The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure

By A. Murray, Wendy Johnson. 2013

297 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7483c4b0c4927bb201574a8d5ca2950e34b2bebe

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In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better (2016, methodology): “To this effect, Murray and Johnson (2013) assumed that the higher-order model representation of cognitive abilities was true and, consequently, specified seven data sets that were contended to be consistent with a higher-order model.”
Are Fit Indices Biased in Favor of Bi-Factor Models in Cognitive Ability Research?: A Comparison of Fit in Correlated Factors, Higher-Order, and Bi-Factor Models via Monte Carlo Simulations (2015, methodology): “Additional attention should also be given to another aspect of model complexity, which was central to the simulation design in Murray and Johnson [36].”
A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality (2016, background): “…and hierarchical models typically neglect item cross-loadings due to the fallible nature of indicators as providing a reflection of one, and only one, construct, which are likely to be expressed through the inflation of the variance attributed to the G factor (e.g., Murray & Johnson, 2013).”
Three Concerns With Applying a Bifactor Model as a Structure of Psychopathology (2017, background): “Moreover, Murray and Johnson (2013) showed that standard statistical fit indices can be biased in favor of the bifactor model, even if the true population model follows a different structure.”
Riskier Tests of the Validity of the Bifactor Model of Psychopathology (2019, background): “…and social scientists alike (Bagby, Taylor, Quilty, & Parker, 2007; Bonifay, Lane, & Reise, 2017; Morgan, Hodge, Wells, & Watkins, 2015; Murray & Johnson, 2013; Vanheule, Desmet, Groenvynck, Rosseel, & Fontiane, 2008), this near exclusive reliance on bifactor Corresponding Author:…”
Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation (2018, background): “Indeed, each of these models is able to absorb unmodeled sources of construct-relevant multidimensionality (e.g., Asparouhov et al., 2015; Morin et al., 2016; Murray & Johnson, 2013).”
Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, result): “…in inflated estimates of factor correlations in CFA, or of the global factor in bifactor-CFA (see Model D in Figure 2), while an unmodeled global factor tends to result in inflated factor correlations in CFA or inflated cross-loadings in ESEM (e.g., Morin et al., 2016a; Murray & Johnson, 2013).”
John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models (2015, background): “For example, Murray and Johnson [65] wrote “there are no explicitly or implicitly bi-factor theories of intelligence” (p. 419).”