Configurational Approaches in Personality Assessment Research: An Introduction with Application to Rorschach Data.

Abstract

This article introduces configurational comparative methods (CCMs), such as qualitative comparative analysis (QCA) and coincidence analysis (CNA), as valuable tools for advancing personality assessment research. Unlike traditional variable-centered approaches, CCMs focus on configurations of indicators that are necessary or sufficient for specific outcomes, aligning more closely with clinical reasoning and idiographic case formulation. Drawing on principles like equifinality and multifinality, CCMs address the complexity and heterogeneity of psychological functioning, particularly in clinical populations. An empirical example using Rorschach and Personality Assessment Inventory (PAI) data from a sample of university students illustrates how CNA can identify distinct combinations of indicators linked to depressive symptomatology in college students. The analysis highlights interpretive asymmetries, showing that the presence of specific Rorschach markers yields clearer inferences than their absence. While CCMs involve challenges such as data calibration and model ambiguity, they offer a unique capacity to integrate quantitative and qualitative findings. As a complement to variable- and person-centered methods, configurational approaches may help assessment researchers formulate and test clinically interpretable hypotheses about patterns of indicators. The paper argues for greater adoption of these methods to better capture the complexity of personality and psychopathology.