Published on 5 May 2022 by Shona McCombes. Revised on 20 March 2023.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:
A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.
You might have to write up a research design as a standalone assignment, or it might be part of a larger research proposal or other project. In either case, you should carefully consider which methods are most appropriate and feasible for answering your question.
Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.
Example: Research question How can teachers adapt their lessons for effective remote learning?
There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.
The first choice you need to make is whether you’ll take a qualitative or quantitative approach.
Qualitative research designs tend to be more flexible and inductive, allowing you to adjust your approach based on what you find throughout the research process.
Example: Qualitative research If you want to generate new ideas for online teaching strategies, a qualitative approach would make the most sense. You can use this type of research to explore exactly what teachers and students struggle with in remote classes.
Quantitative research designs tend to be more fixed and deductive, with variables and hypotheses clearly defined in advance of data collection.
Example: Quantitative research If you want to test the effectiveness of an online teaching method, a quantitative approach is most suitable. You can use this type of research to measure learning outcomes like grades and test scores.
It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.
As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics.
At each stage of the research design process, make sure that your choices are practically feasible.
Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.
Quantitative designs can be split into four main types. Experimental and quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.
With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation).
Example: Correlational design You could use a correlational design to find out if the rise in online teaching in the past year correlates with any change in test scores.
But this design can’t confirm a causal relationship between the two variables. Any change in test scores could have been influenced by many other variables, such as increased stress and health issues among students and teachers.
Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.
Example: Experimental design In an experimental design, you could gather a sample of students and then randomly assign half of them to be taught online and the other half to be taught in person, while controlling all other relevant variables.
By comparing their outcomes in test scores, you can be more confident that it was the method of teaching (and not other variables) that caused any change in scores.
Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.
The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.
Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.
In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.
A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.
For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?
The more precisely you define your population, the easier it will be to gather a representative sample.
Example: Population If you’re studying the effectiveness of online teaching in the UK, it would be very difficult to get a sample that’s representative of all secondary school students in the country.
To make the research more manageable, and to draw more precise conclusions, you could focus on a narrower population – for example, Year 7 students in low-income areas of London.
Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.
To select a sample, there are two main approaches: probability sampling and non-probability sampling. The sampling method you use affects how confidently you can generalise your results to the population as a whole.
Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.
For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.
In some types of qualitative designs, sampling may not be relevant.
For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.
In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.
For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.
Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.
You can choose just one data collection method, or use several methods in the same study.
Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.
Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.
Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.
There are many other ways you might collect data depending on your field and topic.
| Field | Examples of data collection methods |
|---|---|
| Media & communication | Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives |
| Psychology | Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time |
| Education | Using tests or assignments to collect data on knowledge and skills |
| Physical sciences | Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition |
If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.
If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.
With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.
Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.
However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.
As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.
Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.
Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.
If you’re using observations, which events or actions will you count?
Example: Observations To measure student participation in an online course, you could record the number of times students ask and answer questions.
If you’re using surveys, which questions will you ask and what range of responses will be offered?
Example: Surveys To measure teachers’ satisfaction with online learning tools, you could create a questionnaire with a 5-point rating scale.
You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.
Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.
For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.
If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.
As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.
That means making decisions about things like:
If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?
If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?
It’s also important to create a data management plan for organising and storing your data.
Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.
Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.
On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.
In quantitative research, you’ll most likely use some form of statistical analysis. With statistics, you can summarise your sample data, make estimates, and test hypotheses.
Using descriptive statistics, you can summarise your sample data in terms of:
The specific calculations you can do depend on the level of measurement of your variables.
Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs) look for differences in the outcomes of different groups.
Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.
In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.
Two of the most common approaches to doing this are thematic analysis and discourse analysis.
There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.
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.
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.
The research methods you use depend on the type of data you need to answer your research question.
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