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- Researching and Writing a Masters Dissertation
Written by Mark Bennett
All Masters programmes include some form of extended individual project. Research-focussed programmes, such as an MRes , may include multiple independent research components, while taught courses usually culminate with a substantial research task, referred to as the Masters dissertation or thesis.
The advice in this article is designed with the dissertation component of a taught programme in mind, but will also apply more generally to comparable projects forming part of a research degree.
Before you get started on your dissertation, you'll usually need to write a proposal. Read our full guide to Masters dissertation proposals for more information on what this should include!
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What’s the difference between a masters dissertation and an undergraduate dissertation.
The Masters thesis is a bridge between undergraduate study and higher level postgraduate degrees such as the PhD , which are awarded following the completion of an extended research programme over several years.
Therefore, depending on your subject area, a postgraduate dissertation may not look all that different to its undergraduate equivalent. You’ll usually be expected to produce a much longer piece of work, but the essential nature of the task won’t be unfamiliar to you if you’ve already completed a research project for your Bachelors degree.
After all, one of the purposes of an undergraduate dissertation or final year project is to prepare you for more in-depth research work as a postgraduate. That said, there are some important differences between the two levels.
A Masters dissertation will be longer than the undergraduate equivalent – usually it’ll be somewhere between 15,000 and 20,000 words, but this can vary widely between courses, institutions and countries.
To answer your overall research question comprehensively, you’ll be expected to identify and examine specific areas of your topic. This can be like producing a series of shorter pieces of work, similar to those required by individual modules. However, there’s the additional requirement that they collectively support a broader set of conclusions.
This more involved Masters dissertation structure will:
- Give you the scope to investigate your subject in greater detail than is possible at undergraduate level
- Challenge you to be effective at organising your work so that its individual components function as stages in a coherent and persuasive overall argument
- Allow you to develop and hone a suitable research methodology (for example, choosing between qualitative and quantitative methods)
If the individual topics within your overall project require you to access separate sources or datasets, this may also have an impact on your research process.
As a postgraduate, you’ll be expected to establish and assert your own critical voice as a member of the academic community associated with your field .
During your Masters thesis you’ll need to show that you are not just capable of analysing and critiquing original data or primary source material. You should also demonstrate awareness of the existing body of scholarship relating to your topic .
So, if you’ll excuse the pun, a ‘Masters’ degree really is about achieving ‘mastery’ of your particular specialism and the dissertation is where you’ll demonstrate this: showing off the scholarly expertise and research skills that you’ve developed across your programme.
What’s the difference between a dissertation and a thesis?
A dissertation is a long piece of (usually) written work on the same topic. A thesis is a little more specific: it usually means something that presents an original argument based on the interpretation of data, statistics or content.
So, a thesis is almost always presented as a dissertation, but not all dissertations present a thesis.
Masters dissertation structure
As you can probably imagine, no two dissertations follow the exact same structure, especially given the differences found between Masters programmes from university to university and country to country .
That said, there are several key components that make up the structure of a typical Masters dissertation
Usually around 300 words long, the abstract is meant to be a concise summary of your dissertation. It should briefly cover the question(s) you aim to answer, your primary argument and your conclusion.
Introduction
The purpose of the introduction is to provide context for the rest of the dissertation, setting out your aims and the scope of what you want to achieve with your research. The introduction should give a clear overview of the dissertation’s chapters and will usually be around 1,000 words long.
Literature review
This part of the dissertation should examine the scholarship that has already been published in your field, presenting various arguments and counter-arguments while situating your own research within this wider body of work.
You should analyse and evaluate other publications and explain how your dissertation will contribute to the existing literature in your subject area. The literature review sometimes forms part of the introduction or follows immediately on from it. Most literature reviews are up to 1,000 words long.
Research methodology
Not all dissertations will require a section covering research methodology (Arts and Humanities dissertations won’t normally undertake the kind of research that involves a set methodology). However, if you are using a particular method to collect information for your dissertation, you should make sure to explain the rationale behind your choice of methodology. The word count for this part of the dissertation is usually around the 1,500 mark.
Those in the Arts and Humanities will usually outline their theoretical perspectives and approaches as part of the introduction, rather than requiring a detailed explanation of the methodology for their data collection and analysis.
Results / findings
If your research involves some form of survey or experiment, this is where you’ll present the results of your work. Depending on the nature of the study, this might be in the form of graphs, tables or charts – or even just a written description of what the research entailed and what the findings were.
This section forms the bulk of your dissertation and should be carefully structured using a series of related chapters (and sub-chapters). There should be a logical progression from one chapter to the next, with each part building on the arguments of its predecessor.
It can be helpful to think of your Masters dissertation as a series of closely interlinked essays, rather than one overwhelming paper. The size of this section will depend on the overall word count for your dissertation. However, to give you a rough idea for a 15,000-word dissertation, the discussion part will generally be about 12,000 words long.
Here you should draw together the threads of the previous discussion chapters and make your final concluding statements, drawing on evidence and arguments that you’ve already explored over the course of the dissertation. Explain the significance of your findings and point towards directions that future research could follow. This section of the Masters thesis will be around 1,500 words long.
References / bibliography
While planning and writing your dissertation, you should keep an extensive, organised record of any papers, sources or books you’ve quoted (or referred to). This will be a lot easier than leaving all of it until the end and struggling to work out where a particular quotation is from!
Appendices won’t be necessary in many dissertations, but you may need to include supplementary material to support your argument. This could be interview transcripts or questionnaires. If including such content within the body of the dissertation won’t be feasible – i.e. there wouldn’t be enough space or it would break the flow of your writing – you should consult with your supervisor and consider attaching it in an appendix.
It’s worth bearing in mind that these sections won’t always be discretely labelled in every dissertation. For example, everything up to ‘discussion’ might be covered in introductory chapter (rather than as distinct sections). If you’re unsure about the structure of your Masters dissertation, your supervisor will be able to help you map it out.
How long is a Masters dissertation?
Most dissertations in the UK will be between 15,000 and 20,000 words long, although this can vary significantly depending on the nature of the programme.
You should also check with your university exactly which sections of the dissertation count towards the final word count (the abstract, bibliography and appendices won’t usually be included in the total).
How does supervision work for a Masters dissertation?
As a Masters student at the dissertation stage you’ll usually be matched with an academic within your institution who will be tasked with guiding your work. This might be someone who has already taught you, or it may be another scholar whose research interests and expertise align well with what you want to do. You may be able to request a particular supervisor, but taught postgraduates are more likely to be assigned them by their department.
Specific arrangements with your supervisor will vary depending on your institution and subject area. They will usually meet with you at the beginning of the dissertation period to discuss your project and agree a suitable schedule for its undertaking. This timetable will probably set dates for:
- Subsequent discussions and progress checks
- The submission of draft chapters or sections
- Feedback appointments
Though your supervisor is there to help and advise you, it is important to remember that your dissertation is a personal research project with associated expectations of you as an independent scholar.
As a rule of thumb, you can expect your supervisor to read each part of your dissertation once at the draft stage and to offer feedback. Most will not have time to look at lots of subsequent revisions, but may respond favourably to polite requests for exceptions (provided their own workload permits it).
Inundating your supervisor with emails or multiple iterations of draft material is best avoided; they will have their own research to manage (as well as other supervision assignments) and will be able to offer better quality feedback if you stick to an agreed schedule.
How is a Masters dissertation assessed and examined?
On most courses your dissertation will be assessed by an external examiner (as well as additional members of faculty within your university who haven’t been responsible for supervising you), but these will read and critique the work you submit without personally questioning and testing you on it.
Though this examination process is not as challenging as the oral defence or ‘ viva voce ’ required for a PhD thesis, the grading of your Masters dissertation is still a fundamental component of your degree.
How many credits is a Masters dissertation?
It will usually be worth around 60 credits – a third of the total 180 credit value for a UK Masters – and will therefore play a key role in determining your final result.
On some programmes the result awarded to a student’s dissertation may determine the upper grade-band that can be awarded to their degree. For more information on the grading process for UK Masters degrees see our guide to Frequently Asked Questions about Masters study.
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Frequently asked questions
How long is a dissertation.
Dissertation word counts vary widely across different fields, institutions, and levels of education:
- An undergraduate dissertation is typically 8,000–15,000 words
- A master’s dissertation is typically 12,000–50,000 words
- A PhD thesis is typically book-length: 70,000–100,000 words
However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.
Frequently asked questions: Knowledge Base
Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).
In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
Reliability and validity are both about how well a method measures something:
- Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
- Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
If you are doing experimental research , you also have to consider the internal and external validity of your experiment.
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
There are several reasons to conduct a literature review at the beginning of a research project:
- To familiarise yourself with the current state of knowledge on your topic
- To ensure that you’re not just repeating what others have already done
- To identify gaps in knowledge and unresolved problems that your research can address
- To develop your theoretical framework and methodology
- To provide an overview of the key findings and debates on the topic
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a dissertation , thesis, research paper , or proposal .
The literature review usually comes near the beginning of your dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.
Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.
A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.
The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.
In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘ et al. ’
A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.
MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.
Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.
Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12
In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.
When a source has two or three authors, name all of them in your MHRA references . When there are four or more, use only the first name, followed by ‘and others’:
Note that in the bibliography, only the author listed first has their name inverted. The names of additional authors and those of translators or editors are written normally.
A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.
In Vancouver style , you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.
In Vancouver style , when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.
In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.
The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:
- In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
- In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.
The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.
Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).
At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.
A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.
References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.
If you don’t acknowledge your sources, you can get in trouble for plagiarism .
Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:
- Harvard referencing , the most commonly used style in UK universities.
- MHRA , used in humanities subjects.
- APA , used in the social sciences.
- Vancouver , used in biomedicine.
- OSCOLA , used in law.
Your university may have its own referencing style guide.
If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.
To avoid plagiarism , always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.
You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.
In Harvard style , when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).
You can also include page numbers to point the reader towards a passage that you paraphrased . If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.
When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation , first mention the source you want to refer to, and then the source in which you found it. For example:
It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.
In Harvard style referencing , to distinguish between two sources by the same author that were published in the same year, you add a different letter after the year for each source:
- (Smith, 2019a)
- (Smith, 2019b)
Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list .
To create a hanging indent for your bibliography or reference list :
- Highlight all the entries
- Click on the arrow in the bottom-right corner of the ‘Paragraph’ tab in the top menu.
- In the pop-up window, under ‘Special’ in the ‘Indentation’ section, use the drop-down menu to select ‘Hanging’.
- Then close the window with ‘OK’.
Though the terms are sometimes used interchangeably, there is a difference in meaning:
- A reference list only includes sources cited in the text – every entry corresponds to an in-text citation .
- A bibliography also includes other sources which were consulted during the research but not cited.
It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).
The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources . You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.
You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.
For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).
In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.
Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.
Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .
Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.
The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract .
In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.
