How do I create a hanging indent in Word?

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’.

Frequently asked questions: Knowledge Base

What’s the difference between method and methodology?: 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).
Where does the methodology section go?: 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.
What’s the difference between quantitative and qualitative methods?: 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.
What’s the difference between reliability and validity?: 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.
What is sampling?: 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.
What is the purpose of a literature review?: 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.
What is a literature review?: 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.
Where does the literature review go in a dissertation?: 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.
What’s the difference between Harvard and Vancouver referencing styles?: 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.

Harvard style Vancouver style

In-text citation Each referencing style has different rules (Pears and Shields, 2019). Each referencing style has different rules (1).

Reference list Pears, R. and Shields, G. (2019). Cite them right: The essential referencing guide. 11th edn. London: MacMillan. 1. Pears R, Shields G. Cite them right: The essential referencing guide. 11th ed. London: MacMillan; 2019.
When do I need to use a Harvard in-text citation?: 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.
How do I cite a source with multiple authors in Harvard style?: 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.’

In-text citation Reference list

1 author (Smith, 2014) Smith, T. (2014) …

2 authors (Smith and Jones, 2014) Smith, T. and Jones, F. (2014) …

3 authors (Smith, Jones and Davies, 2014) Smith, T., Jones, F. and Davies, S. (2014) …

4+ authors (Smith et al., 2014) Smith, T. et al. (2014) …
Should an MHRA bibliography include sources I didn’t cite?: 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.
How are footnote numbers formatted and placed in MHRA style?: 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.
How do I cite sources with multiple authors in MHRA referencing?: 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’:

Number of authors Footnote example Bibliography example

1 author David Smith Smith, David

2 authors David Smith and Hugh Jones Smith, David, and Hugh Jones

3 authors David Smith, Hugh Jones and Emily Wright Smith, David, Hugh Jones and Emily Wright

4+ authors David Smith and others Smith, David, 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.
When should I use an in-text citation in Vancouver style?: 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.
How do I reference a source with multiple authors in Vancouver style?: 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.’.
What is the difference between a dissertation and a thesis?: 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.
How is a dissertation different from an essay?: The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic, and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).
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.
How long does it take to write a dissertation?: At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.
When do I need to include references?: 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.
Which referencing style should I use?: 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.
How can I avoid plagiarism?: 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.
When should I include page numbers in a Harvard in-text citation?: 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.
How do I cite a source I’ve only seen quoted in another source?: 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:

Smith (1995, cited in Davies, 2005) divides the process into four steps…

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.
How do I cite multiple sources by the same author published in the same year?: 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.
What’s the difference between a bibliography and a reference list?: 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.
How do I know if a website is a reliable source?: 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.
How can I specify a location in a source without page numbers?: 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’).
Whom should I thank in the acknowledgements?: 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.
Do I have to thank my supervisor?: 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.
Where do the acknowledgements go in a thesis or dissertation?: The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract.
How long should the acknowledgements be?: In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.
Can I thank God in my acknowledgements?: 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.
What should be included in a dissertation table of contents page?: 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.
How do I create a 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.

Click OK.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.
Where do you place a table of contents?: The table of contents in a thesis or dissertation always goes between your abstract and your introduction.
What is the difference between an acronym and abbreviation?: 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).
Do you always have to write out 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!
Is a list of abbreviations mandatory in my thesis or dissertation?: 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.
What is a list of abbreviations?: 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.
What is the Scribbr Knowledge Base?: 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.
Who is the Knowledge Base for?: 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.
What other services does Scribbr provide?: 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.
Can I use Scribbr content for teaching purposes?: 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.
How can I make a suggestion for the Knowledge Base?: 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 shona@scribbr.com.
What happens if I plagiarise?: The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole essay by someone else will usually have 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 and/or your job, and you could even face legal consequences for copyright infringement.
Is paraphrasing considered plagiarism?: 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.
Can plagiarism be accidental?: Accidental plagiarism is one of the most common types 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 referencing your sources. Also consider running your work through a plagiarism checker tool prior to submission. These tools work by using advanced database software to scan for matches between your text and existing texts.
Are plagiarism checkers accurate?: 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.

