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Bayesian structural equation modeling for the health index

Author

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  • Ferra Yanuar
  • Kamarulzaman Ibrahim
  • Abdul Aziz Jemain

Abstract

There are many factors which could influence the level of health of an individual. These factors are interactive and their overall effects on health are usually measured by an index which is called as health index. The health index could also be used as an indicator to describe the health level of a community. Since the health index is important, many research have been done to study its determinant. The main purpose of this study is to model the health index of an individual based on classical structural equation modeling (SEM) and Bayesian SEM. For estimation of the parameters in the measurement and structural equation models, the classical SEM applies the robust-weighted least-square approach, while the Bayesian SEM implements the Gibbs sampler algorithm. The Bayesian SEM approach allows the user to use the prior information for updating the current information on the parameter. Both methods are applied to the data gathered from a survey conducted in Hulu Langat, a district in Malaysia. Based on the classical and the Bayesian SEM, it is found that demographic status and lifestyle are significantly related to the health index. However, mental health has no significant relation to the health index.

Suggested Citation

  • Ferra Yanuar & Kamarulzaman Ibrahim & Abdul Aziz Jemain, 2013. "Bayesian structural equation modeling for the health index," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1254-1269, June.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1254-1269
    DOI: 10.1080/02664763.2013.785491
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    Cited by:

    1. Hashem Salarzadeh Jenatabadi & Peyman Babashamsi & Datis Khajeheian & Nader Seyyed Amiri, 2016. "Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling," Sustainability, MDPI, vol. 8(11), pages 1-17, November.
    2. Hashem Salarzadeh Jenatabadi & Che Wan Jasimah Bt Wan Mohamed Radzi & Nadia Samsudin, 2020. "Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach," IJERPH, MDPI, vol. 17(14), pages 1-24, July.
    3. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Martijn Huisman & Martijn Heymans & Jos Twisk, 2022. "Bayesian model selection for multilevel mediation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 219-235, May.
    4. Hashem Salarzadeh Jenatabadi & Sedigheh Moghavvemi & Che Wan Jasimah Bt Wan Mohamed Radzi & Parastoo Babashamsi & Mohammad Arashi, 2017. "Testing students’ e-learning via Facebook through Bayesian structural equation modeling," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    5. Ali Noudoostbeni & Kiran Kaur & Hashem Salarzadeh Jenatabadi, 2018. "A Comparison of Structural Equation Modeling Approaches with DeLone & McLean’s Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    6. Che Wan Jasimah Bt Wan Mohamed Radzi & Hashem Salarzadeh Jenatabadi & Maisarah Binti Hasbullah, 2015. "Firm Sustainability Performance Index Modeling," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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