Date of Award
8-19-2024
Document Type
Open Access Dissertation
Department
Sociology
First Advisor
Bethany Bell
Second Advisor
Maryah Fram
Abstract
Approximately 40% of children living in poverty are in a single parent household headed by their mother (Grall, 2017). The public child support services program exists to provide the service of establishing and enforcing child support orders for parents with primary legal custody of their child. Even with the child support services system in place, child support goes uncollected (Grall, 2020). Many of the non-resident parents (NRP) who are not paying live at poverty level as well. Child support research has focused primarily on NRPs’ ability to pay, but, generally, has drawn on the information contained in the constellation of factors co-occurring with an NRPs’ ability to pay.
Secondary data analysis of an administrative dataset was used to answer two research aims: 1) to identify sub-groups characterized by ability to pay and payment patterns among NRPs with current support orders and arrears and 2) to examine the relationships between sub-group membership and covariates. Latent Class Analysis (LCA) identified four distinct groups of non-resident parents based on their ability to afford current and past-due child support. Subgroups differed in earned income, payment, current support owed, number of concurrent cases, debt burden, and payment patterns. Factors about parents’ ability to pay as well as potential points of intervention meaningful to each group were illuminated. This study contributes substantively to child support scholarship by exploring how a child support agency’s administrative data provides information about the heterogeneity amongst subgroups of parents receiving services to improve operations, processes, service quality, and outcomes.
Rights
© 2024, Victoria Adkins Charles
Recommended Citation
Charles, V. A.(2024). Using Latent Class Analysis to Conceptualize Heterogeneity Among Non-Resident Parents with Current Child Support Orders and Arrears: Identifying Windows of Opportunity. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/7686