how long write phd thesis

• 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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How to write a PhD thesis: a step-by-step guide

A draft isn’t a perfect, finished product; it is your opportunity to start getting words down on paper, writes Kelly Louise Preece

Kelly Louise Preece

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Congratulations; you’ve finished your research! Time to write your PhD thesis. This resource will take you through an eight-step plan for drafting your chapters and your thesis as a whole. 

Organise your material

Before you start, it’s important to get organised. Take a step back and look at the data you have, then reorganise your research. Which parts of it are central to your thesis and which bits need putting to one side? Label and organise everything using logical folders – make it easy for yourself! Academic and blogger Pat Thomson calls this  “Clean up to get clearer” . Thomson suggests these questions to ask yourself before you start writing:

• What data do you have? You might find it useful to write out a list of types of data (your supervisor will find this list useful too.) This list is also an audit document that can go in your thesis. Do you have any for the “cutting room floor”? Take a deep breath and put it in a separate non-thesis file. You can easily retrieve it if it turns out you need it.
• What do you have already written? What chunks of material have you written so far that could form the basis of pieces of the thesis text? They will most likely need to be revised but they are useful starting points. Do you have any holding text? That is material you already know has to be rewritten but contains information that will be the basis of a new piece of text.
• What have you read and what do you still need to read? Are there new texts that you need to consult now after your analysis? What readings can you now put to one side, knowing that they aren’t useful for this thesis – although they might be useful at another time?
• What goes with what? Can you create chunks or themes of materials that are going to form the basis of some chunks of your text, perhaps even chapters?

Once you have assessed and sorted what you have collected and generated you will be in much better shape to approach the big task of composing the dissertation. 

Decide on a key message

A key message is a summary of new information communicated in your thesis. You should have started to map this out already in the section on argument and contribution – an overarching argument with building blocks that you will flesh out in individual chapters.

You have already mapped your argument visually, now you need to begin writing it in prose. Following another of Pat Thomson’s exercises, write a “tiny text” thesis abstract. This doesn’t have to be elegant, or indeed the finished product, but it will help you articulate the argument you want your thesis to make. You create a tiny text using a five-paragraph structure:

• The first sentence addresses the broad context. This locates the study in a policy, practice or research field.
• The second sentence establishes a problem related to the broad context you have set out. It often starts with “But”, “Yet” or “However”.
• The third sentence says what specific research has been done. This often starts with “This research” or “I report…”
• The fourth sentence reports the results. Don’t try to be too tricky here, just start with something like: “This study shows,” or “Analysis of the data suggests that…”
• The fifth and final sentence addresses the “So What?” question and makes clear the claim to contribution.

Here’s an example that Thomson provides:

Secondary school arts are in trouble, as the fall in enrolments in arts subjects dramatically attests. However, there is patchy evidence about the benefits of studying arts subjects at school and this makes it hard to argue why the drop in arts enrolments matters. This thesis reports on research which attempts to provide some answers to this problem – a longitudinal study which followed two groups of senior secondary students, one group enrolled in arts subjects and the other not, for three years. The results of the study demonstrate the benefits of young people’s engagement in arts activities, both in and out of school, as well as the connections between the two. The study not only adds to what is known about the benefits of both formal and informal arts education but also provides robust evidence for policymakers and practitioners arguing for the benefits of the arts. You can  find out more about tiny texts and thesis abstracts on Thomson’s blog.

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Write a plan

You might not be a planner when it comes to writing. You might prefer to sit, type and think through ideas as you go. That’s OK. Everybody works differently. But one of the benefits of planning your writing is that your plan can help you when you get stuck. It can help with writer’s block (more on this shortly!) but also maintain clarity of intention and purpose in your writing.

You can do this by creating a  thesis skeleton or storyboard , planning the order of your chapters, thinking of potential titles (which may change at a later stage), noting down what each chapter/section will cover and considering how many words you will dedicate to each chapter (make sure the total doesn’t exceed the maximum word limit allowed).

Use your plan to help prompt your writing when you get stuck and to develop clarity in your writing.

Some starting points include:

• This chapter will argue that…
• This section illustrates that…
• This paragraph provides evidence that…

Of course, we wish it werethat easy. But you need to approach your first draft as exactly that: a draft. It isn’t a perfect, finished product; it is your opportunity to start getting words down on paper. Start with whichever chapter you feel you want to write first; you don’t necessarily have to write the introduction first. Depending on your research, you may find it easier to begin with your empirical/data chapters.

Vitae advocates for the “three draft approach” to help with this and to stop you from focusing on finding exactly the right word or transition as part of your first draft.

This resource originally appeared on Researcher Development .

Kelly Louse Preece is head of educator development at the University of Exeter.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter .

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Frequently asked questions

How long does it take to write a dissertation.

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.

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).

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.

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

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

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

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

Your university may have its own referencing style guide.

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

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

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

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

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

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

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

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

• (Smith, 2019a)
• (Smith, 2019b)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Your dissertation sometimes contains a list of abbreviations .

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

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

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

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

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

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

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

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

Some synonyms and near synonyms of among include:

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

Some synonyms and near synonyms of between  include:

• In the space separating
• In the time separating

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

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

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

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

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

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

Some synonyms and near synonyms of ensure include:

• Make certain

Some synonyms and near synonyms of assure  include:

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

Some synonyms and near synonyms for council include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some examples of plagiarism include:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Compare two sources for plagiarism

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

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

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

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

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

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

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

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

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

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

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

There are five common approaches to qualitative research :

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

These are four of the most common mixed methods designs :

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

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

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

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

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

There are four main types of triangulation :

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

Triangulation can help:

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

But triangulation can also pose problems:

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

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

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

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

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

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

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

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

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

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

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

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

Advantages:

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

Disadvantages:

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

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

In a factorial design, multiple independent variables are tested.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are two subtypes of construct validity.

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

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

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

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

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

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

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

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

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

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

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

Here are a few common types:

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

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

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

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

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

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

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

Deductive reasoning is also called deductive logic.

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

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

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

In statistics, dependent variables are also called:

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

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

Independent variables are also called:

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

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

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

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

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

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

The difference between explanatory and response variables is simple:

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

There are 4 main types of extraneous variables :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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

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

If something is a mediating variable :

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

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

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

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

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

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

Unstructured interviews are best used when:

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

The four most common types of interviews are:

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

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

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

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

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

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

Overall, your focus group questions should be:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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

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

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

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

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

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

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

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

You can use several tactics to minimise observer bias .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The two main types of social desirability bias are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Snowball sampling is best used in the following cases:

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

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

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

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

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

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

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

Reproducibility and replicability are related terms.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

To define your scope of research, consider the following:

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

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

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

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

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

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

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

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

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

So when does paraphrasing count as plagiarism?

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

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

It’s appropriate to quote when:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Researchability
• Feasibility and specificity
• Relevance and originality

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

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

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

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

Some of the main types of nouns are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use these measures:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some of the main types of adjectives are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some synonyms and near synonyms of  every time include:

• Without exception

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some of the main types of verbs are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

‘Teacher’s aid’ is incorrect.

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

‘Visual aide’ is incorrect.

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

‘Job aide’ is incorrect.

There are numerous synonyms for the various meanings of truly :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).

“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience .

Some synonyms and near synonyms of the verb   compose   (meaning “to make up”) are:

People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing . “Comprise” traditionally means “to be made up of,” not “to make up.”

Some synonyms and near synonyms of the verb comprise are:

• Be composed of
• Be made up of

People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing .

A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:

• Correlation/causation fallacy: Claiming that two events that occur together have a cause-and-effect relationship even though this can’t be proven
• Ecological fallacy : Making inferences about the nature of individuals based on aggregate data for the group
• The sunk cost fallacy : Following through on a project or decision because we have already invested time, effort, or money into it, even if the current costs outweigh the benefits
• The base-rate fallacy : Ignoring base-rate or statistically significant information, such as sample size or the relative frequency of an event, in favor of  less relevant information e.g., pertaining to a single case, or a small number of cases
• The planning fallacy : Underestimating the time needed to complete a future task, even when we know that similar tasks in the past have taken longer than planned

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.

Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:

• A red herring fallacy refers to an attempt to change the subject and divert attention from the original issue. In other words, a seemingly solid but ultimately irrelevant argument is introduced into the discussion, either on purpose or by mistake.
• A straw man argument involves the deliberate distortion of another person’s argument. By oversimplifying or exaggerating it, the other party creates an easy-to-refute argument and then attacks it.

The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.

Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.

The sunk cost fallacy and escalation of commitment (or commitment bias ) are two closely related terms. However, there is a slight difference between them:

• Escalation of commitment (aka commitment bias ) is the tendency to be consistent with what we have already done or said we will do in the past, especially if we did so in public. In other words, it is an attempt to save face and appear consistent.
• Sunk cost fallacy is the tendency to stick with a decision or a plan even when it’s failing. Because we have already invested valuable time, money, or energy, quitting feels like these resources were wasted.

In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).

When you are faced with a straw man argument , the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.

The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.

A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).

This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy .

A slippery slope argument is not always a fallacy.

• When someone claims adopting a certain policy or taking a certain action will automatically lead to a series of other policies or actions also being taken, this is a slippery slope argument.
• If they don’t show a causal connection between the advocated policy and the consequent policies, then they commit a slippery slope fallacy .

There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:

• Slippery slope arguments take advantage of the gray area between an initial action or decision and the possible next steps that might lead to the undesirable outcome. You can point out these missing steps and ask your partner to indicate what evidence exists to support the claimed relationship between two or more events.
• Ask yourself if each link in the chain of events or action is valid. Every proposition has to be true for the overall argument to work, so even if one link is irrational or not supported by evidence, then the argument collapses.
• Sometimes people commit a slippery slope fallacy unintentionally. In these instances, use an example that demonstrates the problem with slippery slope arguments in general (e.g., by using statements to reach a conclusion that is not necessarily relevant to the initial statement). By attacking the concept of slippery slope arguments you can show that they are often fallacious.

People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:

• Cognitive bias is the tendency to make decisions or take action in an illogical way because of our values, memory, socialization, and other personal attributes. In other words, it refers to a fixed pattern of thinking rooted in the way our brain works.
• Logical fallacies relate to how we make claims and construct our arguments in the moment. They are statements that sound convincing at first but can be disproven through logical reasoning.

