Differential Drivers of Psychological Constructs: A Conditional Inference Tree Analysis

Psychological constructs such as anxiety, depression, fatalism, divine control, luck, helplessness, and internality are pressing subjects in the United States (US). Although several studies have explored how specific covariates influence these constructs, like depression and anxiety, less is known about how predictors interact to predict the different subscales of fatalism, namely, fatalism, luck, divine control, helplessness, and internality. This study addresses the gap by using Conditional Inference Trees (CIT) to explore how interactions among predictors influence these constructs. Using a nationally representative survey of 2,000 respondents, CIT was employed to investigate how covariates, including Adverse Childhood Experiences (ACE), age, gender, race, education, and urbanicity, interact to predict each construct. Our analyses revealed that for the scales of fatalism, education, age, and race were key predictors, but their effects varied across the subscales of fatalism- fatalism, divine control, luck, helplessness, and internality. For instance, higher levels of education and younger age were associated with higher levels of fatalism and internality. ACE interacted with race to provide different levels of helplessness and divine control. In addition, by leveraging CIT, we were able to identify subtle interactions between covariates in predicting psychological constructs, primarily those related to the multi-fatalism scale.