Organizational Factors in Using Child Welfare Data
Methods: The Child Welfare Data Use Survey was administered in 2011 as part of the data training element of the North Carolina Reaching Excellence and Accountability in Practice (NC-REAP) project. Data from 237 respondents were analyzed using binominal logistic regression. Five-point Likert scale items were used to assess the independent variables. The dependent variable, child welfare data use in day-to-day work, was dummy coded into non-use and use categories.
Results: The overall model was significant (X2(8) = 169.36; p <.001). Analysis of parameter estimates showed that familiarity (Exp(B) = 18.97, p <.001, 95% CI: 8.31, 43.34) and support from supervisor (Exp(B) = 2.29, p=.007, 95% CI: 1.25, 4.20) were significant predictors of data use. Child welfare workers who are familiar with data were more likely to use data in their day-to-day work. When child welfare workers received more support from their supervisor, they were more likely to use data in their day-to-day work.
Conclusions and Implication: The results underline that the PRECEDE portion of the PRECEDE-PROCEED model is useful in identifying organizational factors that support data-driven decision-making among child welfare workers. These findings are also consistent with established principles of implementation science related to systems change, including the importance of facilitative administration and the need to provide training to increase familiarity (NIRN, 2008-2012). Child welfare organizations should devote attention to methods that make workers increasingly familiar with data, and focus on training or educating supervisors so that they can support and facilitate workers’ use of data.