ERIC Number: EJ1174586
Record Type: Journal
Publication Date: 2018
Abstractor: As Provided
An Improved Estimation Using Polya-Gamma Augmentation for Bayesian Structural Equation Models with Dichotomous Variables
Kim, Seohyun; Lu, Zhenqiu; Cohen, Allan S.
Measurement: Interdisciplinary Research and Perspectives, v16 n2 p81-91 2018
Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables. An empirical example is provided to illustrate the performance of different estimation approaches followed by a simulation study to evaluate the proposed method. The Polya-Gamma method is shown to provide stable results with larger effective sample size than standard Gibbs sampling.
Descriptors: Bayesian Statistics, Structural Equation Models, Computation, Social Science Research, Data Analysis, Sampling, Sample Size, Item Response Theory, Factor Structure, Evaluation Methods, Statistical Distributions
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: DRL1316398