Structural equation modeling
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Path and measurement models
- Path analysis and identification
- Confirmatory factor analysis
- Structural equation models: specification
- Model identification rules
- Diagram notation and syntax (lavaan, Mplus)
Estimation and fit
- Maximum likelihood for SEM
- Fit indices: CFI, TLI, RMSEA, SRMR
- Modification indices and model respecification
- Multigroup SEM (introduction)
- Measurement invariance testing
Advanced SEM topics
- Latent growth models
- Mediation and indirect effects in SEM
- SEM with categorical indicators (overview)
- Bayesian SEM (introduction)
STEM / applied
Applied SEM projects
- Replicating published SEM studies
- Handling missing data: FIML in SEM
- Reporting standards for SEM manuscripts
- Power analysis for SEM (intro)
- Common convergence and identification issues
- Consulting scenarios with latent constructs
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Graduate course in psychology, education, and social science statistics programs. Covers path analysis, CFA, and SEM with software.