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Removing Children From Home: A Multilevel Analysis Of Predictors for Placement in Foster Care

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dc.contributor.advisor Witte, James Fajardo, Valeria
dc.creator Fajardo, Valeria 2013-04-29 2013-09-06T18:08:31Z 2013-09-06T18:08:31Z 2013-09-06
dc.description.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.
dc.language.iso en en_US
dc.subject child maltreatment en_US
dc.subject multilevel models en_US
dc.subject foster care en_US
dc.subject logistic regression en_US
dc.subject out-of-home placement en_US
dc.title Removing Children From Home: A Multilevel Analysis Of Predictors for Placement in Foster Care en_US
dc.type Thesis en Master of Arts in Sociology en_US Master's en Sociology en George Mason University en

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