Social science researchers commonly employ univariate models, yet multivariate models offer the advantage of depicting multiple outcomes and the dependencies between these outcomes simultaneously....Show moreSocial science researchers commonly employ univariate models, yet multivariate models offer the advantage of depicting multiple outcomes and the dependencies between these outcomes simultaneously. De Rooij and Groenen (2021) introduced the MELODIC family, a multivariate approach for addressing these multiple binary outcome variables. The assessment focused on diagnoses and the scales of a behavioural screening questionnaire (SDQ) in a Northern Netherlands outpatient population. Predictive validity gauged content-related scale performance in diagnosis prediction. Discriminative validity was confirmed if only the diagnosis-related scale accurately predicted the presence of the diagnosis. AUC values were used for these comparisons. No significant differences emerged among the models. Since we did not find direct differences across the different models. We tried to elucidate the cause of these non-significant differences by post-hoc analyses: scatterplots and Brier scores. The scatterplots of the ordering of probabilities for a univariate multiple logistic regression and a MELODIC model did not offer any more insights. The Brier scores yielded no additional insights either. There was no more evidence for the predictive and discriminative validity when the MELODIC model was used. The benefit of the utilization of a MELODIC model was that it allowed for the inclusion of all predictors and outcome variables at the same time, eliminating the requirement for multiple separate univariate models and thus reducing the likelihood of errors.Show less
