What is external validity in research?
A brief introduction to external validity
For more best practices see our method overview
Definition of external validity
External validity begs the question “to what populations, settings, and variables can this effect be generalized?” (Campbell, 1957). Like construct validity it is a measure of generalization, however, in particular, the generalization to a different population, setting, outcome, or a variation of treatment.
The other dimensions of research rigor that correspond to external validity are:On our page on rigor you can get an overview for judging whether these are the dimensions to evaluate for your research or if (for qualitative research) you should use framework of trustworthiness instead.
Threats to external validity
External validity should be evaluated in four types of generalization: Interaction of the causal relationship with units, over treatment variations, with outcomes, and with setting (Shadish et al., 2002). The following table presents the potential risks associated with each of these dimensions.
Threat Interaction of causal relationship over units | Consequence Observed effects may not hold for other types of participants. |
Threat Interaction of causal relationship over treatment variations | Consequence Observed effects may not be generalizeable to variations of treatment. |
Threat Interaction of causal relationship with settings | Consequence Observed effects may not hold in settings other than the experimental setting. |
Threat Interaction of causal relationship with outcomes | Consequence Measurement of outcome may be too specific for generalization. |
Strategies to improve external validity.
You can employ a variety of strategies to enhance the generalizability and real-world relevance of your research results:
- Diverse Sampling: Ensuring that the sample used in a study is diverse and representative of the target population is crucial for improving external validity. By including participants from different demographic backgrounds, cultures, and contexts, you can increase the likelihood that your findings will be applicable beyond the immediate study.
- Field Studies: Conducting research in naturalistic settings, such as real-world environments, workplaces, or communities, can enhance the ecological validity of the findings. Unlike controlled laboratory conditions, field studies allow you to observe behaviors and interactions in context, making the results more applicable to real-life situations.
- Randomization: Randomly assigning participants to different experimental groups or conditions is a powerful technique to control for extraneous variables that could otherwise confound the results. By ensuring that participants have an equal chance of being in any group, you increase the internal validity of the study, which in turn enhances the external validity of the findings.
- Replication: Replicating studies across different populations, settings, and contexts is an essential practice to establish the robustness and generalizability of research findings. When consistent results are obtained across multiple studies, the validity of the conclusions becomes more compelling, bolstering the confidence in their applicability beyond individual studies.
- Meta-Analysis: Meta-analysis involves synthesizing the results of multiple studies on the same topic to obtain a comprehensive overview of the phenomenon under investigation. This approach provides a more accurate estimate of the effect size and allows you to assess the consistency of findings across various studies, thus contributing to the overall external validity of the results through Data Triangulation.
Conclusion on external validity.
External validity is a cornerstone of scientific research that determines the real-world applicability of study findings. While you must be cautious of potential threats that can compromise external validity, you can employ various strategies to enhance the generalizability of your results. Diverse sampling, conducting studies in natural settings, randomization, replication, and meta-analysis are all tools that you can use to ensure that your conclusions resonate beyond the confines of your immediate study. By diligently addressing external validity concerns, you contribute to the credibility and practical relevance of your work, ultimately advancing our understanding of the world around us.
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