Leung, Patrick2019-12-22December 22019-12December 2https://hdl.handle.net/10657/5630Turnover within public child welfare (PCW) has been high for decades, and the problem persists through the present day. Title IV-E training stipends have been employed as one method of increasing retention, but recent legislation could threaten continued allocation of these funds. There is a limited body of evidence suggesting Title IV-E training could be beneficial for retention, and that different factors are salient for turnover intentions among Title IV-E-trained workers. However, these results are far from definitive. The current study fills this research gap by analyzing a causal model of turnover intention, with the ability to compare Title IV-E recipients and non-recipients. Multiple-group path analysis revealed several differences between Title IV-E recipients and non-recipients, and some of these differences are indicative of Title IV-E’s possible benefit in reducing turnover. Title IV-E may provide a protective factor against the tendency for MSW graduates and workers in urban locations to express lower intent to remain employed. However, Title IV-E did not buffer against dissatisfaction with other workplace factors, including professional development opportunities, relationships with coworkers and supervisors, workload, and salary. Beyond the substantive findings, many recommendations for future research are provided. This innovative research design provides a template for further inquiry into the contribution of Title IV-E, not only to stem turnover but, ultimately, to improve outcomes for system-involved children and families.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Public child welfareTurnoverTurnover intentRetentionJob satisfactionTitle IV-EMultiple-groupMultiple-samplePath analysisCausal modelMonte Carlo power analysis2019-12-22Thesisborn digital