Methods and Software for Handling Network Data and Text Data in Structural Equation Modeling

Products: The research team will also publish in journals and at conferences, while making available on the website: the software code, the real data used for illustration, and an extensive user's guide for BigSEM.

Publications:

Deng, L., & Yuan, K. H. (2023). 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, 55(3), 1460-1479.

Fang, Y., & Wang, L. (2024). Modeling Intraindividual Variability as Predictors in Longitudinal Research. Multivariate Behavioral Research, 1-3.

Fang, Y., & Wang, L. (2024). Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T. Structural Equation Modeling: A Multidisciplinary Journal, 1-18.

Gomer, B., & Yuan, K. H. (2023). A realistic evaluation of methods for handling missing data when there is a mixture of MCAR, MAR, and MNAR mechanisms in the same dataset. Multivariate Behavioral Research, 58(5), 988-1013.

Hayashi, K., & Yuan, K. H. (2022, July). On the Relationship Between Coefficient Alpha and Closeness Between Factors and Principal Components for the Multi-factor Model. In The Annual Meeting of the Psychometric Society (pp. 173-185). Cham: Springer Nature Switzerland.

Li, R., & Wang, L. (2024). An Analytical Comparison of Three Modeling Approaches for Longitudinal Mediation Analysis. Multivariate Behavioral Research, 1-3.

Li, R., & Wang, L. (2024). Investigating weight constraint methods for causal-formative indicator modeling. Behavior Research Methods, 1-13.

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.

Liu, X., Valentino, K., & Wang, L. (2022). The Impact of Omitting Confounders in Latent Growth Curve Mediation Modeling: Analytical Examination and Methods for Sensitivity Analysis. Multivariate Behavioral Research, 57(1), 153-154.

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.

Liu, X., Zhang, Z., Valentino, K., & Wang, L. (2024). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches. Structural Equation Modeling: A Multidisciplinary Journal, 31(1), 132-150.

Liu, X., Zhang, Z., & Wang, L. (2023). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavior Research Methods, 55(3), 1108-1120.

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), 35-58.

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.

Ming, S., Zhang, H., Zhang, Z., & Wang, L. (2022). Bmemlavaan: an r package for estimating and testing mediation effect in mediation models.

Qin, X., & Wang, L. (2023). Causal moderated mediation analysis: Methods and software. Behavior Research Methods, 1-21.

Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. (2023). Supervised latent Dirichlet allocation with covariates: A Bayesian structural and measurement model of text and covariates. Psychological Methods, 28(5), 1178-1206.

Wyman, A., & Zhang, Z. (2023). API Face Value: Evaluating the Current Status and Potential of Emotion Detection Software in Emotional Deficit Interventions. Journal of Behavioral Data Science, 3(1), 59-69.

Xu, Z. (2022). Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS. Journal of Behavioral Data Science, 2(2), 99-126.

Xu, Z., Gao, F., Fa, A., Qu, W., & Zhang, Z. (2024). Statistical power analysis and sample size planning for moderated mediation models. Behavior Research Methods, 1-20.

Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (2022). Comparison of Methods for Imputing Social Network Data. Journal of Data Science, 21(3), 599-618.

Yuan, K. H. (2023). Comments on the article “Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations” and PLS-SEM in general. European Journal of Marketing, 57(6), 1618-1625.

Yuan, K. H., & Deng, L. (2023). 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, Rönkkö, Trichera, Kline, and Henseler (2023). Structural Equation Modeling: A Multidisciplinary Journal, 30(3), 346-348.

Yuan, K. H., & Fang, Y. (2023). Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites? British Journal of Mathematical and Statistical Psychology, 76(3), 646-678.

Yuan, K. H., & Zhang, Z. (2023). Statistical and psychometric properties of three weighting schemes of the PLS-SEM methodology. In Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 81-112). Cham: Springer International Publishing.

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: A Multidisciplinary Journal, 30(1), 53-69.

Zhang, L., Li, X., & Zhang, Z. (2023). Variety and Mainstays of the R Developer Community. R Journal, 15(3).

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.

Zhao, S., Zhang, Z., & Zhang, H. (2024). Bayesian Inference of Dynamic Mediation Models for Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 31(1), 14-26.