Course: Statistics for Psychosocial Research: Structural Models

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Schedule


Session Topic Activites
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
Fitting

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