Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Simulation Study to Describe Bayesian Analysis of Nonlinear Structural Equation Modeling

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290337,
        author={Ferra  Yanuar and Aidinil  Zetra},
        title={Simulation Study to Describe Bayesian Analysis of Nonlinear Structural Equation Modeling},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={bayesian analysis nonlinear sem simulation study structural equation modeling},
        doi={10.4108/eai.2-8-2019.2290337}
    }
    
  • Ferra Yanuar
    Aidinil Zetra
    Year: 2020
    Simulation Study to Describe Bayesian Analysis of Nonlinear Structural Equation Modeling
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290337
Ferra Yanuar1,*, Aidinil Zetra2
  • 1: Department of Mathematics, Faculty of Mathematics and Natural Science, Andalas University, Kampus Limau Manis, 25163, Padang – Indonesia
  • 2: Department of Political Science, Faculty of Social and Political Science, Andalas University, Kampus Limau Manis, 25163, Padang – Indonesia
*Contact email: ferrayanuar@sci.unand.ac.id

Abstract

Structural equation modeling (SEM) has widely used in many disciplines, such as economic, politic or health. Nonlinear structural equation modeling, as part of SEM, also has been developing analytically but still limited. In this method, the parameter models are estimated using conjugate prior in Bayesian approach. In nonlinear SEM, the models are specified including quadratic forms and/or interactions of latent variables. Posterior mean and posterior variance of the parameters are estimated using iteration approach since it is difficult to estimate those parameters model using analytical approach. The iteration approach used here is Markov Chain Monte Carlo (MCMC) method with Gibbs sampling. The simulation study is done to illustrate the proposed estimation methods for nonlinear model. A group of 300 data are generated to demonstrate the implementation of the proposed method. This study resulted that the proposed nonlinear SEM model could be accepted based on criteria of goodness of fit model.