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Cited by 424

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Suppression situations in psychological research: Definitions, implications, and applications

Tzelgov, Joseph, Henik, Avishai·1991·Psychological Bulletin
DOI 10.1037/0033-2909.109.3.524
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Abstract

In 1941, Horst noticed that a variable can be totally uncorrelated with the criterion and still improve prediction by virtue of being correlated with other predictors. He christened such vari-ables suppressors, a title that implies that such variables suppress criterion-irrelevant variance in other predictors. During the 50 years that have passed since Horst's original analysis, the concept of suppression has been extended and reanalyzed. What follows provides a general approach to the analysis of suppression situations. This approach is based on coupling the analysis of 3 variate suppression situations with the applications of the concept of suppressor to the general linear model. The implications of the analysis are discussed, and some applications of the concept of suppression are provided. Suppose you are trying to predict the success of high-school students in college. \\bu may ask the students in high school whom they expect to succeed in college. A sociometric score for each student can then be defined and used as a predictor. It seems much less likely that you would decide to include a socio-metric variable based on the question "Who is your best

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Crossref · 10.1037/0033-2909.109.3.524

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Metadata

Authors
Tzelgov, J., & Henik, A.
Venue
Psychological Bulletin
Publisher
American Psychological Association (APA)
Published
1991-05
Volume
109
Issue
3
Pages
524-536
ISSN
  • 1939-1455
  • 0033-2909
DOI
10.1037/0033-2909.109.3.524
Cited by
424
References
32
Language
en

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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.