EDF 7412 – Structural Equation Modeling
Fall 2025
Professor: Dr. Walter Leite
E-mail: walter.leite@coe.ufl.edu
Office Hours: Tuesdays 10 am to 12 pm, 4 to 5 pm
Objective
To enable students to understand, critique, apply, and generate models under the structural equation modeling framework, including path analysis, confirmatory factor analysis, latent growth models, and structural equation models.
Student Learning Objectives:
1. Create a plan for survey development, pre-testing, and implementation
2. Write and format surveys for online, mail, group, and mixed-model administration.
3. Implement a survey to maximize response rates
4. Calculate sampling, non-response, and post-stratification weights
5. Identify and use survey weights from national and international surveys
6. Handle missing data in surveys appropriately
7. Estimate and interpret regression models with complex weights
8. Obtain correct standard errors for regression coefficients, adjusting for clustering and strata effects
9. Fit and interpret fixed effects and random effects regression models
10. Evaluate the sensitivity of regression results to missing data assumptions and omitted confounders
Pre-requisite:
The pre-requisite for this class is EDF6403, or EDF6402, or EDF7405
Required books:
Title: PRINCIPLES AND PRACTICE OF STRUCTURAL EQUATION MODELING Author: REX B. KLINE Edition: 5th Copyright: 2023 Publisher: GUILFORD
Software:
I will use R and RStudio software for statistical analyses.
Recommended website for survey data analysis with R.
Topics
- Overview
- Path Analysis
- Implied covariance matrix
- Model identification
- Estimation
- Hypothesis Testing and model fit
- Mediation analysis
- Moderation
- Confirmatory Factor Analysis (CFA) of continuous indicators
- Implied covariance matrix
- Model identification
- Estimation
- Hypothesis testing and model fit
- Confirmatory Factor Analysis (CFA) of categorical indicators
- Polychoric and Tetrachoric correlations
- Model identification
- Estimation
- Hypothesis testing and model fit
- Multiple-group confirmatory factor analysis
- Latent means
- Measurement Invariance
- Structural Equation Models (SEM)
- Implied covariance matrix
- Model identification
- Estimation
- Hypothesis testing and model fit e) Latent variable interactions
- Multilevel SEM
- Latent growth models (LGM)
- Linear models
- Nonlinear models
- Implied covariance matrix
- Model identification
- Estimation
- Hypothesis testing and model fit
- Best practices in SEM and sensitivity analysis