Course: Statistics for Psychosocial Research: Structural Models

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Lecture Materials

These lecture materials correspond to the Fall 2007 offering of Statistics for Psychosocial Research: Structural Models. They are not necessarily representative of subsequent offerings of the course.

» 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



» 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


» Lecture 12: Latent class regression II

Model selection

Violations of assumptions


Model checking


» 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