Applications of SEM in Social Sciences
( 30 Modules )

Module #1
Introduction to Structural Equation Modeling (SEM)
Overview of SEM, its importance in social sciences, and course objectives

Module #2
Foundations of SEM
Basic concepts of SEM, including path diagrams, latent variables, and model estimation

Module #3
SEM Software Overview
Introduction to popular SEM software, including Mplus, R, and Amos

Module #4
Confirmatory Factor Analysis (CFA)
Introduction to CFA, model specification, and interpretation of results

Module #5
Exploratory Factor Analysis (EFA)
Introduction to EFA, model specification, and interpretation of results

Module #6
SEM for Regression Analysis
Using SEM for regression analysis, including mediation and moderation

Module #7
Path Analysis
Introduction to path analysis, including recursive and non-recursive models

Module #8
Mediation Analysis using SEM
Introduction to mediation analysis using SEM, including testing mediation effects

Module #9
Moderation Analysis using SEM
Introduction to moderation analysis using SEM, including testing moderation effects

Module #10
Applications of SEM in Psychology
Use of SEM in psychology research, including examples and case studies

Module #11
Applications of SEM in Education
Use of SEM in education research, including examples and case studies

Module #12
Applications of SEM in Sociology
Use of SEM in sociology research, including examples and case studies

Module #13
Applications of SEM in Business and Management
Use of SEM in business and management research, including examples and case studies

Module #14
Applications of SEM in Health Sciences
Use of SEM in health sciences research, including examples and case studies

Module #15
Model Fit and Evaluation
Assessing model fit, including goodness-of-fit indices and model diagnostics

Module #16
Model Modification and Respecification
Modifying and respecifying SEM models, including theory trimming and model building

Module #17
Common SEM Applications and Case Studies
Real-world examples and case studies of SEM applications in social sciences

Module #18
SEM for Longitudinal and Panel Data
Using SEM for longitudinal and panel data, including growth curve models

Module #19
SEM for Multilevel Data
Using SEM for multilevel data, including hierarchical linear models

Module #20
SEM for Missing Data
Handling missing data in SEM, including multiple imputation and FIML estimation

Module #21
SEM for Non-Normal Data
Handling non-normal data in SEM, including transformations and robust estimation

Module #22
Advanced SEM Topics
Special topics in SEM, including Bayesian estimation and machine learning approaches

Module #23
SEM Best Practices
Best practices for reporting and interpreting SEM results, including transparency and reproducibility

Module #24
Common SEM Errors and Pitfalls
Common errors and pitfalls in SEM, including model misspecification and incorrect interpretation

Module #25
SEM in R and Mplus
Hands-on practice with SEM software, including R and Mplus

Module #26
SEM Project Development
Guided project development using SEM, including research question development and model specification

Module #27
SEM Report Writing
Writing an SEM report, including results interpretation and discussion

Module #28
SEM in Interdisciplinary Research
Using SEM in interdisciplinary research, including integrating multiple perspectives

Module #29
SEM in Policy and Practice
Using SEM to inform policy and practice, including evidence-based decision making

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Applications of SEM in Social Sciences career

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