Bifactor Models of Personality and College Student Performance: A Broad versus Narrow View

McAbee, Samuel T.; Oswald, Frederick L.; Connelly, Brian S.

doi:10.1002/per.1975

Cited by 78 publications

(73 citation statements)

References 76 publications

(168 reference statements)

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“…Observed correlations may be inflated or attenuated as estimates of relations between the broad/narrow psychopathology constructs and other variables (83). For example, the perfectionism facet of Conscientiousness was negatively related to university student physical and mental health after controlling for general Conscientiousness; this relationship was obscured when both sources of variance were combined in the observed subscale score (84). By separating the predictive power of broad and narrow factors, bifactor modeling can provide a clearer picture of the nomological network of psychopathology.…”

Section: Relations Of General and Group Factors With External Variablesmentioning

confidence: 99%

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

Bornovalova

Choate

Fatimah

et al. 2020

Biological Psychiatry

155

129

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Co-occurrence of psychiatric disorders is welldocumented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the 'general psychopathology' or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are wellsuited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data. 1 Sometimes, group factors are called "specific factors." However, "specific factor" more correctly refers to an item's reliable (non-error) variance that is not shared with other items (5).

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Section: Relations Of General and Group Factors With External Variablesmentioning

confidence: 99%

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

Bornovalova

Choate

Fatimah

et al. 2020

Biological Psychiatry

155

129

View full text Add to dashboard Cite

show abstract

“…Residualised facets is the most extreme in that it assigns all common variance to the broad traits. In contrast, the bifactor model (Chen, Hayes, Carver, Laurenceau, & Zhang, ; McAbee, Oswald, & Connelly, ; Perera, Izadikhah, O’Connor, & McIlveen, ) distributes common variance between factors and facets. The bifactor model also provides a way of separating evaluative variance from more descriptive trait variance (Anglim, Morse, et al, ).…”

Section: Recommendations For Researchersmentioning

confidence: 99%

Measurement and research using the Big Five, HEXACO, and narrow traits: A primer for researchers and practitioners

Anglim

O’Connor

2019

Australian Journal of Psychology

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Objective: Personality traits influence human behaviour across a broad range of situations and are consequently relevant to many theoretical and applied disciplines. In this perspective piece, we provide an overview of the logic underpinning personality measurement and review major personality taxonomies. We provide an extensive set of recommendations for researchers and practitioners on when and how to use measures of personality traits. Method: We overview a range of taxonomic representations of personality structure focusing particularly on hierarchical representations and five and six factor models such as the Big Five and HEXACO models. We review the various strengths and weaknesses of each approach. Results: The review outlines the major reasons for the dominance of the Big Five model, and suggests it is a good descriptive framework for studying personality in general. However we suggest that researchers and practitioners also consider alternative taxonomic personality representations such as the HEXACO. We provide a range of scenarios whereby alternative frameworks will be more appropriate than the Big Five and offer recommendations both for choosing measures in general and for implementing studies examining personality facets. Conclusion: Whilst the Big Five represents an excellent general personality framework that is appropriate across multiple situations, researchers and practitioners should be aware of alternative measures and utilise them where appropriate.

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“…In a bifactor model, each indicator loads on the general factor (which influences all measures) and a specific factor (which influences some measures). In fitting a bifactor SEM, ideally, multiple indicators for each specific factor are used (e.g., multiple items from each scale of a personality measure; McAbee, Oswald, & Connelly, 2014). A bifactor model of Edwards et al's (2008) data is shown in Figure 1.…”

Section: Bifactor Modelmentioning

confidence: 99%

How Data Analysis Can Dominate Interpretations of Dominant General Factors

Wiernik

Wilmot

Kostal

2015

Ind. Organ. Psychol.

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438 b r e n ton m . w i e r n i k e t a l . van der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. . Cronbach's α, Revelle's β, and Mc-Donald's ω H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123-133.A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions. Job satisfaction is a quintessential multidimensional construct with a DGF. It comprises several dimensions, each of which reflects attitudes toward different components of the job and all of which are simultaneously c hoic e of a na ly t ic m et hod 439

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Bifactor Models of Personality and College Student Performance: A Broad versus Narrow View

Cited by 78 publications

References 76 publications

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations

Measurement and research using the Big Five, HEXACO, and narrow traits: A primer for researchers and practitioners

How Data Analysis Can Dominate Interpretations of Dominant General Factors

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