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Publications

  • Liu, X., Zhang, Z., Valentino, K., & Wang, L. (accepted). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches. Structural Equation Modeling: A Multidisciplinary Journal.
  • Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. (Accepted). Supervised Latent Dirichlet Allocation with Covariates: A Bayesian Structural and Measurement Model of Text and Covariates. Psychological Methods.
  • Liu, X., Wang, L., & Zhang, Z. (in press). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavioral Research Methods. https://doi.org/10.3758/s13428-022-01860-1
  • Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (in press). Comparison of Methods for Imputing Social Network Data. Journal of Data Science. https://doi.org/10.6339/22-JDS1045
  • Deng, L., & Yuan, K.-H. (in press). Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted-composites. Behavior Research Methods. doi: 10.3758/s13428-022-01838-z
  • Yuan, K.-H. (in press). Comments on the article “Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations” and PLS-SEM in general. European Journal of Marketing.
  • Yuan, K.-H., & Deng, L. (in press). A reply to “Structural parameters under partial least squares and covariance-based structural equation modeling: A comment on Yuan and Deng (2021)” by Schuberth, Rosseel, R¨onkk¨o, Trichera, Kline, and Henseler (2023). Structural Equation Modeling. doi: 10.1080/10705511.2022.2134141
  • Yuan, K.-H., & Fang, Y. (in press). Which method delivers greater signal-to-noise ratio: Structural equation modeling or regression analysis with weighted composites? British Journal of Mathematical and Statistical Psychology. doi: 10.1111/bmsp.12293
  • Yuan, K-H., & Zhang, Z. (2023). Statistical and psychometric properties of three weighting schemes of the PLS-SEM methodology. In H. Latan, J.F. Hair, & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues, and applications (2nd ed.). Cham, Switzerland: Springer.
  • Yuan, K.-H., Wen, Y., & Tang, J. (2023). Sensitivity analysis of the weights of the composites under partial least-squares approach to structural equation modeling. Structural Equation Modeling, 30(1), 53–69. doi: 10.1080/10705511.2022.2106487
  • Mai, Y., Xu, Z., Zhang, Z., & Yuan, K.-H. (2023). An Open Source WYSIWYG Web Application for Drawing Path Diagrams of Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 30(2), 328-335. https://doi.org/10.1080/10705511.2022.2101460
  • 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), 1–24. https://doi.org/10.35566/jbds/v2n2/p2
  • Lu, L., & Zhang, Z. (2022). How to Select the Best Fit Model among Bayesian Latent Growth Models for Complex Data. Journal of Behavioral Data Science, 2(1), 1–24. https://doi.org/10.35566/jbds/v2n1/p2
  • Zhang, Z., & Zhang, D. (2021). What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula. Journal of Behavioral Data Science, 1(1), 1–16. https://doi.org/10.35566/jbds/v1n1/p1
  • Liu, H., & Zhang, Z. (2021). Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship. Journal of Behavioral Data Science, 1(1), 34–52. https://doi.org/10.35566/jbds/v1n1/p3
 
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