Structural Equation Modeling (SEM) is used widely by researchers in a diverse array of fields to find and test complex relationships amongst observed (measured) variables and latent (unobserved) variables and amongst the latent variables themselves. SEM subsumes other analytical techniques (such as factor analysis, regression analysis and path analysis) into one omnibus approach to modeling relationships amongst observed and latent variables. This course is designed to introduce participants to a range of basic structural equation models and the use of the AMOS and/or Mplus software to estimate the model parameters.

Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.

The target audience for this course is post graduate students, academic staff and other researchers needing to learn how to run basic structural equation models using either the AMOS and/or Mplus software.

Participants must have completed an introductory course in statistics (or have equivalent experience). Familiarity with multiple regression and factor analysis is highly desirable, as is experience with a statistical data analysis package such as SPSS, SAS or Stata. However, it is assumed that participants have had little or no experience with either AMOS or Mplus. While not a pre-requisite, participants with no previous exposure to structural equation modeling are strongly encouraged to first complete the course Fundamentals of Structural Equation Modeling.

This course is designed as an applied introduction to the use of both AMOS and Mplus software packages for estimating basic structural equation models.

The target audience for this course is post graduate students, academic staff and other researchers needing to learn how to run basic structural equation models using either the AMOS and/or Mplus software.

This course will run in a classroom. You will need to bring your own laptop with your preferred software AMOS or MPlus.

You will need to advise us in advance which software you will be using, so we can provide the appropriate course notes.

You are encouraged to bring a data set and/or research problem with you.