What Is Generalisability? | Definition & Examples

Generalisability is the degree to which you can apply the results of your study to a broader context. Research results are considered generalisable when the findings can be applied to most contexts, most people, most of the time.

Example: Generalisability
Suppose you want to investigate the shopping habits of people in your city. You stand at the entrance to a high-end shopping street and randomly ask passersby whether they want to answer a few questions for your survey.

Do the people who agree to help you with your survey accurately represent all the people in your city? Probably not. This means that your study can’t be considered generalisable.

Generalisability is determined by how representative your sample is of the target population. This is known as external validity.

What is generalisability?

The goal of research is to produce knowledge that can be applied as widely as possible. However, since it usually isn’t possible to analyse every member of a population, researchers make do by analysing a portion of it, making statements about that portion.

To be able to apply these statements to larger groups, researchers must ensure that the sample accurately resembles the broader population.

In other words, the sample and the population must share the characteristics relevant to the research being conducted. When this happens, the sample is considered representative, and by extension, the study’s results are considered generalisable.

What is generalisability?

In general, a study has good generalisability when the results apply to many different types of people or different situations. In contrast, if the results can only be applied to a subgroup of the population or in a very specific situation, the study has poor generalisability.

Why is generalisability important?

Obtaining a representative sample is crucial for probability sampling. In contrast, studies using non-probability sampling designs are more concerned with investigating a few cases in depth, rather than generalising their findings. As such, generalisability is the main difference between probability and non-probability samples.

There are three factors that determine the generalisability of your study in a probability sampling design:

  • The randomness of the sample, with each research unit (e.g., person, business, or organisation in your population) having an equal chance of being selected.
  • How representative the sample is of your population.
  • The size of your sample, with larger samples more likely to yield statistically significant results.
Generalisability is especially important in psychology research. Historically, the samples used tended to be overwhelmingly white, Western, and individualist. This is called the WWIB: white, Western, individualist bias. These studies suffer from poor generalisability because of their limited focus.

Increasing sample diversity can help researchers develop theories of human nature that reliably explain human behaviour across countries and cultures instead of among only a thin slice of humanity.

Generalisability is one of the three criteria (along with validity and reliability) that researchers use to assess the quality of both quantitative and qualitative research. However, depending on the type of research, generalisability is interpreted and evaluated differently.

Examples of generalisability

Generalisability is crucial for establishing the validity and reliability of your study. In most cases, a lack of generalisability significantly narrows down the scope of your research—i.e., to whom the results can be applied.

Example: Narrowed scope
You are researching the voting intentions of a small town of 3,000 residents. Due to limited time and resources, you plan to take a sample of 100 people.

Luckily, you have access to an anonymised list of all residents. This allows you to establish a sampling frame and proceed with simple random sampling. With the help of an online random number generator, you draw a simple random sample.

After obtaining your results (and prior to drawing any conclusions) you need to consider the generalisability of your results. Using an online sample calculator, you see that the ideal sample size is 341. With a sample of 341, you could be confident that your results are generalisable, but a sample of 100 is too small to be generalisable.

This limitation of your research should be mentioned in your discussion section.

However, research results that cannot be generalised can still have value. It all depends on your research objectives.

Example: Non-generalisable research
Suppose you are conducting a study among visitors of the Getty Museum. You are particularly interested in how families with young children engage with a new interactive exhibit. Your goal is to advise the Getty Museum on future exhibitions geared towards families.

You go to the museum for three consecutive Sundays to make observations.

Your observations yield valuable insights for the Getty Museum, and perhaps even for other museums with similar educational offerings.

However, you can’t claim that your findings represent all the families that visit museums in the country, or even in your city. As you collected a convenience sample, your study results are not generalisable. Nevertheless, in this case, that was not the goal of your research. Your results can still be considered valid for the context in which they were studied.

Types of generalisability

There are two broad types of generalisability:

  • Statistical generalisability, which applies to quantitative research
  • Theoretical generalisability (also referred to as transferability), which applies to qualitative research

Statistical generalisability is critical for quantitative research. The goal of quantitative research is to develop general knowledge that applies to all the units of a population while studying only a subset of these units (sample). Statistical generalisation is achieved when you study a sample that accurately mirrors characteristics of the population. The sample needs to be sufficiently large and unbiased.

In qualitative research, statistical generalisability is not relevant. This is because qualitative research is primarily concerned with obtaining insights on some aspect of human experience, rather than data with solid statistical basis. By studying individual cases, researchers will try to get results that they can extend to similar cases. This is known as theoretical generalisability or transferability.

How do you ensure generalisability in research?

In order to apply your findings on a larger scale, you should take the following steps to ensure your research has sufficient generalisability.

  • Define your population in detail. By doing so, you will establish what it is that you intend to make generalisations about. For example, are you going to discuss students in general, or students on your campus?
  • Use random sampling. If the sample is truly random (i.e., everyone in the population is equally likely to be chosen for the sample), then you can avoid sampling bias and ensure that the sample will be representative of the population.
  • Consider the size of your sample. The sample size must be large enough to support the generalisation being made. If the sample represents a smaller group within that population, then the conclusions have to be downsized in scope.
  • If you’re conducting qualitative research, try to reach a saturation point of important themes and categories. This way, you will have sufficient information to account for all aspects of the phenomenon under study.

After completing your research, take a moment to reflect on the generalisability of your findings. What didn’t go as planned and could impact your generalisability? For example, selection biases such as non-response bias can affect your results. Explain how generalisable your results are, as well as possible limitations, in the discussion section of your research paper.

Other types of research bias

Frequently asked questions about generalisability

What is the difference between internal and external validity?

Internal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables.

External validity is the extent to which your results can be generalised to other contexts.

The validity of your experiment depends on your experimental design.

Why is generalisability important in research?

Generalisability is important because it allows researchers to make inferences for a large group of people, i.e., the target population, by only studying a part of it (the sample).

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specialises in writing about research methods and research bias.