The Performance of Bayesian Analysis in Structural Equation Modelling to Construct The Health Behaviour During Pandemic COVID-19

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.