your Word Counts Be?

If you're thinking right now that these are a lot of numbers to wrap your head around, don't worry! We created this cheatsheet to help you visualize the data. It has everything you might want to know about your novel's word counts — including how long a novel in full should be. Simply enter your email below to unlock it.

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How do you write the chapter break?

Of course, sometimes the best way to chapter your book won’t crystallize until you start editing. So if you want your chapter breaks to pack a punch (whether it’s your first, second, or even fifth draft), these are a few of the ways you can frame the end of each section.

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1. The Promise

Broadly speaking, a break does one of two things: look forward or look back. The former promises more intrigue in the next chapter, often by foreshadowing things to come. This is from the ending of Chapter 3 in The Hobbit :

The next morning was a midsummer’s morning as fair and fresh as could be dreamed: blue sky and never a cloud, and the sun dancing on the water. Now they rode away amid songs of farewell and good speed, with their hearts ready for more adventure, and with a knowledge of the road they must follow over the Misty Mountains to the land beyond. — J.R.R. Tolkien, The Hobbit

This passage promises the reader plenty of upcoming adventure. Of course, its strategic placement at the end of the chapter also sets a reader’s irony senses tingling, telling them that things might not be all so bright and cheery for Bilbo and the gang soon.

2. The Resolution

Looking back on past events is another common way to end a chapter. This technique can simply bring the scene to a close, or it can be a way to summarize. In the exposition of Harry Potter and the Prisoner of Azkaban , J.K. Rowling re-familiarizes us with Harry Potter, an unusual boy wizard who misses some school friends. In this case, the conclusion nicely wraps up the themes and motifs she’s just introduced:

Deciding that he’d worry about the Hogsmeade form when he woke up, Harry got back into bed and reached up to cross off another day on the chart he’d made for himself, counting down the days left until his return to Hogwarts. Then he took off his glasses and lay down; eyes open, facing his three birthday cards. Extremely unusual though he was, at that moment Harry Potter felt just like everyone else — glad, for the first time in his life, that it was his birthday. — J.K. Rowling, Harry Potter and the Prisoner of Azkaban

Though it’s normally a bit of a cliché to close a chapter by getting the character to fall asleep, Rowling does a clever twist on it by making this an occasion worth describing.

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3. The Cliffhanger

You bet we’ve all experienced our fair share of screaming matches with this sort of ending. Charles Dickens, whose serialized novels got readers in the 1800s coming back every week, was a master at writing cliffhangers — sometimes even in the middle of fight scenes .

Here is an example from Chapter 5 of Great Expectations :

But I ran no farther than the house door, for there I ran head-foremost into a party of soldiers with their muskets, one of whom held out a pair of handcuffs to me, saying, “Here you are, look sharp, come on!” — Charles Dickens, Great Expectation s

This time, the chapter ends mid-scene. In addition, this particular cliffhanger rests on a physical act — the soldiers approaching Pip. But it could just as well be a dawning mental or emotional realization that keeps readers on the edge of their seats. Whatever shape the cliffhanger takes, it’s the anticipation you generate that will keep your audience eagerly flipping those pages.

One caveat: you can only use this technique sparingly, or else it’ll start to grow repetitive and lose its impact.

4. The Era or POV Jump

If you’re writing a book that uses multiple perspectives or jumps time periods, the chapter break is an obvious opportunity to switch things up. One of the most prominent practitioners is George R.R. Martin’s A Song of Ice and Fire series, which cycles between a total of 31 characters. Rainbow Rowell’s Eleanor & Park also uses a break effectively to shift POVs :

Park couldn’t think of a way to get rid of her on the bus. Or a way to get rid of himself. So he put his headphones on before the girl sat down and turned the volume all the way up. Thank God she didn’t try to talk to him. — Rainbow Rowell, Eleanor & Park

Then the next section segues smoothly into Eleanor’s take on things, keeping readers intrigued by revealing a new angle on previous events. When there are more than two POV characters, you'll need to make sure that you distribute time equitably between them while making each character interesting in their own right, so that one 7,000-word chapter with a boring character doesn't slow the whole story down.

That said, there are always exceptions, which takes us to our next important point...

Guidelines, not rules

As with everything else when it comes to writing a book, these are only guidelines—not rules. Though the average word count of a chapter is around 2,000 – 5,000 words, it all depends on your story. (We can't emphasize this enough.)

There are plenty of books that purposefully play with the word counts of their chapters. The Luminaries , which won the Man Booker Prize in 2015, has 12 sections that steadily decrease in word count to mirror the waning of the moon. (The first chapter of The Luminaries is 360 pages, whereas the final is two pages.) Then there’s William Faulkner’s As I Lay Dying , in which Vardaman’s famous five-word sentence, “My mother is a fish,” is the whole of Chapter 19. Or consider Fahrenheit 451 , which contains a Part I and Part II — but no chapters.

