Variation in the significance and magnitude of effect size estimates reported across prospective studies has led to replication failures and the weakening of causal inferences about the long-term health effects of child maltreatment. Contamination, or the presence of child maltreatment in comparison conditions, truncates effect size magnitudes and increases Type II errors that lead to replication failures. This project is researching the optimal methods for controlling contamination in child maltreatment research with the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN; N=1354) dataset, a multi-site, multi-wave nationally representative prospective cohort of child maltreatment. Results will help minimize replication failures in future child maltreatment research while generating reproducible effect size estimates across outcomes.
Associate Professor of Human Development and Family Studies