You may acknowledge God in your thesis or dissertation acknowledgements , but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.
All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.
The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .
Do not include the acknowledgements or abstract in the table of contents.
To automatically insert a table of contents in Microsoft Word, follow these steps:
- Apply heading styles throughout the document.
- In the references section in the ribbon, locate the Table of Contents group.
- Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
- Select which levels of headings you would like to include in the table of contents.
Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.
The table of contents in a thesis or dissertation always goes between your abstract and your introduction.
An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).
Your dissertation sometimes contains a list of abbreviations .
As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.
Be sure to add each abbreviation in your list of abbreviations !
If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .
If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.
A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents . It should always be in alphabetical order.
Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.
These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.
Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.
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 citation 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.
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 …’).
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.
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).
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 ‘.
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 ’.
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Blog / Masters dissertation word count and its breakdown
Masters dissertation word count and its breakdown.
Table of Contents
Introduction
If writing a master’s dissertation is in your plans, you likely have an idea as to how many words you will want to include. However, most students do not put a number on the Masters’ dissertation word count, or they fail to understand the relationship between word count and topic. Often students overbook themselves when it comes to the topic and do not leave enough room for the research. This can lead to a rushed ending, and a lot of extra work when it comes to compiling your paper.
It is important that the topic and word count are in line with each other. If the topic does not fit within the word limit, a student should ask the instructor for suggestions regarding the topic. The word limit is usually two thousand words, but this can vary depending on the instructor. A student should make sure the professor is clear on what the word count is before committing to the project.
In addition, there are several class considerations to take into account before beginning the project. For example, are there any required field trips? Are there appropriate and current research citations needed? These factors can be tricky, especially for students who lack a background in the area. The best way to approach this is by figuring out what the student needs to include, and how much research would be required for this.
Usually, a master’s degree is required before writing a dissertation . It is advised to take the time to earn this degree while one is still in their undergrad degree program. There is no better time to get started on this than while one is still in school. It is often possible to secure funding while in school.
Click here to get our experts help to write your master dissertation from scratch at 25% off and win your degree!
Additional time.
In some cases, a student may have to take an elective class in order to earn a master’s degree. This term could mean additional time devoted to writing the thesis. Elective classes are generally considered elective because they do not count towards a major’s requirements. This term should be used with caution, however, because students need to fulfill all requirements for their degree.
Maximum Word
Generally, there is a maximum word limit on a thesis. In many cases, it is possible to double the word limit for a thesis. The reasoning behind this is that the time required to write a thesis is less than the time to do research. This allows a student to spend more time doing the former and less time on the latter. The increase in word limit will affect the graduate’s grade, just as if the thesis were graded too quickly.
Avoid the temptation
Students should avoid the temptation to write their thesis in less than 60% of the allotted time. This can lead to a large decrease in the Masters’ dissertation word count. As a result, students might end up with a thesis that lacks strength and depth and is too long and also unable to break down Masters dissertation word count.
To learn more about the topic of your masters’ dissertation , ask other students who have already earned their degrees about their practices. You can also find out what types of answers professional, academic editors provide when you contact them for feedback. Doing so can help you make sure you have enough space to write your master’s degree successfully, and has great effects on your word count for your masters’ dissertation word count.
One way to ensure that you are not tempted to use any less than sixty percent of the available space is to create an outline of your main arguments. Begin by writing a broad overview of the topic and the research you plan to use. Then, flesh out your outline with each of your individual pieces of data and your overall conclusion.
When beginning the writing process, always begin by checking the university’s web-based deadline for the submission of your master’s degree. Determine your word limit. If it is not specified, choose a word limit in which you feel confident that you will meet it. This is not a guarantee that you will meet the limit, but it gives you a guideline. Other things that affect your word limit are your topic and paper structure. Consult the university’s committee on academic communication for advice about these matters.
Hire an Editor
Another way to help keep track of your word count is to assign an editor with access to your thesis and sources. Having an external eye on your work can help you make sure that you are not overboard in expanding what would be a manageable amount. An editor can help you develop a streamlined plan for writing and proofreading, as well as helping you avoid any grammatical or stylistic errors. An editor can help you achieve your word limit, so do not hesitate to hire an expert .
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Library Guides
Dissertations 1: getting started: starting your dissertation.
- Starting Your Dissertation
- Choosing A Topic
- Devising An Approach/Method
- Thinking Of A Title
- Writing A Proposal
What is a Dissertation?
A dissertation is a research project completed as part of an undergraduate or postgraduate degree. Typically, a dissertation will enable you to present your findings in response to a question that you propose yourself. It is probably the longest piece of academic work you will produce. At undergraduate level, word count requirements can range anywhere from 5,000 to 8,000 words while a Masters level dissertation can be 10,000 to 15,000 words long!
Why are you required to write a dissertation?
A dissertation is a core requirement of most university degrees.
The dissertation will enhance your employability. For instance, you will develop transferable skills in inter-personal communication, data collection and analysis, report writing and effective time-management.
While it is demanding, writing a dissertation is your chance to explore, in depth, a topic that interests you. Therefore, ensuring you choose a topic you are passionate about will make your experience more rewarding and even enjoyable!
Supervision Advice
A supervisor will be assigned to you to assist with guidance on how to prepare, produce and improve your dissertation.
