Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
| Autor: | Diana Zavala-Rojas, Laura Castro-Schilo, Zita Oravecz, Jonathan L. Helm, Anna DeCastellarnau |
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| Rok vydání: | 2017 |
| Předmět: |
Estimation
Bayes estimator Sociology and Political Science Maximum likelihood 05 social sciences Bayesian probability Monte Carlo method 050401 social sciences methods General Decision Sciences Latent variable Missing data 01 natural sciences Structural equation modeling 010104 statistics & probability 0504 sociology Modeling and Simulation Statistics Econometrics 0101 mathematics General Economics Econometrics and Finance Mathematics |
| Zdroj: | Structural Equation Modeling: A Multidisciplinary Journal. 25:71-85 |
| ISSN: | 1532-8007 1070-5511 |
| DOI: | 10.1080/10705511.2017.1378103 |
| Popis: | This article examines whether Bayesian estimation with minimally informed prior distributions can alleviate the estimation problems often encountered with fitting the true score multitrait–multimethod structural equation model with split-ballot data. In particular, the true score multitrait–multimethod structural equation model encounters an empirical underidentification when (a) latent variable correlations are homogenous, and (b) fitted to data from a 2-group split-ballot design; an understudied case of empirical underidentification due to a planned missingness (i.e., split-ballot) design. A Monte Carlo simulation and 3 empirical examples showed that Bayesian estimation performs better than maximum likelihood (ML) estimation. Therefore, we suggest using Bayesian estimation with minimally informative prior distributions when estimating the true score multitrait–multimethod structural equation model with split-ballot data. Furthermore, given the increase in planned missingness designs in psychological res... |
| Databáze: | OpenAIRE |
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