TY - JOUR

T1 - General factor mean difference estimation in bifactor models with ordinal data

AU - Liu, Yixing

AU - Thompson, Marilyn S.

N1 - Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - A simulation study was conducted to explore robustness of general factor mean difference estimation in bifactor measurement models with ordered-categorical measures. Common analysis misspecifications in which generated bifactor data were fitted using a unidimensional model and/or ordered-categorical data were treated as continuous were compared under generation conditions varying by sample size, number of response categories, effect size of the general factor mean difference, and loadings on the specific factors. Fitting bifactor data using unidimensional models resulted in estimation bias in the general factor mean difference, with magnitude largely determined by degree of unidimensionality and size of the general factor mean difference. Although bifactor models produced less estimation bias, estimates were less precise. Modeling the data as categorical and employing the WLSMV estimator provided somewhat more power, whereas modeling the data as continuous and applying MLR produced relatively less estimation bias when unidimensional models were specified for strongly bifactor data.

AB - A simulation study was conducted to explore robustness of general factor mean difference estimation in bifactor measurement models with ordered-categorical measures. Common analysis misspecifications in which generated bifactor data were fitted using a unidimensional model and/or ordered-categorical data were treated as continuous were compared under generation conditions varying by sample size, number of response categories, effect size of the general factor mean difference, and loadings on the specific factors. Fitting bifactor data using unidimensional models resulted in estimation bias in the general factor mean difference, with magnitude largely determined by degree of unidimensionality and size of the general factor mean difference. Although bifactor models produced less estimation bias, estimates were less precise. Modeling the data as categorical and employing the WLSMV estimator provided somewhat more power, whereas modeling the data as continuous and applying MLR produced relatively less estimation bias when unidimensional models were specified for strongly bifactor data.

KW - Bifactor models

KW - general factor mean difference

KW - multiple-group categorical CFA

KW - ordinal data

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U2 - 10.1080/10705511.2020.1833732

DO - 10.1080/10705511.2020.1833732

M3 - Article

AN - SCOPUS:85096092893

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

ER -