Measuring the hierarchical general factor model of psychopathology in young adults. Issue 1 (8th October 2017)

• Record Type: Journal Article
• Authors: Lahey, Benjamin B.; Zald, David H.; Perkins, Scott F.; Villalta‐Gil, Victoria; Werts, Katherine B.; Van Hulle, Carol A.; Rathouz, Paul J.; Applegate, Brooks; Class, Quetzal A.; Poore, Holly E.; Watts, Ashley L.; Waldman, Irwin D.
• Is Part Of: International journal of methods in psychiatric research. Volume 27:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-10-08
• Subjects: general factor of psychopathology -- hierarchical taxonomy of psychopathology -- p factor; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1593
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: There is evidence that models of psychopathology specifying a general factor and specific second‐order factors fit better than competing structural models. Nonetheless, additional tests are needed to examine the generality and boundaries of the general factor model. In a selected second wave of a cohort study, first‐order dimensions of psychopathology symptoms in 499 23‐ to 31‐year‐old twins were analyzed. Using confirmatory factor analysis, a bifactor model specifying a general factor and specific internalizing and externalizing factors fit better than competing models. Factor loadings in this model were sex invariant despite greater variances in the specific internalizing factor among females and greater variances in the general and specific externalizing factors among males. The bifactor structure was robust to the exclusion of any single first‐order dimension of psychopathology. Furthermore, the results were essentially unchanged when all overlapping symptoms that define multiple disorders were excluded from symptom dimensions. Furthermore, the best‐fitting bifactor model also emerged in exploratory structural equation modeling with freely estimated cross‐loadings. The general factor of psychopathology was robust across variations in measurement and analysis.