The supervisor’s role is to:
Assist in the organisation of the project in the early stages of preparation
Advise you on the feasibility of what you plan to do
Advise on methods and ethics of your research
The supervisor is not expected to:
Proofread your work
Provide you with a topic or research question
Direct the research
Ensure that a dissertation is of sufficient quality to pass: this is your responsibility
To get the best out of your time with your supervisor, you should:
Check formal requirements early
Check arrangements for supervisions and how your supervisor likes to work
Organise regular supervision meetings and prepare work for each one
Let your supervisor know how you work best
Using Dissertation Marking Criteria
Your dissertation, like your previous assessments, will be marked against a set of assessment criteria which is published in your module or course handbook and posted on Blackboard.
Assessment criteria are intended to:
Ensure you meet the learning outcomes.
Help you understand how your work is assessed.
Allow tutors to focus their feedback. They will let you know what you are doing well and what needs improvement.
Dissertation assessment criteria usually specifies what the tutor expects in terms of:
Clarity: have you expressed your ideas clearly?
Relevance: does your work fit into/fill a gap in existing research/literature on similar topics?
Originality: does it offer a fresh perspective on a topic?
Meeting course requirements: does it meet the word count / deadlines, for example?
Before starting your dissertation, it is essential that you check what is expected of you and how your work will be graded. It is also useful to regularly check what you have written every few weeks and after you have finished to see if you are on track to meet the assessment criteria.
First Steps
Ready to get started but uncertain how to begin? These are normally the first steps of dissertation writing:
Choose a topic
Conduct a literature search
Devise research question(s) / hypotheses
Devise your approach (e.g. if undertaking primary research, you will need to devise your methodology, methods, etc.)
Think of a title
Plan your time
Write a proposal (if requested)
These steps are addressed in the tabs of this guide.
- Next: Choosing A Topic >>
- Last Updated: Jan 26, 2022 4:55 PM
- URL: https://libguides.westminster.ac.uk/starting-your-dissertation
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Dissertations (15k word)
Guidance on 15k-word dissertation supervision, formatting and submission for MSc by Research students within the School of Literatures, Languages and Cultures.
The Dissertation Component
Masters taught degree programmes include submission of a dissertation which may be the written output of a practical or investigational project. Students should make full use of the experience and guidance of staff members, and plan their progress through the dissertation with the help of staff, especially the supervisor.
The dissertation is distinguishable from assessed courses by the greater depth of investigation, analysis, comprehension and critique demonstrated. Masters students are not expected to research their work as exhaustively as is typical of a higher research degree. Students, supervisors and examiners should be aware that the masters dissertation is primarily a teaching, learning and examining medium, not a medium for the presentation of research outcomes to public and peers. The dissertation may vary in the breadth of coverage, but it must have a clear focus with definable objectives and boundaries, achievable in the time and word limit available. The relative importance of theoretical and investigational content also varies. Some programmes require students to conduct primary empirical work; others do not. Investigational work should add something to the study which is not available in the literature, and be manageable given the time and resources available.
If you have questions or concerns about the dissertation process, about the allocation of supervision, or about the level of support you are receiving during the writing of your supervision you should contact your Programme Director/ Subject Officer.
Dissertation Supervision
Supervisors are normally selected on the basis that they have expertise in the field or topic chosen for the dissertation, and are available and willing to support the student during the period of study. Specific expertise may not always be available for all dissertation topics; but general expertise in the broad area of the dissertation is usually adequate for a Masters dissertation. Students and supervisors are expected to agree a timetable in writing; this will specify at what points student and supervisor will be in contact. There should be a minimum of 3 supervision meetings held during the dissertation period.
A short written proposal or concept note should be prepared in advance of your first meeting with your supervisor, indicating the aims and justification of the dissertation, specific research questions, literature and investigative work to be covered and a provisional outline of chapter titles and sub-headings. There should also be a written plan or timetable indicating how the work is going to be undertaken and identifying critical points when the supervisor and student should meet or otherwise be in contact with each other.
Students have responsibilities to meet their supervisors regularly and to hand in material at agreed times. Students experiencing difficulties in meeting with supervisors, or other staff members, should contact the Programme Director/ Subject Officer without delay.
Supervisors have responsibilities to respond promptly and appropriately, by making constructive suggestions both at the planning stage and in response to the material submitted. The responsibility for the academic quality of the dissertation is ultimately the student's alone.
Students should be made aware that approval by a supervisor, and the following of the advice and guidance of the supervisor carries no guarantee of success at examination. Any such approving or guiding comments cannot constitute grounds for subsequent appeal.
Students should be aware of the particular importance of acknowledging the work of others and of avoiding plagiarism.
Students should always get in touch with their dissertation supervisor and/or Programme Director as soon as any problems emerge.
Dissertation Submission
Printed copies of the dissertation are not required. Students must submit their dissertation electronically ONLY via Learn no later than 4pm on Thursday 12th August 2021 , unless you have received confirmation of an alternative deadline from the School. You should check the Learn site to confirm your deadline and any guidance specific to your programme. You should submit the dissertation to the relevant drop box in Learn. Please ensure that you include your examination number in the name of the document that you upload to Learn. You will need to complete a Declaration of Own Work on Learn before you will be able to access the dissertation drop box, so be sure to allow time for this before the deadline.
Every effort should be made to adhere to deadlines. In the event of late submission, the School of Literatures, Languages and Cultures upholds the University's standard penalty for late submission of coursework.
If exceptional circumstances prevent you meeting the deadline, you should apply online for an extension before the deadline.
