The Experts below are selected from a list of 100686 Experts worldwide ranked by ideXlab platform
Zita Oravecz - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Estimation of the true score multitrait multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Diana Zavalarojas, Anna Decastellarnau, Laura Castroschilo, Zita OraveczAbstract: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...
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Diana Zavala-rojas, Laura Castro-schilo, Zita OraveczAbstract: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...
Jonathan L Helm - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Estimation of the true score multitrait multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Diana Zavalarojas, Anna Decastellarnau, Laura Castroschilo, Zita OraveczAbstract: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...
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Diana Zavala-rojas, Laura Castro-schilo, Zita OraveczAbstract: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...
Anna Decastellarnau - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Estimation of the true score multitrait multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Diana Zavalarojas, Anna Decastellarnau, Laura Castroschilo, Zita OraveczAbstract: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...
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Diana Zavala-rojas, Laura Castro-schilo, Zita OraveczAbstract: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...
Laura Castro-schilo - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Estimation of the True Score Multitrait–Multimethod Model With a Split-Ballot Design
Structural Equation Modeling, 2017Co-Authors: Jonathan L Helm, Anna Decastellarnau, Diana Zavala-rojas, Laura Castro-schilo, Zita OraveczAbstract: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...
Diana Zavalarojas - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Estimation of the true score multitrait multimethod model with a split ballot design
Structural Equation Modeling, 2018Co-Authors: Jonathan L Helm, Diana Zavalarojas, Anna Decastellarnau, Laura Castroschilo, Zita OraveczAbstract: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...
