Comparing Bayesian and Maximum Likelihood Predictors in Structural Equation Modeling of Children's Lifestyle Index

作者：Radzi Che Wan Jasimah bt Wan Mohamed^*; Hui Huang; Jenatabadi Hashem Salarzadeh

来源：Symmetry-Basel, 2016, 8(12): 141.

DOI：10.3390/sym8120141

• Bayesian structural equation modeling
• public health
• maximum likelihood structural equation modeling
• Gibbs sampler algorithm

Several factors may influence children's lifestyle. The main purpose of this study is to introduce a children's lifestyle index framework and model it based on structural equation modeling (SEM) with Maximum likelihood (ML) and Bayesian predictors. This framework includes parental socioeconomic status, household food security, parental lifestyle, and children's lifestyle. The sample for this study involves 452 volunteer Chinese families with children 7-12 years old. The experimental results are compared in terms of root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error metrics. An analysis of the proposed causal model suggests there are multiple significant interconnections among the variables of interest. According to both Bayesian and ML techniques, the proposed framework illustrates that parental socioeconomic status and parental lifestyle strongly impact children's lifestyle. The impact of household food security on children's lifestyle is rejected. However, there is a strong relationship between household food security and both parental socioeconomic status and parental lifestyle. Moreover, the outputs illustrate that the Bayesian prediction model has a good fit with the data, unlike the ML approach. The reasons for this discrepancy between ML and Bayesian prediction are debated and potential advantages and caveats with the application of the Bayesian approach in future studies are discussed.

• 出版日期2016-12