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Research Design | Step-by-Step Guide with Examples
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:
- Your overall aims and approach
- The type of research design you’ll use
- Your sampling methods or criteria for selecting subjects
- Your data collection methods
- The procedures you’ll follow to collect data
- Your data analysis methods
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.
Table of contents
Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.
- Introduction
Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.
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.
Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.
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.
Practical and ethical considerations when designing research
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 .
- How much time do you have to collect data and write up the research?
- Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
- Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
- Will you need ethical approval ?
At each stage of the research design process, make sure that your choices are practically feasible.
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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.
Types of quantitative research designs
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 ).
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.
Types of qualitative research designs
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.
Defining the population
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.
Sampling methods
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.
Case selection in qualitative research
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.
Survey methods
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.
Observation methods
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.
Other methods of data collection
There are many other ways you might collect data depending on your field and topic.
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.
Secondary data
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.
Operationalisation
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?
If you’re using surveys , which questions will you ask and what range of responses will be offered?
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 and validity
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.
Sampling procedures
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:
- How many participants do you need for an adequate sample size?
- What inclusion and exclusion criteria will you use to identify eligible participants?
- How will you contact your sample – by mail, online, by phone, or in person?
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?
Data management
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.
Quantitative data analysis
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 distribution of the data (e.g., the frequency of each score on a test)
- The central tendency of the data (e.g., the mean to describe the average score)
- The variability of the data (e.g., the standard deviation to describe how spread out the scores are)
The specific calculations you can do depend on the level of measurement of your variables.
Using inferential statistics , you can:
- Make estimates about the population based on your sample data.
- Test hypotheses about a relationship between 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.
Qualitative data analysis
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 .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
- If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
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