The Dissertation will be 15,000 words for most programmes; however, some programmes have variations in word counts so you must follow the directions of your Programme Director/Supervisor if you have been given a dissertation length other than 15,000 words. The School does not permit a margin of 10% in either direction. Special permission is required from the Programme Director, in writing, for dissertations that extend beyond the stated word limit. The word count for dissertations does not include the bibliography, cover sheet, abstract (if required) or appendices; the word count includes: in-text references, tables of contents, quotations, footnotes, list of figures, captions and all other elements of your submitted dissertation.
Dissertation Formatting
Because the dissertations are marked anonymously, it is essential that you ensure that your name does not appear anywhere in the dissertation.
- You must not include any acknowledgements as these might compromise the anonymity of the marking process.
- For your document setttings, use A4 paper size with standard margins and a 12-point font, preferably Times or Garamond ; double space and page number the entire document. It is essential that line spacing is at least 1.5 , except for poems, which should be single spaced.
- You should include a cover sheet (provided on Learn).
- In-text quotes less than 40 words have to appear in inverted commas followed by their reference. Any quote more than 40 words should be presented as indented quotes, without inverted commas, in font 11, followed by the reference.
- If you rephrased a scholar’s concept, you still need to give the reference in brackets in-text.
- In order to ease cross-referencing within your dissertation, please number the lines on your ST and TT if you chose to do translation and commentary.
- Spell-check and grammar-check the dissertation. If this is not sufficient to produce readable and accurate English, have your dissertation proofread. If you have chosen to write a translation and commentary, only the commentary can be proofread.
- Unless you are given specific instruction otherwise, you are welcome to use British or American spelling as long as the chosen spelling is applied systematically and consistently throughout your work.
- A programme may have a required referencing style; please consult your supervisor or personal tutor to see if a particular referencing style is required. Otherwise, the generally recommended referencing system is Harvard; however, you are allowed and are free to use another system (such as MLA) as long as it is applied systematically and consistently throughout your Dissertation.
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Formatting Your Dissertation
On this page:
Language of the Dissertation
Page and text requirements, body of text, tables, figures, and captions, dissertation acceptance certificate, copyright statement.
- Table of Contents
Front and Back Matter
Supplemental material, dissertations comprising previously published works, top ten formatting errors, further questions.
Related Contacts and Forms
When preparing the dissertation for submission, students must follow strict formatting requirements. Any deviation from these requirements may lead to rejection of the dissertation and delay in the conferral of the degree.
The language of the dissertation is ordinarily English, although some departments whose subject matter involves foreign languages may accept a dissertation written in a language other than English.
Most dissertations are 100 to 300 pages in length. All dissertations should be divided into appropriate sections, and long dissertations may need chapters, main divisions, and subdivisions.
- 8½ x 11 inches, unless a musical score is included
At least 1 inch for all margins
Body of text: double spacing
Block quotations, footnotes, and bibliographies: single spacing within each entry but double spacing between each entry
Table of contents, list of tables, list of figures or illustrations, and lengthy tables: single spacing may be used
FONTS AND POINT SIZE
Use 10-12 point size. Fonts must be embedded in the PDF file to ensure all characters display correctly.
Recommended Fonts
If you are unsure whether your chosen font will display correctly, use one of the following fonts:
If fonts are not embedded, non-English characters may not appear as intended. Fonts embedded improperly will be published to DASH as-is. It is the student’s responsibility to make sure that fonts are embedded properly prior to submission.
Instructions for Embedding Fonts
To embed your fonts in recent versions of word, follow these instructions from microsoft:.
Click the File tab and then click Options .
In the left column, select the Save tab.
At the bottom, under Preserve fidelity when sharing this document , select the Embed fonts in the file check box.
Clear the Do not embed common system fonts check box.
For reference, below are some instructions from ProQuest UMI for embedding fonts in older file formats:
To embed your fonts in Microsoft Word 2010:
In the File pull-down menu click on Options .
Choose Save on the left sidebar.
- Check the box next to Embed fonts in the file.
- Click the OK button.
- Save the document.
Note that when saving as a PDF, make sure to go to “more options” and save as “PDF/A compliant”
To embed your fonts in Microsoft Word 2007:
- Click the circular Office button in the upper left corner of Microsoft Word.
- A new window will display. In the bottom right corner select Word Options .
- Choose Save from the left sidebar.
Using Microsoft Word on a Mac:
Microsoft Word 2008 on a Mac OS X computer will automatically embed your fonts while converting your document to a PDF file.
If you are converting to PDF using Acrobat Professional (instructions courtesy of the Graduate Thesis Office at Iowa State University):
- Open your document in Microsoft Word.
- Click on the Adobe PDF tab at the top. Select "Change Conversion Settings."
- Click on Advanced Settings.
- Click on the Fonts folder on the left side of the new window. In the lower box on the right, delete any fonts that appear in the "Never Embed" box. Then click "OK."
- If prompted to save these new settings, save them as "Embed all fonts."
- Now the Change Conversion Settings window should show "embed all fonts" in the Conversion Settings drop-down list and it should be selected. Click "OK" again.
- Click on the Adobe PDF link at the top again. This time select Convert to Adobe PDF. Depending on the size of your document and the speed of your computer, this process can take 1-15 minutes.
- After your document is converted, select the "File" tab at the top of the page. Then select "Document Properties."
- Click on the "Fonts" tab. Carefully check all of your fonts. They should all show "(Embedded Subset)" after the font name.
- If you see "(Embedded Subset)" after all fonts, you have succeeded.
The font used in the body of the text must also be used in headers, page numbers, and footnotes. Exceptions are made only for tables and figures created with different software and inserted into the document.
