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Dr Aja Murray and Prof Manuel Eisner publish a paper on quantifying the strength of general factors in psychopathology

last modified Jun 13, 2018 04:58 PM

Dr Aja Murray and Professor Manuel Eisner have published a paper called Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation, BSEM, and ESEM/EFA Bifactor Approaches in the Journal of Personality Assessment.

Co-authors are Dr Ingrid Obsuth, School of Health in Social Science, University of Edinburgh; Dr Tom Booth, Department of Psychology, University of Edinburgh and Dr Denis Ribeaud, Jacobs Centre for Productive Youth Development, University of Zurich. This quantitative paper explores psychopathology research focusing on comorbidity and the factors of internalizing and externalizing. Internalizing includes symptoms such as major depression, anxiety disorder, dysthymia, phobias, post-traumatic stress and panic disorder. Externalizing encompasses symptoms such as substance use, conduct disorder and others with behavioural disinhibition, impulsivity or “acting out”.



Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).


You can find the paper here.



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