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.
Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments, surveys, statistical tests).
In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results, discussion and conclusion.
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
Reliability and validity are both about how well a method measures something:
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
There are several reasons to conduct a literature review at the beginning of a research project:
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question.
It is often written as part of a dissertation, thesis, research paper, or proposal.
The literature review usually comes near the beginning of your dissertation. After the introduction, it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology.
Harvard referencing uses an author–date system. Sources are cited by the author’s last name and the publication year in brackets. Each Harvard in-text citation corresponds to an entry in the alphabetised reference list at the end of the paper.
Vancouver referencing uses a numerical system. Sources are cited by a number in parentheses or superscript. Each number corresponds to a full reference at the end of the paper.
| 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. |
A Harvard in-text citation should appear in brackets every time you quote, paraphrase, or refer to information from a source.
The citation can appear immediately after the quotation or paraphrase, or at the end of the sentence. If you’re quoting, place the citation outside of the quotation marks but before any other punctuation like a comma or full stop.
In Harvard referencing, up to three author names are included in an in-text citation or reference list entry. When there are four or more authors, include only the first, followed by ‘et al.’
| 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) … |
A bibliography should always contain every source you cited in your text. Sometimes a bibliography also contains other sources that you used in your research, but did not cite in the text.
MHRA doesn’t specify a rule about this, so check with your supervisor to find out exactly what should be included in your bibliography.
Footnote numbers should appear in superscript (e.g. 11 ). You can use the ‘Insert footnote’ button in Word to do this automatically; it’s in the ‘References’ tab at the top.
Footnotes always appear after the quote or paraphrase they relate to. MHRA generally recommends placing footnote numbers at the end of the sentence, immediately after any closing punctuation, like this. 12
In situations where this might be awkward or misleading, such as a long sentence containing multiple quotations, footnotes can also be placed at the end of a clause mid-sentence, like this; 13 note that they still come after any punctuation.
When a source has two or three authors, name all of them in your MHRA references. When there are four or more, use only the first name, followed by ‘and others’:
| 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.
A citation should appear wherever you use information or ideas from a source, whether by quoting or paraphrasing its content.
In Vancouver style, you have some flexibility about where the citation number appears in the sentence – usually directly after mentioning the author’s name is best, but simply placing it at the end of the sentence is an acceptable alternative, as long as it’s clear what it relates to.
In Vancouver style, when you refer to a source with multiple authors in your text, you should only name the first author followed by ‘et al.’. This applies even when there are only two authors.
In your reference list, include up to six authors. For sources with seven or more authors, list the first six followed by ‘et al.’.
The words ‘dissertation’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:
The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.
Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic, and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).
Dissertation word counts vary widely across different fields, institutions, and levels of education:
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.
At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.
A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.
References should be included in your text whenever you use words, ideas, or information from a source. A source can be anything from a book or journal article to a website or YouTube video.
If you don’t acknowledge your sources, you can get in trouble for plagiarism.
Your university should tell you which referencing style to follow. If you’re unsure, check with a supervisor. Commonly used styles include:
Your university may have its own referencing style guide.
If you are allowed to choose which style to follow, we recommend Harvard referencing, as it is a straightforward and widely used style.
To avoid plagiarism, always include a reference when you use words, ideas or information from a source. This shows that you are not trying to pass the work of others off as your own.
You must also properly quote or paraphrase the source. If you’re not sure whether you’ve done this correctly, you can use the Scribbr Plagiarism Checker to find and correct any mistakes.
In Harvard style, when you quote directly from a source that includes page numbers, your in-text citation must include a page number. For example: (Smith, 2014, p. 33).
You can also include page numbers to point the reader towards a passage that you paraphrased. If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.
When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation, first mention the source you want to refer to, and then the source in which you found it. For example:
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.
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:
Add ‘a’ to the first one you cite, ‘b’ to the second, and so on. Do the same in your bibliography or reference list.
To create a hanging indent for your bibliography or reference list:
Though the terms are sometimes used interchangeably, there is a difference in meaning:
It’s important to assess the reliability of information found online. Look for sources from established publications and institutions with expertise (e.g. peer-reviewed journals and government agencies).
The CRAAP test (currency, relevance, authority, accuracy, purpose) can aid you in assessing sources, as can our list of credible sources. You should generally avoid citing websites like Wikipedia that can be edited by anyone – instead, look for the original source of the information in the “References” section.
You can generally omit page numbers in your in-text citations of online sources which don’t have them. But when you quote or paraphrase a specific passage from a particularly long online source, it’s useful to find an alternate location marker.
For text-based sources, you can use paragraph numbers (e.g. ‘para. 4’) or headings (e.g. ‘under “Methodology”’). With video or audio sources, use a timestamp (e.g. ‘10:15’).
In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.
Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.
Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation.
Even if you feel your supervisor did not contribute greatly to the final product, you still should acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.
The acknowledgements are generally included at the very beginning of your thesis or dissertation, directly after the title page and before the abstract.
In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.
You may acknowledge God in your thesis or dissertation acknowledgements, but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.
To automatically insert a table of contents in Microsoft Word, follow these steps:
Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.
The table of contents in a thesis or dissertation always goes between your abstract and your introduction.
An abbreviation is a shortened version of an existing word, such as Dr for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).
Your dissertation sometimes contains a list of abbreviations.
As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like UK or PC), then you can just use the abbreviated version straight away.
Be sure to add each abbreviation in your list of abbreviations!
If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations.
If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.
A list of abbreviations is a list of all the abbreviations you used in your thesis or dissertation. It should appear at the beginning of your document, immediately after your table of contents. It should always be in alphabetical order.
Fishbone diagrams have a few different names that are used interchangeably, including herringbone diagram, cause-and-effect diagram, and Ishikawa diagram.
These are all ways to refer to the same thing– a problem-solving approach that uses a fish-shaped diagram to model possible root causes of problems and troubleshoot solutions.
Fishbone diagrams (also called herringbone diagrams, cause-and-effect diagrams, and Ishikawa diagrams) are most popular in fields of quality management. They are also commonly used in nursing and healthcare, or as a brainstorming technique for students.
Some synonyms and near synonyms of among include:
Some synonyms and near synonyms of between include:
In spite of is a preposition used to mean ‘regardless of‘, ‘notwithstanding’, or ‘even though’.
It’s always used in a subordinate clause to contrast with the information given in the main clause of a sentence (e.g., ‘Amy continued to watch TV, in spite of the time’).
Despite is a preposition used to mean ‘regardless of‘, ‘notwithstanding’, or ‘even though’.
It’s used in a subordinate clause to contrast with information given in the main clause of a sentence (e.g., ‘Despite the stress, Joe loves his job’).
‘Log in’ is a phrasal verb meaning ‘connect to an electronic device, system, or app’. The preposition ‘to’ is often used directly after the verb; ‘in’ and ‘to’ should be written as two separate words (e.g., ‘log in to the app to update privacy settings’).
‘Log into’ is sometimes used instead of ‘log in to’, but this is generally considered incorrect (as is ‘login to’).
Some synonyms and near synonyms of ensure include:
Some synonyms and near synonyms of assure include:
Rest assured is an expression meaning ‘you can be certain’ (e.g., ‘Rest assured, I will find your cat’). ‘Assured’ is the adjectival form of the verb assure, meaning ‘convince’ or ‘persuade’.
Some synonyms and near synonyms for council include:
There are numerous synonyms and near synonyms for the two meanings of counsel:
| Advise (verb) | Advice (noun) |
|---|---|
| Direct | Direction |
| Guide | Guidance |
| Instruct | Instruction |
AI writing tools can be used to perform a variety of tasks.
Generative AI writing tools (like ChatGPT) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.
These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. Y ou can also use Scribbr’s free paraphrasing tool, summarising tool, and grammar checker, which are designed specifically for these purposes.
Using AI writing tools (like ChatGPT) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.
However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.
The Scribbr Knowledge Base is a collection of free resources to help you succeed in academic research, writing, and citation. Every week, we publish helpful step-by-step guides, clear examples, simple templates, engaging videos, and more.
The Knowledge Base is for students at all levels. Whether you’re writing your first essay, working on your bachelor’s or master’s dissertation, or getting to grips with your PhD research, we’ve got you covered.
As well as the Knowledge Base, Scribbr provides many other tools and services to support you in academic writing and citation:
Yes! We’re happy for educators to use our content, and we’ve even adapted some of our articles into ready-made lecture slides.
You are free to display, distribute, and adapt Scribbr materials in your classes or upload them in private learning environments like Blackboard. We only ask that you credit Scribbr for any content you use.
We’re always striving to improve the Knowledge Base. If you have an idea for a topic we should cover, or you notice a mistake in any of our articles, let us know by emailing shona@scribbr.com.
The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole paper by someone else will have the most severe consequences, while accidental citation errors are considered less serious.
If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.
As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding or your job, and you could even face legal consequences for copyright infringement.
Paraphrasing without crediting the original author is a form of plagiarism, because you’re presenting someone else’s ideas as if they were your own.
However, paraphrasing is not plagiarism if you correctly reference the source. This means including an in-text referencing and a full reference, formatted according to your required citation style (e.g., Harvard, Vancouver).
