Neural network modeling of risk assessment in child protective services.

Abstract

The advantages of using neural network methodology for the modeling of complex social science data are demonstrated, and neural network analysis is applied to Washington State Child Protective Services risk assessment data. Neural network modeling of the association between social worker overall assessment of risk and the 37 separate risk factors from the State of Washington Risk Assessment Matrix is shown to provide case classification results superior to linear or logistic multiple regression. The improvement in case prediction and classification accuracy is attributed to the superiority of neural networks for modeling nonlinear relationships between interacting variables; in this respect the mathematical framework of neural networks is a better approximation to the actual process of human decision making than linear, main effects regression. The implications of this modeling advantage for evaluating social science data within the framework of ecological theories are discussed.

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