Day 1: Introductory concepts

9-10am

Lecture: Refresher on multiple regression and connection with path models

·Review GLM structure and principles

·Case studies of applications of the GLM approach in psychology

·Limitations of the GLM approach with more complex phenomena

·Introduce path models as an alternate

10-10:45am

Workshop on GLM and introductory path models

·Based around provided datasets

10:45-11am

Morning tea

11am-12pm

Lecture / demonstration: Introduction to AMOS

·How to navigate AMOS

·Links with SPSS

·Specifying models

·Examining output: text and graphical

·Coefficients / variances / covariances

12pm-1pm

Lunch

1pm-1:45pm

Workshop on using AMOS

·Repeat of SPSS exercise using AMOS

·Practice additional options such as bootstrapping variance estimates

2pm-3pm

Lecture: Further path models

·Path model logic

·Causal path models

·Mediation models

·Recursive and non-recursive models

3pm-3:45pm

Workshop on further path models

·Based around earlier data but fitting models that represent an hypothesised causal path

Day 2: Latent variables

9-10am

Lecture: Confirmatory factor models

·Meaurement models

·Common factor models

·Multiple factor models

·Super-factors

·Uncorrelated and correlated factors

·Comparison with EFA

10-10:45am

Workshop on measurement models (CFA)

·Based around provided datasets

10:45-11am

Morning tea

11am-12pm

Lecture: Structural equation models (SEM)

·Combine measurement and structural models

·Test causal hypotheses

·Test the empirical validity of psychological models

·Longitudinal applications

·Theoretical requirements for causality

12-1pm

Lunch

1pm-1:45pm

Workshop on SEM

·Based around provided datasets

2-3pm

Lecture: SEM refinements

·Moderation

·Missing values and bootstrapping

·Improving model fit

3-3:45pm

Workshop on SEMs

4pm

Finish