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Cited by 1,603

The DOI matches its Crossref record; 204 of the 204 references it cites were found in a registry or an index.

Building on the Foundation of General Strain Theory: Specifying the Types of Strain Most Likely to Lead to Crime and Delinquency

AGNEW, ROBERT·2001·Journal of Research in Crime and Delinquency

Abstract

General strain theory (GST) is usually tested by examining the effect of strain on crime. Researchers, however, have little guidance when it comes to selecting among the many hundreds of types of strain and have trouble explaining why only some of them are related to crime. This article builds on GST by describing the characteristics of strainful events and conditions that influence their relationship to crime. Strains are said to be most likely to result in crime when they (1) are seen as unjust, (2) are seen as high in magnitude, (3) are associated with low social control, and (4) create some pressure or incentive to engage in criminal coping. Drawing on these characteristics, it is predicted that some types of strain will not be related to crime, including types that have dominated the research on strain theory, and that others will be related to crime, including types that have been neglected by empirical researchers.

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Metadata

Authors
AGNEW, R.
Venue
Journal of Research in Crime and Delinquency
Published
2001-11
Volume
38
Issue
4
Pages
319-361
ISSN
  • 0022-4278 (ISSN-L)
  • 1552-731X
OpenAlex
W2141206318
Cited by
1,603
References
204
FWCI
43.52
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Building on the Foundation of General Strain Theory: Specifying the Types of Strain Most Likely to Lead to Crime and Delinquency. https://app.cochat.ai/papers/10.1177/0022427801038004001
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AGNEW, R. (2001). Building on the Foundation of General Strain Theory: Specifying the Types of Strain Most Likely to Lead to Crime and Delinquency. Journal of Research in Crime and Delinquency. https://doi.org/10.1177/0022427801038004001

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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.