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A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making.
Abstract
Gender bias continues to be a concern in many work settings, leading researchers to identify factors that influence workplace decisions. In this study we examine several of these factors, using an organizing framework of sex distribution within jobs (including male- and female-dominated jobs as well as sex-balanced, or integrated, jobs). We conducted random effects meta-analyses including 136 independent effect sizes from experimental studies (N = 22,348) and examined the effects of decision-maker gender, amount and content of information available to the decision maker, type of evaluation, and motivation to make careful decisions on gender bias in organizational decisions. We also examined study characteristics such as type of participant, publication year, and study design. Our findings revealed that men were preferred for male-dominated jobs (i.e., gender-role congruity bias), whereas no strong preference for either gender was found for female-dominated or integrated jobs. Second, male raters exhibited greater gender-role congruity bias than did female raters for male-dominated jobs. Third, gender-role congruity bias did not consistently decrease when decision makers were provided with additional information about those they were rating, but gender-role congruity bias was reduced when information clearly indicated high competence of those being evaluated. Fourth, gender-role congruity bias did not differ between decisions that required comparisons among ratees and decisions made about individual ratees. Fifth, decision makers who were motivated to make careful decisions tended to exhibit less gender-role congruity bias for male-dominated jobs. Finally, for male-dominated jobs, experienced professionals showed smaller gender-role congruity bias than did undergraduates or working adults.
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Metadata
- Authors
- Koch, A. J., D'Mello, S. D., & Sackett, P. R.
- Venue
- Journal of Applied Psychology
- Published
- 2015-01
- Volume
- 100
- Issue
- 1
- Pages
- 128-161
- ISSN
- 0021-9010 (ISSN-L)
- 1939-1854
- OpenAlex
- W1997439558
- Cited by
- 583
- References
- 172
- FWCI
- 7.50
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
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.