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Jennifer Crocker

Researcher at Ohio State University

Publications -  198
Citations -  27085

Jennifer Crocker is an academic researcher from Ohio State University. The author has contributed to research in topics: Medicine & Self-esteem. The author has an hindex of 67, co-authored 169 publications receiving 25405 citations. Previous affiliations of Jennifer Crocker include University at Buffalo & Harvard University.

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Social stigma and self-esteem: The self-protective properties of stigma.

TL;DR: In this article, it is proposed that members of stigmatized groups may attribute negative feedback to prejudice against their group, compare their outcomes with those of the ingroup, rather than with the relatively advantaged outgroup, and selectively devalue those dimensions on which their group fares poorly and value those dimensions that their group excels.
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A Collective Self-Esteem Scale: Self-Evaluation of One's Social Identity

TL;DR: In this paper, a scale was constructed to assess individual differences in collective, rather than personal, self-esteem, with four subscales (Membership esteem, public collective selfesteem, private collective self esteem, and importance to identity), and evidence for reliability and validity of the scale was provided by three studies.
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Contingencies of self-worth.

TL;DR: This paper presented a model of global self-esteem that builds on James' insights and emphasizes contingencies of self-worth, which can help to understand how selfesteem is implicated in affect, cognition, and self-regulation of behavior.
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The Costly Pursuit of Self-Esteem.

TL;DR: The authors propose that the importance of self-esteem lies more in how people strive for it rather than whether it is high or low, and argue that in domains in which their self-worth is invested, people adopt the goal to validate their abilities and qualities, and hence theirself-worth.
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Contingencies of self-worth in college students: theory and measurement.

TL;DR: In confirmatory factor analyses on data from 1,418 college students, a 7-factor model fit to the data acceptably well and significantly better than several plausible alternative models.