## Course: Statistics for Psychosocial Research: Structural Models

# Lecture Materials

## » Lecture 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

## » Lecture 2: Regression analysis for items

Generalized estimating equations (GEE)/marginal models

Model specification, interpretation, and fitting

## » Lecture 3: Introduction to path analysis

Path diagram

Decomposing covariances and correlations

Direct, Indirect, and Total Effects

Identification

Estimation

## » Lecture 4: Introduction to structural equations with latent variables

Measurement models

Structural models

Model specification, Estimation

Example: confirmatory factor analysis

## » Lecture 5: Inference using structural equations with latent variables

Parameterizing hypotheses

Parameter constraints

Model identification

Model checking

## » Lecture 6: Examples of path analysis

Behavior genetics

Status attainment

Evaluation of treatment effects

## » Lecture 7: Commonly applied structural models with latent variables

MIMIC (multiple indicators and multiple causes of a single latent variable) models

Group comparisons

Application (example)

## » Lecture 8: Advanced structural equations models I

Longitudinal analysis

Growth curves

## » Lecture 9: Advanced structural equations models II

Multilevel Models

## » Lecture 10: Models for dichotomous outcomes

Dichotomous variable factor analysis

Latent variable structural equations models with discrete data

## » Lecture 11: Latent class regression I

Motivating examples

Model specification

Assumptions

Fitting

## » Lecture 12: Latent class regression II

Model selection

Violations of assumptions

Identifiability

Model checking

Example

## » Lecture 13: Concluding topics

Design, power, sample size

Pros and cons of latent variable models

Using observed and latent variable models in parallel

Causal inference