Tables and figures must be placed as close as possible to their first mention in the text. They may be placed on a page with no text above or below, or they may be placed directly into the text. If a table or a figure is alone on a page (with no narrative), it should be centered within the margins on the page. Tables may take up more than one page as long as they obey all rules about margins. Tables and figures referred to in the text may not be placed at the end of the chapter or at the end of the dissertation.
Given the standards of the discipline, dissertations in the Department of History of Art and Architecture and the Department of Architecture, Landscape Architecture, and Urban Planning often place illustrations at the end of the dissertation.
Figure and table numbering must be continuous throughout the dissertation or by chapter (e.g., 1.1, 1.2, 2.1, 2.2, etc.). Two figures or tables cannot be designated with the same number. If you have repeating images that you need to cite more than once, label them with their number and A, B, etc.
Headings should be placed at the top of tables. While no specific rules for the format of table headings and figure captions are required, a consistent format must be used throughout the dissertation (contact your department for style manuals appropriate to the field).
Captions should appear at the bottom of any figures. If the figure takes up the entire page, the caption should be placed alone on the preceding page, centered vertically and horizontally within the margins.
Each page receives a separate page number. When a figure or table title is on a preceding page, the second and subsequent pages of the figure or table should say, for example, “Figure 5 (Continued).” In such an instance, the list of figures or tables will list the page number containing the title. The word “figure” should be written in full (not abbreviated), and the “F” should be capitalized (e.g., Figure 5). In instances where the caption continues on a second page, the “(Continued)” notation should appear on the second and any subsequent page. The figure/table and the caption are viewed as one entity and the numbering should show correlation between all pages. Each page must include a header.
Landscape orientation figures and tables must be positioned correctly and bound at the top so that the top of the figure or table will be at the left margin. Figure and table headings/captions are placed with the same orientation as the figure or table when on the same page. When on a separate page, headings/captions are always placed in portrait orientation, regardless of the orientation of the figure or table. Page numbers are always placed as if the figure were vertical on the page.
If a graphic artist does the figures, GSAS will accept lettering done by the artist only within the figure. Figures done with software are acceptable if the figures are clear and legible. Legends and titles done by the same process as the figures will be accepted if they too are clear, legible, and run at least 10 or 12 characters per inch. Otherwise, legends and captions should be printed with the same font used in the text.
Original illustrations, photographs, and fine arts prints may be scanned and included, centered between the margins on a page with no text above or below.
Use of Third-Party Content
In addition to the student's own writing, dissertations often contain third-party content or in-copyright content owned by parties other than you, the student who authored the dissertation. The Office for Scholarly Communication recommends consulting the information below about fair use, which allows individuals to use in-copyright content, on a limited basis and for specific purposes, without seeking permission from copyright holders.
Because your dissertation will be made available for online distribution through DASH , Harvard's open-access repository, it is important that any third-party content in it may be made available in this way.
Fair Use and Copyright
What is fair use?
Fair use is a provision in copyright law that allows the use of a certain amount of copyrighted material without seeking permission. Fair use is format- and media-agnostic. This means fair use may apply to images (including photographs, illustrations, and paintings), quoting at length from literature, videos, and music regardless of the format.
How do I determine whether my use of an image or other third-party content in my dissertation is fair use?
There are four factors you will need to consider when making a fair use claim.
1) For what purpose is your work going to be used?
Nonprofit, educational, scholarly, or research use favors fair use. Commercial, non-educational uses, often do not favor fair use.
A transformative use (repurposing or recontextualizing the in-copyright material) favors fair use. Examining, analyzing, and explicating the material in a meaningful way, so as to enhance a reader's understanding, strengthens your fair use argument. In other words, can you make the point in the thesis without using, for instance, an in-copyright image? Is that image necessary to your dissertation? If not, perhaps, for copyright reasons, you should not include the image.
2) What is the nature of the work to be used?
Published, fact-based content favors fair use and includes scholarly analysis in published academic venues.
Creative works, including artistic images, are afforded more protection under copyright, and depending on your use in light of the other factors, may be less likely to favor fair use; however, this does not preclude considerations of fair use for creative content altogether.
3) How much of the work is going to be used?
Small, or less significant, amounts favor fair use. A good rule of thumb is to use only as much of the in-copyright content as necessary to serve your purpose. Can you use a thumbnail rather than a full-resolution image? Can you use a black-and-white photo instead of color? Can you quote select passages instead of including several pages of the content? These simple changes bolster your fair use of the material.
4) What potential effect on the market for that work may your use have?
If there is a market for licensing this exact use or type of educational material, then this weighs against fair use. If however, there would likely be no effect on the potential commercial market, or if it is not possible to obtain permission to use the work, then this favors fair use.
For further assistance with fair use, consult the Office for Scholarly Communication's guide, Fair Use: Made for the Harvard Community and the Office of the General Counsel's Copyright and Fair Use: A Guide for the Harvard Community .
What are my options if I don’t have a strong fair use claim?
Consider the following options if you find you cannot reasonably make a fair use claim for the content you wish to incorporate:
Seek permission from the copyright holder.
Use openly licensed content as an alternative to the original third-party content you intended to use. Openly-licensed content grants permission up-front for reuse of in-copyright content, provided your use meets the terms of the open license.
Use content in the public domain, as this content is not in-copyright and is therefore free of all copyright restrictions. Whereas third-party content is owned by parties other than you, no one owns content in the public domain; everyone, therefore, has the right to use it.
For use of images in your dissertation, please consult this guide to Finding Public Domain & Creative Commons Media , which is a great resource for finding images without copyright restrictions.
Who can help me with questions about copyright and fair use?