So, don’t write a chapter with only one eye on your story and the other on your word count. When you're outlining your book and writing your first draft, concentrate on making the content of your story the best it can be. Then you can always circle back to adjust word counts afterward, with pacing and reader experience in mind.

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What do you think about chapter word counts? Have any tips for your fellow writers? Share your thoughts in the comments below!

29 responses

Robert Run says:

19/10/2017 – 19:22

In as many as I could, in my book, I wrote the Chapters so they can stand alone as little fables and tales of their own. In hopes one can enjoy the Chapter w/out having to know the entire plot.

writerkris says:

19/10/2017 – 20:21

At least ten pages per chapter. No idea how many words that might be, but I might go and check. And always at an interesting point so they'll hopefully keep reading. Not a cliffhanger often, because I find those personally annoying.

↪️ Brittany Myers replied:

27/08/2019 – 12:01

For my book, my goal is to write ten pages per chapter. It'll be 10 10.

robert easterbrook says:

19/10/2017 – 20:47

It's all about pacing. Not word count. Scene breaks, yes. And chapter breaks, for sure. Fuck statistics. Stop trying to make novelists confirm to 'formulaic' notions of writing. Your last paragraph is titled Guidelines, not rules, yet you managed to squeeze in a nugget about 'word count'. This is like claiming something is true in research before you've done the research, then doing the research and finding no support for your claim, but then concluding your claim is still true despite the evidence. Contradictory bullshit.

↪️ Reedsy replied:

20/10/2017 – 09:02

You're absolutely right, it's all about pacing. However, word count *does* have an incidence on pacing, doesn't it? There is only so much you can write at the same pace… And yes, our last paragraph explains that these are merely guidelines. Offering an average word count that 90% of novels tend to fall into is exactly that: a guideline. We're not saying: "you should do like 90% of other authors," we're saying "here's how 90% of novels out there look like, and here's a rational explanation as to why (pacing, rhythm). If you want to do it differently, all the more power to you."

nancy says:

19/10/2017 – 23:37

Curious you didn't mention All the Light We Cannot See. It's been a best-seller for so long, and the chapters are one and two pages. Any comments?

20/10/2017 – 09:03

We couldn't cover all the bestsellers, however we're working on expanding our data set for the infographic and will hopefully be able to include more of those. All the Light We Cannot See is an interesting example though, so thanks for mentioning it!

madman says:

19/10/2017 – 23:59

It seems like the takeaway is not to focus on trying to hit any standard measure, but to use chapter breaks deliberately for effect. This seems to be the case with most aspects of writing (and art in general).

Livingstone Thuranira M says:

20/10/2017 – 08:19

A very insightful post.

J. Conrad Guest says:

22/10/2017 – 15:20

A chapter should be as long as it needs to be. I don’t believe in adhering to word counts. A chapter can be broken up into sections; the passage of time or a change of venue divided by an extra paragraph break. This, too, like a chapter ending, gives the reader a chance to put down the book, to come back later to continue reading. Best practices are far too concerned about manipulating and catering to the reader. As writers we all manipulate the reader. But as a reader, I don’t like feeling as if I’m being manipulated. A two-paragraph chapter, used as a sort of cliff-hanger, is but one of many bells and whistles writers are encouraged to use to keep the reader turning pages. Exquisite writing, the turn of a phrase, how a writer tells their story, can just as easily keep a reader immersed in the text. A book is only as good as what its words make happen inside the reader's head, and no two readers are alike. No number of best practices can save a poorly written story.

Jeff Haanen says:

28/12/2017 – 22:23

thanks! helpful.

Trish says:

03/01/2018 – 10:27

In the Table of Contents, the last section is titled, "Rules, Not Guidelines". When I read that, I thought it was strange! Is the author tired of the writing teachers emphasizing that so many bits of writing advice are guidelines, not rules? Is this some kind of backlash? Is the author an uptight martinet? Arriving at the actual section, which of course is "Guidelines, Not Rules", I realized that no, it's none of the above. It's simply less-than-stellar proofreading. You might want to fix that.

03/01/2018 – 10:38

Fixed, thank you for kindly letting us know about it. Hope this didn't detract you from the main message of the article.

↪️ Trish replied:

03/01/2018 – 17:17

No, it didn't distract me from the main message. The article was informative and helpful.

Geoff says:

28/03/2018 – 23:08

Excellent data and advice! Thank you.