In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.

An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy .

It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.

For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy .

An ad hominem (Latin for “to the person”) is a type of informal logical fallacy . Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.

This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.

Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).

Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.

In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy .

Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”

Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.

The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence .

It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.

To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:

• In statistics , if we want to draw inferences about an entire population, we need to make sure that the sample is random and representative of the population . We can achieve that by using a probability sampling method , like simple random sampling or stratified sampling .
• In academic writing , use precise language and measured phases. Try to avoid making absolute claims, cite specific instances and examples without applying the findings to a larger group.
• As readers, we need to ask ourselves “does the writer demonstrate sufficient knowledge of the situation or phenomenon that would allow them to make a generalization?”

The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:

• The hasty generalization fallacy involves genuinely considering an example or case (i.e., the evidence comes first and then an incorrect conclusion is drawn from this).
• The anecdotal evidence fallacy (also known as “cherry-picking” ) is knowing in advance what conclusion we want to support, and then selecting the story (or a few stories) that support it. By overemphasizing anecdotal evidence that fits well with the point we are trying to make, we overlook evidence that would undermine our argument.

Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:

• Begging the question fallacy occurs when you assume that an argument is true in order to justify a conclusion. If something begs the question, what you are actually asking is, “Is the premise of that argument actually true?” For example, the statement “Snakes make great pets. That’s why we should get a snake” begs the question “are snakes really great pets?”
• Circular reasoning fallacy on the other hand, occurs when the evidence used to support a claim is just a repetition of the claim itself.  For example, “People have free will because they can choose what to do.”

In other words, we could say begging the question is a form of circular reasoning.

Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.

An example of a non sequitur is the following statement:

“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”

Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy .

The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.

In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.

An example of post hoc fallacy is the following line of reasoning:

“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”

Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.

Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:

• Post hoc fallacy is assuming a cause and effect relationship between two events, simply because one happened after the other.
• Hasty generalisation fallacy is drawing a general conclusion from a small sample or little evidence.

In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.

The fallacy of composition is similar to and can be confused with the hasty generalization fallacy . However, there is a difference between the two:

• The fallacy of composition involves drawing an inference about the characteristics of a whole or group based on the characteristics of its individual members.
• The hasty generalization fallacy involves drawing an inference about a population or class of things on the basis of few atypical instances or a small sample of that population or thing.

In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.

The opposite of the fallacy of composition is the fallacy of division . In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”

Base rate fallacy can be avoided by following these steps:

• Avoid making an important decision in haste. When we are under pressure, we are more likely to resort to cognitive shortcuts like the availability heuristic and the representativeness heuristic . Due to this, we are more likely to factor in only current and vivid information, and ignore the actual probability of something happening (i.e., base rate).
• Take a long-term view on the decision or question at hand. Look for relevant statistical data, which can reveal long-term trends and give you the full picture.
• Talk to experts like professionals. They are more aware of probabilities related to specific decisions.

Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.

When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy .

Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy .

In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.

Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.

The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.

Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.

For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.

The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:

• In the ad hominem fallacy , the goal is to discredit the argument by discrediting the person currently making the argument.
• In the genetic fallacy , the goal is to discredit the argument by discrediting the history or origin (i.e., genesis) of an argument.

False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.

The false dilemma fallacy works in two ways:

• By presenting only two options as if these were the only ones available
• By presenting two options as mutually exclusive (i.e., only one option can be selected or can be true at a time)

In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).

The best way to avoid a false dilemma fallacy is to pause and reflect on two points:

• Are the options presented truly the only ones available ? It could be that another option has been deliberately omitted.
• Are the options mentioned mutually exclusive ? Perhaps all of the available options can be selected (or be true) at the same time, which shows that they aren’t mutually exclusive. Proving this is called “escaping between the horns of the dilemma.”

Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.

For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”

The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:

• A complex question fallacy occurs when someone asks a question that presupposes the answer to another question that has not been established or accepted by the other person. For example, asking someone “Have you stopped cheating on tests?”, unless it has previously been established that the person is indeed cheating on tests, is a fallacy.
• Begging the question fallacy occurs when we assume the very thing as a premise that we’re trying to prove in our conclusion. In other words, the conclusion is used to support the premises, and the premises prove the validity of the conclusion. For example: “God exists because the Bible says so, and the Bible is true because it is the word of God.”

In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.

“ No true Scotsman ” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.

For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.

No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.

The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.

To identify an appeal to authority fallacy , you can ask yourself the following questions:

• Is the authority cited really a qualified expert in this particular area under discussion? For example, someone who has formal education or years of experience can be an expert.
• Do experts disagree on this particular subject? If that is the case, then for almost any claim supported by one expert there will be a counterclaim that is supported by another expert. If there is no consensus, an appeal to authority is fallacious.
• Is the authority in question biased? If you suspect that an expert’s prejudice and bias could have influenced their views, then the expert is not reliable and an argument citing this expert will be fallacious.To identify an appeal to authority fallacy, you ask yourself whether the authority cited is a qualified expert in the particular area under discussion.

Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.

Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.

The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”

This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.

The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.

Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:

• An ad populum fallacy tries to persuade others by claiming that something is true or right because a lot of people think so.
• An appeal to authority fallacy tries to persuade by claiming a group of experts believe something is true or right, therefore it must be so.

To identify a false cause fallacy , you need to carefully analyse the argument:

• When someone claims that one event directly causes another, ask if there is sufficient evidence to establish a cause-and-effect relationship. 
• Ask if the claim is based merely on the chronological order or co-occurrence of the two events. 
• Consider alternative possible explanations (are there other factors at play that could influence the outcome?).

By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.

False cause fallacy examples include: 

• Believing that wearing your lucky jersey will help your team win 
• Thinking that everytime you wash your car, it rains
• Claiming that playing video games causes violent behavior 

In each of these examples, we falsely assume that one event causes another without any proof.

The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:

• The planning fallacy describes our inability to correctly estimate how long a future task will take, mainly due to optimism bias and a strong focus on the best-case scenario.
• Procrastination refers to postponing a task, usually by focusing on less urgent or more enjoyable activities. This is due to psychological reasons, like fear of failure.

In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.

A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.

Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.

The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.

An example of appeal to pity fallacy is the following appeal by a student to their professor:

“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”

While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.

While both the appeal to pity fallacy and   red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:

• Appeal to pity fallacy attempts to evoke feelings of sympathy, pity, or guilt in an audience, so that they accept the speaker’s conclusion as truthful.
• Red herring fallacy attempts to introduce an irrelevant piece of information that diverts the audience’s attention to a different topic.

Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.

Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy . It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.

Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.

For example, the sentence “ To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to  many”, but there’s no particular reason to do so. Both versions are fine.

Some people argue that you shouldn’t end a sentence with a preposition , but that “rule” can also be ignored, since it’s not supported by serious language authorities.

Yes, it’s fine to end a sentence with a preposition . The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.

Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about ” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.

No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing . While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.

Specifically, the CRAAP test for evaluating sources includes five criteria: currency , relevance , authority , accuracy , and purpose . ChatGPT fails to meet at least three of them:

• Currency: The dataset that ChatGPT was trained on only extends to 2021, making it slightly outdated.
• Authority: It’s just a language model and is not considered a trustworthy source of factual information.
• Accuracy: It bases its responses on patterns rather than evidence and is unable to cite its sources .

So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source .

ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.

It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.

So ChatGPT is not always trustworthy . It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.

Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source .

No, it is not possible to cite your sources with ChatGPT . You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.

Instead, use a tool designed for this purpose, like the Scribbr Citation Generator .

But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.

GPT  stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.

The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.

ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.

OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.

ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.

In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content , and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.

Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.

ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.

ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.

Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations : because its answers are based on probabilities, they’re not always trustworthy .

OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.

Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.

The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.

ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.

However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.

Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.

Try using a prompt like this one:

“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”

Yes, there are a variety of ways to use ChatGPT for language learning , including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.

AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT ) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors , we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.

Because of how AI detectors work , they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.

Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.

But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.

You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.

Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.

While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.

Try the QuillBot summarizer for free

Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.

Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.

A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):

• Gives the tool a role to explain what type of answer you expect from it
• Is precisely formulated and gives enough context
• Is free from bias
• Has been tested and improved by experimenting with the tool

ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.

ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.

Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.

To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.

A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.

You can access ChatGPT by signing up for a free account:

• Follow this link to the ChatGPT website.
• Click on “Sign up” and fill in the necessary details (or use your Google account). It’s free to sign up and use the tool.
• Type a prompt into the chat box to get started!

A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.

According to OpenAI’s terms of use, users have the right to reproduce text generated by ChatGPT during conversations.

However, publishing ChatGPT outputs may have legal implications , such as copyright infringement.

Users should be aware of such issues and use ChatGPT outputs as a source of inspiration instead.

According to OpenAI’s terms of use, users have the right to use outputs from their own ChatGPT conversations for any purpose (including commercial publication).

However, users should be aware of the potential legal implications of publishing ChatGPT outputs. ChatGPT responses are not always unique: different users may receive the same response.

Furthermore, ChatGPT outputs may contain copyrighted material. Users may be liable if they reproduce such material.

ChatGPT can sometimes reproduce biases from its training data , since it draws on the text it has “seen” to create plausible responses to your prompts.

For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.

The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts . It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.

Information extraction  refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP) .

For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity 1 ,celebrity 2 ,date) .

The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.

Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.

An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.

A related concept is information extraction , concerned with how to get structured information from unstructured sources.

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.

However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).

However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well. Alternatively, you can get advice using Scribbr’s essay editing service .

No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.

Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.

However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).

ChatGPT and other AI writing tools can have unethical uses. These include:

• Reproducing biases and false information
• Using ChatGPT to cheat in academic contexts
• Violating the privacy of others by inputting personal information

However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:

• Following your institution’s guidelines
• Critically evaluating outputs
• Being transparent about how you used the tool

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Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7.

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

How does the sample edit work?

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

Read more about how the sample edit works

Yes, you can upload your document in sections.

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

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

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

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

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

Yes, our editors also work during the weekends and holidays.

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

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

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

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

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

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

View an example

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:

• Graduation projects
• Dissertations
• Admissions essays
• College essays
• Application essays
• Personal statements
• Process reports
• Reflections
• Internship reports
• Academic papers
• Research proposals
• Prospectuses

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The fastest turnaround time is 24 hours.