As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.
Accidental plagiarism is one of the most common examples of plagiarism. Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from, and aren’t totally sure if it’s original or not.
These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re citing your sources. Also consider running your work through a plagiarism checker tool prior to submission, which work by using advanced database software to scan for matches between your text and existing texts.
Scribbr’s Plagiarism Checker takes less than 10 minutes and can help you turn in your paper with confidence.
The accuracy depends on the plagiarism checker you use. Per our in-depth research, Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.
Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts. Their accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared).
To avoid plagiarism when summarising an article or other source, follow these two rules:
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.
The most surefire way to avoid plagiarism is to always cite your sources. When in doubt, cite!
Global plagiarism means taking an entire work written by someone else and passing it off as your own. This can include getting someone else to write an essay or assignment for you, or submitting a text you found online as your own work.
Global plagiarism is one of the most serious types of plagiarism because it involves deliberately and directly lying about the authorship of a work. It can have severe consequences for students and professionals alike.
Verbatim plagiarism means copying text from a source and pasting it directly into your own document without giving proper credit.
If the structure and the majority of the words are the same as in the original source, then you are committing verbatim plagiarism. This is the case even if you delete a few words or replace them with synonyms.
If you want to use an author’s exact words, you need to quote the original source by putting the copied text in quotation marks and including an in-text citation.
Patchwork plagiarism, also called mosaic plagiarism, means copying phrases, passages, or ideas from various existing sources and combining them to create a new text. This includes slightly rephrasing some of the content, while keeping many of the same words and the same structure as the original.
While this type of plagiarism is more insidious than simply copying and pasting directly from a source, plagiarism checkers like Turnitin’s can still easily detect it.
To avoid plagiarism in any form, remember to reference your sources.
Yes, reusing your own work without citation is considered self-plagiarism. This can range from resubmitting an entire assignment to reusing passages or data from something you’ve handed in previously.
Self-plagiarism often has the same consequences as other types of plagiarism. If you want to reuse content you wrote in the past, make sure to check your university’s policy or consult your professor.
If you are reusing content or data you used in a previous assignment, make sure to cite yourself. You can cite yourself the same way you would cite any other source: simply follow the directions for the citation style you are using.
Keep in mind that reusing prior content can be considered self-plagiarism, so make sure you ask your instructor or consult your university’s handbook prior to doing so.
Most institutions have an internal database of previously submitted student assignments. Turnitin can check for self-plagiarism by comparing your paper against this database. If you’ve reused parts of an assignment you already submitted, it will flag any similarities as potential plagiarism.
Online plagiarism checkers don’t have access to your institution’s database, so they can’t detect self-plagiarism of unpublished work. If you’re worried about accidentally self-plagiarising, you can use Scribbr’s Self-Plagiarism Checker to upload your unpublished documents and check them for similarities.
Plagiarism has serious consequences and can be illegal in certain scenarios.
While most of the time plagiarism in an undergraduate setting is not illegal, plagiarism or self-plagiarism in a professional academic setting can lead to legal action, including copyright infringement and fraud. Many scholarly journals do not allow you to submit the same work to more than one journal, and if you do not credit a coauthor, you could be legally defrauding them.
Even if you aren’t breaking the law, plagiarism can seriously impact your academic career. While the exact consequences of plagiarism vary by institution and severity, common consequences include a lower grade, automatically failing a course, academic suspension or probation, and even expulsion.
Self-plagiarism means recycling work that you’ve previously published or submitted as an assignment. It’s considered academic dishonesty to present something as brand new when you’ve already gotten credit and perhaps feedback for it in the past.
If you want to refer to ideas or data from previous work, be sure to cite yourself.
Academic integrity means being honest, ethical, and thorough in your academic work. To maintain academic integrity, you should avoid misleading your readers about any part of your research and refrain from offences like plagiarism and contract cheating, which are examples of academic misconduct.
Academic dishonesty refers to deceitful or misleading behavior in an academic setting. Academic dishonesty can occur intentionally or unintentionally, and it varies in severity.
It can encompass paying for a pre-written essay, cheating on an exam, or committing plagiarism. It can also include helping others cheat, copying a friend’s homework answers, or even pretending to be sick to miss an exam.
Academic dishonesty doesn’t just occur in a classroom setting, but also in research and other academic-adjacent fields.
Consequences of academic dishonesty depend on the severity of the offence and your institution’s policy. They can range from a warning for a first offence to a failing grade in a course to expulsion from your university.
For those in certain fields, such as nursing, engineering, or lab sciences, not learning fundamentals properly can directly impact the health and safety of others. For those working in academia or research, academic dishonesty impacts your professional reputation, leading others to doubt your future work.
Academic dishonesty can be intentional or unintentional, ranging from something as simple as claiming to have read something you didn’t to copying your neighbour’s answers on an exam.
You can commit academic dishonesty with the best of intentions, such as helping a friend cheat on a paper. Severe academic dishonesty can include buying a pre-written essay or the answers to a multiple-choice test, or falsifying a medical emergency to avoid taking a final exam.
Plagiarism means presenting someone else’s work as your own without giving proper credit to the original author. In academic writing, plagiarism involves using words, ideas, or information from a source without including a citation.
Plagiarism can have serious consequences, even when it’s done accidentally. To avoid plagiarism, it’s important to keep track of your sources and cite them correctly.
Common knowledge does not need to be cited. However, you should be extra careful when deciding what counts as common knowledge.
Common knowledge encompasses information that the average educated reader would accept as true without needing the extra validation of a source or citation.
Common knowledge should be widely known, undisputed, and easily verified. When in doubt, always cite your sources.
Most online plagiarism checkers only have access to public databases, whose software doesn’t allow you to compare two documents for plagiarism.
However, in addition to our Plagiarism Checker, Scribbr also offers an Self-Plagiarism Checker. This is an add-on tool that lets you compare your paper with unpublished or private documents. This way you can rest assured that you haven’t unintentionally plagiarised or self-plagiarised.
The research methods you use depend on the type of data you need to answer your research question.
Methodology refers to the overarching strategy and rationale of your research project. It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys, and statistical tests).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section.
In a longer or more complex research project, such as a thesis or dissertation, you will probably include a methodology section, where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
In mixed methods research, you use both qualitative and quantitative data collection and analysis methods to answer your research question.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.
There are various approaches to qualitative data analysis, but they all share five steps in common:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis, thematic analysis, and discourse analysis.
There are five common approaches to qualitative research:
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses, by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
Operationalisation means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data, it’s important to consider how you will operationalise the variables that you want to measure.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Triangulation is mainly used in qualitative research, but it’s also commonly applied in quantitative research. Mixed methods research always uses triangulation.
These are four of the most common mixed methods designs:
An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.
The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.
Exploratory research explores the main aspects of a new or barely researched question.
Explanatory research explains the causes and effects of an already widely researched question.
Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.
To design a successful experiment, first identify:
When designing the experiment, first decide:
There are four main types of triangulation:
Triangulation can help:
But triangulation can also pose problems:
A confounding variable, also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design, it’s important to identify potential confounding variables and plan how you will reduce their impact.
In a between-subjects design, every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design, each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.
The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.
A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment.
Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.
Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.
While a between-subjects design has fewer threats to internal validity, it also requires more participants for high statistical power than a within-subjects design.
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
Probability sampling means that every member of the target population has a known chance of being included in the sample.
In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.
In multistage sampling, or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.
Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.
Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population. Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.
The American Community Survey is an example of simple random sampling. In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity. However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,
If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
Cluster sampling is more time- and cost-efficient than other probability sampling methods, particularly when it comes to large samples spread across a wide geographical area.
However, it provides less statistical certainty than other methods, such as simple random sampling, because it is difficult to ensure that your clusters properly represent the population as a whole.
There are three types of cluster sampling: single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
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.
For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling, systematic sampling, or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.
But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples.
In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).
Once divided, each subgroup is randomly sampled using another probability sampling method.
You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.
Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure.
For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.
For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.
There are three key steps in systematic sampling:
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling.
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
A statistic refers to measures about the sample, while a parameter refers to measures about the population.
A sampling error is the difference between a population parameter and a sample statistic.
There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction, and attrition.
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
Attrition bias is a threat to internal validity. In experiments, differential rates of attrition between treatment and control groups can skew results.
This bias can affect the relationship between your independent and dependent variables. It can make variables appear to be correlated when they are not, or vice versa.
The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.
The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).
There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment, and situation effect.
Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.
With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity, which includes construct validity, face validity, and criterion validity.
There are two subtypes of construct validity.
When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity, because it covers all of the other types. You need to have face validity, content validity, and criterion validity to achieve construct validity.
Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity.
Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.
Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.
Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.
While experts have a deep understanding of research methods, the people you’re studying can provide you with valuable insights you may have missed otherwise.
There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.
Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.
In inductive research, you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottom-up reasoning.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.
Deductive reasoning is also called deductive logic.
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research.
In research, you might have come across something called the hypothetico-deductive method. It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.
A dependent variable is what changes as a result of the independent variable manipulation in experiments. It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.
In statistics, dependent variables are also called:
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:
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
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.
There are 4 main types of extraneous 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.
‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. That way, you can isolate the control variable’s effects from the relationship between the variables of interest.
Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity.
If you don’t control relevant extraneous variables, they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable.
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
In statistics, ordinal and nominal variables are both considered categorical variables.
Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).
The process of turning abstract concepts into measurable variables and indicators is called operationalisation.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.
In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching, you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable.
In statistical control, you include potential confounders as variables in your regression.
In randomisation, you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause, while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables.
Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables, or even find a causal relationship where none exists.
Yes, but including more than one of either type requires multiple research questions.
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable.
To ensure the internal validity of an experiment, you should only change one independent variable at a time.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.
You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment.
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results.
Discrete and continuous variables are two types of quantitative variables:
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:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.
Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
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.
When conducting research, collecting original data has significant advantages:
However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.
There is a risk of an interviewer effect in all types of interviews, but it can be mitigated by writing really high-quality interview questions.
A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
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:
The four most common types of interviews are:
A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews.
Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys, but is most common in semi-structured interviews, unstructured interviews, and focus groups.
Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.
This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions, which can bias your responses.
Overall, your focus group questions should be:
The third variable and directionality problems are two main reasons why correlation isn’t causation.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
Controlled experiments establish causality, whereas correlational studies only show associations between variables.
In general, correlational research is high in external validity while experimental research is high in internal validity.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. The Pearson product-moment correlation coefficient (Pearson’s r) is commonly used to assess a linear relationship between two quantitative variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research.
A correlation reflects the strength and/or direction of the association between two or more variables.
Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
The 1970 British Cohort Study, which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study.
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.
Longitudinal studies and cross-sectional studies are two different types of research design. In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
| 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 |
Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study.
Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.
Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘x affects y because …’).
A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study, the statistical hypotheses correspond logically to the research hypothesis.
Individual Likert-type questions are generally considered ordinal data, because the items have clear rank order, but don’t have an even distribution.
Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.
The type of data determines what statistical tests you should use to analyse your data.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.
To use a Likert scale in a survey, you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.
A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.
A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.
However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).
For strong internal validity, it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
In a controlled experiment, all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables.
Questionnaires can be self-administered or researcher-administered.
Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.
You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.
Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.
Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.
You can think of naturalistic observation as ‘people watching’ with a purpose.
Naturalistic observation is a valuable tool because of its flexibility, external validity, and suitability for topics that can’t be studied in a lab setting.
The downsides of naturalistic observation include its lack of scientific control, ethical considerations, and potential for bias from observers and subjects.
You can use several tactics to minimise observer bias.
The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.
Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics.
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias.
Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors, but cleaning your data helps you minimise or resolve these.
Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.
In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.
Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.
For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.
After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.
Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.
Dirty data can come from any part of the research process, including poor research design, inappropriate measurement materials, or flawed data entry.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
Random selection, or random sampling, is a way of selecting members of a population for your study’s sample.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.
To implement random assignment, assign a unique number to every member of your study’s sample.
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.
Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.
You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.
Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.
Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research.
Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.
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.
Many academic fields use peer review, largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.
However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.
Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.
It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.
Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.
In general, the peer review process follows the following steps:
Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.
For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.
Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations.
You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.
You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.
These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others.
These considerations protect the rights of research participants, enhance research validity, and maintain scientific integrity.
A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
The two main types of social desirability bias are:
Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.
Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.
Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias.
When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method.
This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
A sampling frame is a list of every member in the entire population. It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.
However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.
In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.
Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.
On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.
The main difference is that in stratified sampling, you draw a random sample from each subgroup (probability sampling). In quota sampling you select a predetermined number or proportion of units, in a non-random manner (non-probability sampling).
Snowball sampling is best used in the following cases:
Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.
Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .
Snowball sampling is a non-probability sampling method, where there is not an equal chance for every member of the population to be included in the sample.
This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research. As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research.
Snowball sampling is a non-probability sampling method. Unlike probability sampling (which involves some form of random selection), the initial individuals selected to be studied are the ones who recruit new participants.
Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.
The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.
Convergent validity and discriminant validity are both subtypes of construct validity. Together, they help you evaluate whether a test measures the concept it was designed to measure.
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.
Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.
In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.
The higher the content validity, the more accurate the measurement of the construct.
If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.
Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion (operationalisation).
On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.
Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.
When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.
For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).
On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.
A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.
Convergent validity and discriminant validity are both subtypes of construct validity. Together, they help you evaluate whether a test measures the concept it was designed to measure.
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
Criterion validity and construct validity are both types of measurement validity. In other words, they both show you how accurately a method measures something.
While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.
Construct validity is often considered the overarching type of measurement validity. You need to have face validity, content validity, and criterion validity in order to achieve construct validity.
Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.
Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group. As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased.
Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.
Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:
Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity:
Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs.
On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.
Although both types of validity are established by calculating the association or correlation between a test score and another variable, they represent distinct validation methods.
The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.
Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables. In other words, they prioritise internal validity over external validity, including ecological validity.
Inclusion and exclusion criteria are predominantly used in non-probability sampling. In purposive sampling and snowball sampling, restrictions apply as to who can be included in the sample.
Scope of research is determined at the beginning of your research process, prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.
Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation. A scope is needed for all types of research: quantitative, qualitative, and mixed methods.
To define your scope of research, consider the following:
To make quantitative observations, you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.
Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.
The Scribbr Reference Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js. It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.
You can find all the citation styles and locales used in the Scribbr Reference Generator in our publicly accessible repository on Github.
To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:
The main point is to ensure you don’t just copy the structure of the original text, but instead reformulate the idea in your own words.
Plagiarism means using someone else’s words or ideas and passing them off as your own. Paraphrasing means putting someone else’s ideas into your own words.
So when does paraphrasing count as plagiarism?
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:
A quote is an exact copy of someone else’s words, usually enclosed in quotation marks and credited to the original author or speaker.
Every time you quote a source, you must include a correctly formatted in-text citation. This looks slightly different depending on the citation style.
For example, a direct quote in APA is cited like this: ‘This is a quote’ (Streefkerk, 2020, p. 5).
Every in-text citation should also correspond to a full reference at the end of your paper.
In scientific subjects, the information itself is more important than how it was expressed, so quoting should generally be kept to a minimum. In the arts and humanities, however, well-chosen quotes are often essential to a good paper.
In social sciences, it varies. If your research is mainly quantitative, you won’t include many quotes, but if it’s more qualitative, you may need to quote from the data you collected.
As a general guideline, quotes should take up no more than 5–10% of your paper. If in doubt, check with your instructor or supervisor how much quoting is appropriate in your field.
If you’re quoting from a text that paraphrases or summarises other sources and cites them in parentheses, APA recommends retaining the citations as part of the quote:
Footnote or endnote numbers that appear within quoted text should be omitted.
If you want to cite an indirect source (one you’ve only seen quoted in another source), either locate the original source or use the phrase ‘as cited in’ in your citation.
A block quote is a long quote formatted as a separate ‘block’ of text. Instead of using quotation marks, you place the quote on a new line, and indent the entire quote to mark it apart from your own words.
APA uses block quotes for quotes that are 40 words or longer.
A credible source should pass the CRAAP test and follow these guidelines:
Common examples of primary sources include interview transcripts, photographs, novels, paintings, films, historical documents, and official statistics.
Anything you directly analyze or use as first-hand evidence can be a primary source, including qualitative or quantitative data that you collected yourself.
Common examples of secondary sources include academic books, journal articles, reviews, essays, and textbooks.
Anything that summarizes, evaluates or interprets primary sources can be a secondary source. If a source gives you an overview of background information or presents another researcher’s ideas on your topic, it is probably a secondary source.
To determine if a source is primary or secondary, ask yourself:
Some types of sources are nearly always primary: works of art and literature, raw statistical data, official documents and records, and personal communications (e.g. letters, interviews). If you use one of these in your research, it is probably a primary source.
Primary sources are often considered the most credible in terms of providing evidence for your argument, as they give you direct evidence of what you are researching. However, it’s up to you to ensure the information they provide is reliable and accurate.
Always make sure to properly cite your sources to avoid plagiarism.
A fictional movie is usually a primary source. A documentary can be either primary or secondary depending on the context.
If you are directly analysing some aspect of the movie itself – for example, the cinematography, narrative techniques, or social context – the movie is a primary source.
If you use the movie for background information or analysis about your topic – for example, to learn about a historical event or a scientific discovery – the movie is a secondary source.
Whether it’s primary or secondary, always properly cite the movie in the citation style you are using. Learn how to create an MLA movie citation or an APA movie citation.
Articles in newspapers and magazines can be primary or secondary depending on the focus of your research.
In historical studies, old articles are used as primary sources that give direct evidence about the time period. In social and communication studies, articles are used as primary sources to analyse language and social relations (for example, by conducting content analysis or discourse analysis).
If you are not analysing the article itself, but only using it for background information or facts about your topic, then the article is a secondary source.
In academic writing, there are three main situations where quoting is the best choice:
Don’t overuse quotes; your own voice should be dominant. If you just want to provide information from a source, it’s usually better to paraphrase or summarise.
To paraphrase effectively, don’t just take the original sentence and swap out some of the words for synonyms. Instead, try:
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.
Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.
