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Abstract

Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The epidemiological model is required to provide evidence for public health policymakers to reduce the spread of COVID-19. Health behaviour is assumed could reduce the spread of this virus.  This study purposes to construct an acceptable model of health behaviour. To achieve this goal, a Bayesian structural equation modelling (SEM) is implemented. This current study is also purposed to evaluate the performance of Bayesian SEM, including the sensitivity, adequacy, and the acceptability of parameters estimated with the result that the acceptable model is obtained. The sensitivity of the Bayesian SEM estimator is evaluated by choosing several types of prior and the model results are compared. The adequacy of the Bayesian SEM estimate is checked by doing the convergence test of the corresponding model parameters. The acceptability of the Bayesian approach and its associated algorithm in recovering the true parameters are monitored by the Bootstrap simulation study. The Bayesian SEM applies the Gibbs sample approach in estimating model parameters. This method is applied to the primary data gathered from an online survey from March to May 2020 during COVID-19 to individuals living in West Sumatera, Indonesia. It is found that health motivation is significantly related to health behaviour. Whereas socio-demographic and perceived susceptibility has no significant effect on health behaviour. 

Keywords

Structural Equation Modeling Bayesian SEM Health Behavior Health Motivation

Article Details

How to Cite
Yanuar, F., & Zetra, A. (2022). The Performance of Bayesian Analysis in Structural Equation Modelling to Construct The Health Behaviour During Pandemic COVID-19. Pakistan Journal of Statistics and Operation Research, 18(3), 575-587. https://doi.org/10.18187/pjsor.v18i3.4096

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References

  1. Arora, T., & Grey, I. (2020). Health behaviour changes during COVID-19 and the potential consequences: A mini-review. Journal of Health Psychology, 25(9), 1155–1163. https://doi.org/10.1177/1359105320937053 DOI: https://doi.org/10.1177/1359105320937053
  2. Asparouhov, T., & Muthén, B. (2021). Advances in Bayesian Model Fit Evaluation for Structural Equation Models. Structural Equation Modelling: A Multidisciplinary Journal, 28(1), 1–14. https://doi.org/10.1080/10705511.2020.1764360 DOI: https://doi.org/10.1080/10705511.2020.1764360
  3. Cain, M. K., & Zhang, Z. (2019). Fit for a Bayesian: An Evaluation of PPP and DIC for Structural Equation Modelling. Structural Equation Modelling: A Multidisciplinary Journal, 26(1), 39–50. https://doi.org/10.1080/10705511.2018.1490648 DOI: https://doi.org/10.1080/10705511.2018.1490648
  4. Choompunuch, B., Suksatan, W., Sonsroem, J., Kutawan, S., & In-udom, A. (2021). Stress, adversity quotient, and health behaviours of undergraduate students in a Thai university during COVID-19 outbreak. Belitung Nursing Journal, 7(1), 1–7. https://doi.org/10.33546/bnj.1276 DOI: https://doi.org/10.33546/bnj.1276
  5. Conner, M., & Norman, P. (Eds.). (2007). Predicting health behaviour: Research and practice with social cognition models (2. ed., repr). Open Univ. Press.
  6. Garnier-Villarreal, M., & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modelling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70. https://doi.org/10.1037/met0000224 DOI: https://doi.org/10.1037/met0000224
  7. Glanz, K., Rimer, B. K., & Viswanath, K. (Eds.). (2008). Health behaviour and health education: Theory, research, and practice (4th ed). Jossey-Bass.
  8. Hou, W., Zhang, W., Jin, R., Liang, L., Xu, B., & Hu, Z. (2020). Risk factors for disease progression in hospitalized patients with COVID-19: A retrospective cohort study. Infectious Diseases, 52(7), 498–505. https://doi.org/10.1080/23744235.2020.1759817 DOI: https://doi.org/10.1080/23744235.2020.1759817
  9. Kelava, A., Nagengast, B., & Brandt, H. (2014). A Nonlinear Structural Equation Mixture Modelling Approach for Nonnormally Distributed Latent Predictor Variables. Structural Equation Modelling: A Multidisciplinary Journal, 21(3), 468–481. https://doi.org/10.1080/10705511.2014.915379 DOI: https://doi.org/10.1080/10705511.2014.915379
  10. Khoso, P. A., Yew, V. W. C., & Mutalib, M. H. A. (2016). Comparing and Contrasting Health Behaviour With Illness Behaviour. 11(2), 12.
  11. Lee, S.-Y. (2007). Structural Equation Modelling: A Bayesian Approach. John Wiley & Sons, Ltd. DOI: https://doi.org/10.1002/9780470024737
  12. Muharisa, C., Yanuar, F., & Devianto, D. (2018). Simulation Study of the Using of Bayesian Quantile Regression in Non- normal Error. Cauchy - Jurnal Matematika Murni Dan Aplikasi, 5(November), 121–126. DOI: https://doi.org/10.18860/ca.v5i3.5633
  13. Nam, E. J., Lee, E. K., & Oh, M.-S. (2018). Bayesian quantile regression analysis of Korean Jeonse deposit. Communications for Statistical Applications and Methods, 25(5), 489–499. https://doi.org/10.29220/CSAM.2018.25.5.489 DOI: https://doi.org/10.29220/CSAM.2018.25.5.489
  14. Ntzoufras, I. (2009). Bayesian modelling using WinBUGS. Wiley. DOI: https://doi.org/10.1002/9780470434567
  15. Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The Performance of ML, GLS, and WLS Estimation in Structural Equation Modelling Under Conditions of Misspecification and Nonnormality. Structural Equation Modelling: A Multidisciplinary Journal, 7(4), 557–595. https://doi.org/10.1207/S15328007SEM0704_3 DOI: https://doi.org/10.1207/S15328007SEM0704_3
  16. Parekh, N., & Deierlein, A. L. (2020). Health behaviours during the coronavirus disease 2019 pandemic: Implications for obesity. Public Health Nutrition, 23(17), 3121–3125. https://doi.org/10.1017/S1368980020003031 DOI: https://doi.org/10.1017/S1368980020003031
  17. Perlman, S. (2020). Another Decade, Another Coronavirus. The New England Journal of Medicine, 3. DOI: https://doi.org/10.1056/NEJMe2001126
  18. Rahmadita, A., Yanuar, F., & Devianto, D. (2018). The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modelling Approach. CAUCHY, 5(2), 73. https://doi.org/10.18860/ca.v5i2.5039 DOI: https://doi.org/10.18860/ca.v5i2.5039
  19. Suksatan, W., Choompunuch, B., Koontalay, A., Posai, V., & Abusafia, A. H. (2021). Predictors of Health Behaviours Among Undergraduate Students During the COVID-19 Pandemic: A Cross-Sectional Predictive Study. Journal of Multidisciplinary Healthcare, Volume 14, 727–734. https://doi.org/10.2147/JMDH.S306718 DOI: https://doi.org/10.2147/JMDH.S306718
  20. Worldometers. (2021). COVID-19 Coronavirus pandemic. Https://Www.Worldometers.Info/Coronavirus/.
  21. Y. Thanoon, T., Adnan, R., & Saffari, S. E. (2016). Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data. Pakistan Journal of Statistics and Operation Research, 12(1), 125–140. https://doi.org/10.18187/pjsor.v12i1.952 DOI: https://doi.org/10.18187/pjsor.v12i1.952
  22. Yanuar, F. (2016). The Health Status Model in Urban and Rural Society in West Sumatera, Indonesia: An Approach of Structural Equation Modelling. Indian Journal of Science and Technology, 9(4). https://doi.org/10.17485/ijst/2016/v9i4/72601 DOI: https://doi.org/10.17485/ijst/2016/v9i4/72601
  23. Yanuar, F., Ibrahim, K., & Aziz Jemain, A. (2013). Bayesian structural equation modelling for the health index. Journal of Applied Statistics, 40(6), 1254–1269. DOI: https://doi.org/10.1080/02664763.2013.785491
  24. Yanuar, F., Ibrahim, K., & Jemain, A. A. (2010). On the application of structural equation modelling for the construction of a health index. Environmental Health and Preventive Medicine, 15(5), 285–291. https://doi.org/10.1007/s12199-010-0140-7 DOI: https://doi.org/10.1007/s12199-010-0140-7
  25. Yanuar, F., Yozza, H., & Zetra, A. (2022). Modified Quantile Regression for Modelling the Low Birth Weight. Frontiers in Applied Mathematics and Statistics, 8, 890028. https://doi.org/10.3389/fams.2022.890028 DOI: https://doi.org/10.3389/fams.2022.890028
  26. Zhang, X., & Savalei, V. (2016). Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modelling. Structural Equation Modelling: A Multidisciplinary Journal, 23(3), 392–408. https://doi.org/10.1080/10705511.2015.1118692 DOI: https://doi.org/10.1080/10705511.2015.1118692
  27. Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G. F., & Tan, W. (2020). A Novel Coronavirus from Patients with Pneumonia in China, 2019. New England Journal of Medicine, 382(8), 727–733. https://doi.org/10.1056/NEJMoa2001017 DOI: https://doi.org/10.1056/NEJMoa2001017
  28. Zvolensky, M. J., Garey, L., Rogers, A. H., Schmidt, N. B., Vujanovic, A. A., Storch, E. A., Buckner, J. D., Paulus, D. J., Alfano, C., Smits, J. A. J., & O’Cleirigh, C. (2020). Psychological, addictive, and health behaviour implications of the COVID-19 pandemic. Behaviour Research and Therapy, 134, 1–16. https://doi.org/10.1016/j.brat.2020.103715 DOI: https://doi.org/10.1016/j.brat.2020.103715