Latent Variable Modeling
- Observed and latent variables
- Classical latent variable theory and factor-analytic paradigm
- Exploratory and confirmatory factor analysis
- Observed and implied covariance structures
- Model identification
- Maximum likelihood estimation
- Model fit statistics (test of exact fit, test of close fit, noncentrality-based fit statistics, incremental fit indexes, residual-based fit indexes)
- Null hypothesis testing and confidence intervals for parameters
- Estimation of reliability and validity measures
- Mediation and moderation analyses
- Robustness against nonnormality and small sample sizes
- Trends: Reflective versus formative latent variables, common method bias, etc.
- Causality
- Latent variable models and experimental data
- Overview: Extensions of the basic latent variable model (e.g., mixture modeling)
Note: We currently plan to offer the course in person (room C-107, Campus Vallendar).
Date | Time |
---|---|
Thursday, 29.09.2022 | 08:00 - 19:00 |
Friday, 30.09.2022 | 08:00 - 19:00 |