You can upload your document at any time and choose between four deadlines:

At Scribbr, we promise to make every customer 100% happy with the service we offer. Our philosophy: Your complaint is always justified – no denial, no doubts.

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.

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

How to Write a PhD Thesis: 13 Tips For PhD Thesis Writing 

Completing a successful PhD research thesis is extremely challenging, and how to write a PhD thesis is often a question in students’ minds. Fret not, there are many ways to make the process of PhD thesis writing less bumpy. This article will provide some PhD thesis writing tips to simplify the writing process and help you complete your thesis on time, while keeping your sanity mostly intact. 

Only about 50% of students enrolled in a PhD program ever complete it 1 . They drop out at many different points during the process for many different reasons. Some leave because the course work is too difficult or time consuming. Some leave for personal or financial reasons. One common cause of non-completion, or late completion, is the daunting spectre of PhD thesis writing. 

PhD thesis writing tips: How to overcome the challenge of writing your PhD thesis

First, remember that although writing a PhD thesis is difficult, this can be accomplished. Here are some things to consider that will increase your confidence and make the task of PhD thesis writing a bit less scary. 

• Create an outline before you start writing – The most effective way to keep your work organized is to first create an outline based on the PhD thesis structure required by your university. Using an outline for your PhD paper writing has tremendous benefits. It creates a handy space to keep and organize all the little snippets of information and questions you will have during your preparation. It allows you to effectively plan your work and manage your time and makes the actual writing much easier. A thesis is shaped more than written, and an outline provides it the required PhD thesis structure. 
• Follow all university guides – Be careful to ensure that you are meeting all the requirements of your university. This includes everything from topic selection to structure to writing style. It is extremely frustrating to spend a lot of time and effort on a section only to have to do it over because you didn’t follow the proper guidelines. Read all relevant material from your university over and over until you have it memorized. Then, check it again. 
• Section order – It is usually best not to do your PhD thesis writing in chronological order. For researchers, the easiest parts to write are usually the Method and Results. So, gain some confidence first and write the Introduction and Conclusion last to tie it all together. 
• Work extensively with your supervisor – Don’t forget that in the process of PhD thesis writing, help is right there when you ask for it. Do not hesitate to ask for guidance from your supervisor, advisors, or other committee members when you get stuck. Clear and regular communication with these important resources can save you untold heartache during the PhD research and thesis writing processes. This should not be a solo exercise; they have all been where you are now. 
• Plan carefully, create rough drafts, and refine 2 – This is so important and basic to all academic research that it bears repeating. You will not write the final PhD thesis on your first try. Do not become frustrated, trust the process. 
• Produce quality writing – Make sure your ideas flow easily and are clear and easy to read. This is not a strong skill for most beginning researchers, but it’s a skill that can be learned with a lot of practice. Therefore, edit, edit, and edit some more. If you need it, there are many places to get PhD thesis writing help and assistance. 
• Details matter – Pay attention to the small things, especially with the document formatting. If you start out using the proper format, you will be saving a tremendous amount of time and grief later. 
• Avoid plagiarism – Quote accurately, otherwise paraphrase. There is no excuse for being a lazy writer. Consider using a smart tool or service to check for plagiarism during your PhD thesis editing process to make sure you did not unintentionally copy any material. 
• Rein in the references – Use a database, such as EndNote or Mendeley, to keep them organized and under control; check and double check citations and references with the bibliography to ensure they all match. Don’t forget to use the PhD thesis style required by your university. 
• Keep it simple – Remember, this is only the start of your career, not your ultimate work 3 ; perfectionism can be a disaster. 
• Make consistent progress – Try to write at least a little every day; check quotations and references when writing seems too difficult. 3  
• Keep your reader in mind – As with all writing, your PhD thesis is meant to be read, so be considerate of those who read it; be concise, include all necessary data/information to support your argument but nothing extra. Strive to be understood and avoid unnecessary words. 
• Be persistent and eager – Writing a doctoral thesis becomes easier if you are consistent and dedicated. All other things being equal, your attitude will ultimately determine your success. Have patience and work hard. Create work you will be proud of for a lifetime. 
• Cassuto, L. Ph.D. attrition: How much is too much? The Chronicle of Higher Education.    https://www.chronicle.com/article/ph-d-attrition-how-much-is-too-much/?cid=gen_sign_in [Accessed 20 July 2022]
• Curl, I. 10 tips for writing a PhD thesis. Times Higher Education. https://www.timeshighereducation.com/blog/10-tips-writing-phd-thesis [Accessed 20 July 2022]
• Thomas, K. Finishing your PhD thesis: 15 top tips from those in the know. The Guardian. https://www.theguardian.com/higher-education-network/blog/2014/aug/27/finishing-phd-thesis-top-tips-experts-advice [Accessed 20 July 2022]

A PhD thesis includes several key components, that are essential for the work to be considered seriously. These may vary depending on the research field and specific requirements of the institution, but generally include: ·         an introduction that presents the research question and context, ·         a literature review that surveys existing knowledge and research, ·         a methodology section describing the research design and methods employed, ·         a presentation of findings or results, ·         a discussion section interpreting the results and their implications, and ·         a conclusion that summarizes the main findings and contributions. Additionally, appendices may contain supplementary materials such as data, charts, or technical details.  

The time required for writing a PhD thesis can vary significantly depending on factors such as the research topic, the individual’s research progress, the specific requirements of the institution, and the researcher’s writing process. On average, it can take several months to a few years to complete a PhD thesis. The research, data collection, and analysis stages can span several years, with the PhD thesis writing phase itself often lasting several months. Here, AI writing assistants like Paperpal, designed for academics, can help you write better. Explore Paperpal and see the difference for yourself!

To select a suitable topic for your PhD thesis, start by identifying your research interests and areas of expertise. Consider the gaps or unresolved questions in your field of study and explore potential research avenues and read extensively in your area of interest. Consult with your advisor or mentors, who can offer guidance and help narrow down your options. Once you have a tentative topic, conduct a literature review to ensure its novelty and feasibility. It’s important to choose a topic that aligns with your passion, has potential for meaningful contribution, and is feasible given available resources and time constraints.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Confusing Elements of a Research Paper That Trip Up Most Academics
• How to Present Data and Statistics in Your Research Paper: Language Matters 
• 3 Easy Ways for Researchers to Improve Their Academic Vocabulary
• What is an Expository Essay and How to Write It

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How To Write A Dissertation Or Thesis

8 straightforward steps to craft an a-grade dissertation.

By: Derek Jansen (MBA) Expert Reviewed By: Dr Eunice Rautenbach | June 2020

Writing a dissertation or thesis is not a simple task. It takes time, energy and a lot of will power to get you across the finish line. It’s not easy – but it doesn’t necessarily need to be a painful process. If you understand the big-picture process of how to write a dissertation or thesis, your research journey will be a lot smoother.  

In this post, I’m going to outline the big-picture process of how to write a high-quality dissertation or thesis, without losing your mind along the way. If you’re just starting your research, this post is perfect for you. Alternatively, if you’ve already submitted your proposal, this article which covers how to structure a dissertation might be more helpful.

How To Write A Dissertation: 8 Steps

• Clearly understand what a dissertation (or thesis) is
• Find a unique and valuable research topic
• Craft a convincing research proposal
• Write up a strong introduction chapter
• Review the existing literature and compile a literature review
• Design a rigorous research strategy and undertake your own research
• Present the findings of your research
• Draw a conclusion and discuss the implications

Step 1: Understand exactly what a dissertation is

This probably sounds like a no-brainer, but all too often, students come to us for help with their research and the underlying issue is that they don’t fully understand what a dissertation (or thesis) actually is.

So, what is a dissertation?

At its simplest, a dissertation or thesis is a formal piece of research , reflecting the standard research process . But what is the standard research process, you ask? The research process involves 4 key steps:

• Ask a very specific, well-articulated question (s) (your research topic)
• See what other researchers have said about it (if they’ve already answered it)
• If they haven’t answered it adequately, undertake your own data collection and analysis in a scientifically rigorous fashion
• Answer your original question(s), based on your analysis findings

In short, the research process is simply about asking and answering questions in a systematic fashion . This probably sounds pretty obvious, but people often think they’ve done “research”, when in fact what they have done is:

• Started with a vague, poorly articulated question
• Not taken the time to see what research has already been done regarding the question
• Collected data and opinions that support their gut and undertaken a flimsy analysis
• Drawn a shaky conclusion, based on that analysis

If you want to see the perfect example of this in action, look out for the next Facebook post where someone claims they’ve done “research”… All too often, people consider reading a few blog posts to constitute research. Its no surprise then that what they end up with is an opinion piece, not research. Okay, okay – I’ll climb off my soapbox now.

The key takeaway here is that a dissertation (or thesis) is a formal piece of research, reflecting the research process. It’s not an opinion piece , nor a place to push your agenda or try to convince someone of your position. Writing a good dissertation involves asking a question and taking a systematic, rigorous approach to answering it.

If you understand this and are comfortable leaving your opinions or preconceived ideas at the door, you’re already off to a good start!

Step 2: Find a unique, valuable research topic

As we saw, the first step of the research process is to ask a specific, well-articulated question. In other words, you need to find a research topic that asks a specific question or set of questions (these are called research questions ). Sounds easy enough, right? All you’ve got to do is identify a question or two and you’ve got a winning research topic. Well, not quite…

A good dissertation or thesis topic has a few important attributes. Specifically, a solid research topic should be:

Let’s take a closer look at these:

Attribute #1: Clear

Your research topic needs to be crystal clear about what you’re planning to research, what you want to know, and within what context. There shouldn’t be any ambiguity or vagueness about what you’ll research.

Here’s an example of a clearly articulated research topic:

An analysis of consumer-based factors influencing organisational trust in British low-cost online equity brokerage firms.

As you can see in the example, its crystal clear what will be analysed (factors impacting organisational trust), amongst who (consumers) and in what context (British low-cost equity brokerage firms, based online).

Need a helping hand?

Attribute #2:   Unique

Your research should be asking a question(s) that hasn’t been asked before, or that hasn’t been asked in a specific context (for example, in a specific country or industry).

