Finding optimal factor analytic representations of the structure of personality
Creators: name: Hong, Hyeri
Publisher: name: University of Iowa
Year: 2024
Subjects: Bayesian Structural Equation Modeling
Confirmatory Factor Analysis
Exploratory Structural Equation Modeling
IPIP-NEO-120
Method Effects
Personality Instrument
public abstract

description: Factor analysis has served as an essential tool for understanding the nature of personality and other constructs. Over the years, factor analysis techniques have evolved from simple exploratory analyses to more sophisticated approaches that include: (a) confirmatory factor analyses (CFAs) that allow for direct testing of hypothesized models; (b) estimation procedures that adjust for non-normal distributions, correct for ordinal-level data, and enhance model convergence; (c) alterations of models to take method effects (e.g., negative phrasing of items) into account; and (d) extensions of simple structure models to allow for small cross-loadings for non-targeted items. The purpose of this dissertation was to investigate the effects of estimation method, negative item wording, and factor loading constraints on model fit indices and magnitude of factor loadings for correlated factor, hierarchical, and bifactor models of personality using a sample of 447,500 respondents who completed the International Personality Item Pool – Neuroticism, Extraversion, and Openness (IPIP-NEO-120) questionnaire. The IPIP-NEO-120 is available free of charge, computer-administered, brief yet comprehensive in coverage, and amenable to evaluation of multiple types of factor models representing domain and nested facet scores congruence with the Big Five Factor model.
description: Overall, results across the three types of factor models highlighted the importance of controlling for scale coarseness, accounting for method variance, allowing small off-target loadings when using maximum likelihood (ML) and robust weighted least squares estimation (WLSMV), and including informative priors when using Bayesian estimation. When properly configured, Bayesian with informative priors and WLSMV estimation provided noticeably better model fits than did Bayesian with non-informative priors and ML estimation. The best fits were yielded by bifactor, correlated factor, and hierarchical models, respectively, but excellent fits were achieved for each type of model when relationships were appropriately represented. Performing these analyses also significantly expanded evidence of reliability and validity for IPIP-NEO-120 domain and facet scores.
description: Taken collectively, findings from this dissertation support models of the structure of personality within several theoretical frameworks and contribute to ongoing research in refining factor analytic techniques. Methods demonstrated here are widely applicable to self-report measures in general and may serve as templates for future investigations into other multidimensional psychological constructs. Recommendations for future research into factor analytic techniques and further refinements of personality and other self-report measures are discussed. Code in Mplus for analyzing all models is included in an appendix at the end of the dissertation.
description: