2015
DOI: 10.1080/00223891.2015.1035381
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Testing the Factor Structure and Measurement Invariance Across Gender of the Big Five Inventory Through Exploratory Structural Equation Modeling

Abstract: Confirmatory factor analyses (CFAs) typically fail to support the a priori 5-factor structure of Big Five self-report instruments, due in part to the overly restrictive CFA assumptions. We show that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis, overcomes these problems in relation to responses to the 44-item Big Five Inventory (BFI) administered to a large Italian community sample. ESEM fitted the data better and resulted in less correlated factors than… Show more

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Cited by 52 publications
(49 citation statements)
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“…Although the ICU was originally designed to have 4 subscales, CFA models were necessary, because exploratory factor analytic research has found evidence for a 3-factor structure of CU traits in adolescence, and the best-fitting factor structures have varied across studies (Kimonis et al, 2008; Essau, Sasagawa & Frick, 2006). Because the factor structure of the Big Five (including using unit-weighted sum scores) has been well validated (Chiorri, Marsh, Ubbiali & Donati, 2015; John, Naumann, & Soto, 2008; Marsh et al, 2010; Marsh, Nagengast, & Morin, 2013), we did not investigate its factor structure here.…”
Section: Resultsmentioning
confidence: 99%
“…Although the ICU was originally designed to have 4 subscales, CFA models were necessary, because exploratory factor analytic research has found evidence for a 3-factor structure of CU traits in adolescence, and the best-fitting factor structures have varied across studies (Kimonis et al, 2008; Essau, Sasagawa & Frick, 2006). Because the factor structure of the Big Five (including using unit-weighted sum scores) has been well validated (Chiorri, Marsh, Ubbiali & Donati, 2015; John, Naumann, & Soto, 2008; Marsh et al, 2010; Marsh, Nagengast, & Morin, 2013), we did not investigate its factor structure here.…”
Section: Resultsmentioning
confidence: 99%
“…To test the associations between the variables, structural equation modeling (SEM) analyses with latent variables were conducted with robust maximum likelihood estimation to examine the relationship pattern between Big Five factors, Tinder motivations, and problematic Tinder use. Following previous applications ( Chiorri, Marsh, Ubbiali, & Donati, 2016 ; Marsh et al., 2010 ), the Big Five factors were estimated in the exploratory SEM framework (for more details, see Morin, Marsh, & Nagengast, 2013 or Tóth-Király, Bőthe, Rigó, & Orosz, 2017 ). The same fit indices and guidelines were applied as in Study 1.…”
Section: Methodsmentioning
confidence: 99%
“…(Note that contrary to coding instructions for the SCS, Neff, , Pfattheicher et al did not reverse code the negative items, leading to a positive correlation between RUS and neuroticism.) However, many have argued that ESEM is a better way to model the facets of the Five Factor Personality Inventory regardless of the instruments at hand including 15 items (Marsh, Nagengast, & Morin, ), 44 items (Chiorri, Marsh, Ubbiali, & Donati, ), 60 items (Marsh et al, ), 240 items (Furnham, Guenole, Levine, & Chamorro‐Premuzic, ), or even a smaller proportion of the factors (Marsh, Lüdtke, Nagengast, Morin, & Von Davier, ). Therefore, we opted to explore the factor structure of the NEO PI‐R with ESEM as well.…”
Section: Studymentioning
confidence: 99%