Employees' Intentions to Remain Employed in Child Welfare: Testing a Conceptual Model

Abstract

The purpose of the current study was to extend prior research on retention of child welfare workers by constructing and testing a conceptual model for worker retention that combined individual and organizational factors. The sample used in this study (n = 244) was drawn from a Department of Human Services-Division of Family and Children Services (US DHS-DFCS) from a Southeastern state of the United States. Structural equation modeling was used to test the proposed model. The results indicated that professional organizational culture, satisfaction with extrinsic rewards, emotional exhaustion, and professional commitment were significantly related to workers' intentions to remain employed in child welfare. Although organizational policies were not directly related to workers' intentions to remain employed in child welfare, there was an indirect relationship through emotional exhaustion. Implications for practice and future research are discussed.

KEYWORDS:

• public child welfare
• retention
• professional organizational culture
• organizational policies
• satisfaction with extrinsic rewards
• emotional exhaustion
• professional commitment

Notes

Note.*p < .05

**p < .01

***p < .001.

1. In confirmatory factor analysis (CFA), parcels are often preferred over single items because using item parcels is are more likely to meet the assumptions of multivariate normality that underlie the maximum likelihood of estimation methods, reduce various sources of sampling error, and reduce the number of parameters to be estimated, thus avoid requiring a large sample size (Nasser & Wisenbaker, 2003; Rogers & Schmitt, 2004; Russell, Kahn, Spoth, & Altmaier, 1998). We generated nine item parcels (three parcels for each factor) using the item-to-construct balance technique (Little, Cunningham, Shahar, & Widaman, 2002). Specifically, we conducted separate exploratory factor analyses for each factor and the items for each factor were fit into one factor solution. Items were then ranked according to their loadings on this factor from highest to lowest. The three items with the highest loadings were used to anchor the three parcels. The three items with the next highest loadings were assigned to the parcels in an inverted order. The process continued until all items were assigned to parcels.