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The Age-Graded Consequences of Victimization

Award Information

Award #
2014-IJ-CX-0006
Location
Congressional District
Status
Closed
Funding First Awarded
2014
Total funding (to date)
$32,000

Description of original award (Fiscal Year 2014, $32,000)

Statement of the Problem: A large body of research links victimization to various harms. Yet it remains unclear why victimization leads to these outcomes, or how the effects of victimization vary over the life course. Accordingly, this study seeks to advance the literature and inform victim service interventions by: 1) examining the effects of violent victimization on multiple behavioral, social, psychological, and health-related consequences; 2) specifying theoretically and measuring directly the intervening processes (i.e., supportive social ties) that explain why some victims are more resilient than others; and 3) testing these relationships across multiple stages of the life course. In doing so, the broader purpose of this research is to inform evidence based-interventions that will be appropriate for victims at different stages in the life span. Research Design and Methods: Existing data from Waves I (1994-1995), II (1996), III (2001-2002), and IV (2008-2009) of the National Longitudinal Study of Adolescent Health (Add Health) are used. The Add Health is a nationally representative sample of over 20,000 American adolescents enrolled in middle and high school during the 1994-1995 school year. On average, respondents are 15 years of age at Wave I (11-18 years), 16 years of age at Wave II (ranging from 12 to 20 years), 22 years of age at Wave III (ranging from 18 to 26 years), and 29 years of age at Wave IV (ranging from 24 to 32 years). Analysis: After conducting descriptive and bivariate analyses, multivariate regression models (e.g., ordinary least-squares, logistic and negative binomial models) are used to assess the effects of violent victimization on the various behavioral, social, psychological, and health-related outcomes at each wave of data. Two-stage sample selection models are estimated to correct for attrition bias, and missing data due to item non-response are handled using multiple imputation.

Products, Reports, and Data Archiving: An official copy of the dissertation will be provided to NIJ. These results will be used to prepare a webinar, three manuscripts for academic and practitioner audiences, and will be presented at a national conference.
ca/ncf

Date Created: September 14, 2014