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

Size of the database

Many free plagiarism checkers only check your paper against websites – not against books, journals, or papers previously submitted by other students. Therefore, these plagiarism checkers are not very accurate, as they miss a lot of plagiarism.

Algorithm

Most plagiarism checkers are only able to detect ‘direct plagiarism‘, or instances where the sentences are exactly the same as in the original source. However, a good plagiarism checker is also able to detect ‘patchwork plagiarism‘ (sentences where some words are changed or synonyms are used).
How can I summarise a source without plagiarising?: 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.
How is plagiarism detected?: 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.
What are some examples of plagiarism?: 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!
What is global plagiarism?: 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.
What is verbatim plagiarism?: 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.
What is patchwork plagiarism?: 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.
Can I plagiarise myself?: 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.
When do I need to cite myself?: 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.
Does Turnitin check for self-plagiarism?: 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.
Is plagiarism illegal?: 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.
What is self-plagiarism?: 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.
How do I decide which research methods to use?: 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.
What’s the difference between method and methodology?: 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.
What is mixed methods research?: In mixed methods research, you use both qualitative and quantitative data collection and analysis methods to answer your research question.
What is data collection?: 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.
How do I analyse qualitative data?: 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.
What are the main qualitative research approaches?: 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.
What is hypothesis testing?: 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.
What is operationalisation?: 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.
What is triangulation in research?: 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.
What are the main types of mixed methods research designs?: 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.
How do you define an observational study?: 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.
How does an observational study differ from an experiment?: 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.
What’s the difference between exploratory and explanatory research?: 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.
What is the definition of an experimental design?: 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
What are the types of triangulation?: 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
What are the pros and cons of triangulation?: 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.
What is a confounding variable?: 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.
What’s the difference between within-subjects and between-subjects designs?: 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.
What is a quasi-experiment?: 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.
What is random assignment?: 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.
When should I use a quasi-experimental design?: 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.
What are the pros and cons of a within-subjects design?: 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
Can I use a within- and between-subjects design in the same study?: 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.
What is a factorial design?: 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.
What are the pros and cons of a between-subjects design?: 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.

Advantages:

Prevents carryover effects of learning and fatigue.

Shorter study duration.

Disadvantages:

