Vol. 2 No. 2 (2022): Volume 2 Number 2

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• Zhang, T., Tong, X., & Zhou, J. (2022). Disentangling the Influence of Data Contamination in Growth Curve Modeling: A Median Based Bayesian Approach. Journal of Behavioral Data Science, 2(2), 1–22. https://doi.org/10.35566/jbds/v2n2/p1
• Liu, H. ., Qu, W., Zhang, Z., & Wu, H. (2022). A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter. Journal of Behavioral Data Science, 2(2), 23–46. https://doi.org/10.35566/jbds/v2n2/p2
• Du, H., Ke, Z., Jiang, G., & Huang, S. (2022). The Performances of Gelman-Rubin and Geweke’s Convergence Diagnostics of Monte Carlo Markov Chains in Bayesian Analysis. Journal of Behavioral Data Science, 2(2), 47–72. https://doi.org/10.35566/jbds/v2n2/p3
• Suzuki, H., & Gonzalez, O. (2022). Relative Predictive Performance of Treatments of Ordinal Outcome Variables across Machine Learning Algorithms and Class Distributions. Journal of Behavioral Data Science, 2(2), 73–98. https://doi.org/10.35566/jbds/v2n2/suzuki
• Xu, Z. (2022). Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS. Journal of Behavioral Data Science, 2(2), 99–126. https://doi.org/10.35566/jbds/v2n2/xu
• Qiu, M. (2022). A Tutorial on Bayesian Latent Class Analysis Using JAGS. Journal of Behavioral Data Science, 2(2), 127–155. https://doi.org/10.35566/jbds/v2n2/qiu
• Shao, S. (2022). A Tutorial on Bayesian Analysis of Count Data Using JAGS. Journal of Behavioral Data Science, 2(2), 156–173. https://doi.org/10.35566/jbds/v2n2/shao