Difficulties in interpersonal behavior are often measured by the circumplex-based Inventory of Interpersonal Problems. Its eight scales can be represented by a three-factor structure with two circumplex factors, Dominance and Love, and a general problem factor, Distress. Bayesian confirmatory factor analysis is well-suited to evaluate the higher-level structure of interpersonal problems because circumplex loading priors allow for data-driven adjustments and a more flexible investigation of the ideal circumplex pattern than maximum likelihood confirmatory factor analysis. Using a nonclinical sample from an online questionnaire study (N = 822), we replicated the three-factor structure of the IIP by maximum likelihood and Bayesian confirmatory factor analysis and found great proximity of the Bayesian loadings to perfect circumplexity. We also investigated higher-level scores for Dominance, Love, and Distress using traditional regression factor scores, posterior mean factor scores from Bayesian confirmatory factor analysis, and weighted sum scores. We found excellent reliability (with Rtt ≥ .90) for Dominance, Love, and Distress for all scoring methods. We found high congruence of the higher-level scores with the underlying factors and good circumplex properties of the scoring models. The correlation pattern with external measures – Agreeableness, Extraversion, and Neuroticism from the Big Five and subclinical grandiose narcissism – were in line with theoretical expectations. We encourage the use of Bayesian modeling when dealing with circumplex structure and recommend the use of higher-level scores for interpersonal problems as parsimonious, reliable, and valid measures.
Overcoming limitations of the independent clusters model for CFA by means of Bayes-estimation and buffered simple structure