Structural equation modelling—or structural equations with latent variables—is a very general statistical model and widely used method. For example, SEM is used in fundamental disciplines such as the social, economic and psychological sciences, the biological sciences, and applied disciplines such as education, health and marketing.

SEM has become popular for several reasons, apart from its generality:

1. all SEM models can be represented visually,

2. a standard notation helps researchers to communicate, and

3. several software packages for estimating SEM models are readily available (e.g., AMOS, LISREL, Mplus, R).

This course provides an overview of the fundamentals of SEM. As well as the statistical theory, an overview of the many applications and capabilities of SEM is given. The course is not particularly mathematical, but instead places emphasis on the fundamental concepts of SEM and how it is used by applied researchers.

General aims of the course are for students to develop a readiness for using SEM software and to develop the requisite knowledge for applying SEM methods and models in an intelligent way. Note that participants may be invited to briefly present their own research on the last day of class. This exercise, along with the formal lecture material, might help participants to chart a direction forward in their study and application of SEM.

This course is designed as an introductory course for applied researchers and as such, is suitable for participants who want to develop a fundamental knowledge of SEM techniques.

Day 1

• Review of linear regression

Day 2

• Building regression and path models using an SEM framework

• General stages of SEM modelling - specification, identification, estimation, testing and modification

• Reporting SEM research

• Exploratory factor analysis

• Confirmatory factor analysis and latent variables

Day 3

• Related groups models and higher order latent factors

• Multilevel models

• Review of logistic and Poisson regression

Day 4

• Latent Growth Models

• Diagnostics including assessing normality, outliers, linearity

Day 5

• Accounting for missing data

• Tests for mediation and moderation

Approximately half of the time during this course will be spent in PowerPoint presentations, and half of the time in computer demonstrations and self-paced computer exercises. The majority of the computer exercises will be conducted in AMOS, the remaining secondary exercises will be conducted in SPSS.

Familiarity with analysis of variance, factor analysis or regression is desirable, but not strictly necessary. It is assumed that participants have little or no familiarity of structural equations with latent variables.