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.
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.
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 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.
- In quantitative research, generalisability helps to make inferences about the population.
- In qualitative research, generalisability helps to compare the results to other results from similar situations.
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.
However, research results that cannot be generalised can still have value. It all depends on your research objectives.
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?
The validity of your experiment depends on your experimental design.
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