Contact your Copyright First Responder . Please note, Copyright First Responders assist with questions concerning copyright and fair use, but do not assist with the process of obtaining permission from copyright holders.
Pages should be assigned a number except for the Dissertation Acceptance Certificate . Preliminary pages (abstract, table of contents, list of tables, graphs, illustrations, and preface) should use small Roman numerals (i, ii, iii, iv, v, etc.). All pages must contain text or images.
Count the title page as page i and the copyright page as page ii, but do not print page numbers on either page .
For the body of text, use Arabic numbers (1, 2, 3, 4, 5, etc.) starting with page 1 on the first page of text. Page numbers must be centered throughout the manuscript at the top or bottom. Every numbered page must be consecutively ordered, including tables, graphs, illustrations, and bibliography/index (if included); letter suffixes (such as 10a, 10b, etc.) are not allowed. It is customary not to have a page number on the page containing a chapter heading.
Check pagination carefully. Account for all pages.
A copy of the Dissertation Acceptance Certificate (DAC) should appear as the first page. This page should not be counted or numbered. The DAC will appear in the online version of the published dissertation.
The dissertation begins with the title page; the title should be as concise as possible and should provide an accurate description of the dissertation.
- Do not print a page number on the title page: It is understood to be page i for counting purposes only.
A copyright notice should appear on a separate page immediately following the title page and include the copyright symbol ©, the year of first publication of the work, and the name of the author:
© [ year ] [ Author’s Name ] All rights reserved.
Alternatively, students may choose to license their work openly under a Creative Commons license. The author remains the copyright holder while at the same time granting up-front permission to others to read, share, and (depending on the license) adapt the work, so long as proper attribution is given. (By default, under copyright law, the author reserves all rights; under a Creative Commons license, the author reserves some rights.)
- Do not print a page number on the copyright page. It is understood to be page ii for counting purposes only.
An abstract, numbered as page iii , should immediately follow the copyright page and should state the problem, describe the methods and procedures used, and give the main results or conclusions of the research. The abstract will appear in the online and bound versions of the dissertation and will be published by ProQuest. There is no maximum word count for the abstract.
- double-spaced
- left-justified
- indented on the first line of each paragraph
- The author’s name, right justified
- The words “Dissertation Advisor:” followed by the advisor’s name, left-justified (a maximum of two advisors is allowed)
- Title of the dissertation, centered, several lines below author and advisor
Dissertations divided into sections must contain a table of contents that lists, at minimum, the major headings in the following order:
- Front Matter
- Body of Text
- Back Matter
Front matter includes (if applicable):
acknowledgements of help or encouragement from individuals or institutions
a dedication
a list of illustrations or tables
a glossary of terms
one or more epigraphs.
Back matter includes (if applicable):
bibliography
supplemental materials, including figures and tables
an index (in rare instances).
Supplemental figures and tables must be placed at the end of the dissertation in an appendix, not within or at the end of a chapter. If additional digital information (including audio, video, image, or datasets) will accompany the main body of the dissertation, it should be uploaded as a supplemental file through ProQuest ETD . Supplemental material will be available in DASH and ProQuest and preserved digitally in the Harvard University Archives.
As a matter of copyright, dissertations comprising the student's previously published works must be authorized for distribution from DASH. The guidelines in this section pertain to any previously published material that requires permission from publishers or other rightsholders before it may be distributed from DASH. Please note:
Authors whose publishing agreements grant the publisher exclusive rights to display, distribute, and create derivative works will need to seek the publisher's permission for nonexclusive use of the underlying works before the dissertation may be distributed from DASH.
Authors whose publishing agreements indicate the authors have retained the relevant nonexclusive rights to the original materials for display, distribution, and the creation of derivative works may distribute the dissertation as a whole from DASH without need for further permissions.
It is recommended that authors consult their publishing agreements directly to determine whether and to what extent they may have transferred exclusive rights under copyright. The Office for Scholarly Communication (OSC) is available to help the author determine whether she has retained the necessary rights or requires permission. Please note, however, the Office of Scholarly Communication is not able to assist with the permissions process itself.
Missing Dissertation Acceptance Certificate. The first page of the PDF dissertation file should be a scanned copy of the Dissertation Acceptance Certificate (DAC). This page should not be counted or numbered as a part of the dissertation pagination.
Conflicts Between the DAC and the Title Page. The DAC and the dissertation title page must match exactly, meaning that the author name and the title on the title page must match that on the DAC. If you use your full middle name or just an initial on one document, it must be the same on the other document.
Abstract Formatting Errors. The advisor name should be left-justified, and the author's name should be right-justified. Up to two advisor names are allowed. The Abstract should be double spaced and include the page title “Abstract,” as well as the page number “iii.” There is no maximum word count for the abstract.
Pagination
The front matter should be numbered using Roman numerals (iii, iv, v, …). The title page and the copyright page should be counted but not numbered. The first printed page number should appear on the Abstract page (iii).
The body of the dissertation should be numbered using Arabic numbers (1, 2, 3, …). The first page of the body of the text should begin with page 1. Pagination may not continue from the front matter.
All page numbers should be centered either at the top or the bottom of the page.
Figures and tables Figures and tables must be placed within the text, as close to their first mention as possible. Figures and tables that span more than one page must be labeled on each page. Any second and subsequent page of the figure/table must include the “(Continued)” notation. This applies to figure captions as well as images. Each page of a figure/table must be accounted for and appropriately labeled. All figures/tables must have a unique number. They may not repeat within the dissertation.
