The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better

By Gilles E. Gignac. 2016

207 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/405394d3fbe26bfd42d94f7bedf60d8c53922268

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Citations in the corpus

Investigating the Multidimensionality of Need Fulfillment: A Bifactor Exploratory Structural Equation Modeling Representation (2018, background): “Although elegant at first sight, this operationalization relies on one extremely stringent assumption that is almost never empirically verified (Gignac, 2016).”
Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation (2018, unknown): “2012; Yung, Thissen, & McLeod, 1999) or to make sense theoretically (Gignac, 2016), thus positioning bifactor models as the more robust modeling procedure.”
Riskier Tests of the Validity of the Bifactor Model of Psychopathology (2019, methodology): “In addition, further research might extend existing simulation studies (Gignac, 2016; Greene et al., in press; MaydeuOlivares & Coffman, 2006; Murray & Johnson, 2013) by examining the conditions under which we artificially identify bifactor models on the basis of preferential model fit as well as c”
Testing multidimensional models of youth civic engagement: Model comparisons, measurement invariance, and age differences (2017, methodology): “Our bifactor model included several low loadings, which can be substantively interesting in illustrating the amount of variance an item contributes to a general versus specific factor (Gignac, 2016).”
Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, background): “” It has thus been argued that unless researchers can theoretically justify the presence of the proportionality constraints and of indirect associations between the indicators and the global factors, bifactor models should be preferred (Gignac, 2016).”