Removing Children From Home: A Multilevel Analysis Of Predictors for Placement in Foster Care
Date
2013-09-06
Authors
Fajardo, Valeria
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Abstract
The foster care system is intended as a temporary safety net to protect children and youth at risk of harm. Difficult decisions are made every day to place victims of child abuse or neglect in foster care. The focus of this study is to identify what characteristics, at either the individual or state level, increase the likelihood of placement and assess changes over time in factors that impact likelihood of placement. Secondary data from the National Child Abuse and Neglect Data System are used in a multilevel logistic regression model. Results show that infants, neglect, and prior victimizations all increase the likelihood of foster care placement. Foster care placement has decreased over time, despite tremendous variation between states. This thesis contributes to the existing literature in the field on indicators of increased likelihood of foster care placement, as well as provides a pedagogical example of how multilevel models can address important structural issues within the social sciences.
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Keywords
Child maltreatment, Multilevel models, Foster care, Logistic regression, Out-of-home placement