Lists of figures and tables are often not required, and they aren’t particularly common. They specifically aren’t required for APA Style, though you should be careful to follow their other guidelines for figures and tables.
If you have many figures and tables in your thesis or dissertation, include one may help you stay organised. Your educational institution may require them, so be sure to check their guidelines.
Copyright information can usually be found wherever the table or figure was published. For example, for a diagram in a journal article, look on the journal’s website or the database where you found the article. Images found on sites like Flickr are listed with clear copyright information.
If you find that permission is required to reproduce the material, be sure to contact the author or publisher and ask for it.
A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.
APA doesn’t require you to include a list of tables or a list of figures. However, it is advisable to do so if your text is long enough to feature a table of contents and it includes a lot of tables and/or figures.
A list of tables and list of figures appear (in that order) after your table of contents, and are presented in a similar way.
A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.
Definitional terms often fall into the category of common knowledge, meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.
However, if you’d prefer to cite your sources, you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.
A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.
Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.
A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.
The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.
The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.
Usually, no title page is needed in an MLA paper. A header is generally included at the top of the first page instead. The exceptions are when:
In those cases, you should use a title page instead of a header, listing the same information but on a separate page.
When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation.
A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organise your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.
Generally, an outline contains information on the different sections included in your thesis or dissertation, such as:
While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.
A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation.
A theoretical framework can sometimes be integrated into a literature review chapter , but it can also be included as its own chapter or section in your dissertation. As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.
An abstract is a concise summary of an academic text (such as a journal article or dissertation). It serves two main purposes:
Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.
The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.
Avoid citing sources in your abstract. There are two reasons for this:
There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.
The abstract appears on its own page, after the title page and acknowledgements but before the table of contents.
Results are usually written in the past tense, because they are describing the outcome of completed actions.
The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.
In qualitative research, results and discussion are sometimes combined. But in quantitative research, it’s considered important to separate the objective results from your interpretation of them.
Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement.
However, it should also fulfill criteria in three main areas:
The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.
A noun is a word that represents a person, thing, concept, or place (e.g., ‘John’, ‘house’, ‘affinity’, ‘river’). Most sentences contain at least one noun or pronoun.
Nouns are often, but not always, preceded by an article (‘the’, ‘a’, or ‘an’) and/or another determiner such as an adjective.
There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.
Some of the main types of nouns are:
Pronouns are words like ‘I’, ‘she’, and ‘they’ that are used in a similar way to nouns. They stand in for a noun that has already been mentioned or refer to yourself and other people.
Pronouns can function just like nouns as the head of a noun phrase and as the subject or object of a verb. However, pronouns change their forms (e.g., from ‘I’ to ‘me’) depending on the grammatical context they’re used in, whereas nouns usually don’t.
Common nouns are words for types of things, people, and places, such as ‘dog’, ‘professor’, and ‘city’. They are not capitalised and are typically used in combination with articles and other determiners.
Proper nouns are words for specific things, people, and places, such as ‘Max’, ‘Dr Prakash’, and ‘London’. They are always capitalised and usually aren’t combined with articles and other determiners.
A proper adjective is an adjective that was derived from a proper noun and is therefore capitalised .
Proper adjectives include words for nationalities, languages, and ethnicities (e.g., ‘Japanese’, ‘Inuit’, ‘French’) and words derived from people’s names (e.g., ‘Bayesian’, ‘Orwellian’).
The names of seasons (e.g., ‘spring’) are treated as common nouns in English and therefore not capitalised. People often assume they are proper nouns, but this is an error.
The names of days and months, however, are capitalised since they’re treated as proper nouns in English (e.g., ‘Wednesday’, ‘January’).
No, as a general rule, academic concepts, disciplines, theories, models, etc. are treated as common nouns, not proper nouns, and therefore not capitalised. For example, ‘five-factor model of personality’ or ‘analytic philosophy’.
However, proper nouns that appear within the name of an academic concept (such as the name of the inventor) are capitalised as usual. For example, ‘Darwin’s theory of evolution’ or ‘Student’s t table‘.
Collective nouns are most commonly treated as singular (e.g., ‘the herd is grazing’), but usage differs between US and UK English:
The plural of “crisis” is “crises”. It’s a loanword from Latin and retains its original Latin plural noun form (similar to “analyses” and “bases”). It’s wrong to write “crisises”.
For example, you might write “Several crises destabilized the regime.”
Normally, the plural of “fish” is the same as the singular: “fish”. It’s one of a group of irregular plural nouns in English that are identical to the corresponding singular nouns (e.g., “moose”, “sheep”). For example, you might write “The fish scatter as the shark approaches.”
If you’re referring to several species of fish, though, the regular plural “fishes” is often used instead. For example, “The aquarium contains many different fishes, including trout and carp.”
The correct plural of “octopus” is “octopuses”.
People often write “octopi” instead because they assume that the plural noun is formed in the same way as Latin loanwords such as “fungus/fungi”. But “octopus” actually comes from Greek, where its original plural is “octopodes”. In English, it instead has the regular plural form “octopuses”.
For example, you might write “There are four octopuses in the aquarium.”
The plural of “moose” is the same as the singular: “moose”. It’s one of a group of plural nouns in English that are identical to the corresponding singular nouns. So it’s wrong to write “mooses”.
For example, you might write “There are several moose in the forest.”
Bias in research affects the validity and reliability of your findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.
Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behaviour and external factors (difficult circumstances) to justify the same behaviour in themselves.
Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.
Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen either because people are not willing or not able to participate.
In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.
Demand characteristics are common problems in psychology experiments and other social science studies because they can bias your research findings.
Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results.
These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity. You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.
You can control demand characteristics by taking a few precautions in your research design and materials.
Use these measures:
Some attrition is normal and to be expected in research. However, the type of attrition is important because systematic research bias can distort your findings. Attrition bias can lead to inaccurate results because it affects internal and/or external validity.
To avoid attrition bias, applying some of these measures can help you reduce participant dropout (attrition) by making it easy and appealing for participants to stay.
If you have a small amount of attrition bias, you can use a few statistical methods to try to make up for this research bias.
Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.
Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.
The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective.
Although there is no definite answer to what causes the placebo effect, researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.
Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.
Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.
Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.
Positivity bias is phenomenon that occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias, or the e xpectation that things will work out well, even if rationality suggests that problems are inevitable in life.
Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.
There are many ways to categorize adjectives into various types. An adjective can fall into one or more of these categories depending on how it is used.
Some of the main types of adjectives are:
Cardinal numbers (e.g., one, two, three) can be placed before a noun to indicate quantity (e.g., one apple). While these are sometimes referred to as ‘numeral adjectives‘, they are more accurately categorised as determiners or quantifiers.
Proper adjectives are adjectives formed from a proper noun (i.e., the name of a specific person, place, or thing) that are used to indicate origin. Like proper nouns, proper adjectives are always capitalised (e.g., Newtonian, Marxian, African).
The cost of proofreading depends on the type and length of text, the turnaround time, and the level of services required. Most proofreading companies charge per word or page, while freelancers sometimes charge an hourly rate.
For proofreading alone, which involves only basic corrections of typos and formatting mistakes, you might pay as little as £0.01 per word, but in many cases, your text will also require some level of editing, which costs slightly more.
It’s often possible to purchase combined proofreading and editing services and calculate the price in advance based on your requirements.
Use to and used to are commonly confused words. In the case of ‘used to do’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.
There are numerous synonyms and near synonyms for the various meanings of “favour”:
| Prefer (verb) | Approval (noun) |
| Advocate | Adoration |
| Approve of | Appreciation |
| Endorse | Praise |
| Support | Respect |
There are numerous synonyms and near synonyms for the two meanings of “favoured”:
| Preferred (verb) | Most-liked (adjective) |
| Advocated | Adored |
| Approved of | Appreciated |
| Endorsed | Praised |
| Supported | Preferred |
No one (two words) is an indefinite pronoun meaning ‘nobody’. People sometimes mistakenly write ‘noone’, but this is incorrect and should be avoided. ‘No-one’, with a hyphen, is also acceptable in UK English.
Nobody and no one are both indefinite pronouns meaning ‘no person’. They can be used interchangeably (e.g., ‘nobody is home’ means the same as ‘no one is home’).
Some synonyms and near synonyms of every time include:
‘Everytime’ is sometimes used to mean ‘each time’ or ‘whenever’. However, this is incorrect and should be avoided. The correct phrase is every time (two words).
Yes, the conjunction because is a compound word, but one with a long history. It originates in Middle English from the preposition “bi” (“by”) and the noun “cause”. Over time, the open compound “bi cause” became the closed compound “because”, which we use today.
Though it’s spelled this way now, the verb “be” is not one of the words that makes up “because”.
Yes, today is a compound word, but a very old one. It wasn’t originally formed from the preposition “to” and the noun “day”; rather, it originates from their Old English equivalents, “tō” and “dæġe”.
In the past, it was sometimes written as a hyphenated compound: “to-day”. But the hyphen is no longer included; it’s always “today” now (“to day” is also wrong).
IEEE citation format is defined by the Institute of Electrical and Electronics Engineers and used in their publications.
It’s also a widely used citation style for students in technical fields like electrical and electronic engineering, computer science, telecommunications, and computer engineering.
