Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-23T14:47:09.800Z Has data issue: false hasContentIssue false
  • English
  • Français

Neighborhood as a predictor of non-aggressive, but not aggressive, antisocial behaviors in adulthood

Published online by Cambridge University Press:  04 June 2015

S. A. Burt ,
K. L. Klump ,
D. A. Kashy ,
D. Gorman-Smith  and
J. M. Neiderhiser
S. A. Burt*
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
K. L. Klump
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
D. A. Kashy
Affiliation:
Department of Psychology, Michigan State University, East Lansing, MI, USA
D. Gorman-Smith
Affiliation:
School of Social Service Administration, University of Chicago, Chicago, IL, USA
J. M. Neiderhiser
Affiliation:
Department of Psychology, The Pennsylvania State University, University Park, PA, USA
*
* Address for correspondence: S. A. Burt, Ph.D., Department of Psychology, Michigan State University, 107D Psychology Building, East Lansing, MI 48824, USA. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Prior meta-analytic work has highlighted important etiological distinctions between aggressive (AGG) and non-aggressive rule-breaking (RB) dimensions of antisocial behavior. Among these is the finding that RB is influenced by the environment more than is AGG. Relatively little research, however, has sought to identify the specific environmental experiences that contribute to this effect. The current study sought to do just this.

Method

We examined whether unrelated adults residing in the same neighborhood (n = 1915 participants in 501 neighborhoods) were more similar in their AGG and RB than would be expected by chance. Analyses focused on simple multi-level models, with the participant as the lower-level unit and the neighborhood as the upper-level unit.

Results

Results revealed little to no evidence of neighborhood-level variance in AGG. By contrast, 11+% of the variance in RB could be predicted from participant neighborhood, results that persisted even when considering the possibility of genetic relatedness across participants and neighborhood selection effects. Moreover, 17% of this neighborhood-level variance in RB was accounted for by neighborhood structural characteristics and social processes.

Conclusions

Findings bolster prior suggestions that broader contextual experiences, like the structural and social characteristics of one's neighborhood, contribute in a meaningful way to RB in particular. Our results also tentatively imply that this association may be environmental in origin. Future work should seek to develop additional, stronger designs capable of more clearly leveraging genetic un-relatedness to improve causal inferences regarding the environment.

Keywords

Aggressionantisocial behaviorenvironmentneighborhoodrule-breaking
Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 13 , October 2015 , pp. 2897 - 2907
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Breslau, J, Borges, G, Saito, N, Tancredi, DJ, Benjet, C, Hinton, L, Medina-Mora, ME (2011). Migration from Mexico to the United States and conduct disorder: a cross-national study. Archives of General Psychiatry 68, 1284–1293.CrossRefGoogle Scholar
Buhrmester, M, Kwang, T, Gosling, SD (2011). Amazon's mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science 6, 3–5.Google Scholar
Burt, SA (2009). Are there meaningful etiological differences within antisocial behavior? Results of a meta-analysis. Clinical Psychology Review 29, 163–178.Google Scholar
Burt, SA (2012). How do we optimally conceptualize the heterogeneity within antisocial behavior? An argument for aggressive versus non-aggressive behavioral dimensions. Clinical Psychology Review 32, 263–279.Google Scholar
Burt, SA (2014). The shared environment as a key source of variability in child and adolescent psychiatry. Journal of Child Psychology and Psychiatry 55, 304–312.CrossRefGoogle Scholar
Burt, SA, Donnellan, MB (2009). Development and validation of the Sub-Types of Antisocial Behavior questionnaire (STAB). Aggressive Behavior 35, 376–398.Google Scholar
Burt, SA, Klump, KL (2012). Etiological distinctions between aggressive and non-aggressive antisocial behavior: results from a nuclear twin family model. Journal of Abnormal Child Psychology 40, 1059–1071.Google Scholar
Gorman-Smith, D, Tolan, PH, Henry, D (2000). A developmental–ecological model of the relation of family functioning to patterns of delinquency. Journal of Quantitative Criminology 16, 169–198.Google Scholar
Gorman-Smith, D, Tolan, PH, Zelli, A, Huesmann, LR (1996). The relation of family functioning to violence among inner-city minority youths. Journal of Family Psychology 10, 115–129.CrossRefGoogle Scholar
Klahr, AM, Klump, KL, Burt, SA (2014). The etiology of the association between child antisocial behavior and maternal negativity varies across aggressive and non-aggressive rule-breaking forms of antisocial behavior. Journal of Abnormal Child Psychology 42, 1299–1311.CrossRefGoogle ScholarPubMed
Leventhal, T, Brooks-Gunn, J (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin 126, 309–337.CrossRefGoogle ScholarPubMed
Lynam, DR, Caspi, A, Moffitt, TE, Wikstrom, PH, Loeber, R, Novak, S (2000). The interaction between impulsivity and neighborhood context on offending: the effects of impulsivity are stronger in poorer neighborhoods. Journal of Abnormal Psychology 109, 563–574.CrossRefGoogle ScholarPubMed
Odgers, CL, Moffitt, TE, Tach, LM, Sampson, RJ, Taylor, A, Matthews, CL, Caspi, A (2009). The protective effects of neighborhood collective efficacy on British children growing up in deprivation: a developmental analysis. Developmental Psychology 45, 942–957.Google Scholar
Plomin, R, DeFries, JC, Knopik, VS, Neiderhiser, JM (2013). Behavioral Genetics, 6th edn. Worth Publishers: New York.Google Scholar
Raine, A, Brennan, P, Mednick, SA (1994). Birth complications combined with early maternal rejection at age 1 year predipose to violent crime at age 18 years. Archives of General Psychiatry 51, 984–988.CrossRefGoogle Scholar
Sampson, RJ, Morenoff, JD, Gannon-Rowley, T (2002). Assessing “neighborhood effects”: social processes and new directions in research. Annual Review of Sociology 28, 443–478.Google Scholar
Snijders, TAB, Bosker, RJ (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd edn. Sage Publishers: London.Google Scholar
Tackett, JL, Krueger, RF, Iacono, WG, McGue, M (2005). Symptom-based subfactors of DSM-defined conduct disorder: evidence for etiologic distinctions. Journal of Abnormal Psychology 114, 483–487.Google Scholar
Tolan, PH, Gorman-Smith, D, Henry, DB (2003). The developmental ecology of urban males’ youth violence. Developmental Psychology 39, 274–291.Google Scholar
Tremblay, RE (2010). Developmental origins of disruptive behaviour problems: the ‘original sin’ hypothesis, epigenetics and their consequences for prevention. Journal of Child Psychology and Psychiatry 51, 341–367.Google Scholar