Date of Conferral

1-1-2010

Degree

Ph.D.

School

Public Policy and Administration

Advisor

Gloria Billingsley

Abstract

Although structured decision making and risk assessment protocols have successfully been used in human service programs, little research has been done on their applicability in the child support program. In this study, problems identified with child support case management were examined, along with positive and negative attributes of various risk assessment tools utilized in other arenas. The overall research problem asserted that there are no structured decision making protocols in the child support program to support case assignment by enforcement difficulty. The primary research question asked whether or not a process stratified by risk and level of enforcement difficulty could be developed to increase child support collections and improve program cost-effectiveness using custodial parent data obtained at time of intake. The theoretical foundation of the study revolved around descriptive decision theory and specifically, risk assessment as means to stratify child support caseloads. A nonparametric quantitative research methodology was utilized to examine 1501 cases from the program. The goal was to identify those variables that had the greatest impact on case payment so that they could be incorporated into a structured decision making protocol. The results of the data analysis, using a Cramer's V test for association, indicated that of the 11 independent variables chosen for the study, seven variables appeared to be very strongly associated with the dependent variable. Those variables were custodial parent age, gender, ethnicity, welfare status, number of children, relationship to each child and the ages of the children. Ultimately, the social change implication is to improve collection of child support payments for low income children and families. Enhancing the economic lifestyles of these individuals has the potential to reduce government dependency and to improve economic self sufficiency.