External validity: Difference between revisions
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Latest revision as of 17:07, 22 March 2025
External validity is a term in research design that refers to the extent to which the results of a study can be generalized or applied to other settings or groups. It is one of the two types of validity (the other being internal validity) that researchers consider when designing a study.
Definition[edit]
External validity is concerned with the applicability of the research findings outside of the specific context in which the data was collected. It is about the degree to which the results of a study can be generalized to other populations, settings, and conditions. This is often considered in terms of population validity and ecological validity.
Factors affecting external validity[edit]
Several factors can affect the external validity of a study. These include:
- Sampling bias: This occurs when the sample used in the study is not representative of the population the researcher is interested in. This can limit the generalizability of the study's findings.
- Setting: The environment in which the study is conducted can also affect its external validity. If the study is conducted in a controlled laboratory setting, its findings may not be applicable to real-world settings.
- Time: The time at which the study is conducted can also affect its external validity. If the study is conducted at a particular time, its findings may not be applicable at other times.
Improving external validity[edit]
There are several strategies that researchers can use to improve the external validity of their studies. These include:
- Random sampling: This involves selecting participants for the study at random from the population of interest. This can help to ensure that the sample is representative of the population.
- Naturalistic observation: This involves conducting the study in a real-world setting rather than a controlled laboratory environment. This can help to ensure that the findings are applicable to real-world settings.
- Longitudinal studies: These involve collecting data over a long period of time. This can help to ensure that the findings are applicable at different times.
