What is a literature review?
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question.
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.
Reliability and validity are both about how well a method measures something:
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.
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.
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.
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.
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 can also include page numbers to point the reader towards a passage that you paraphrased. If you refer to the general ideas or findings of the source as a whole, you don’t need to include a page number.
When you want to use a quote but can’t access the original source, you can cite it indirectly. In the in-text citation, first mention the source you want to refer to, and then the source in which you found it. For example:
It’s advisable to avoid indirect citations wherever possible, because they suggest you don’t have full knowledge of the sources you’re citing. Only use an indirect citation if you can’t reasonably gain access to the original source.
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.
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.
In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.
You may acknowledge God in your thesis or dissertation acknowledgements, but be sure to follow academic convention by also thanking the relevant members of academia, as well as family, colleagues, and friends who helped you.
All level 1 and 2 headings should be included in your table of contents. That means the titles of your chapters and the main sections within them.
The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list.
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).
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.
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:
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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.
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The consequences of plagiarism vary depending on the type of plagiarism and the context in which it occurs. For example, submitting a whole essay by someone else will usually have severe consequences, while accidental citation errors are considered less serious.
If you’re a student, then you might fail the course, be suspended or expelled, or be obligated to attend a workshop on plagiarism. It depends on whether it’s your first offence or you’ve done it before.
As an academic or professional, plagiarising seriously damages your reputation. You might also lose your research funding and/or your job, and you could even face legal consequences for copyright infringement.
However, paraphrasing is not plagiarism if you correctly reference the source. This means including an in-text citation and a full reference, formatted according to your required citation style (e.g. Harvard, Vancouver).
As well as referencing your source, make sure that any paraphrased text is completely rewritten in your own words.
Accidental plagiarism is one of the most common types of plagiarism. Perhaps you forgot to cite a source, or paraphrased something a bit too closely. Maybe you can’t remember where you got an idea from and aren’t totally sure if it’s original or not.
These all count as plagiarism, even though you didn’t do it on purpose. When in doubt, make sure you’re referencing your sources. Also consider running your work through a plagiarism checker tool prior to submission. These tools work by using advanced database software to scan for matches between your text and existing texts.
The accuracy depends on the plagiarism checker you use. Per our in-depth research, Scribbr is the most accurate plagiarism checker. Many free plagiarism checkers fail to detect all plagiarism or falsely flag text as plagiarism.
The accuracy is determined by two factors: the algorithm (which recognises the plagiarism) and the size of the database (with which your document is compared). Plagiarism checkers work by using advanced database software to scan for matches between your text and existing texts.
Many free plagiarism checkers only check your paper against websites – not against books, journals, or papers previously submitted by other students. Therefore, these plagiarism checkers are not very accurate, as they miss a lot of plagiarism.
Most plagiarism checkers are only able to detect ‘direct plagiarism‘, or instances where the sentences are exactly the same as in the original source. However, a good plagiarism checker is also able to detect ‘patchwork plagiarism‘ (sentences where some words are changed or synonyms are used).
Plagiarism can be detected by your professor or readers if the tone, formatting, or style of your text is different in different parts of your paper, or if they’re familiar with the plagiarised source.
Many universities also use plagiarism detection software like Turnitin’s, which compares your text to a large database of other sources, flagging any similarities that come up.
It can be easier than you think to commit plagiarism by accident. Consider using a plagiarism checker prior to submitting your essay to ensure you haven’t missed any citations.
Some examples of plagiarism include:
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.
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.
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.
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.
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:
There are five common approaches to qualitative research:
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.
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.
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.
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 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.
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.
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
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.
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.
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.
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.
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.
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.
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:
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.
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.
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 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 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:
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.
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.
The difference between explanatory and response variables is simple:
There are 4 main types of extraneous variables:
‘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.
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 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.
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.
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).
Discrete and continuous variables are two types of quantitative variables:
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 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.
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:
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.
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 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.
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 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 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.
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.
You can use several tactics to minimise observer bias.
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. Observer bias is also called detection bias or ascertainment bias.
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.
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.
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.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
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.
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.
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.
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.
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.
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.
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.
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:
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.
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 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.
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.
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.
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:
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