An IEEE in-text citation consists of a number in brackets at the relevant point in the text, which points the reader to the right entry in the numbered reference list at the end of the paper. For example, ‘Smith [1] states that …’
A location marker such as a page number is also included within the brackets when needed: ‘Smith [1, p. 13] argues …’
The IEEE reference page consists of a list of references numbered in the order they were cited in the text. The title ‘References’ appears in bold at the top, either left-aligned or centered.
The numbers appear in square brackets on the left-hand side of the page. The reference entries are indented consistently to separate them from the numbers. Entries are single-spaced, with a normal paragraph break between them.
If you cite the same source more than once in your writing, use the same number for all of the IEEE in-text citations for that source, and only include it on the IEEE reference page once. The source is numbered based on the first time you cite it.
For example, the fourth source you cite in your paper is numbered [4]. If you cite it again later, you still cite it as [4]. You can cite different parts of the source each time by adding page numbers [4, p. 15].
A verb is a word that indicates a physical action (e.g., ‘drive’), a mental action (e.g., ‘think’) or a state of being (e.g., ‘exist’). Every sentence contains a verb.
Verbs are almost always used along with a noun or pronoun to describe what the noun or pronoun is doing.
There are many ways to categorize verbs into various types. A verb can fall into one or more of these categories depending on how it is used.
Some of the main types of verbs are:
Regular verbs are verbs whose simple past and past participle are formed by adding the suffix ‘-ed’ (e.g., ‘walked’).
Irregular verbs are verbs that form their simple past and past participles in some way other than by adding the suffix ‘-ed’ (e.g., ‘sat’).
The indefinite articles a and an are used to refer to a general or unspecified version of a noun (e.g., a house). Which indefinite article you use depends on the pronunciation of the word that follows it.
Indefinite articles can only be used with singular countable nouns. Like definite articles, they are a type of determiner.
Editing and proofreading are different steps in the process of revising a text.
Editing comes first, and can involve major changes to content, structure and language. The first stages of editing are often done by authors themselves, while a professional editor makes the final improvements to grammar and style (for example, by improving sentence structure and word choice).
Proofreading is the final stage of checking a text before it is published or shared. It focuses on correcting minor errors and inconsistencies (for example, in punctuation and capitalization). Proofreaders often also check for formatting issues, especially in print publishing.
Whether you’re publishing a blog, submitting a research paper, or even just writing an important email, there are a few techniques you can use to make sure it’s error-free:
If you want to be confident that an important text is error-free, it might be worth choosing a professional proofreading service instead.
There are many different routes to becoming a professional proofreader or editor. The necessary qualifications depend on the field – to be an academic or scientific proofreader, for example, you will need at least a university degree in a relevant subject.
For most proofreading jobs, experience and demonstrated skills are more important than specific qualifications. Often your skills will be tested as part of the application process.
To learn practical proofreading skills, you can choose to take a course with a professional organisation such as the Society for Editors and Proofreaders. Alternatively, you can apply to companies that offer specialised on-the-job training programmes, such as the Scribbr Academy.
Though they’re pronounced the same, there’s a big difference in meaning between its and it’s.
Its and it’s are often confused, but its (without apostrophe) is the possessive form of ‘it’ (e.g., its tail, its argument, its wing). You use ‘its’ instead of ‘his’ and ‘her’ for neuter, inanimate nouns.
| Examples: Then in a sentence | Examples: Than in a sentence |
|---|---|
| Mix the dry ingredients first, and then add the wet ingredients. | Max is a better saxophonist than you. |
| I was working as a teacher then. | I usually like coaching a team more than I like playing soccer myself. |
Use to and used to are commonly confused words. In the case of ‘used to be’, the latter (with ‘d’) is correct, since you’re describing an action or state in the past.
A grammar checker is a tool designed to automatically check your text for spelling errors, grammatical issues, punctuation mistakes, and problems with sentence structure. You can check out our analysis of the best free grammar checkers to learn more.
A paraphrasing tool edits your text more actively, changing things whether they were grammatically incorrect or not. It can paraphrase your sentences to make them more concise and readable or for other purposes. You can check out our analysis of the best free paraphrasing tools to learn more.
Some tools available online combine both functions. Others, such as QuillBot, have separate grammar checker and paraphrasing tools. Be aware of what exactly the tool you’re using does to avoid introducing unwanted changes.
Good grammar is the key to expressing yourself clearly and fluently, especially in professional communication and academic writing. Word processors, browsers, and email programs typically have built-in grammar checkers, but they’re quite limited in the kinds of problems they can fix.
If you want to go beyond detecting basic spelling errors, there are many online grammar checkers with more advanced functionality. They can often detect issues with punctuation, word choice, and sentence structure that more basic tools would miss.
Not all of these tools are reliable, though. You can check out our research into the best free grammar checkers to explore the options.
Our research indicates that the best free grammar checker available online is the QuillBot grammar checker.
We tested 10 of the most popular checkers with the same sample text (containing 20 grammatical errors) and found that QuillBot easily outperformed the competition, scoring 18 out of 20, a drastic improvement over the second-place score of 13 out of 20.
It even appeared to outperform the premium versions of other grammar checkers, despite being entirely free.
A teacher’s aide is a person who assists in teaching classes but is not a qualified teacher. Aide is a noun meaning ‘assistant’, so it will always refer to a person.
‘Teacher’s aid’ is incorrect.
A visual aid is an instructional device (e.g., a photo, a chart) that appeals to vision to help you understand written or spoken information. Aid is often placed after an attributive noun or adjective (like ‘visual’) that describes the type of help provided.
‘Visual aide’ is incorrect.
A job aid is an instructional tool (e.g., a checklist, a cheat sheet) that helps you work efficiently. Aid is a noun meaning ‘assistance’. It’s often placed after an adjective or attributive noun (like ‘job’) that describes the specific type of help provided.
‘Job aide’ is incorrect.
There are numerous synonyms for the various meanings of truly:
| In a truthful way | Absolutely | Properly |
| Candidly | Completely | Accurately |
| Honestly | Really | Correctly |
| Openly | Totally | Exactly |
| Truthfully | Undoubtedly | Precisely |
Yours truly is a phrase used at the end of a formal letter or email. It can also be used (typically in a humorous way) as a pronoun to refer to oneself (e.g., ‘The dinner was cooked by yours truly‘). The latter usage should be avoided in formal writing.
A pathetic fallacy can be a short phrase or a whole sentence and is often used in novels and poetry. Pathetic fallacies serve multiple purposes, such as:
AMA citation format is a citation style designed by the American Medical Association. It’s frequently used in the field of medicine.
You may be told to use AMA style for your student papers. You will also have to follow this style if you’re submitting a paper to a journal published by the AMA.
An AMA in-text citation consists of the number of the relevant reference on your AMA reference page, written in superscript 1 at the point in the text where the source is used.
It may also include the page number or range of the relevant material in the source (e.g., the part you quoted 2(p46) ). Multiple sources can be cited at one point, presented as a range or list (with no spaces 3,5–9 ).
An AMA reference usually includes the author’s last name and initials, the title of the source, information about the publisher or the publication it’s contained in, and the publication date. The specific details included, and the formatting, depend on the source type.
References in AMA style are presented in numerical order (numbered by the order in which they were first cited in the text) on your reference page. A source that’s cited repeatedly in the text still only appears once on the reference page.
An AMA in-text citation just consists of the number of the relevant entry on your AMA reference page, written in superscript at the point in the text where the source is referred to.
You don’t need to mention the author of the source in your sentence, but you can do so if you want. It’s not an official part of the citation, but it can be useful as part of a signal phrase introducing the source.
On your AMA reference page, author names are written with the last name first, followed by the initial(s) of their first name and middle name if mentioned.
There’s a space between the last name and the initials, but no space or punctuation between the initials themselves. The names of multiple authors are separated by commas, and the whole list ends in a period, e.g., ‘Andreessen F, Smith PW, Gonzalez E’.
The names of up to six authors should be listed for each source on your AMA reference page, separated by commas. For a source with seven or more authors, you should list the first three followed by ‘et al’: ‘Isidore, Gilbert, Gunvor, et al’.
In the text, mentioning author names is optional (as they aren’t an official part of AMA in-text citations). If you do mention them, though, you should use the first author’s name followed by ‘et al’ when there are three or more: ‘Isidore et al argue that …’
Note that according to AMA’s rather minimalistic punctuation guidelines, there’s no period after ‘et al’ unless it appears at the end of a sentence. This is different from most other styles, where there is normally a period.
Yes, you should normally include an access date in an AMA website citation (or when citing any source with a URL). This is because webpages can change their content over time, so it’s useful for the reader to know when you accessed the page.
When a publication or update date is provided on the page, you should include it in addition to the access date. The access date appears second in this case, e.g., ‘Published June 19, 2021. Accessed August 29, 2022.’
Don’t include an access date when citing a source with a DOI (such as in an AMA journal article citation).
Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.
However, for other variables, you can choose the level of measurement. For example, income is a variable that can be recorded on an ordinal or a ratio scale:
If you have a choice, the ratio level is always preferable because you can analyse data in more ways. The higher the level of measurement, the more precise your data is.
