Todd I Herrenkohl1, Jungeun Lee, J David Hawkins. 1. Social Development Research Group, School of Social Work, University of Washington, Seattle, Washington 98105-6299, USA. tih@u.washington.edu
Abstract
BACKGROUND: Numerous studies have examined predictors of youth violence associated with the individual child, the family, school, and the surrounding neighborhood or community. However, few studies have examined predictors using a systematic approach to differentiate and compare risk and direct protective factors. PURPOSE: This study examines risk and protective factors associated with youth violence in an ongoing longitudinal panel study of 808 students from 18 Seattle public elementary schools followed since 1985 when they were in 5th grade. Predictors span the individual, family, school, peer, and neighborhood domains. METHODS: Data were collected annually, beginning in 1985, to age 16 years, and then again at age 18 years. This paper provides findings of analyses in which continuous predictor variables, measured at ages 10-12 years, were trichotomized to reflect a risk end of the variable, a direct protective end, and a middle category of scores. Youth violence was measured at ages 13-14 years and 15-18 years. RESULTS: Bivariate analyses of risk and direct protective factors identified the following predictors of violence at ages 13-14 years and 15-18 years. Risk for violence was increased by earlier antisocial behavior (e.g., prior violence, truancy, nonviolent delinquency), attention problems, family conflict, low school commitment, and living in a neighborhood where young people were in trouble. Direct protective factors at ages 10-12 years include a low level of attention problems, low risk-taking, refusal skills, school attachment, and low access and exposure to marijuana at ages 10-12 years. Multivariate regressions showed neighborhood risk factors to be among the most salient and consistent predictors of violence after accounting for all other variables in the tested models. CONCLUSIONS: Relatively few direct protective factors were identified in these statistical tests, suggesting the need for further review and possible refinement of the measures and methods that were applied. Implications provide important information for programs and policy.
BACKGROUND: Numerous studies have examined predictors of youth violence associated with the individual child, the family, school, and the surrounding neighborhood or community. However, few studies have examined predictors using a systematic approach to differentiate and compare risk and direct protective factors. PURPOSE: This study examines risk and protective factors associated with youth violence in an ongoing longitudinal panel study of 808 students from 18 Seattle public elementary schools followed since 1985 when they were in 5th grade. Predictors span the individual, family, school, peer, and neighborhood domains. METHODS: Data were collected annually, beginning in 1985, to age 16 years, and then again at age 18 years. This paper provides findings of analyses in which continuous predictor variables, measured at ages 10-12 years, were trichotomized to reflect a risk end of the variable, a direct protective end, and a middle category of scores. Youth violence was measured at ages 13-14 years and 15-18 years. RESULTS: Bivariate analyses of risk and direct protective factors identified the following predictors of violence at ages 13-14 years and 15-18 years. Risk for violence was increased by earlier antisocial behavior (e.g., prior violence, truancy, nonviolent delinquency), attention problems, family conflict, low school commitment, and living in a neighborhood where young people were in trouble. Direct protective factors at ages 10-12 years include a low level of attention problems, low risk-taking, refusal skills, school attachment, and low access and exposure to marijuana at ages 10-12 years. Multivariate regressions showed neighborhood risk factors to be among the most salient and consistent predictors of violence after accounting for all other variables in the tested models. CONCLUSIONS: Relatively few direct protective factors were identified in these statistical tests, suggesting the need for further review and possible refinement of the measures and methods that were applied. Implications provide important information for programs and policy.
Authors: Jeffrey E Hall; Thomas R Simon; James A Mercy; Rolf Loeber; David P Farrington; Rosalyn D Lee Journal: Am J Prev Med Date: 2012-08 Impact factor: 5.043