Data-Driven Analysis Exploring the Development of Empathy in an Iranian Context

Abstract

In the present study, we explored the best regression models that explain the developmental path of dispositional empathy among Iranian participants using Bayesian Model Averaging (BMA). BMA, a data-driven analysis method, was employed to identify the most likely candidate regression models given the collected data. We reported the best regression model for each dependent variable and different components of applied questionnaires by evaluating and comparing multiple model fit indicators—Akaike information criterion, Bayesian information criterion, adjusted R2, Bayes Factor model, and leave-one-out cross-validation root-mean-square error—among candidate regression models identified by BMA. We discussed the theoretical implications of the findings regarding factors associated with empathy development and the methodological implications of using data-driven analysis in the field.