Traditional statistical approaches may produce poor results in light of such conditions and may require very large samples in order to model multiple, complex interactions among predictor variables. Artificial neural networks have been purported to excel at prediction and classification problems, particularly when there are a large number of inputs that are related in nonlinear ways. In prediction, neural networks are an alternative to multiple regression, logistic regression, structural equation models, and expert systems. Neural networks may outperform traditional statistical procedures where problems lack discernable structure, data are incomplete, and many competing inputs are related in nonlinear ways. The present study compares the predictive accuracy of an artificial neural network model in predicting child physical abuse recurrence against a logistic regression model.
Logistic regression is a commonly used statistical analysis for predicting dichotomous outcomes.
The architecture of the neural network will be discussed, as well as applicability of this method to other research areas. The information of this presentation is applicable to other researchers and clinicians interested in the use of artificial neural networks for a range of problems related to human services outcome prediction. No prerequisite knowledge is necessary, however the presentation may be of particular interest to those with a foundation in statistics and research methods. Be familiar with the methodological obstacles to predicting recurrence of child abuse Have knowledge of specific correlates of physical re-abuse within the U.S. Air Force community. Have an increased understanding of the relative utility of logistic regression and artificial neural networks in prediction and classification problems.