Advanced Techniques in Structural Equation Modeling
( 24 Modules )
Module #1 Introduction to Advanced SEM Overview of the course, importance of advanced SEM techniques, and review of fundamental SEM concepts
Module #2 Modeling Non-Normal Data Techniques for dealing with non-normal data in SEM, including transformations, robust estimators, and bootstrapping
Module #3 Handling Missing Data in SEM Methods for handling missing data in SEM, including listwise deletion, pairwise deletion, and multiple imputation
Module #4 Modeling Non-recursive Relationships Advanced techniques for modeling non-recursive relationships, including non-recursive models and generalized SEM
Module #5 Testing Mediation and Moderation Advanced techniques for testing mediation and moderation in SEM, including bootstrapping and Bayesian methods
Module #6 Modeling Multigroup Data Techniques for modeling multigroup data in SEM, including multiple group analysis and measurement invariance testing
Module #7 Bayesian SEM Introduction to Bayesian SEM, including Bayesian estimation and model comparison
Module #8 Bayesian Model Averaging Advanced Bayesian SEM techniques, including Bayesian model averaging and Bayesian variable selection
Module #9 SEM with Complex Survey Data Techniques for analyzing complex survey data in SEM, including survey weights and clustered data
Module #10 SEM with Longitudinal Data Advanced techniques for analyzing longitudinal data in SEM, including growth curve models and latent growth curve models
Module #11 SEM with Multilevel Data Techniques for analyzing multilevel data in SEM, including multilevel modeling and cross-classified models
Module #12 Modeling Count and Censored Data Advanced techniques for modeling count and censored data in SEM, including Poisson regression and Tobit models
Module #13 SEM with Non-linear Relationships Techniques for modeling non-linear relationships in SEM, including polynomial and spline models
Module #14 Modeling Heterogeneity in SEM Advanced techniques for modeling heterogeneity in SEM, including finite mixture models and latent class analysis
Module #15 SEM with Machine Learning Algorithms Introduction to using machine learning algorithms in SEM, including neural networks and decision trees
Module #16 SEM with Natural Language Processing Advanced techniques for analyzing text data in SEM, including topic modeling and sentiment analysis
Module #17 SEM with Spatial Data Techniques for analyzing spatial data in SEM, including spatial autocorrelation and spatial regression
Module #18 SEM with Network Data Advanced techniques for analyzing network data in SEM, including social network analysis and exponential random graph models
Module #19 SEM with High-Dimensional Data Techniques for analyzing high-dimensional data in SEM, including dimension reduction and regularization
Module #20 SEM with Small Sample Sizes Advanced techniques for analyzing small sample sizes in SEM, including bias correction and bootstrapping
Module #21 SEM Model Fit and Evaluation Advanced techniques for evaluating SEM model fit, including cross-validation and predictive model selection
Module #22 SEM Model Identification and Equivalence Advanced topics in SEM model identification and equivalence, including model equivalence testing and identification strategies
Module #23 SEM Software and Programming Overview of SEM software and programming, including Mplus, R, and Python
Module #24 Course Wrap-Up & Conclusion Planning next steps in Advanced Techniques in Structural Equation Modeling career
