Cumulative versus Multiple-Risk Models in the Prediction of Anxiety Symptoms

Abstract

The present study aimed to advance understanding of the cumulative and incremental influences of anxiety sensitivity, behavioral inhibition, and interpretive and judgment biases on anxiety outcomes in a large sample of emerging adults (N = 862; mean age = 18.75 years, SD = 1.04). Participants completed a battery of questionnaires assessing the constructs of interest and cumulative- and multiple-risk models were tested. Linear, hierarchical regression analyses showed that cumulative- and multiple-risk models significantly predicted anxiety outcomes, although the statistical prediction offered by the latter model was superior. Additionally, variability in each risk factor significantly predicted anxiety outcomes after controlling for the total number of risks, supporting the value of the severity of risk to the prediction of anxiety outcomes. Findings, implications for intervention, and limitations are discussed.