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Effects of relative deprivation on change in displaced aggression and the underlying motivation mechanism: A three‐wave cross‐lagged analysis.

  • Published In: British Journal of Psychology, 2024, v. 115, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Guo, Yongfen; Li, Rui; Xia, Ling‐Xiang 3 of 3

Abstract

Displaced aggression refers to pernicious acts against innocent people. To date, little is known about mechanisms underlying dynamic changes in displaced aggression. The present study constructed a cross‐lagged model to examine the dynamic effects of relative deprivation on displaced aggression and the mediation mechanisms underlying these effects. A total of 1130 undergraduate students participated in this three‐wave longitudinal study. The results showed that relative deprivation predicted changes in displaced aggression through concurrent changes in levels of hostile attribution bias and moral disengagement. Hostile attribution bias and moral disengagement could predict each other longitudinally. The relationships between relative deprivation and displaced aggression, and relative deprivation and hostile attribution bias were mutual. This multiple mediation model with two mutually predicting mediators was explained from the aggressive motivation perspective. The findings help inform aggression theories and have implications for the prevention of and interventions against displaced aggression. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:British Journal of Psychology. 2024/02, Vol. 115, Issue 1, p1
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0007-1269
  • DOI:10.1111/bjop.12674
  • Accession Number:174818551
  • Copyright Statement:Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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