Markov chain Monte Carlo Estimation Method of Confirmatory Factor Analysis Model with Mixed Data

Thanoon Thanoon; Hasmek warttan; Robiah Adnan

Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020

Research Article

Markov chain Monte Carlo Estimation Method of Confirmatory Factor Analysis Model with Mixed Data

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@INPROCEEDINGS{10.4108/eai.28-6-2020.2297911,
    author={Thanoon  Thanoon and Hasmek  warttan and Robiah  Adnan},
    title={Markov chain Monte Carlo Estimation Method of Confirmatory Factor Analysis Model with Mixed Data},
    proceedings={Proceedings of the 1st International Multi-Disciplinary Conference Theme: Sustainable Development and Smart Planning, IMDC-SDSP 2020, Cyperspace, 28-30 June 2020},
    publisher={EAI},
    proceedings_a={IMDC-SDSP},
    year={2020},
    month={9},
    keywords={confirmatory factor analysis(cfa) bayesian inference gibbs sampling mixed data},
    doi={10.4108/eai.28-6-2020.2297911}
}

Thanoon Thanoon
Hasmek warttan
Robiah Adnan
Year: 2020
Markov chain Monte Carlo Estimation Method of Confirmatory Factor Analysis Model with Mixed Data
IMDC-SDSP
EAI
DOI: 10.4108/eai.28-6-2020.2297911

Thanoon Thanoon¹^,*, Hasmek warttan¹, Robiah Adnan²

1: Department of Business Management Techniques, Administrative Technical College, Northern Technical University, Mosul, Iraq
2: (Department of Mathematical Sciences, Faculty of science, University Technology Malaysia, Johor, Malaysia

*Contact email: thanoon.younis@ntu.edu.iq

Abstract

This paper provides a general overview of (Bayesian Confirmatory Factor Analysis) with mixed ordinal and binary data. Mixed variables with specific cut-points are used and the simulation (Gibbs sampling) of the Markov chain Monte Carlo (MCMC) as an estimation tool. The problem of qualitative data is handled using censoring methods with specific cut points. Some additional tools, which contain on the Bayesian estimator, standard deviations (SD), Markov chain error (MC error) and highest posterior density (HPD) interval, are interpreted. The developed approach is discussed with the findings derived from the OpenBUGS program using the information on the quality of life (QOL)

Keywords: confirmatory factor analysis(cfa) bayesian inference gibbs sampling mixed data

Published: 2020-09-03
Publisher: EAI

: http://dx.doi.org/10.4108/eai.28-6-2020.2297911

Markov chain Monte Carlo Estimation Method of Confirmatory Factor Analysis Model with Mixed Data

Abstract

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