Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

By Thomas A. Schmitt, Daniel A. Sass. 2011

269 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c8817a2766eeac24856a7865c6569e7c61c27b25

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In Computer Networks and Communications

In Management Science and Operations Research

Citations in the corpus

Motivation profiles at work: A self-determination theory approach (2016, result): “This decision is also based on the results from simulation studies (Asparouhov & Muthén, 2009; Sass & Schmitt, 2010; Schmitt & Sass, 2011) and studies of simulated data (Marsh, Lüdtke, Nagengast, Morin, & Von Davier, 2013; Morin, Arens, & Marsh, 2015) showing that forcing cross-loadings (even as…”
Bayesian Structural Equation Modeling With Cross-Loadings and Residual Covariances (2015, methodology): “…most indicators will present both some level of random noise and also some level of constructrelevant association with other constructs (Marsh, Lüdtke, Nagengast, Morin, & Von Davier, 2013; Marsh, Morin, Parker, & Kaur, 2014; Morin et al., in press; Sass & Schmitt, 2010; Schmitt & Sass, 2011).”
The Latent Structure of Spatial Skills and Mathematics: A Replication of the Two-Factor Model (2017, methodology): “Once the model with the best fit was identified, we determined which tasks loaded onto each factor significantly using z values derived by dividing the factor loading for each measure by its standard error (Cudeck & O’Dell, 1994; Schmitt & Sass, 2011).”
Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis (2011, background): “Hypothesis testing of standard errors are discussed and demonstrated in the Illustration, as well as in Schmitt and Sass (2011).”
A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality (2016, background): “…still show that, notwithstanding this issue, ESEM tends to provide factor correlation estimates that are closer to true population values, even if they are themselves imperfect due to rotational indeterminacy (e.g., Asparouhov & Muthén, 2009; Marsh, Lüdtke, et al., 2013; Schmitt & Sass, 2011).”
A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis (2019, methodology): “Moreover, when there is no crossloading in the population model, ESEM/EFA can still provide unbiased estimates of factor correlations (Marsh et al., 2013; Sass & Schmitt, 2010; Schmitt & Sass, 2011).”
Complementary Variable- and Person-Centered Approaches to the Dimensionality of Psychometric Constructs: Application to Psychological Wellbeing at Work (2016, background): “, cross-loadings) to be zero, the only way for them to be expressed is through the inflation of the factor correlations, in turn leading to biased estimates of the relations among constructs according to a variety of simulation studies (Asparouhov and Muthén 2009; Sass and Schmitt 2010; Schmitt and ”
Cross-validation of the short form of the physical self-inventory (PSI-S) using exploratory structural equation modeling (ESEM) (2011, methodology): “…with previous ESEM applications (Marsh, Liem, et al., in press; Marsh, Lüdtke, et al., 2010; Marsh et al., 2009; Marsh, Nagengast, et al., in press), we also explored solutions based on alternative rotation procedures following Sass and Schmitt (2010; Schmitt & Sass, 2011) recommendations.”
Should Regularization Replace Simple Structure Rotation in Exploratory Factor Analysis? (2019, methodology): “Furthermore, oblique rotations tend to yield inflated factor correlations and underestimated cross-loadings in the presence of high cross-loadings in the population factor loading pattern (Asparouhov & Muthén, 2009; Sass & Schmitt, 2010; Schmitt & Sass, 2011).”