Statistical Verification of Facet Intercorrelations Using Parametric Bootstrapping in Differential Personality Diagnostics

Building on our previous work that examined intra-individual intercorrelations in differential psychological diagnostics, this paper focuses on the statistical verification of normative facet intercorrelations using parametric bootstrapping^[2]. Traditional methods rely on Pearson correlations and z-scores to compare subject profiles to a normative matrix^[6][1]. However, these approaches assume a normal distribution of correlations—a premise that may not hold under all conditions^[4]. Here, we describe how parametric bootstrapping, with direct normal approximation and Fisher-z transformation^[2][3], can empirically assess the variability and distributional characteristics of correlation coefficients. The insights gained help establish an objective diagnostic threshold for identifying significant deviations in individual profiles. This methodological enhancement is critical to refining personality diagnostics, merging rigorous statistical validation with clinical applicability.