For example, sticking organisational trust topic above, it’s quite likely that organisational trust factors in the UK have been investigated before, but the context (online low-cost equity brokerages) could make this research unique. Therefore, the context makes this research original.

One caveat when using context as the basis for originality – you need to have a good reason to suspect that your findings in this context might be different from the existing research – otherwise, there’s no reason to warrant researching it.

Attribute #3: Important

Simply asking a unique or original question is not enough – the question needs to create value. In other words, successfully answering your research questions should provide some value to the field of research or the industry. You can’t research something just to satisfy your curiosity. It needs to make some form of contribution either to research or industry.

For example, researching the factors influencing consumer trust would create value by enabling businesses to tailor their operations and marketing to leverage factors that promote trust. In other words, it would have a clear benefit to industry.

So, how do you go about finding a unique and valuable research topic? We explain that in detail in this video post – How To Find A Research Topic . Yeah, we’ve got you covered 😊

Step 3: Write a convincing research proposal

Once you’ve pinned down a high-quality research topic, the next step is to convince your university to let you research it. No matter how awesome you think your topic is, it still needs to get the rubber stamp before you can move forward with your research. The research proposal is the tool you’ll use for this job.

So, what’s in a research proposal?

The main “job” of a research proposal is to convince your university, advisor or committee that your research topic is worthy of approval. But convince them of what? Well, this varies from university to university, but generally, they want to see that:

• You have a clearly articulated, unique and important topic (this might sound familiar…)
• You’ve done some initial reading of the existing literature relevant to your topic (i.e. a literature review)
• You have a provisional plan in terms of how you will collect data and analyse it (i.e. a methodology)

At the proposal stage, it’s (generally) not expected that you’ve extensively reviewed the existing literature , but you will need to show that you’ve done enough reading to identify a clear gap for original (unique) research. Similarly, they generally don’t expect that you have a rock-solid research methodology mapped out, but you should have an idea of whether you’ll be undertaking qualitative or quantitative analysis , and how you’ll collect your data (we’ll discuss this in more detail later).

Long story short – don’t stress about having every detail of your research meticulously thought out at the proposal stage – this will develop as you progress through your research. However, you do need to show that you’ve “done your homework” and that your research is worthy of approval .

So, how do you go about crafting a high-quality, convincing proposal? We cover that in detail in this video post – How To Write A Top-Class Research Proposal . We’ve also got a video walkthrough of two proposal examples here .

Step 4: Craft a strong introduction chapter

Once your proposal’s been approved, its time to get writing your actual dissertation or thesis! The good news is that if you put the time into crafting a high-quality proposal, you’ve already got a head start on your first three chapters – introduction, literature review and methodology – as you can use your proposal as the basis for these.

Handy sidenote – our free dissertation & thesis template is a great way to speed up your dissertation writing journey.

What’s the introduction chapter all about?

The purpose of the introduction chapter is to set the scene for your research (dare I say, to introduce it…) so that the reader understands what you’ll be researching and why it’s important. In other words, it covers the same ground as the research proposal in that it justifies your research topic.

What goes into the introduction chapter?

This can vary slightly between universities and degrees, but generally, the introduction chapter will include the following:

• A brief background to the study, explaining the overall area of research
• A problem statement , explaining what the problem is with the current state of research (in other words, where the knowledge gap exists)
• Your research questions – in other words, the specific questions your study will seek to answer (based on the knowledge gap)
• The significance of your study – in other words, why it’s important and how its findings will be useful in the world

As you can see, this all about explaining the “what” and the “why” of your research (as opposed to the “how”). So, your introduction chapter is basically the salesman of your study, “selling” your research to the first-time reader and (hopefully) getting them interested to read more.

How do I write the introduction chapter, you ask? We cover that in detail in this post .

Step 5: Undertake an in-depth literature review

As I mentioned earlier, you’ll need to do some initial review of the literature in Steps 2 and 3 to find your research gap and craft a convincing research proposal – but that’s just scratching the surface. Once you reach the literature review stage of your dissertation or thesis, you need to dig a lot deeper into the existing research and write up a comprehensive literature review chapter.

What’s the literature review all about?

There are two main stages in the literature review process:

Literature Review Step 1: Reading up

The first stage is for you to deep dive into the existing literature (journal articles, textbook chapters, industry reports, etc) to gain an in-depth understanding of the current state of research regarding your topic. While you don’t need to read every single article, you do need to ensure that you cover all literature that is related to your core research questions, and create a comprehensive catalogue of that literature , which you’ll use in the next step.

Reading and digesting all the relevant literature is a time consuming and intellectually demanding process. Many students underestimate just how much work goes into this step, so make sure that you allocate a good amount of time for this when planning out your research. Thankfully, there are ways to fast track the process – be sure to check out this article covering how to read journal articles quickly .

Literature Review Step 2: Writing up

Once you’ve worked through the literature and digested it all, you’ll need to write up your literature review chapter. Many students make the mistake of thinking that the literature review chapter is simply a summary of what other researchers have said. While this is partly true, a literature review is much more than just a summary. To pull off a good literature review chapter, you’ll need to achieve at least 3 things:

• You need to synthesise the existing research , not just summarise it. In other words, you need to show how different pieces of theory fit together, what’s agreed on by researchers, what’s not.
• You need to highlight a research gap that your research is going to fill. In other words, you’ve got to outline the problem so that your research topic can provide a solution.
• You need to use the existing research to inform your methodology and approach to your own research design. For example, you might use questions or Likert scales from previous studies in your your own survey design .

As you can see, a good literature review is more than just a summary of the published research. It’s the foundation on which your own research is built, so it deserves a lot of love and attention. Take the time to craft a comprehensive literature review with a suitable structure .

But, how do I actually write the literature review chapter, you ask? We cover that in detail in this video post .

Step 6: Carry out your own research

Once you’ve completed your literature review and have a sound understanding of the existing research, its time to develop your own research (finally!). You’ll design this research specifically so that you can find the answers to your unique research question.

There are two steps here – designing your research strategy and executing on it:

1 – Design your research strategy

The first step is to design your research strategy and craft a methodology chapter . I won’t get into the technicalities of the methodology chapter here, but in simple terms, this chapter is about explaining the “how” of your research. If you recall, the introduction and literature review chapters discussed the “what” and the “why”, so it makes sense that the next point to cover is the “how” –that’s what the methodology chapter is all about.

In this section, you’ll need to make firm decisions about your research design. This includes things like:

• Your research philosophy (e.g. positivism or interpretivism )
• Your overall methodology (e.g. qualitative , quantitative or mixed methods)
• Your data collection strategy (e.g. interviews , focus groups, surveys)
• Your data analysis strategy (e.g. content analysis , correlation analysis, regression)

If these words have got your head spinning, don’t worry! We’ll explain these in plain language in other posts. It’s not essential that you understand the intricacies of research design (yet!). The key takeaway here is that you’ll need to make decisions about how you’ll design your own research, and you’ll need to describe (and justify) your decisions in your methodology chapter.

2 – Execute: Collect and analyse your data

Once you’ve worked out your research design, you’ll put it into action and start collecting your data. This might mean undertaking interviews, hosting an online survey or any other data collection method. Data collection can take quite a bit of time (especially if you host in-person interviews), so be sure to factor sufficient time into your project plan for this. Oftentimes, things don’t go 100% to plan (for example, you don’t get as many survey responses as you hoped for), so bake a little extra time into your budget here.

Once you’ve collected your data, you’ll need to do some data preparation before you can sink your teeth into the analysis. For example:

• If you carry out interviews or focus groups, you’ll need to transcribe your audio data to text (i.e. a Word document).
• If you collect quantitative survey data, you’ll need to clean up your data and get it into the right format for whichever analysis software you use (for example, SPSS, R or STATA).

Once you’ve completed your data prep, you’ll undertake your analysis, using the techniques that you described in your methodology. Depending on what you find in your analysis, you might also do some additional forms of analysis that you hadn’t planned for. For example, you might see something in the data that raises new questions or that requires clarification with further analysis.

The type(s) of analysis that you’ll use depend entirely on the nature of your research and your research questions. For example:

• If your research if exploratory in nature, you’ll often use qualitative analysis techniques .
• If your research is confirmatory in nature, you’ll often use quantitative analysis techniques
• If your research involves a mix of both, you might use a mixed methods approach

Again, if these words have got your head spinning, don’t worry! We’ll explain these concepts and techniques in other posts. The key takeaway is simply that there’s no “one size fits all” for research design and methodology – it all depends on your topic, your research questions and your data. So, don’t be surprised if your study colleagues take a completely different approach to yours.

Step 7: Present your findings

Once you’ve completed your analysis, it’s time to present your findings (finally!). In a dissertation or thesis, you’ll typically present your findings in two chapters – the results chapter and the discussion chapter .

What’s the difference between the results chapter and the discussion chapter?

While these two chapters are similar, the results chapter generally just presents the processed data neatly and clearly without interpretation, while the discussion chapter explains the story the data are telling  – in other words, it provides your interpretation of the results.

For example, if you were researching the factors that influence consumer trust, you might have used a quantitative approach to identify the relationship between potential factors (e.g. perceived integrity and competence of the organisation) and consumer trust. In this case:

• Your results chapter would just present the results of the statistical tests. For example, correlation results or differences between groups. In other words, the processed numbers.
• Your discussion chapter would explain what the numbers mean in relation to your research question(s). For example, Factor 1 has a weak relationship with consumer trust, while Factor 2 has a strong relationship.

Depending on the university and degree, these two chapters (results and discussion) are sometimes merged into one , so be sure to check with your institution what their preference is. Regardless of the chapter structure, this section is about presenting the findings of your research in a clear, easy to understand fashion.

Importantly, your discussion here needs to link back to your research questions (which you outlined in the introduction or literature review chapter). In other words, it needs to answer the key questions you asked (or at least attempt to answer them).

For example, if we look at the sample research topic:

In this case, the discussion section would clearly outline which factors seem to have a noteworthy influence on organisational trust. By doing so, they are answering the overarching question and fulfilling the purpose of the research .

For more information about the results chapter , check out this post for qualitative studies and this post for quantitative studies .

