PREDICTING SEVERITY OF CHILD-ABUSE INJURY WITH ORDINAL PROBIT REGRESSION

To compensate for insufficient resources, many child protection agenci es use screening instruments to prioritize reports of abuse for invest igations. Like most risk assessment instruments, however, the variable s included in the screens are selected on the basis of clinical observ ations. Because an empirically derived screen may lead to more-accurat e predictions, this article identifies predictors of injury severity c aused by physical abuse from information included in the abuse reports . Subjects included one physically abused child from 789 families. The criterion variable had four levels: no, mild, moderate, and severe in jury. Predictors included child, report, perpetrator, and maternal cha racteristics. Results of an ordinal probit regression analysis identif ied that a model with four predictors (perpetrator identity, reporter identity, severity of allegations, and season report was mode) and two interaction terms (child's age by mother's age and child's age by chi ld's gender) successfully predicted whether a third was injured.