Bayesian Structural Equation Modeling With Cross-Loadings and Residual Covariances

By T. Asparouhov, B. Muthén, Alexandre J. S. Morin. 2015

387 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6ad2f7e5634b968ce38dfbcb860c7da8c41f8a16

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In Computer Networks and Communications

In Food Science

In Management Science and Operations Research

Citations in the corpus

Using Bifactor Exploratory Structural Equation Modeling to Test for a Continuum Structure of Motivation (2018, methodology): “However, it is important to reinforce that, irrespective of parsimony, current evidence suggests that there is no risk to adopting an ESEM parameterization of the data even when no cross-loadings are present in the population model (Asparouhov et al., 2015).”
Further Reflections on Disentangling Shape and Level Effects in Person-Centered Analyses: An Illustration Exploring the Dimensionality of Psychological Health (2016, background): “…ignoring any one of these two sources of construct-relevant multidimensionality, when they are present in the data, may lead to inflated estimates of the other source or to inflated estimates of factor correlations when both sources are ignored (Asparouhov et al., 2015; Morin, Arens et al., 2015).”
An Updated Guideline for Assessing Discriminant Validity (2020, methodology): “Second, a factor model where each item loads on only one factor may be too constraining for applied research (Asparouhov et al., 2015; Marsh et al., 2014; Morin et al., 2017; Rodriguez et al., 2016).”
Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, methodology): “Consistent with the statistical research showing that ESEM tends to provide reduced, and more exact, estimates of factor correlations (Asparouhov et al., 2015), the ESEM solution resulted in substantially smaller estimates of factor correlations between AMS subscales.”
Investigating the Multidimensionality of Need Fulfillment: A Bifactor Exploratory Structural Equation Modeling Representation (2018, background): “However, based on statistical evidence showing that ESEM results in more exact estimates of factor correlations when cross-loadings are present in the population model but unbiased estimates otherwise (Asparouhov et al., 2015), the key comparison involves the factor correlations.”
A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis (2019, methodology): “In this sense, BSEM reflects better researchers’ theories or prior beliefs and detects more easily the possible misspecifications than CFA does (Asparouhov et al., 2015; Muthén & Asparouhov, 2012).”