Module #1 Introduction to Structural Equation Modeling Overview of SEM, its application, and importance
Module #2 Software Overview:Getting Familiar with SEM Software Introduction to popular SEM software, interface, and basic functionality
Module #3 Research Questions and Hypotheses in SEM Formulating research questions and hypotheses in the context of SEM
Module #4 Data Preparation for SEM Preparing data for SEM analysis, including data cleaning and preprocessing
Module #5 Measurement Models in SEM Introduction to measurement models, including factor analysis and item response theory
Module #6 Structural Models in SEM Introduction to structural models, including path analysis and regression models
Module #7 Model Specification:Defining Latent Variables and Relationships Specifying SEM models, including defining latent variables and relationships
Module #8 ModelIdentification and Evaluation Evaluating model identification, including model fit indices and modification indices
Module #9 Estimation and Model Fit in SEM Estimation methods in SEM, including maximum likelihood and Bayesian estimation
Module #10 Model Fit Indices and Model Selection Evaluating model fit using fit indices, including chi-square, RMSEA, and CFI
Module #11 Model Modification and Respecification Modifying and respecifying SEM models based on fit indices and modification indices
Module #12 MEDiation Analysis in SEM Conducting mediation analysis using SEM, including direct and indirect effects
Module #13 Moderation Analysis in SEM Conducting moderation analysis using SEM, including interaction effects
Module #14 Multi-Group Analysis in SEM Conducting multi-group analysis using SEM, including invariance testing
Module #15 SEM with Non-Normal Data Dealing with non-normal data in SEM, including robust estimation methods
Module #16 Power Analysis and Sample Size Determination in SEM Conducting power analysis and determining sample size for SEM studies
Module #17 Reporting and Interpreting SEM Results Reporting and interpreting SEM results, including visualizing models and results
Module #18 Common Pitfalls and Errors in SEM Avoiding common pitfalls and errors in SEM, including model misspecification and overfitting
Module #19 Advances in SEM:Latent Class Analysis and Mixture Modeling Introduction to latent class analysis and mixture modeling using SEM
Module #20 Advances in SEM:Bayesian SEM and Machine Learning Introduction to Bayesian SEM and machine learning approaches using SEM
Module #21 SEM in Various Fields:Applications and Case Studies Applications and case studies of SEM in various fields, including psychology, education, and business
Module #22 SEM Software Tutorial 1:Using [Software Name] for Basic SEM Analysis Hands-on tutorial using [Software Name] for basic SEM analysis
Module #23 SEM Software Tutorial 2:Using [Software Name] for Advanced SEM Analysis Hands-on tutorial using [Software Name] for advanced SEM analysis
Module #24 Best Practices in SEM:Writing a SEM Research Paper Best practices in writing a SEM research paper, including reporting guidelines
Module #25 Course Wrap-Up & Conclusion Planning next steps in Modeling and Analysis Using SEM Software career
