Co-Principal Investigators: Yuan, Ke-Hai; Wang, Lijuan

Purpose: The purpose of this grant is to combine structural equation modeling (SEM) techniques and data science methods to model network and text data. Network and text data are increasingly collected in many fields of research, business, and government. For example, to study student behaviors, it is important to understand the context of behaviors because students are not independent entities but are typically connected with one another, which naturally leads to the collection and analysis of network data. For teacher evaluations, narrative comments on different aspects of teaching can provide teachers rich information and valuable feedback over and beyond numerical ratings. Such data, however, present analytical challenges which have not yet been fully met by existing statistical techniques and software.

Project Activities: The research team will create an easy-to-use software package called BigSEM to implement the proposed methods for analyzing network and text data. The research team will develop BigSEM as both an R package to allow future growth in capability and a web application (https://bigsem.org) so that researchers can conduct complex data analysis through drawing a path diagram. As part of the software development process, the team will conduct simulation studies to test the functionality of BigSEM, prepare real-data examples of the software's capabilities, and conduct multiple rounds of user-testing.

Products and Publications

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.