How Well Does An Intent to Leave Proxy Predict Child Welfare Workers' Actual Turnover?
Methods: Data for this study was obtained from a sample of 77 child welfare workers employed at a voluntary child welfare agency in a large northeastern city. Voluntary agencies in this locale are private agencies that are under contract with the city and provide preventive services. Workers in the sample represent various roles within the agency and included administrators, supervisors, social workers, caseworkers, and case planners. The workers filled out an extensive survey that contained items on their intention to leave including, “Have you considered looking for a new job within the past year?” The researchers returned to the agency 12 months later to obtain data on who actually left. Binary logistic regression was utilized to test how well the proxy “intention to leave” predicted actual turnover. Stata 12.1 was used to conduct the analysis.
Results: There were several factors that differentiated those who left from those who remained. For example, 43% (n = 33) of the workers actually left the agency within a year. Workers who left had a significantly (t = 3.0; p <. 001) lower mean number of years at the agency (2.3 years) compared to those who remained (6.5 years). A majority of workers with a social work degree (80%) left compared to 30% of those without a degree (X2=15.2; p < .001). Almost 70% of those who considered looking for a new job actually left the agency. Workers who indicated that they did intend to leave had a 2.5 times greater likelihood of actually leaving than those who did not. (OR = 2.5, p = < .05).
Conclusions and Implications: The results of this study indicate that the proxy “have you considered looking for a new job within the past year?” is a strong predictor of actual turnover. Personal characteristics including years employed and having a social work degree were also strong predictors. Although the results of this study need to be replicated, using a proxy for attrition provides a method for administrators to predict planned turnover at their agencies.