A Multiverse Analysis Examining Measurement Factors of Potentially Traumatic Events That Influence Predictability of Developmental Functioning Among Children

Research that seeks to better understand the connection between potentially traumatic events (PTEs) and children’s well-being continues to develop from inadequate and inconsistent assessment practices. Not only does variability exist within what PTE characteristics are collected, but there is also variability in how this information is used to create and analyze PTE exposure. This study used a multiverse analysis to examine the utility of assessing multiple PTE characteristics when predicting children’s level of developmental functioning, and whether the operationalization technique influenced these relations. Preschool-age children (N = 325; M_age = 4.19; 49.5% female; 73% Black) were administered developmental and cognitive assessments, and caregivers reported on their child’s PTE. Children’s PTE history was then examined in relation to classification of being at-risk of poor developmental functioning using logistic regression and machine learning approaches based on different characteristics of PTE exposure (i.e., polyvictimization, frequency, and duration) and methods for operationalizing these characteristics (i.e., sum, mean, and max). Results suggested that PTE was not associated with developmental functioning; however, divergence from this pattern was observed with certain PTE characterizations and operationalizations. Findings illustrate the importance of evaluating how data processing decisions may influence findings and why multiverse analysis frameworks may be helpful when examining PTE exposure.