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.
Why are samples used in research?: Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
What is probability sampling?: 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.
What is non-probability 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.
What is multistage 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.
What is sampling bias?: Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.
What is simple random sampling?: 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.
What is an example of simple random sampling?: 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.
When should I use simple random sampling?: 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.
What are some advantages and disadvantages of cluster 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.
What are the types of cluster sampling?: 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.
What is cluster sampling?: 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.
Is multistage sampling a probability or non-probability sampling method?: 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.
What are the pros and cons of multistage sampling?: 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.
What is stratified sampling?: 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.
When do I use stratified sampling?: 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.
Can I stratify by multiple characteristics at once?: 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.
How do I perform systematic sampling?: 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 kth member of the population as your sample.
What is systematic sampling?: 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.
When are populations used in research?: 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.
What’s the difference between a statistic and a parameter?: A statistic refers to measures about the sample, while a parameter refers to measures about the population.
What is sampling error?: A sampling error is the difference between a population parameter and a sample statistic.
What are threats to internal validity?: There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction, and attrition.
What is internal validity?: 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.
How does attrition threaten internal validity?: 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.
What is external validity?: The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.
What are the two types of external validity?: 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).
What are threats to external validity?: There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment, and situation effect.
How does attrition threaten external validity?: 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.
What is the definition of construct validity?: 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
Why does construct validity matter?: 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.
How do I measure construct validity?: Statistical analyses are often applied to test validity with data from your measures. You test convergent 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.
What is the definition of face 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.
Why is face validity important?: 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.
Who should assess face validity?: 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.
What are some types of inductive reasoning?: 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.
What’s the difference between inductive and deductive reasoning?: 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.
How is inductive reasoning used in research?: 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.
What is the definition of inductive reasoning?: 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.
What is the definition of deductive 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.
How do you use deductive reasoning in research?: 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.
What’s the definition of a dependent variable?: 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)
What’s the definition of an independent variable?: 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)
How many variables are in a correlation?: A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
How do you plot explanatory and response variables on a graph?: 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.
How do explanatory variables differ from independent variables?: 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.
What are explanatory and response variables?: 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.
What are the types of extraneous 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
What’s the difference between extraneous and confounding variables?: 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.
What does ‘controlling for a variable’ mean?: ‘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.
Why are control variables important?: 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.
What’s the definition of a control 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.
Are ordinal variables categorical or quantitative?: 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.
What’s the difference between concepts, variables and indicators?: 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.
How do I prevent confounding variables from interfering with my research?: 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.
What’s the difference between confounding, independent, and dependent 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.
Why do confounding variables matter for my research?: 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.
Can I include more than one independent or dependent variable in a study?: 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.
Can a variable be both independent and dependent?: 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.
What is an example of an independent and dependent variable?: 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.
Why are independent and dependent variables important?: 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.
What’s the difference between quantitative and categorical variables?: 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.
What’s the difference between discrete and continuous variables?: 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).
What are independent and dependent variables?: 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.
Why should you include mediators and moderators in your study?: 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.
How can you tell if something is a mediator?: 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
What’s the difference between a mediator and a confounder?: 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.
What’s the difference between a mediator and moderator?: A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
What is data collection?: 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.
What are the benefits of collecting original data?: 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.
When should you use a structured interview?: 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.
What is an interviewer effect?: 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.
When should you use a semi-structured interview?: 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.
When should you use an unstructured interview?: 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
What are the four main types of interviews?: 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.
What is the definition of a focus group?: 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.
What is social desirability bias?: 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 can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.
How do you write focus group questions?: 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
Why doesn’t correlation imply causation?: 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.
What’s the difference between correlational and experimental research?: 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.
What is the definition of a correlation coefficient?: 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.
What is the definition of correlational research?: 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.
What is the definition of a correlation?: 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.
How long is a longitudinal study?: Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
What is an example of a longitudinal study?: 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.
What are the pros and cons 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.
What is the difference between a longitudinal and a cross-sectional study?: 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.

Longitudinal study Cross-sectional study

Repeated observations Observations at a single point in time

Observes the same group multiple times Observes different groups (a ‘cross-section’) in the population

Follows changes in participants over time Provides snapshot of society at a given point
What are the disadvantages of a cross-sectional study?: 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.
Why do a cross-sectional 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.
What is the definition of a hypothesis?: 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).
What’s the difference between a research hypothesis and a statistical hypothesis?: 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.
Are Likert scales ordinal or interval scales?: 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.
What is the definition of a Likert scale?: 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.
What’s the difference between a questionnaire and a survey?: 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.
Do experiments always require a control group?: 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.
What’s the difference between a control group and an experimental group?: 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.
What are the requirements for a controlled experiment?: 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.
How do you administer questionnaires?: 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.
How do you order a questionnaire?: 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.
What’s the difference between closed-ended and open-ended questions?: 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.
What’s the definition of a naturalistic observation?: 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.
What are the pros and cons of naturalistic observation?: 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.
How can I minimise observer bias in my research?: 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.
What is the observer-expectancy effect?: 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.
What is the definition of observer bias?: 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. Observer bias is also called detection bias or ascertainment bias.
Why does data cleaning matter?: 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.
What is the definition of data cleaning?: 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.
When do you clean data?: 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.
What is the difference between clean and dirty data?: 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.
When do you use random assignment?: 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.
What’s the difference between random selection and random assignment?: 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.
How do you randomly assign participants to a group?: 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.
When should I use exploratory research?: 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.
What is the definition of exploratory research?: 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.
When should I use explanatory research?: 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.
What is the definition of explanatory 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.
What is the definition of blinding in research?: Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.
Why is blinding important in research?: 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.
What’s the difference between single-blind, double-blind and triple-blind studies?: 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.
What types of documents are usually peer-reviewed?: 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.
Why is peer review important?: 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.
How does the peer review process work?: In general, the peer review process follows the following steps:

First, the author submits the manuscript to the editor.

The editor can either:

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.
What is the definition of peer review?: 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.
What’s the difference between anonymity and confidentiality?: 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.
What is research misconduct?: 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.
Why do research ethics matter?: 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.
What are ethical considerations in research?: 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.
Is a systematic review primary research?: A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
What are the two main types of social desirability bias?: 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.
What’s the difference between demand characteristics and social desirability bias?: Demand characteristics are aspects of experiments that may give away the research purpose 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.
What is the definition of response bias?: 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.
How do I paraphrase effectively?: 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.
What’s the difference between plagiarism and paraphrasing?: 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.
When should I quote instead of paraphrasing?: 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
What is a quote?: A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.
How do I cite a quote in academic writing?: 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.
How many quotes should I use?: 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.
How do I quote text that contains a citation?: 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.
What is a block quote?: 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.
Where does your list of figures and tables go?: Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.
Is a list of figures and tables mandatory?: 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.
How can I find copyright information on tables and figures from other sources?: 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.
What is a list of figures and tables?: 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.
Should I include lists of my tables and figures in an APA paper?: 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.
What is the definition of a 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. 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.
Are citations necessary in a glossary?: 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.
What’s the difference between a glossary and an index?: 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.
Is a glossary mandatory?: 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.
What is the difference between a glossary and a dictionary?: 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.
What should your dissertation title page include?: The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.
Where does your dissertation title page go?: The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.
Do I need a title page for my MLA paper?: 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.
How should you refer to chapters in your thesis or dissertation?: 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.
What is a thesis or dissertation outline?: 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.)

	Harvard style	Vancouver style
In-text citation	Each referencing style has different rules (Pears and Shields, 2019).	Each referencing style has different rules (1).
Reference list	Pears, R. and Shields, G. (2019). Cite them right: The essential referencing guide. 11th edn. London: MacMillan.	1. Pears R, Shields G. Cite them right: The essential referencing guide. 11th ed. London: MacMillan; 2019.

	In-text citation	Reference list
1 author	(Smith, 2014)	Smith, T. (2014) …
2 authors	(Smith and Jones, 2014)	Smith, T. and Jones, F. (2014) …
3 authors	(Smith, Jones and Davies, 2014)	Smith, T., Jones, F. and Davies, S. (2014) …
4+ authors	(Smith et al., 2014)	Smith, T. et al. (2014) …

Number of authors	Footnote example	Bibliography example
1 author	David Smith	Smith, David
2 authors	David Smith and Hugh Jones	Smith, David, and Hugh Jones
3 authors	David Smith, Hugh Jones and Emily Wright	Smith, David, Hugh Jones and Emily Wright
4+ authors	David Smith and others	Smith, David, and others

Longitudinal study	Cross-sectional study
Repeated observations	Observations at a single point in time
Observes the same group multiple times	Observes different groups (a ‘cross-section’) in the population
Follows changes in participants over time	Provides snapshot of society at a given point

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Types of editing	Available at Scribbr?
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Copy editing Focus on grammar, syntax, style, tone and the conventions of the field. The editor also considers the internal logic of the text and flags any obvious contradictions.	Yes! This is the “editing” in Scribbr’s standard service. It can only be selected in combination with proofreading.
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