The level at which you measure a variable determines how you can analyse your data.
Depending on the level of measurement, you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis.
Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:
Statistical analysis is the main method for analyzing quantitative research data. It uses probabilities and models to test predictions about a population from sample data.
The null hypothesis is often abbreviated as H0. When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).
The alternative hypothesis is often abbreviated as Ha or H1. When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).
As the degrees of freedom increase, Student’s t distribution becomes less leptokurtic, meaning that the probability of extreme values decreases. The distribution becomes more and more similar to a standard normal distribution.
When there are only one or two degrees of freedom, the chi-square distribution is shaped like a backwards ‘J’. When there are three or more degrees of freedom, the distribution is shaped like a right-skewed hump. As the degrees of freedom increase, the hump becomes less right-skewed and the peak of the hump moves to the right. The distribution becomes more and more similar to a normal distribution.
‘Looking forward in hearing from you’ is an incorrect version of the phrase looking forward to hearing from you. The phrasal verb ‘looking forward to’ always needs the preposition ‘to’, not ‘in’.
Some synonyms and near synonyms for the expression looking forward to hearing from you include:
People sometimes mistakenly write ‘looking forward to hear from you’, but this is incorrect. The correct phrase is looking forward to hearing from you.
The phrasal verb ‘look forward to’ is always followed by a direct object, the thing you’re looking forward to. As the direct object has to be a noun phrase, it should be the gerund ‘hearing’, not the verb ‘hear’.
Traditionally, the sign-off Yours sincerely is used in an email message or letter when you are writing to someone you have interacted with before, not a complete stranger.
Yours faithfully is used instead when you are writing to someone you have had no previous correspondence with, especially if you greeted them as ‘Dear Sir or Madam’.
Just checking in is a standard phrase used to start an email (or other message) that’s intended to ask someone for a response or follow-up action in a friendly, informal way. However, it’s a cliché opening that can come across as passive-aggressive, so we recommend avoiding it in favor of a more direct opening like “We previously discussed …”
In a more personal context, you might encounter “just checking in” as part of a longer phrase such as “I’m just checking in to see how you’re doing”. In this case, it’s not asking the other person to do anything but rather asking about their well-being (emotional or physical) in a friendly way.
“Earliest convenience” is part of the phrase at your earliest convenience , meaning “as soon as you can”.
It’s typically used to end an email in a formal context by asking the recipient to do something when it’s convenient for them to do so.
ASAP is an abbreviation of the phrase “as soon as possible”.
It’s typically used to indicate a sense of urgency in highly informal contexts (e.g., “Let me know ASAP if you need me to drive you to the airport”).
“ASAP” should be avoided in more formal correspondence. Instead, use an alternative like at your earliest convenience.
Some synonyms and near synonyms of the verb compose (meaning “to make up”) are:
People increasingly use “comprise” as a synonym of “compose.” However, this is normally still seen as a mistake, and we recommend avoiding it in your academic writing. “Comprise” traditionally means “to be made up of,” not “to make up.”
Some synonyms and near synonyms of the verb comprise are:
People increasingly use “comprise” interchangeably with “compose,” meaning that they consider words like “compose,” “constitute,” and “form” to be synonymous with “comprise.” However, this is still normally regarded as an error, and we advise against using these words interchangeably in academic writing.
A fallacy is a mistaken belief, particularly one based on unsound arguments or one that lacks the evidence to support it. Common types of fallacy that may compromise the quality of your research are:
The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.
For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias or positivity bias.
Although both red herring fallacy and straw man fallacy are logical fallacies or reasoning errors, they denote different attempts to “win” an argument. More specifically:
The red herring fallacy is a problem because it is flawed reasoning. It is a distraction device that causes people to become sidetracked from the main issue and draw wrong conclusions.
Although a red herring may have some kernel of truth, it is used as a distraction to keep our eyes on a different matter. As a result, it can cause us to accept and spread misleading information.
The sunk cost fallacy and escalation of commitment (or commitment bias) are two closely related terms. However, there is a slight difference between them:
In other words, escalating commitment is a manifestation of the sunk cost fallacy: an irrational escalation of commitment frequently occurs when people refuse to accept that the resources they’ve already invested cannot be recovered. Instead, they insist on more spending to justify the initial investment (and the incurred losses).
When you are faced with a straw man argument, the best way to respond is to draw attention to the fallacy and ask your discussion partner to show how your original statement and their distorted version are the same. Since these are different, your partner will either have to admit that their argument is invalid or try to justify it by using more flawed reasoning, which you can then attack.
The straw man argument is a problem because it occurs when we fail to take an opposing point of view seriously. Instead, we intentionally misrepresent our opponent’s ideas and avoid genuinely engaging with them. Due to this, resorting to straw man fallacy lowers the standard of constructive debate.
A straw man argument is a distorted (and weaker) version of another person’s argument that can easily be refuted (e.g., when a teacher proposes that the class spend more time on math exercises, a parent complains that the teacher doesn’t care about reading and writing).
This is a straw man argument because it misrepresents the teacher’s position, which didn’t mention anything about cutting down on reading and writing. The straw man argument is also known as the straw man fallacy.
A slippery slope argument is not always a fallacy.
There are a number of ways you can deal with slippery slope arguments especially when you suspect these are fallacious:
People sometimes confuse cognitive bias and logical fallacies because they both relate to flawed thinking. However, they are not the same:
In other words, cognitive bias refers to an ongoing predisposition, while logical fallacy refers to mistakes of reasoning that occur in the moment.
An appeal to ignorance (ignorance here meaning lack of evidence) is a type of informal logical fallacy.
It asserts that something must be true because it hasn’t been proven false—or that something must be false because it has not yet been proven true.
For example, “unicorns exist because there is no evidence that they don’t.” The appeal to ignorance is also called the burden of proof fallacy.
An ad hominem (Latin for “to the person”) is a type of informal logical fallacy. Instead of arguing against a person’s position, an ad hominem argument attacks the person’s character or actions in an effort to discredit them.
This rhetorical strategy is fallacious because a person’s character, motive, education, or other personal trait is logically irrelevant to whether their argument is true or false.
Name-calling is common in ad hominem fallacy (e.g., “environmental activists are ineffective because they’re all lazy tree-huggers”).
Ad hominem is a persuasive technique where someone tries to undermine the opponent’s argument by personally attacking them.
In this way, one can redirect the discussion away from the main topic and to the opponent’s personality without engaging with their viewpoint. When the opponent’s personality is irrelevant to the discussion, we call it an ad hominem fallacy.
Ad hominem tu quoque (‘you too”) is an attempt to rebut a claim by attacking its proponent on the grounds that they uphold a double standard or that they don’t practice what they preach. For example, someone is telling you that you should drive slowly otherwise you’ll get a speeding ticket one of these days, and you reply “but you used to get them all the time!”
Argumentum ad hominem means “argument to the person” in Latin and it is commonly referred to as ad hominem argument or personal attack. Ad hominem arguments are used in debates to refute an argument by attacking the character of the person making it, instead of the logic or premise of the argument itself.
The opposite of the hasty generalization fallacy is called slothful induction fallacy or appeal to coincidence.
It is the tendency to deny a conclusion even though there is sufficient evidence that supports it. Slothful induction occurs due to our natural tendency to dismiss events or facts that do not align with our personal biases and expectations. For example, a researcher may try to explain away unexpected results by claiming it is just a coincidence.
To avoid a hasty generalization fallacy we need to ensure that the conclusions drawn are well-supported by the appropriate evidence. More specifically:
The hasty generalization fallacy and the anecdotal evidence fallacy are similar in that they both result in conclusions drawn from insufficient evidence. However, there is a difference between the two:
Although many sources use circular reasoning fallacy and begging the question interchangeably, others point out that there is a subtle difference between the two:
In other words, we could say begging the question is a form of circular reasoning.
Circular reasoning fallacy uses circular reasoning to support an argument. More specifically, the evidence used to support a claim is just a repetition of the claim itself. For example: “The President of the United States is a good leader (claim), because they are the leader of this country (supporting evidence)”.
An example of a non sequitur is the following statement:
“Giving up nuclear weapons weakened the United States’ military. Giving up nuclear weapons also weakened China. For this reason, it is wrong to try to outlaw firearms in the United States today.”
Clearly there is a step missing in this line of reasoning and the conclusion does not follow from the premise, resulting in a non sequitur fallacy.
The difference between the post hoc fallacy and the non sequitur fallacy is that post hoc fallacy infers a causal connection between two events where none exists, whereas the non sequitur fallacy infers a conclusion that lacks a logical connection to the premise.
In other words, a post hoc fallacy occurs when there is a lack of a cause-and-effect relationship, while a non sequitur fallacy occurs when there is a lack of logical connection.
An example of post hoc fallacy is the following line of reasoning:
“Yesterday I had ice cream, and today I have a terrible stomachache. I’m sure the ice cream caused this.”
Although it is possible that the ice cream had something to do with the stomachache, there is no proof to justify the conclusion other than the order of events. Therefore, this line of reasoning is fallacious.