Step 8: The Final Step Draw a conclusion and discuss the implications

Last but not least, you’ll need to wrap up your research with the conclusion chapter . In this chapter, you’ll bring your research full circle by highlighting the key findings of your study and explaining what the implications of these findings are.

What exactly are key findings? The key findings are those findings which directly relate to your original research questions and overall research objectives (which you discussed in your introduction chapter). The implications, on the other hand, explain what your findings mean for industry, or for research in your area.

Sticking with the consumer trust topic example, the conclusion might look something like this:

Key findings

This study set out to identify which factors influence consumer-based trust in British low-cost online equity brokerage firms. The results suggest that the following factors have a large impact on consumer trust:

While the following factors have a very limited impact on consumer trust:

Notably, within the 25-30 age groups, Factors E had a noticeably larger impact, which may be explained by…

Implications

The findings having noteworthy implications for British low-cost online equity brokers. Specifically:

The large impact of Factors X and Y implies that brokers need to consider….

The limited impact of Factor E implies that brokers need to…

As you can see, the conclusion chapter is basically explaining the “what” (what your study found) and the “so what?” (what the findings mean for the industry or research). This brings the study full circle and closes off the document.

Let’s recap – how to write a dissertation or thesis

You’re still with me? Impressive! I know that this post was a long one, but hopefully you’ve learnt a thing or two about how to write a dissertation or thesis, and are now better equipped to start your own research.

To recap, the 8 steps to writing a quality dissertation (or thesis) are as follows:

• Understand what a dissertation (or thesis) is – a research project that follows the research process.
• Find a unique (original) and important research topic
• Craft a convincing dissertation or thesis research proposal
• Write a clear, compelling introduction chapter
• Undertake a thorough review of the existing research and write up a literature review
• Undertake your own research
• Present and interpret your findings

Once you’ve wrapped up the core chapters, all that’s typically left is the abstract , reference list and appendices. As always, be sure to check with your university if they have any additional requirements in terms of structure or content.  

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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20 Comments

thankfull >>>this is very useful

Thank you, it was really helpful

unquestionably, this amazing simplified way of teaching. Really , I couldn’t find in the literature words that fully explicit my great thanks to you. However, I could only say thanks a-lot.

Great to hear that – thanks for the feedback. Good luck writing your dissertation/thesis.

This is the most comprehensive explanation of how to write a dissertation. Many thanks for sharing it free of charge.

Very rich presentation. Thank you

Thanks Derek Jansen|GRADCOACH, I find it very useful guide to arrange my activities and proceed to research!

Thank you so much for such a marvelous teaching .I am so convinced that am going to write a comprehensive and a distinct masters dissertation

It is an amazing comprehensive explanation

This was straightforward. Thank you!

I can say that your explanations are simple and enlightening – understanding what you have done here is easy for me. Could you write more about the different types of research methods specific to the three methodologies: quan, qual and MM. I look forward to interacting with this website more in the future.

Thanks for the feedback and suggestions 🙂

Hello, your write ups is quite educative. However, l have challenges in going about my research questions which is below; *Building the enablers of organisational growth through effective governance and purposeful leadership.*

Very educating.

Just listening to the name of the dissertation makes the student nervous. As writing a top-quality dissertation is a difficult task as it is a lengthy topic, requires a lot of research and understanding and is usually around 10,000 to 15000 words. Sometimes due to studies, unbalanced workload or lack of research and writing skill students look for dissertation submission from professional writers.

Thank you 💕😊 very much. I was confused but your comprehensive explanation has cleared my doubts of ever presenting a good thesis. Thank you.

thank you so much, that was so useful

Hi. Where is the excel spread sheet ark?

could you please help me look at your thesis paper to enable me to do the portion that has to do with the specification

my topic is “the impact of domestic revenue mobilization.

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Trapped in dissertation revisions?

How long does it take to write a dissertation, published by steve tippins on july 11, 2019 july 11, 2019.

Last Updated on: 2nd February 2024, 05:00 am

How long does it take to write a dissertation? The most accurate (and least helpful) answer is, it depends. Since that’s probably not the answer you’re looking for, I’ll use the rest of the article to address the realities of how long it takes to write a dissertation.

How Long Does It Take to Write a Dissertation?

Based on my experience, writing your dissertation should take somewhere between 13-20 months. These are average numbers based upon the scores of doctoral students that I have worked with over the years, and they generally hold true.

I have seen people take less time and more time, but I believe that with concerted effort, the 13-20 month timeframe is reasonable. 

“Based on my experience, writing your dissertation should take somewhere between 13-20 months.”

University Requirements

Once you hit the dissertation stage, some schools require a minimum number of hours in the dissertation area before you can graduate. Many schools require the equivalent of one year of dissertation credits to graduate. 

So, even if you can finish your dissertation in three months, you will still have to pay for nine more months of dissertation credits before you can graduate. However, unless research and writing is your superpower, I wouldn’t worry about having to pay extra tuition.

But this requirement does offer some insight into how long it takes to write a dissertation. Based on this requirement, it’s reasonable to expect that writing your dissertation will take a year of more. This is consistent with my experience.

However, this timeframe is based on several assumptions. First, I am assuming that you are continually working towards finishing your dissertation. This means that no family emergencies, funding conundrums, or work issues get in the way of completing. Second, there are no major changes in your dissertation committee. Third, you will have access to the data that you need. 

Assuming these assumptions hold true, this article should give you a general idea of how long it might take to write your dissertation.

How Long Does it Take to Write A Dissertation? Stage-By-Stage

Let’s break down each stage of the dissertation writing process and how long it takes. 

Prospectus 

This is the hardest one to judge, as this is where you lay the groundwork for the rest of your dissertation and get buy-in from committee members. Normally this takes from 3-6 months. Not all of this is writing time, though–much of it is spent refining your topic and your approach.

Why does this stage take so long? For many people, starting to express themselves using an academic voice can take time. This can hold up the review process as your committee members ask for writing-related revisions before they even get to evaluating the content. Don’t worry, once you learn the academic language things will start to flow more easily.

One common mistake students make is lack of specificity, both in their writing in general and in their topic focus. 

Proposal (Chapters 1-3)

Chapter 1 is often an expansion of your Prospectus. However, you’ll be expected to develop your ideas more and have even more specificity on things like your research question and methodology, so don’t underestimate how long this chapter will take.

Chapter 2 can take some time as you will be digging deep into the literature but I think this can be done in 3-4 months. One caution, some people, and committees, like to start with Chapter 2 so that you are immersed in the literature before completing Chapters 1 and 3. Regardless of where you start, 3-4 months is a good estimate.

Chapter 3 requires an in-depth explanation of your methodology. I suggest working closely with your Chair on this one to avoid multiple submissions and revisions. Get clear on your methodology and make sure you and your chair are on the same page before you write, and continue to check in with your chair, if possible, throughout the process.

IRB Approval

While this step can be full of details and require several iterations it seems that allowing 2 months is sufficient. Most schools have an IRB form that must be submitted. To save time you can usually start filling out the form while your committee is reviewing your Proposal.

Collecting Data

This step varies a great deal. If you are using readily available secondary data this can take a week but if you are interviewing hard to get individuals or have trouble finding a sufficient number of people for your sample this can take 4 months or more. I think 1-4 months should be appropriate

Chapters 4 and 5

These two chapters are the easiest to write as in Chapter 4 you are reporting your results and in Chapter 5 you explain what the results mean. I believe that these two chapters can be written in 2 months.

Defense and Completion

You will need to defend your dissertation and then go through all of the university requirements to finalize the completion of your dissertation. I would allow 2 months for this process.

Variables That Affect How Long It Takes to Write A Dissertation 

When students say something like, “I’m going to finish my dissertation in three months,” they likely aren’t considering all of the variables besides the actual writing. Even if you’re a fast writer, you’ll have to wait on your committee’s comments, 

Timing Issues

Many schools have response times for committee members. This is important when looking at how long it takes to finish a dissertation. For example, it you have two committee members and they each get up to 2 weeks for a review, it can take up to a month to get a document reviewed, each time you submit. So, plan for these periods of time when thinking about how long that it will take you.

Addressing Comments

How long it takes to write your dissertation also depends on your ability to address your committee’s comments thoroughly. It’s not uncommon for a committee member to send a draft back several times, even if their comments were addressed adequately, because they notice new issues each time they read it. Save yourself considerable time by making sure you address their comments fully, thus avoiding unnecessary time waiting to hear the same feedback.

This is the biggest variable in the dissertation model. How dedicated are you to the process? How much actual time do you have? How many outside interests/requirements do you have? Are you easily distracted? How clean does your workspace need to be? (This may seem like a strange thing to discuss, but many people need to work in a clean space and can get very interested in cleaning if they have to write). Are you in a full-time program or in a part time program? Are you holding down a job? Do you have children? 

All of these things will affect how much time you have to put into writing–or rather, how disciplined you need to be about making time to write.

One of the things that can influence how long it takes to write your dissertation is your committee. Choose your committee wisely. If you work under the assumption that the only good dissertation is a done dissertation, then you want a committee that will be helpful and not trying to prove themselves on your back. When you find a Chair that you can work with ask her/him which of their colleagues they work well with (it’s also worth finding out who they don’t work well with).

Find out how they like to receive material to review. Some members like to see pieces of chapters and some like to see completed documents. Once you know their preferences, you can efficiently submit what they want when they want it.

How Long Does it Take to Write a Dissertation? Summary

Barring unforeseen events, the normal time range for finishing a dissertation seems to be 13-19 months, which can be rounded to one to one and a half years. If you are proactive and efficient, you can usually be at the shorter end of the time range. 

That means using downtime to do things like changing the tense of your approved Proposal from future tense to past tense and completing things like you Abstract and Acknowledgement sections before final approval.

I hope that you can be efficient in this process and finish as quickly as possible. Remember, “the only good dissertation is a done dissertation.”

On that note, I offer coaching services to help students through the dissertation writing process, as well as editing services for those who need help with their writing.

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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• Manuscript Preparation

Know How to Structure Your PhD Thesis

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Table of Contents

In your academic career, few projects are more important than your PhD thesis. Unfortunately, many university professors and advisors assume that their students know how to structure a PhD. Books have literally been written on the subject, but there’s no need to read a book in order to know about PhD thesis paper format and structure. With that said, however, it’s important to understand that your PhD thesis format requirement may not be the same as another student’s. The bottom line is that how to structure a PhD thesis often depends on your university and department guidelines.

But, let’s take a look at a general PhD thesis format. We’ll look at the main sections, and how to connect them to each other. We’ll also examine different hints and tips for each of the sections. As you read through this toolkit, compare it to published PhD theses in your area of study to see how a real-life example looks.

Main Sections of a PhD Thesis

In almost every PhD thesis or dissertation, there are standard sections. Of course, some of these may differ, depending on your university or department requirements, as well as your topic of study, but this will give you a good idea of the basic components of a PhD thesis format.

• Abstract : The abstract is a brief summary that quickly outlines your research, touches on each of the main sections of your thesis, and clearly outlines your contribution to the field by way of your PhD thesis. Even though the abstract is very short, similar to what you’ve seen in published research articles, its impact shouldn’t be underestimated. The abstract is there to answer the most important question to the reviewer. “Why is this important?”
• Introduction : In this section, you help the reviewer understand your entire dissertation, including what your paper is about, why it’s important to the field, a brief description of your methodology, and how your research and the thesis are laid out. Think of your introduction as an expansion of your abstract.
• Literature Review : Within the literature review, you are making a case for your new research by telling the story of the work that’s already been done. You’ll cover a bit about the history of the topic at hand, and how your study fits into the present and future.
• Theory Framework : Here, you explain assumptions related to your study. Here you’re explaining to the review what theoretical concepts you might have used in your research, how it relates to existing knowledge and ideas.
• Methods : This section of a PhD thesis is typically the most detailed and descriptive, depending of course on your research design. Here you’ll discuss the specific techniques you used to get the information you were looking for, in addition to how those methods are relevant and appropriate, as well as how you specifically used each method described.
• Results : Here you present your empirical findings. This section is sometimes also called the “empiracles” chapter. This section is usually pretty straightforward and technical, and full of details. Don’t shortcut this chapter.
• Discussion : This can be a tricky chapter, because it’s where you want to show the reviewer that you know what you’re talking about. You need to speak as a PhD versus a student. The discussion chapter is similar to the empirical/results chapter, but you’re building on those results to push the new information that you learned, prior to making your conclusion.
• Conclusion : Here, you take a step back and reflect on what your original goals and intentions for the research were. You’ll outline them in context of your new findings and expertise.

Tips for your PhD Thesis Format

As you put together your PhD thesis, it’s easy to get a little overwhelmed. Here are some tips that might keep you on track.

• Don’t try to write your PhD as a first-draft. Every great masterwork has typically been edited, and edited, and…edited.
• Work with your thesis supervisor to plan the structure and format of your PhD thesis. Be prepared to rewrite each section, as you work out rough drafts. Don’t get discouraged by this process. It’s typical.
• Make your writing interesting. Academic writing has a reputation of being very dry.
• You don’t have to necessarily work on the chapters and sections outlined above in chronological order. Work on each section as things come up, and while your work on that section is relevant to what you’re doing.
• Don’t rush things. Write a first draft, and leave it for a few days, so you can come back to it with a more critical take. Look at it objectively and carefully grammatical errors, clarity, logic and flow.
• Know what style your references need to be in, and utilize tools out there to organize them in the required format.
• It’s easier to accidentally plagiarize than you think. Make sure you’re referencing appropriately, and check your document for inadvertent plagiarism throughout your writing process.

PhD Thesis Editing Plus

Want some support during your PhD writing process? Our PhD Thesis Editing Plus service includes extensive and detailed editing of your thesis to improve the flow and quality of your writing. Unlimited editing support for guaranteed results. Learn more here , and get started today!

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Emeritus Professor, Edinburgh Napier University

How long does it take to write a PhD thesis?

My short answer is 68 days, but please read the detail below…

Bold resolutions: “Piled Higher and Deeper” by Jorge Cham www.phdcomics.com

As a PhD supervisor I have often been asked ‘How long do you think it will take me to write up my thesis?’ My answer always begins ‘It depends…’ We then continue the conversation with an audit of material already drafted that may contribute (in edited format) to the final thesis. These include the initial literature review from the first year transfer report, and posters, conference papers and journal articles presented and/or published from the on-going work.

For example third year PhD student John Mowbray , who is currently based within the Centre for Social Informatics (CSI) at Edinburgh Napier University , has a strong basis for his literature review chapter in the form of a conference paper delivered at CoLIS 2016 , which is due to be published in full in Information Research later this year. Similarly John’s fellow student Frances Ryan has already published an account of research design for her study. This paper will underpin the writing of her methods chapter.

Then we consider less formal sources, such as any discussions or debates that the student has documented publicly elsewhere, for example in blog posts. See, for instance Lyndsey Jenkins ‘ recent thoughts about the importance of research domain at http://lyndseyjenkins.org. These may well contribute to a section of Lyndsey’s methods chapter when she comes to write up her work in 2018.

The students also have ‘non-public’ material about their work that will be adapted for their theses. These include interim reports for their supervisors and/or other stakeholders. For example, last semester CSI PhD student Iris Buunk wrote a report on some of the empirical work that she has conducted for the body that gave her access to survey respondents. Handwritten ideas and remarks kept in notebooks over the course of PhD registration are also very valuable ‘private’ resources.

Once we have completed this audit, the challenge of transforming all the work completed to date into an 80,000 word thesis appears not to be so great – but of course, it still all needs to be done!

Records from writing up my own PhD have also recently served as another source for answering questions about preparing the main output of the doctoral study. I undertook my PhD part-time over a period of just over four years while working full-time. Throughout this period there were weeks when I could not progress my work at all. This was largely due to other commitments in intensive periods related to teaching such as the marking season towards the end of each semester. There were other times when it was much easier to devote myself to my PhD. For example, I took annual leave in University vacation time for this purpose (rather than went away on holiday). To guard against losing track of my PhD at times when I was too busy to devote any time to it I kept detailed notes of my progress. As a result of this, I know exactly how much time I spent writing up each chapter for the final version of the thesis. Although all PhD theses are different, the proportion of time on each type of chapter may be helpful to those who have resolved to submit their theses in 2017.

In total it took me 68 days to write up my thesis (NB 68 to write up the work, not 68 days to complete the PhD!) This is the equivalent of approximately 14 working weeks, assuming a five day week. It needs to be borne in mind, however, that I was a part-time student. In practice the writing up was done over the last seven months of the four and half years in which I worked on the entire doctoral study.

The largest portion of the writing-up time – around three quarters – was spent on the two chapters that related the findings of my research, and about a fifth on the discussion chapter. My literature review took very little time to write up (just 5 days) because I had already presented much of it in published form. The methods and conclusions chapters did not take very long either (3.5 and 2.5 days respectively) largely because their content was straightforward. My introductory chapter was very short at a page and a half and was thus drafted in just a couple of hours.

As might be deduced from the time allocations given above, I found the results and discussion chapters most heavy-going. The former was due to the quantity of empirical data to convert into a fluent account of the findings, and the latter because of the intellectual challenge of expressing the meaning of the findings and how the outcomes of my study represented an original contribution to the domain. However, once these two elements were ‘cracked’ it was a relatively easy task to pull all the other chapters together.

If you are reading this blog post as a PhD student in the later stages of your work, I would advise you to be prepared for the long haul of writing up your results and the discussion chapters, and ensure that you allocate a high proportion of your write-up time to these accordingly. It is also worth noting that I found that the closer I came to the target of completing my write-up, the more important it was for me to avoid other distractions. You cannot control for all of them (for example, illness), but I would caution against getting actively involved in anything that will take you away from your PhD at this intensive stage, such as planning a big event (for example, a major holiday, a house move, or a family wedding) or starting a new job.

If you are still in the early stages of your doctoral study, my first piece of advice is to plan your conference participation and journal paper publishing activity with the final thesis in mind. Be selective and strategic so that you prioritise engagement in external events that are valuable to the completion of your thesis and/or your future career. Each piece of work that you present externally should progress your study by encouraging you to write-up as you go along (for example in the form of a poster, a set of slides, a full paper), defend your ideas in person within your academic community, seek feedback on work completed to date, and solicit advice on the later stages. You should also be documenting any thoughts or ideas that may be valuable to writing up in a format that make sense to you, whether this be in a set of handwritten notes or in a more public format such as a series of structured blog posts.

Good luck to all those who will submit their theses in 2017!

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x == (s || z). You say it kwontized

Rule of Three: advice on writing a PhD thesis

PhD students sometimes get the same bad advice on writing their thesis. I call this advice the Rule of Three . Typically, they get told that their thesis:

• Will take 3 months to write
• Should have 3 results chapters
• Should be 300 pages

These bits of advice have one thing in common: they are all wrong.

• If you have been organised (see below), it should not take 3 months to write a PhD thesis. It certainly shouldn’t involve leaving the lab 3 months before your hand-in date to write up.
• Theses can have one results chapter or they can have more. How many chapters depends on your project, and your results. Trying to make three results chapters out of one chapter ends up in a weak or overlong thesis.
• A thesis is like a piece of string and it will be as long as it needs to be. Aim for brevity and not producing a magnus opus (see below).

Disclaimer: what follows is some different advice. As with all “advice”, your mileage will vary. It is written for the people in my lab but likely applies to UK PhD students doing biomedical research.

Rule 1: aim for a thesis that is good enough

Who will read your thesis? Two people. Your examiners. OK, some parts – such as the Methods section – will be useful to future lab members (although with electronic lab notebooks this function is becoming redundant). Maybe your thesis will be downloaded by someone from the repository, but essentially, it will only be read by your examiners.

How long does an examiner spend reading your thesis? A few hours. One day maximum. They simply have no more time. Do you really want to spend three months of your life writing something that will be read for just a few hours by two people?

It’s for these reasons that spending too much effort writing a perfect thesis is a waste of time. It just needs to be good enough.

As well as being just good enough, it only needs to be long enough . A big mistake students make is to produce a really long thesis because they think that that is what theses should be (rule of 3). What happens is the examiner will receive the thesis, look at how many pages there are, subtract the bibliography, and their heart will sink if it is too long.

You might now be wondering: is writing a thesis a waste of time?

No, because you have to do it to get your PhD.

No, because you learn important writing skills. You also learn how to assemble a large document (it’s often how students learn to use Word properly or up their LaTeX game). It’s good training for writing papers and other technical documents down the line. Employers know this when they hire you.

But that is about it. So you just need to write something that is good enough to pass.

Rule 2: prioritise papers and the thesis will follow

Papers are the priority. They are more useful to you and to your PI. But this advice isn’t motivated by self-interest. If you go into the viva and the work in your thesis is already peer reviewed and published, it’s harder for the examiners to criticise it. At least, they will not approach your thesis with the question: is this work publishable? This is one criteria for passing your PhD, so demonstrating that it is publishable means you are (almost) there.

This was the one bit of advice I received when doing my PhD and it is still true today. OK, it is harder these days to get a first author paper published before you submit your thesis. However a preprint on bioRxiv before you begin writing will help you to prepare your thesis and will still tick the publishable box.

How long should it take to write your thesis?

There is tension here because you are at your most useful in the lab as you near the end of your PhD. One week of labwork now is worth one month (or more) earlier in your PhD. You are most valuable to your lab/PI/science/career at this point and keeping working in the lab will yield more rewards. But it won’t get your thesis written.

The first bit of writing is busywork and can be done around lab work. “Deep writing” and reading does need time away for most students.

If you have only collected data in the lab and not analysed it, if you’ve not presented your work very often, if you are disorganised… yes, it will take you a full three months to write your thesis.

All the folks in my lab are encouraged to get figures ready, analyse as they go and they also give regular talks. It should not take anyone in this position three months away from the lab to write their thesis.

Agreeing a timeline with your PI for when you begin writing is really important. Regular deadlines and a commitment to timely feedback from your supervisor make thesis writing easier. The discussion needs to be based on facts though. Often students want to budget a lot of time to writing, because of the rule of 3 or because they believe they are “bad at writing”. It helps to see some evidence. Writing draft chapters earlier in the PhD – which is a requirement at some universities – can reveal difficulties and weaknesses.

Reality check

If you hear the rule of 3 from everyone and your supervisor is giving you different advice. It might be time for a reality check. Have a look at past theses from the lab. How long were they? How many chapters? Information is good.

You can see that all theses are fewer than 300 pages in length, many substantially so. Four have three chapters and two have two. Although looking closer, two of the theses with three chapters use a results chapter as an expanded methods chapter.

Ultimately, the thesis is your work but you will get input from your supervisor. Regardless of what is written here or how many people tell you about the rule of 3, your supervisor will have their own ideas about how your thesis should be. Agreeing a sensible plan with them is the way to get started productively.

Getting started

This is not a comprehensive guide but in order to write a good enough thesis, you first need a plan.

• Make a figure list. This should be every single figure you can think of. You can cross off ones you don’t need later if they don’t fit or are insignificant.
• Plan the narrative. There is usually more than one way to put together the figures to make a thesis. Be prepared for this to change after you start writing! Sometimes the writing process reveals ways in which the narrative should be rearranged.
• How many results chapters? Start with the idea that you will have one. Does it need dividing? If yes, then what are the titles of the two chapters? If you have difficulty titling them you may need to split to a 3rd.

Now you have a plan. It’s time to get going.

Set some goals – but make them small. Having a goal of “I am going to complete my thesis” is too demoralising. You need to feel like you are making progress constantly to stay motivated. Break it down into smaller chunks. “I will finish this chapter by next Friday”. “I will write the cloning section this morning and then go for a walk”.

Write the materials and methods first . It’s the easiest bit to write because it is all technical writing with little wordcraft required. You can fit it around labwork. In fact, it is easier to write whilst in the lab because you can look up all the stuff you need. Importantly, it gets over the “blank page syndrome”.

Next get your figures together . This should already be done if you have been organised.

Then write the figure legends . You already have the title for each figure from your plan. All you need to do is describe each panel. Again, quite low energy writing required for this task.

Now write the results sections ! This is the same way that we put papers together. The results parts of the thesis are more extended but in principle you will guide the reader though the figures that you’ve made. Remember, you already have the legends written. So you are already partly on your way.

Time to regroup . At this point you can refine your plan for the introduction and check the rest of your plan still makes sense. Now is the time for some deep writing and reading.

The post title comes from “Rule of Three” by The Lemonheads.

Easily understand how to write a PhD thesis introduction

Feb 26, 2019

Have you checked out the rest of  The PhD Knowledge Base ? It’s home to hundreds more free resources and guides, written especially for PhD students. 

Get the introduction right and the rest of your dissertation will follow. 

What is the purpose of a PhD thesis introduction?

1. establish your research territory (by situating your research in a broader context), 2. establish and justify your niche (by describing why your research is needed) , 3. explain the significance of your research (by describing how you conducted the research).

• What your thesis is about
• Why it is important
• How it was conducted
• How it is laid out

The introduction as a whole should outline the significance and relevance of the thesis. The main criteria for a PhD is its role as an original contribution to knowledge, so the introduction is the space in which you very clearly outline that contribution.

How to structure an PhD thesis introduction

A typical PhD thesis introduction follows the following format:

• Introduction to the introduction: a short version (of only a few paragraphs) of the thesis’ aims, research questions, contribution, objectives and findings.
• State the overarching topic and aims of the thesis in more detail Provide a brief review of the literature related to the topic (this will be very brief if you have a separate literature review chapter)
• Define the terms and scope of the topic
• Critically evaluate the current state of the literature on that topic and identify your gap
• Outline why the research is important and the contribution that it makes
• Outline your epistemological and ontological position
• Clearly outline the research questions and problem(s) you seek to address
• State the hypotheses (if you are using any)
• Detail the most important concepts and variables
• Briefly describe your methodology
• Discuss the main findings
• Discuss the layout of the thesis

Much like the abstract, the reader shouldn’t have to wait long before they understand the contribution, what you are doing and how you are doing it. So, you’ll start by presenting your research in a clear, concise way in the opening few paragraphs. These opening paragraphs should briefly summarise the aims, objectives, research questions, main argument and contribution.

The reader shouldn’t have to wait long before they understand the contribution, what you are doing and how you are doing it.

A useful exercise here is to try and write the core elements of an introduction on a Post-it note. Keep trying until they fit. When they do, use that as the basis for these first few paragraphs. This is the same technique you use when filling out the PhD Writing Template .

As you go through the chapter, you will dial down into more and more detail. That means that the next stage, after the first few paragraphs, is to provide some context (steps 2-10 above).

Here you provide all the detail necessary to situate the study and make sense of the opening few paragraphs.

But, there are two things to bear in mind.

You will need to ease into the detail gently. Don’t launch straight from your opening paragraphs into huge amounts of detail. Follow the order of the 13 steps above and you will gradually ease into your discussion.

The danger of presenting too much information too soon is that you will confuse the reader. They will struggle to understand how the information you present is relevant and will struggle to understand how it relates to your thesis aims and objectives.  

Simply follow the steps above.

You need to bear in mind that the level of detail you will go into (and therefore the length of the introduction) depends on the structure of your thesis.

If you have a standalone literature review, you will go into less detail about the current state of the literature and the gaps within it.

Similarly, if you have a dedicated theory chapter, you will not need to spend too much time on developing your theory framework.

The same is true for your methods.

The goal in any case is to present enough context to situate and make sense of your research questions but not overburden the reader with information that is superfluous to the goal of situating the research and which you will repeat at a later juncture anyhow.

Your PhD thesis. All on one page.

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

Common problems when writing your introduction

When we proofread PhDs , we see the same mistakes again and again.

Providing too much detail

There is a tendency to provide too much background information in the introduction. As we saw above, quite how much information you present in your thesis will depend on whether you have a standalone literature review or methods chapter. What you want to avoid is any unnecessary repetition.

Sometimes there is necessary repetition though. You need to present just enough information to contextualise your study and to be able to situate your aims, research questions an argument, but not too much that you end up confusing and bombarding the reader. Keep things simple here; it’s fine to overlook some of the more technical detail at this stage. Think of a newspaper article: the first couple of paragraphs provide a brief overview of the story. The detail comes later.

Not providing enough detail

On the flip side, some students don’t provide enough detail. The danger here is that the reader is left asking questions at the end of the introduction. Remember: they should be able to understand what your thesis is about, how it was conducted and why it is important just from reading the introduction. If you present too little detail then they won’t be able to. Read through your own introduction; is it clear what your contribution is and why it is important? If not, you haven’t got enough detail.

Launching into too much detail

Make sure you introduce gently. Don’t suddenly rush into lots of detail. Instead, you should make the aims, questions and contribution clear in the opening lines and then gradually layer on more detail. That way, the reader can keep up. Present too much detail too soon and the reader will become confused. The last place you want confusion is in the introduction; if the reader can’t follow your introduction, they won’t understand the thesis.

Not following a coherent structure so that the reader is left confused

Some students don’t follow a coherent logic when they write their introductions, which means that the reader is left confused.

For example, if you present too much background information and literature review before you outline the aim and purpose of the research, the reader will struggle to follow, because they won’t know why the background information is important.

The same is true if you discuss the methods before your research questions.

What we see often is important information being spread throughout the introduction in such a way that the reader has to hunt for it. Follow our layout guide above so that each piece of vital information is contained in its own mini section. Make your reader’s job as easy as possible.

Using too much technical language not properly defined

It’s more than likely that your research relies upon lots of technical terms, concepts and techniques. If you must talk about any of these in the introduction, be sure to offer clear and concise definitions. A failure to do so means that the reader is left confused.

Conducting a literature review

Unless you are explicitly avoiding a standalone literature review chapter, the introduction is not the place to review the literature. Sure, you will need to situate your study in a body of literature, but the introduction isn’t the place to critically discuss it or justify its inclusion in that literature. It’s enough to say that you will contribute to X body of literature and briefly discuss its core features and shortcomings. The literature review is the place to justify that decision and elaborate upon its features. Read our guide to writing literature reviews and our guide to being critical when you do so.

Finalising your thesis introduction

Once you have finished your thesis, ask yourself the following questions:

• Does the first line of the introduction discuss the problem that your thesis is addressing and the contribution that it is making?
• Does the introduction provide an overview of the thesis and end with a brief discussion on the content of each chapter?
• Does the introduction make a case for the research?
• Have the research questions/problems/hypotheses been clearly outlined (preferably early on)?