Horizontal Figures and Tables
Any figures/tables placed in a horizontal orientation must be placed with the top of the figure/ table on the left-hand side. The top of the figure/table should be aligned with the spine of the dissertation when it is bound.
Page numbers must be placed in the same location on all pages of the dissertation, centered, at the bottom or top of the page. Page numbers may not appear under the table/ figure.
Supplemental Figures and Tables. Supplemental figures and tables must be placed at the back of the dissertation in an appendix. They should not be placed at the back of the chapter.
Permission Letters Copyright. permission letters must be uploaded as a supplemental file, titled ‘do_not_publish_permission_letters,” within the dissertation submission tool.
DAC Attachment. The signed Dissertation Acceptance Certificate must additionally be uploaded as a document in the "Administrative Documents" section when submitting in Proquest ETD . Dissertation submission is not complete until all documents have been received and accepted.
Overall Formatting. The entire document should be checked after all revisions, and before submitting online, to spot any inconsistencies or PDF conversion glitches.
You can view dissertations successfully published from your department in DASH . This is a great place to check for specific formatting and area-specific conventions.
Contact the Office of Student Affairs with further questions.
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Word Limits in Master’s Dissertations
Word Limits in Master’s Dissertations. A frequent question from Master’s students when doing their project dissertations is how many words am I aiming for? What is the word count? This blog will explore Word Count limits for dissertations, and also includes some tips for using word count in WORD. This will also be useful for bachelor’s final year projects when students are asking “How long is the final year project required to be?”
Here is a short video clip on the subject.
Bachelors Final Year Project Word Count
This is often the first time a student has to set their own question for an assignment, and a typical bachelors final year project may be 6,000 – 10,000 words. Many of the following tips may also apply at bachelor’s level.
Masters Final Year Project Word Count
How long is a Master’s dissertation? Essentially the project is again a student setting their own question, but this time at a Master’s level, so it requires more critical evaluation, and a demonstration of mastery in their research area. Therefore, the word limit may be up to 20,000 words, but do check with your institution for exact limits.
Approach the course/module coordinator for the dissertation rather than your project supervisor, as although the project supervisor may be the technical expert in your subject, they may not however understand the module/project requirements.
How is the Word Count Calculated?
Usually from the start of chapter 1 to the end of the Conclusion/Further Work/Recommendations chapter. This means it excludes the abstract, table of contents, list of figures, acknowledgements, declaration of originality at the beginning, and also excludes the references, bibliography and appendices at the end to of the document, but please do check with your institution.
MBA, MSc, or MA qualification?
I wrote a separate blog on the differences between these qualifications here .
MBA students are encouraged to deliver short, concise business documents. Therefore the limit for an MBA might be just 12,000 words in total (including everything in some cases – check with your institution). It is often very difficult to cover everything is so few words.
An MSc dissertation should be displaying the student as a technical expert in a deep and technical area. The dissertation may be solving a problem. A minimum of 12,000 words might be required, and an advised maximum of 16,000 words. I find it easy for some students to write too much, and remind them to refer back to their objectives – is what they are writing contributing to meeting the research aims and objectives? If not, take it out!
An MA in Social Science, Education, History or English, may include much more debate and written in an essay writing style. Therefore, the word count may be higher, perhaps 20,000 – 25,000 words.
Check with your institution and find out if there are penalties for being more than 10% over, or under word count.
Tips for Word Count in Word
These tips are for the PC application of MS WORD.
- Firstly, ‘turn it on’ if it isn’t there, by right clicking in status bar.
- Select just a portion of text to count just the selection.
- Use the “Review” Tab and click “Word Count” as an alternative method.
- Look for the details on characters, lines, paragraphs, and pages, by right clicking on the word count in the status bar.
- Use the navigation pane to select sections automatically (by right-clicking), and then count the words in each section. This is useful for excluding or subtracting sections from the total word count.
With submission of paper copies, nobody is really going to check your word count by counting each word. However, there may be a more subjective comment such as – “It’s only 55 pages” or “there were over 200 pages with few diagrams, and no real structure”.
With the recent move to the submission of electronic copies, your word count is there for everybody to see, so pay attention to the limits and penalties that your institution operates.
Check with your institution regarding exact limits, tolerance bands, and penalties. My view is as long as it is easy to read, well structured, has good headings, plenty of diagrams, tables, and bullet points, then I’m not too worried about long dissertations. However short Master’s MSc dissertations are always an area for concern!
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COMMENTS
A Masters dissertation will be longer than the undergraduate equivalent – usually it'll be somewhere between 15,000 and 20,000 words, but this can vary
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
The word limit is usually two thousand words, but this can vary depending on the instructor. A student should make sure the professor is clear on what the word
My Masters diss max word count was 20,000, with a 5% penalty for every 500 words over the limit. However the numpty who re-wrote the postgrad handbook for our
It is probably the longest piece of academic work you will produce. At undergraduate level, word count requirements can range anywhere from
The master's thesis is a carefully argued scholarly paper of approximately 12,000 – 13,000 words (roughly 50 pages). It should present an original argument that
The Dissertation will be 15,000 words for most programmes; however, some programmes have variations in word counts so you must follow the
Most dissertations are 100 to 300 pages in length. All dissertations should be divided into appropriate sections, and long dissertations may need chapters
Discussion on the Word Count Limits for projects and dissertations with some tips to ... 2.8K views 5 years ago Masters Dissertation Support.
An MSc dissertation should be displaying the student as a technical expert in a deep and technical area. The dissertation may be solving a