This is the schedule used to deliver educational content offered by JHSPH. As a result, some of the information and/or materials listed here may not be relevant to or available for an OCW user's self-directed study.
Schedule
| SESSION # | TOPIC | ACTIVITIES |
|---|---|---|
| 1 | Introduction: Structural regression |
Model specification Motivating examples Three approaches: score then analyze, analyze then summarize, LV Role of measurement error Model assumptions Path diagram |
| 2 | Regression analysis for items |
Generalized estimating equations (GEE)/marginal models Model specification, interpretation, and fitting |
| 3 | Introduction to path analysis |
Path diagram Decomposing covariances and correlations Direct, Indirect, and Total Effects Identification Estimation |
| 4 | Introduction to structural equations with latent variables |
Measurement models Structural models Model specification, Estimation Example: confirmatory factor analysis |
| 5 | Inference using structural equations with latent variables |
Parameterizing hypotheses Parameter constraints Model identification Model checking |
| 6 | Examples of path analysis |
Behavior genetics Status attainment Evaluation of treatment effects |
| 7 | Commonly applied structural models with latent variables |
MIMIC (multiple indicators and multiple causes of a single latent variable) models Group comparisons Application (example) |
| 8 | Advanced structural equations models I |
Longitudinal analysis Growth curves |
| 9 | Advanced structural equations models II | Multilevel Models |
| 10 | Models for dichotomous outcomes |
Dichotomous variable factor analysis Latent variable structural equations models with discrete data |
| 11 | Latent class regression I |
Motivating examples Model specification Assumptions |
| 12 | Latent class regression II |
Model selection Violations of assumptions Identifiability Model checking Example |
| 13 | Concluding topics |
Design, power, sample size Pros and cons of latent variable models Using observed and latent variable models in parallel Causal inference |
