Abstract
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 conventional maximum likelihood confirmatory factor analysis. Using a non-clinical 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 found additional support for the validity of the three-factor model of the IIP by including external criteria-Agreeableness, Extraversion, and Neuroticism from the Big Five and subclinical grandiose narcissism-in the analysis. We also investigated higher-level scores for Dominance, Love, and Distress using traditional regression factor scores and weighted sum scores. We found excellent reliability (with Rtt ≥ 0.90) for Dominance, Love, and Distress for the two scoring methods. We found high congruence of the higher-level scores with the underlying factors and good circumplex properties of the scoring models. The correlational pattern with the external measures was in line with theoretical expectations and similar to the results from the factor analysis. 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.
Highlights
The Inventory of Interpersonal Problems (IIP) is one of the most widely used measures of difficulties in the interaction with other people (Horowitz et al, 1988, 2017)
Before investigating the higher-level model of the IIP by maximum-likelihood confirmatory factors analysis (CFA) (MCFA) and Bayesian CFA (BCFA), we analyzed the correlational pattern of the IIP octants as implied by the Circular Stochastic Process Model (SPMC) (Browne, 1992; Nagy et al, 2019)
As this was the minimum correlation, it indicated that the IIP scales showed an overall positive correlation with each other, supporting the importance of a third, general factor causing the positive correlations in addition to the two circumplex factors
Summary
The Inventory of Interpersonal Problems (IIP) is one of the most widely used measures of difficulties in the interaction with other people (Horowitz et al, 1988, 2017). Higher-Level Structure of Interpersonal Problems have shown that the IIP scales can be used to classify certain psychological disorders (Alden and Phillips, 1990; Pincus and Wiggins, 1990) and to evaluate treatment outcome of psychotherapy (Horowitz et al, 1988; Ruiz et al, 2004). The scales are LM/Overly nurturant (0◦), NO/Intrusive (45◦), PA/Domineering (90◦), BC/Vindictive (135◦), DE/Cold (180◦), Abbreviations: IIP, Inventory of Interpersonal Problems; SPMC, Circular Stochastic Process Model; MCFA, Confirmatory factor analysis with maximumlikelihood estimation; BCFA, Bayesian confirmatory factor analysis; TEFA, Exploratory factor analysis with subsequent target rotation toward the perfect circumplex; PSR, Potential scale reduction
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