1. Review

Probability, independence, conditional probability, probability density functions, likelihood and log-likelihood functions, expectations, matrix operations.

2. Exploratory factor analysis (EFA)

Model likelihood, implied moments, identifiability, model tests and fit, rotations

3. Ordinal EFA

Cumulative link model, identifiability, full and limited information methods

Notes, exercise, CFCS data, dark triad data

4. Confirmatory factor analysis (CFA) and structural equation modeling (SEM)

Path models, CFA measures of fit, higher-order models, latent path models

5. Latent class models (LCMs)

Mixture models, identifiability, polytomous response data example