Lulubunny says:

13/05/2018 – 05:04

You can say that this stuff doesn't matter all you want, but it really does. I for one get super irritated with marathon chapters. If a book is plugging along at 10-15 pages a chapter and then all of a sudden you throw in a 40 pager, I very well might stop reading. It makes me thinks the author doesn't know/care about form. A painter picks a canvas before painting and a writer makes decisions about pacing and length. Adhering to formulas or convention does not necessarily diminish art. In fact, it can elevate it. Just look at classical music.

Louise Viv says:

24/06/2018 – 19:29

i have just started to write a true life story and the local authorities (social services) unsure of how many chapters i should write as this is over 40 years failing on there behalf. And from what i have seen 5000 words per chapter is not a lot as i am on 8000 right now la little worried any advice thank you in advance

Idiots of Perth says:

15/09/2018 – 04:20

Dry cleaner near me

John H. Carroll says:

29/12/2018 – 16:18

I like the disclaimer at the end that these are guidelines, not rules. It's a very informative article. I write epic fantasy, which is more forgiving of meatier chapters I think. I average about 4-5k words per chapter. One of the conclusions I came to was that fewer chapters in a book make it easier to format. So my formatter's hat always pushes me to write bigger chapters, lol. That said, I write the story and let the words fall where they will. I have short chapters and long ones. An story that uses words efficiently has more effect on readers holding onto that book. Chapters are a tool of writing just like sentences and paragraph.

Sasha Winslow says:

17/05/2019 – 00:19

As a new writer, this blog was tough to swallow. However, I am grateful for the time it took to compile the statistics and example. Very helpful. I am still stuck on a chapter with 300 plus pages. Being new to this my goal is to just write and go with the flow. Once I get to the point of editing then I will work on ( the daunting) but needed word count. Thank you

Giulia says:

03/06/2019 – 07:12

In my story, a few chapters are thirty pages long and one even more. I publish it on a website but readers never told me it was too long or that I annoyed them, even if many times I should have divided it in two. In the end, however, I didn't change a thing. Normally, however, my chapters are fifteen pages long.

17/06/2019 – 12:12

I didn't know it actually posted the first one.

Alice says:

18/07/2019 – 19:39

Thank you for making this! This is a great guide, and I love the chart. I write so slowly, I have a hard time knowing how long a chapter I am working on would feel to a reader. Obviously I don't spend my energy trying to adhere to a specific number, but it helps me to know if I'm roughly at the length of a Jane Eyre chapter or a Name of the Wind chapter or a Giver chapter, since I know how the pacing in those books feels when I read them. Very useful.

deirdre says:

23/07/2019 – 12:19

hi the harry potter quote is from prisoner of azkaban, not from sorcerer's stone :)

↪️ Martin Cavannagh replied:

25/07/2019 – 09:55

Ack! So it is! Now corrected — thanks :)

Dan Clinton says:

03/08/2019 – 10:39

I'm also writing a fantasy but or young adults. I'm just 13 in high school. Is 28 pages per chapter enough for a 368pg book?

↪️ Yvonne replied:

08/08/2019 – 02:04

Page count is a bit more nebulous of a standard, since it depends on so many factors (margins, font size, type of font being used). If you email us at [email protected] with the average word count per chapter, we'll be able to give you some more detailed thoughts :)

orkida says:

20/08/2019 – 20:13

I found this blog accidentally and I love it

YASMINE says:

18/04/2020 – 21:15

Thank you for this article it was very insightful and right on point! The comparison table is perfect.

Comments are currently closed.

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Why is writing essays so hard?

Patterns and boring topics imposed by schools and universities are not very conducive to creativity and human development. Such essays are very difficult to write, because many are not interested in this and do not see the meaning of the text. There are a number of criteria that make it impossible to write essays:

• Boring and incomprehensible topics. Many topics could be more interesting, but teachers formulate them in a way that makes you want to yawn.
• Templates. 90% do not know how to make an essay interesting, how to turn this detailed answer to a question into a living story.
• Fear of not living up to expectations. It seems to many that the essay is stupid and that they simply did not understand the question. There is a fear of getting a bad mark and disappointing the professor, parents and classmates. There is a fear of looking stupid and embarrassing in front of the team.
• Lack of experience. People don't know what and how to write about. In order to make a good essay, you need to have a perfect understanding of the topic and have the skills of a writer.

That is why the company EssaysWriting provides its services. We remove the responsibility for the result from the clients and do everything to ensure that the scientific work is recognized.

Perfect Essay

Customer Reviews

To describe something in great detail to the readers, the writers will do my essay to appeal to the senses of the readers and try their best to give them a live experience of the given subject.