Now you know how to present your research as clearly and concisely as possible. Your reader (and examiner) will thank you, because they’ll be able to understand exactly what your study is about just from reading the introductory pages. Keep this guide to hand, whatever stage of the writing process you are at.

Have you downloaded our free one page PhD Writing Template ? It’s a really effective way to visualise your entire thesis on one page.

If you’re still struggling to structure your introduction, or you need any other support as you write your thesis, check out our  one-on-one PhD coaching . It’s like having a personal trainer, but for your PhD. 

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12 comments.

I was struggling with writing the introduction chapter. Really had no idea on how to organise my ideas. Completely lost and desolate. I have no one to encourage or support me. I prayed to God to give me knowledge and wisdom and guide me. After a while I found this site. Praise the Lord. I can’t thank u enough for addressing exactly what’s in my mind. Thank you. Glory be to God for directing me to this site.

All we aim to do here is to make life a little bit easier for PhD students. I know how hard I found it when I was completing mine, so I want to give something back to the community. I’m so pleased you found it useful. Good luck with writing up. If you need any support or if you have any questions at all, email me: [email protected]

Thank you very very much for your information it is resourceful. I was having a problem how to start my introduction.

It’s great to hear you found it useful. Thanks!

Hey, this is so useful thankyou! I’m wondering does this apply broadly to all PhD’s including humanities?

Yep – it sure does.

I found this piece of information helpful. I am preparing for my proposal defense in two weeks and needed to refine my introduction. Thank you very much. God bless you richly. I wish we can have a skype conversation.

Thank you again.

thank you, learned from this

Your advice and guidance has become my constant companion in what has been a very stressful time. You write with empathy and understanding. What a wonderful job you are doing for those of us who are too proud to seek advice and support from supervisors or colleagues. Sincerest thanks for taking the loneliness out of writing.

Such nice words. Thank you so much.

this is really useful!!!

Thanks Nora! I’m glad you found it useful.

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AI For Dissertation: Best AI Tools For Masters & PhD Thesis Writing

Writing a dissertation can be a daunting task for master’s and PhD students, but AI tools are transforming this challenging process.

From generating detailed mind maps to providing accurate citations and real-time research insights, AI-powered writing assistants streamline every aspect of thesis writing.

In this article, we explore the best AI tools available, highlighting how they can:

• enhance your academic writing,
• simplify complex tasks, and
• help produce high-quality, well-structured content.

Best AI Tools For Masters & PhD Thesis Writing

Heuristi.ca – mind map maker.

Heuristi.ca is an AI-powered writing tool designed to streamline your dissertation writing process.

This AI tool helps you write your thesis or research paper by creating detailed mind maps tailored to academic writing.

You input a topic, like “organic photovoltaics,” and the AI generates related concepts and real-time insights. This assists in organising your literature review and structuring your academic writing.

The AI assistant ensures coherence by connecting related ideas, making writing more manageable and efficient. It helps avoid plagiarism by providing original content and AI-driven citation suggestions. 

By automating and streamlining aspects of the writing process, Heuristi.ca helps you meet deadlines and maintain academic standards.

Open Read – Generate Summaries 

Open Read is an AI-powered writing tool designed to enhance your thesis writing process. With Open Read, you can upload research papers and receive AI-generated summaries, making literature reviews more manageable.

The tool offers real-time features like paper Q&A and AI summaries, which streamline your academic writing tasks.

For example, you can upload a PDF and get a concise summary, along with detailed insights into the paper’s background and significance.

This AI tool helps you write your dissertation by breaking down complex information into bite-sized chunks, saving you time and effort.

Open Read also helps ensure academic integrity by providing accurate citations and checking for plagiarism. 

Explain Paper

Explain Paper is an AI tool that simplifies the academic writing process.E xplain Paper supports dissertation writing by making research more digestible and manageable. 

To use Explain Paper, simply:

• upload a research paper,
• select the text you want to understand, and
• choose an explanation level. 

The AI provides clear explanations tailored to different educational levels, making complex research accessible. 

You can select a dense paragraph and have it explained as if to a middle schooler or a college student. This helps in breaking down intricate concepts, streamlining your literature review.

The tool also ensures your writing adheres to academic standards by providing coherent summaries and related resources.

Paper Brain

Paper Brain is an AI tool designed to streamline your academic writing process. You upload a research paper, and the AI analyzes and generates concise summaries, making it easier to grasp complex topics.

This AI-powered assistant helps you write your dissertation by providing clear and concise explanations, ensuring coherence and adherence to academic standards.

You can upload a paper on graphene thickness measurement, and Paper Brain will summarise its key points and answer specific questions about the research.

This tool is invaluable for literature reviews, as it simplifies the extraction of relevant information. Graduate students writing a thesis can benefit from Paper Brain’s AI technology, which makes writing tasks more manageable and efficient. 

Einblick is an AI-powered writing tool that simplifies data visualisation for your dissertation or thesis.

Einblick can analyze and present data coherently, helping you write your dissertation with accurate, well-organized charts and graphs.

This tool is simple to use – you upload your dataset, describe the chart you need, and Einblick generates it.

This tool helps you visualise complex data quickly and efficiently, which is crucial for academic writing and literature reviews.

Let’s say if you need a scatterplot of N2O versus CH4 emissions. Einblick creates it in seconds. This AI assistant streamlines the thesis writing process by providing clear, tailored visual aids, ensuring your research paper meets academic standards.

Tavily is an AI-powered research assistant designed to streamline your dissertation writing process. You simply input your research topic, and Tavily creates an AI agent that scours the internet for relevant information.

This AI tool ensures that the data you receive is accurate and up-to-date, helping you write your dissertation or thesis with confidence.

If you’re researching organic photovoltaic devices, Tavily will provide:

• detailed summaries,
• key findings, and
• relevant sources.

This AI writing assistant makes the literature review process more manageable by automating and streamlining the search for academic papers.

It also offers tailored content that adheres to academic standards, ensuring coherence and quality in your writing.

Graduate students find Tavily invaluable for meeting deadlines and enhancing academic writing skills. The tool’s ability to analyse and synthesise vast amounts of data in real-time allows you to focus on the writing process. 

Using AI technology, Tavily helps you achieve academic success by providing a solid foundation for your thesis or dissertation.

Power Drill

Power Drill is an AI-powered writing tool designed to assist you in the dissertation writing process. To use Power Drill, you start by uploading your data sets, which can be:

• web pages, or
• files. 

The AI then analyses these data sources, providing insightful summaries and answers to specific questions.

This helps streamline the research and writing process, making it easier to organize your thoughts and structure your thesis.

You can upload a research paper, and Power Drill will highlight key points and generate concise summaries. This AI tool helps you write your dissertation by breaking down complex information into manageable parts, saving you time and effort.

Graduate students find Power Drill particularly helpful for meeting deadlines and adhering to academic standards.

The tool ensures coherence in your writing by offering accurate citations and reducing the risk of plagiarism.

Power Drill can also scan vast databases and provide real-time insights, helping you achieve academic success and write a compelling thesis or dissertation.

SciSpace is an AI-powered writing tool designed to simplify the academic writing process for dissertations and theses.

You upload your research papers, and SciSpace provides detailed summaries and key insights, making literature reviews more manageable. This AI tool uses natural language processing to extract essential information, helping you organise and streamline your writing tasks.

When you upload a PDF, SciSpace offers a concise “too long; didn’t read” summary, highlighting conclusions and significant points.

This is especially useful for graduate students who need to write their dissertation or thesis efficiently.

SciSpace also includes an AI writing assistant, which helps ensure coherence and adherence to academic standards by generating accurate citations and checking for plagiarism.

Using AI technology, SciSpace can analyze vast databases and provide real-time insights, making your writing process more manageable.

This tool helps you meet deadlines and achieve academic success by automating and streamlining various aspects of academic writing, ultimately supporting you in creating well-structured and high-quality written content.

Next Net – For Drug & Health Research

Next Net is an AI-powered tool tailored for those in the drug and health research fields. This AI assistant helps you write your dissertation or thesis by providing comprehensive, real-time searches of the latest literature and research data.

You input your query, and Next Net uses advanced AI technology to scan vast databases for relevant information.

For example, if you’re researching new drugs, Next Net offers detailed insights into recent developments, gene expressions, and clinical trials.

This AI tool streamlines the literature review process by organizing complex data into manageable, easy-to-understand summaries.

Graduate students find Next Net invaluable for its ability to generate accurate citations and ensure academic integrity.

Next Net’s AI-driven approach makes writing a thesis more efficient, allowing you to focus on creating well-structured, high-quality content. This AI tool is essential for achieving academic success in the health research domain.

Chat GPT, Perplexity, Bing

ChatGPT , Perplexity, and Bing are AI-powered writing tools that significantly enhance the academic writing process. Each tool leverages artificial intelligence to assist with various aspects of dissertation and thesis writing.

ChatGPT uses advanced natural language processing to generate coherent, detailed responses to research queries. You can ask it to help:

• outline your thesis,
• draft sections of your dissertation, or
• provide insights on complex topics.

It’s particularly useful for brainstorming and refining ideas, making writing tasks more manageable. You can also create templates to help you generate texts faster.

Perplexity , another AI tool, excels at providing real-time, accurate references for your research paper. By asking Perplexity detailed questions, you get precise, sourced answers that streamline your literature review. 

Bing, integrated with AI, offers a balanced approach to research. You can specify the type of response you need—creative, balanced, or precise—making it a versatile assistant for academic writing.

Bing’s AI can scan vast databases to find relevant information, helping you write your dissertation or thesis with comprehensive, up-to-date data.

By automating and streamlining the writing process, ChatGPT, Perplexity, and Bing help you achieve academic success with well-researched and well-written content.

AI Tools For Thesis and Dissertation Writing

From Heuristi.ca’s mind mapping to ChatGPT’s brainstorming capabilities, these AI-powered assistants streamline literature reviews, ensure academic standards, and provide accurate citations.

Tools like Open Read and Explain Paper simplify complex concepts, while Einblick and Next Net offer data visualization and real-time research insights.

By leveraging these AI tools, graduate students can enhance their academic writing skills, meet deadlines, and achieve academic success efficiently.

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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