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Cited by 1,150

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Stereotypes as Dynamic Constructs: Women and Men of the Past, Present, and Future

Open access
Diekman, Amanda B., Eagly, Alice H.·2000·Personality and Social Psychology Bulletin

Abstract

Dynamic stereotypes characterize social groups that are thought to have changed from the attributes they manifested in the past and even to continue to change in the future. According to social role theory’s assumption that the role behavior of group members shapes their stereotype, groups should have dynamic stereotypes to the extent that their typical social roles are perceived to change over time. Applied to men and women, this theory makes two predictions about perceived change: (a) perceivers should think that sex differences are eroding because of increasing similarity of the roles of men and women and (b) the female stereotype should be particularly dynamic because of greater change in the roles of women than of men. This theory was tested and confirmed in five experiments that examined perceptions of the roles and the personality, cognitive, and physical attributes of men and women of the past, present, and future.

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Crossref · 10.1177/0146167200262001

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Metadata

Authors
Diekman, A. B., & Eagly, A. H.
Venue
Personality and Social Psychology Bulletin
Published
2000-11
Volume
26
Issue
10
Pages
1171-1188
ISSN
  • 0146-1672 (ISSN-L)
  • 1552-7433
OpenAlex
W1997319562
Cited by
1,150
References
3
FWCI
46.13
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Stereotypes as Dynamic Constructs: Women and Men of the Past, Present, and Future. https://app.cochat.ai/papers/10.1177/0146167200262001
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Diekman, A. B., & Eagly, A. H. (2000). Stereotypes as Dynamic Constructs: Women and Men of the Past, Present, and Future. Personality and Social Psychology Bulletin. https://doi.org/10.1177/0146167200262001

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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.