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Rejoinder to MacCallum, Edwards, and Cai (2012) and Rindskopf (2012): Mastering a New Method
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Muthen, Bengt; Asparouhov, Tihomir – Psychological Methods, 2012
This rejoinder discusses the general comments on how to use Bayesian structural equation modeling (BSEM) wisely and how to get more people better trained in using Bayesian methods. Responses to specific comments cover how to handle sign switching, nonconvergence and nonidentification, and prior choices in latent variable models. Two new…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Statistical Analysis
Next Steps in Bayesian Structural Equation Models: Comments on, Variations of, and Extensions to Muthen and Asparouhov (2012)
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Rindskopf, David – Psychological Methods, 2012
Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By…
Descriptors: Structural Equation Models, Bayesian Statistics, Factor Analysis, Computation
Hopes and Cautions in Implementing Bayesian Structural Equation Modeling
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MacCallum, Robert C.; Edwards, Michael C.; Cai, Li – Psychological Methods, 2012
Muthen and Asparouhov (2012) have proposed and demonstrated an approach to model specification and estimation in structural equation modeling (SEM) using Bayesian methods. Their contribution builds on previous work in this area by (a) focusing on the translation of conventional SEM models into a Bayesian framework wherein parameters fixed at zero…
Descriptors: Structural Equation Models, Bayesian Statistics, Computation, Expertise
The Relationship between Mean Square Differences and Standard Error of Measurement: Comment on Barchard (2012)
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Pan, Tianshu; Yin, Yue – Psychological Methods, 2012
In the discussion of mean square difference (MSD) and standard error of measurement (SEM), Barchard (2012) concluded that the MSD between 2 sets of test scores is greater than 2(SEM)[superscript 2] and SEM underestimates the score difference between 2 tests when the 2 tests are not parallel. This conclusion has limitations for 2 reasons. First,…
Descriptors: Error of Measurement, Geometric Concepts, Tests, Structural Equation Models
On the Meaning of the Latent Variables in the CT-C(M-1) Model: A Comment on Maydeu-Olivares and Coffman (2006)
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Geiser, Christian; Eid, Michael; Nussbeck, Fridtjof W. – Psychological Methods, 2008
In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006, see EJ751121) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an…
Descriptors: Factor Structure, Factor Analysis, Structural Equation Models, Questionnaires
Interpretational Confounding Is Due to Misspecification, Not to Type of Indicator: Comment on Howell, Breivik, and Wilcox (2007)
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Bollen, Kenneth A. – Psychological Methods, 2007
R. D. Howell, E. Breivik, and J. B. Wilcox (2007) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal…
Descriptors: Researchers, Structural Equation Models, Formative Evaluation, Transformative Learning