Post hoc fallacy and hasty generalisation fallacy are similar in that they both involve jumping to conclusions. However, there is a difference between the two:
In other words, post hoc fallacy involves a leap to a causal claim; hasty generalisation fallacy involves a leap to a general proposition.
The fallacy of composition is similar to and can be confused with the hasty generalization fallacy. However, there is a difference between the two:
In other words, the fallacy of composition is using an unwarranted assumption that we can infer something about a whole based on the characteristics of its parts, while the hasty generalization fallacy is using insufficient evidence to draw a conclusion.
The opposite of the fallacy of composition is the fallacy of division. In the fallacy of division, the assumption is that a characteristic which applies to a whole or a group must necessarily apply to the parts or individual members. For example, “Australians travel a lot. Gary is Australian, so he must travel a lot.”
Base rate fallacy can be avoided by following these steps:
Suppose there is a population consisting of 90% psychologists and 10% engineers. Given that you know someone enjoyed physics at school, you may conclude that they are an engineer rather than a psychologist, even though you know that this person comes from a population consisting of far more psychologists than engineers.
When we ignore the rate of occurrence of some trait in a population (the base-rate information) we commit base rate fallacy.
Cost-benefit fallacy is a common error that occurs when allocating sources in project management. It is the fallacy of assuming that cost-benefit estimates are more or less accurate, when in fact they are highly inaccurate and biased. This means that cost-benefit analyses can be useful, but only after the cost-benefit fallacy has been acknowledged and corrected for. Cost-benefit fallacy is a type of base rate fallacy.
In advertising, the fallacy of equivocation is often used to create a pun. For example, a billboard company might advertise their billboards using a line like: “Looking for a sign? This is it!” The word sign has a literal meaning as billboard and a figurative one as a sign from God, the universe, etc.
Equivocation is a fallacy because it is a form of argumentation that is both misleading and logically unsound. When the meaning of a word or phrase shifts in the course of an argument, it causes confusion and also implies that the conclusion (which may be true) does not follow from the premise.
The fallacy of equivocation is an informal logical fallacy, meaning that the error lies in the content of the argument instead of the structure.
Fallacies of relevance are a group of fallacies that occur in arguments when the premises are logically irrelevant to the conclusion. Although at first there seems to be a connection between the premise and the conclusion, in reality fallacies of relevance use unrelated forms of appeal.
For example, the genetic fallacy makes an appeal to the source or origin of the claim in an attempt to assert or refute something.
The ad hominem fallacy and the genetic fallacy are closely related in that they are both fallacies of relevance. In other words, they both involve arguments that use evidence or examples that are not logically related to the argument at hand. However, there is a difference between the two:
False dilemma fallacy is also known as false dichotomy, false binary, and “either-or” fallacy. It is the fallacy of presenting only two choices, outcomes, or sides to an argument as the only possibilities, when more are available.
In both cases, by using the false dilemma fallacy, one conceals alternative choices and doesn’t allow others to consider the full range of options. This is usually achieved through an“either-or” construction and polarised, divisive language (“you are either a friend or an enemy”).
The best way to avoid a false dilemma fallacy is to pause and reflect on two points:
Begging the question fallacy is an argument in which you assume what you are trying to prove. In other words, your position and the justification of that position are the same, only slightly rephrased.
For example: “All freshmen should attend college orientation, because all college students should go to such an orientation.”
The complex question fallacy and begging the question fallacy are similar in that they are both based on assumptions. However, there is a difference between them:
In other words, begging the question is about drawing a conclusion based on an assumption, while a complex question involves asking a question that presupposes the answer to a prior question.
“No true Scotsman” arguments aren’t always fallacious. When there is a generally accepted definition of who or what constitutes a group, it’s reasonable to use statements in the form of “no true Scotsman”.
For example, the statement that “no true pacifist would volunteer for military service” is not fallacious, since a pacifist is, by definition, someone who opposes war or violence as a means of settling disputes.
No true Scotsman arguments are fallacious because instead of logically refuting the counterexample, they simply assert that it doesn’t count. In other words, the counterexample is rejected for psychological, but not logical, reasons.
The appeal to purity or no true Scotsman fallacy is an attempt to defend a generalisation about a group from a counterexample by shifting the definition of the group in the middle of the argument. In this way, one can exclude the counterexample as not being “true”, “genuine”, or “pure” enough to be considered as part of the group in question.
To identify an appeal to authority fallacy, you can ask yourself the following questions:
Appeal to authority is a fallacy when those who use it do not provide any justification to support their argument. Instead they cite someone famous who agrees with their viewpoint, but is not qualified to make reliable claims on the subject.
Appeal to authority fallacy is often convincing because of the effect authority figures have on us. When someone cites a famous person, a well-known scientist, a politician, etc. people tend to be distracted and often fail to critically examine whether the authority figure is indeed an expert in the area under discussion.
The ad populum fallacy is common in politics. One example is the following viewpoint: “The majority of our countrymen think we should have military operations overseas; therefore, it’s the right thing to do.”
This line of reasoning is fallacious, because popular acceptance of a belief or position does not amount to a justification of that belief. In other words, following the prevailing opinion without examining the underlying reasons is irrational.
The ad populum fallacy plays on our innate desire to fit in (known as “bandwagon effect”). If many people believe something, our common sense tells us that it must be true and we tend to accept it. However, in logic, the popularity of a proposition cannot serve as evidence of its truthfulness.
Ad populum (or appeal to popularity) fallacy and appeal to authority fallacy are similar in that they both conflate the validity of a belief with its popular acceptance among a specific group. However there is a key difference between the two:
To identify a false cause fallacy , you need to carefully analyse the argument:
By carefully analysing the reasoning, considering alternative explanations, and examining the evidence provided, you can identify a false cause fallacy and discern whether a causal claim is valid or flawed.
In each of these examples, we falsely assume that one event causes another without any proof.
The planning fallacy and procrastination are not the same thing. Although they both relate to time and task management, they describe different challenges:
In other words, the planning fallacy refers to inaccurate predictions about the time we need to finish a task, while procrastination is a deliberate delay due to psychological factors.
A real-life example of the planning fallacy is the construction of the Sydney Opera House in Australia. When construction began in the late 1950s, it was initially estimated that it would be completed in four years at a cost of around $7 million.
Because the government wanted the construction to start before political opposition would stop it and while public opinion was still favorable, a number of design issues had not been carefully studied in advance. Due to this, several problems appeared immediately after the project commenced.
The construction process eventually stretched over 14 years, with the Opera House being completed in 1973 at a cost of over $100 million, significantly exceeding the initial estimates.
An example of appeal to pity fallacy is the following appeal by a student to their professor:
“Professor, please consider raising my grade. I had a terrible semester: my car broke down, my laptop got stolen, and my cat got sick.”
While these circumstances may be unfortunate, they are not directly related to the student’s academic performance.
While both the appeal to pity fallacy and red herring fallacy can serve as a distraction from the original discussion topic, they are distinct fallacies. More specifically:
Both fallacies can be used as a tool of deception. However, they operate differently and serve distinct purposes in arguments.
Argumentum ad misericordiam (Latin for “argument from pity or misery”) is another name for appeal to pity fallacy. It occurs when someone evokes sympathy or guilt in an attempt to gain support for their claim, without providing any logical reasons to support the claim itself. Appeal to pity is a deceptive tactic of argumentation, playing on people’s emotions to sway their opinion.
Yes, it’s quite common to start a sentence with a preposition, and there’s no reason not to do so.
For example, the sentence “To many, she was a hero” is perfectly grammatical. It could also be rephrased as “She was a hero to many”, but there’s no particular reason to do so. Both versions are fine.
Some people argue that you shouldn’t end a sentence with a preposition, but that “rule” can also be ignored, since it’s not supported by serious language authorities.
Yes, it’s fine to end a sentence with a preposition. The “rule” against doing so is overwhelmingly rejected by modern style guides and language authorities and is based on the rules of Latin grammar, not English.
Trying to avoid ending a sentence with a preposition often results in very unnatural phrasings. For example, turning “He knows what he’s talking about” into “He knows about what he’s talking” or “He knows that about which he’s talking” is definitely not an improvement.
No, ChatGPT is not a credible source of factual information and can’t be cited for this purpose in academic writing. While it tries to provide accurate answers, it often gets things wrong because its responses are based on patterns, not facts and data.
Specifically, the CRAAP test for evaluating sources includes five criteria: currency, relevance, authority, accuracy, and purpose. ChatGPT fails to meet at least three of them:
So you shouldn’t cite ChatGPT as a trustworthy source for a factual claim. You might still cite ChatGPT for other reasons – for example, if you’re writing a paper about AI language models, ChatGPT responses are a relevant primary source.
ChatGPT is an AI language model that was trained on a large body of text from a variety of sources (e.g., Wikipedia, books, news articles, scientific journals). The dataset only went up to 2021, meaning that it lacks information on more recent events.
It’s also important to understand that ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data.
So ChatGPT is not always trustworthy. It can usually answer general knowledge questions accurately, but it can easily give misleading answers on more specialist topics.
Another consequence of this way of generating responses is that ChatGPT usually can’t cite its sources accurately. It doesn’t really know what source it’s basing any specific claim on. It’s best to check any information you get from it against a credible source.
