Schein (1973) Revisited: The Think Manager-Think Male Effect is a Statistical Artifact

Schein (1973) is a highly cited article in research on sex and gender biases. The original article concluded that people are biased against women regarding requisite management characteristics. However, the present paper replicates Schein (1973) and demonstrates that the findings were a result of an imbalanced ratio of items which exhibited mean differences between men and women targets. In addition, the use of intraclass correlations creates an illusion of large differences or similarities between targets when the actual mean rating differences are practically trivial and statistically nonsignificant. A bias against women, against men, and no bias are obtained by altering the number of male and female items, or by applying the intraclass correlation to more appropriate data. The implications of the results for the measurement of sex and gender biases are discussed. Broader concerns are raised about ideological biases which allow for conclusions and theories to propagate without empirical support.

Authors

Publication Details

Journal
Journal of Open Inquiry in the Behavioral Sciences
Published
2024-12-22
DOI
https://doi.org/10.58408/issn.2992-9253.2024.02.02.0001
Primary Topic
Gender Diversity and Inequality
Type
article
Field-Weighted Citation Impact
0.00
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article

Schein (1973) Revisited: The Think Manager-Think Male Effect is a Statistical Artifact

Matthew W. Lauritsen
Journal of Open Inquiry in the Behavioral Sciences
Gender Diversity and Inequality
article

Schein (1973) Revisited: The Think Manager-Think Male Effect is a Statistical Artifact

Matthew W. Lauritsen
article en

Abstract

Schein (1973) is a highly cited article in research on sex and gender biases. The original article concluded that people are biased against women regarding requisite management characteristics. However, the present paper replicates Schein (1973) and demonstrates that the findings were a result of an imbalanced ratio of items which exhibited mean differences between men and women targets. In addition, the use of intraclass correlations creates an illusion of large differences or similarities between targets when the actual mean rating differences are practically trivial and statistically nonsignificant. A bias against women, against men, and no bias are obtained by altering the number of male and female items, or by applying the intraclass correlation to more appropriate data. The implications of the results for the measurement of sex and gender biases are discussed. Broader concerns are raised about ideological biases which allow for conclusions and theories to propagate without empirical support.

Journal of Open Inquiry in the Behavioral SciencesVol. 3(4)
Gender equality
Openalex Percentile: Top 65%
Gender Diversity and Inequality
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