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In defense of external invalidity.
Abstract
Many psychological investigations are accused of failure to generalize to the real because of sample bias or artificiality of setting. It is argued in this article that such generalizations often are not intended. Rather than making predic- tions about the real world from the laboratory, we may test predictions that specify what ought to hap- pen in the lab. We may regard even artificial find- ings as interesting because they show what can occur, even if it rarely does. Or, where we do make gener- alizations, they may have added force because of artificiality of sample or setting. A misplaced preoc- cupation with external validity can lead us to dismiss good research for which generalization to real life is not intended or meaningful.
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Citations
Cited by
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Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0999152x · Cited by 12,641
Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004) · American Psychologist · doi:10.1037/0003-066x.59.2.93 · Cited by 2,932
Gilbert, D. T., & Malone, P. S. (1995) · Psychological Bulletin · doi:10.1037/0033-2909.117.1.21 · Cited by 1,897
Glasgow, R. E., Lichtenstein, E., & Marcus, A. C. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.8.1261 · Cited by 1,836
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Metadata
- Authors
- Mook, D. G.
- Venue
- American Psychologist
- Published
- 1983-04
- Volume
- 38
- Issue
- 4
- Pages
- 379-387
- ISSN
- 0003-066X (ISSN-L)
- 1935-990X
- OpenAlex
- W2125292601
- Cited by
- 1,212
- References
- 0
- FWCI
- 34.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: In defense of external invalidity. https://app.cochat.ai/papers/10.1037/0003-066x.38.4.379
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- Mook, D. G. (1983). In defense of external invalidity. American Psychologist. https://doi.org/10.1037/0003-066x.38.4.379
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