No, it is not possible to cite your sources with ChatGPT. You can ask it to create citations, but it isn’t designed for this task and tends to make up sources that don’t exist or present information in the wrong format. ChatGPT also cannot add citations to direct quotes in your text.
Instead, use a tool designed for this purpose, like the Scribbr Citation Generator.
But you can use ChatGPT for assignments in other ways, to provide inspiration, feedback, and general writing advice.
GPT stands for “generative pre-trained transformer”, which is a type of large language model: a neural network trained on a very large amount of text to produce convincing, human-like language outputs. The Chat part of the name just means “chat”: ChatGPT is a chatbot that you interact with by typing in text.
The technology behind ChatGPT is GPT-3.5 (in the free version) or GPT-4 (in the premium version). These are the names for the specific versions of the GPT model. GPT-4 is currently the most advanced model that OpenAI has created. It’s also the model used in Bing’s chatbot feature.
ChatGPT was created by OpenAI, an AI research company. It started as a nonprofit company in 2015 but became for-profit in 2019. Its CEO is Sam Altman, who also co-founded the company. OpenAI released ChatGPT as a free “research preview” in November 2022. Currently, it’s still available for free, although a more advanced premium version is available if you pay for it.
OpenAI is also known for developing DALL-E, an AI image generator that runs on similar technology to ChatGPT.
ChatGPT is owned by OpenAI, the company that developed and released it. OpenAI is a company dedicated to AI research. It started as a nonprofit company in 2015 but transitioned to for-profit in 2019. Its current CEO is Sam Altman, who also co-founded the company.
In terms of who owns the content generated by ChatGPT, OpenAI states that it will not claim copyright on this content, and the terms of use state that “you can use Content for any purpose, including commercial purposes such as sale or publication”. This means that you effectively own any content you generate with ChatGPT and can use it for your own purposes.
Be cautious about how you use ChatGPT content in an academic context. University policies on AI writing are still developing, so even if you “own” the content, you’re often not allowed to submit it as your own work according to your university or to publish it in a journal.
ChatGPT is a chatbot based on a large language model (LLM). These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter.
ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes.
Essentially, ChatGPT’s answers are based on predicting the most likely responses to your inputs based on its training data, with a reward system on top of this to incentivise it to give you the most helpful answers possible. It’s a bit like an incredibly advanced version of predictive text. This is also one of ChatGPT’s limitations: because its answers are based on probabilities, they’re not always trustworthy.
OpenAI may store ChatGPT conversations for the purposes of future training. Additionally, these conversations may be monitored by human AI trainers.
Users can choose not to have their chat history saved. Unsaved chats are not used to train future models and are permanently deleted from ChatGPT’s system after 30 days.
The official ChatGPT app is currently only available on iOS devices. If you don’t have an iOS device, only use the official OpenAI website to access the tool. This helps to eliminate the potential risk of downloading fraudulent or malicious software.
ChatGPT conversations are generally used to train future models and to resolve issues/bugs. These chats may be monitored by human AI trainers.
However, users can opt out of having their conversations used for training. In these instances, chats are monitored only for potential abuse.
Yes, using ChatGPT as a conversation partner is a great way to practice a language in an interactive way.
Try using a prompt like this one:
“Please be my Spanish conversation partner. Only speak to me in Spanish. Keep your answers short (maximum 50 words). Ask me questions. Let’s start the conversation with the following topic: [conversation topic].”
Yes, there are a variety of ways to use ChatGPT for language learning, including treating it as a conversation partner, asking it for translations, and using it to generate a curriculum or practice exercises.
AI detectors aim to identify the presence of AI-generated text (e.g., from ChatGPT) in a piece of writing, but they can’t do so with complete accuracy. In our comparison of the best AI detectors, we found that the 10 tools we tested had an average accuracy of 60%. The best free tool had 68% accuracy, the best premium tool 84%.
Because of how AI detectors work, they can never guarantee 100% accuracy, and there is always at least a small risk of false positives (human text being marked as AI-generated). Therefore, these tools should not be relied upon to provide absolute proof that a text is or isn’t AI-generated. Rather, they can provide a good indication in combination with other evidence.
Tools called AI detectors are designed to label text as AI-generated or human. AI detectors work by looking for specific characteristics in the text, such as a low level of randomness in word choice and sentence length. These characteristics are typical of AI writing, allowing the detector to make a good guess at when text is AI-generated.
But these tools can’t guarantee 100% accuracy. Check out our comparison of the best AI detectors to learn more.
You can also manually watch for clues that a text is AI-generated – for example, a very different style from the writer’s usual voice or a generic, overly polite tone.
Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.
While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.
Deep learning requires a large dataset (e.g., images or text) to learn from. The more diverse and representative the data, the better the model will learn to recognise objects or make predictions. Only when the training data is sufficiently varied can the model make accurate predictions or recognise objects from new data.
Deep learning models can be biased in their predictions if the training data consist of biased information. For example, if a deep learning model used for screening job applicants has been trained with a dataset consisting primarily of white male applicants, it will consistently favour this specific population over others.
A good ChatGPT prompt (i.e., one that will get you the kinds of responses you want):
ChatGPT prompts are the textual inputs (e.g., questions, instructions) that you enter into ChatGPT to get responses.
ChatGPT predicts an appropriate response to the prompt you entered. In general, a more specific and carefully worded prompt will get you better responses.
Yes, ChatGPT is currently available for free. You have to sign up for a free account to use the tool, and you should be aware that your data may be collected to train future versions of the model.
To sign up and use the tool for free, go to this page and click “Sign up”. You can do so with your email or with a Google account.
A premium version of the tool called ChatGPT Plus is available as a monthly subscription. It currently costs £16 and gets you access to features like GPT-4 (a more advanced version of the language model). But it’s optional: you can use the tool completely free if you’re not interested in the extra features.
You can access ChatGPT by signing up for a free account:
A ChatGPT app is also available for iOS, and an Android app is planned for the future. The app works similarly to the website, and you log in with the same account for both.
According to OpenAI’s terms of use, users have the right to reproduce text generated by ChatGPT during conversations.
However, publishing ChatGPT outputs may have legal implications, such as copyright infringement.
Users should be aware of such issues and use ChatGPT outputs as a source of inspiration instead.
According to OpenAI’s terms of use, users have the right to use outputs from their own ChatGPT conversations for any purpose (including commercial publication).
However, users should be aware of the potential legal implications of publishing ChatGPT outputs. ChatGPT responses are not always unique: different users may receive the same response.
Furthermore, ChatGPT outputs may contain copyrighted material. Users may be liable if they reproduce such material.
ChatGPT can sometimes reproduce biases from its training data, since it draws on the text it has “seen” to create plausible responses to your prompts.
For example, users have shown that it sometimes makes sexist assumptions such as that a doctor mentioned in a prompt must be a man rather than a woman. Some have also pointed out political bias in terms of which political figures the tool is willing to write positively or negatively about and which requests it refuses.
The tool is unlikely to be consistently biased toward a particular perspective or against a particular group. Rather, its responses are based on its training data and on the way you phrase your ChatGPT prompts. It’s sensitive to phrasing, so asking it the same question in different ways will result in quite different answers.
Information extraction refers to the process of starting from unstructured sources (e.g., text documents written in ordinary English) and automatically extracting structured information (i.e., data in a clearly defined format that’s easily understood by computers). It’s an important concept in natural language processing (NLP).
For example, you might think of using news articles full of celebrity gossip to automatically create a database of the relationships between the celebrities mentioned (e.g., married, dating, divorced, feuding). You would end up with data in a structured format, something like MarriageBetween(celebrity1,celebrity2,date).
The challenge involves developing systems that can “understand” the text well enough to extract this kind of data from it.
Knowledge representation and reasoning (KRR) is the study of how to represent information about the world in a form that can be used by a computer system to solve and reason about complex problems. It is an important field of artificial intelligence (AI) research.
An example of a KRR application is a semantic network, a way of grouping words or concepts by how closely related they are and formally defining the relationships between them so that a machine can “understand” language in something like the way people do.
A related concept is information extraction, concerned with how to get structured information from unstructured sources.
Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.
You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.
Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.
However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.
Yes, you use ChatGPT to help write your college essay by having it generate feedback on certain aspects of your work (consistency of tone, clarity of structure, etc.).
However, ChatGPT is not able to adequately judge qualities like vulnerability and authenticity. For this reason, it’s important to also ask for feedback from people who have experience with college essays and who know you well.
Alternatively, you can get advice using Scribbr’s essay editing service .
No, having ChatGPT write your college essay can negatively impact your application in numerous ways. ChatGPT outputs are unoriginal and lack personal insight.
Furthermore, Passing off AI-generated text as your own work is considered academically dishonest . AI detectors may be used to detect this offense, and it’s highly unlikely that any university will accept you if you are caught submitting an AI-generated admission essay.
However, you can use ChatGPT to help write your college essay during the preparation and revision stages (e.g., for brainstorming ideas and generating feedback).
ChatGPT and other AI writing tools can have unethical uses. These include:
However, when used correctly, AI writing tools can be helpful resources for improving your academic writing and research skills. Some ways to use ChatGPT ethically include:
