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Autor/inn/enMuthen, Bengt; Asparouhov, Tihomir
TitelBayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory
QuelleIn: Psychological Methods, 17 (2012) 3, S.313-335 (23 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1082-989X
DOI10.1037/a0026802
SchlagwörterFactor Analysis; Cognitive Ability; Science Achievement; Structural Equation Models; Bayesian Statistics; Correlation; Surveys; Personality Measures; Longitudinal Studies; Monte Carlo Methods; Measurement; Prediction; United Kingdom
AbstractThis article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988. (Contains 3 figures and 18 tables.) (As Provided).
AnmerkungenAmerican Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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