Structural Equation Modelling using Stata: Online - (2 days)

This master-class provides a foundation for those wishing to utilise structural equation modelling (SEM) to explore and test complex relationships.


The course is designed as an applied introduction to SEM using Stata, aimed at providing participants with a sound understanding of when to use SEM and how to assess and report their models.



This course will be offered online via Zoom And will run to the following timetable:


Day 1:

9.00am -10.30am: Instructional Zoom session
10.30am-11.00am: Break
11.00am-12.30pm: Instructional Zoom session
12.30pm-1.30pm: Lunch
1.30pm-3.00pm: Instructional Zoom session
3.00pm-3.30pm: Break
3.30pm-5.00pm:  Exercises


Day 2:

9.00am -10.30am: Instructional Zoom session
10.30am-11.00am: Break
11.00am-12.30pm: Exercises
12.30pm-1.30pm: Lunch
1.30pm-3.00pm: Instructional Zoom session
3.00pm-3.30pm: Break
3.30pm-5.00pm:  Exercises

Master Class - runs over 2 days

Dr Joanna Dipnall is an applied statistician with interests in the advanced statistical methods, including machine learning and deep learning techniques. She completed her Honours in Econometrics with Monash University and her PhD with IMPACT SRC, School of Medicine, Deakin University. Joanna works extensively with registry and linked medical data and collaborates extensively with the Faculty of IT at Monash to supervise Masters and PhD students to integrate artificial intelligence within health research. Joanna teaches within the Monash Biostatistics Unit and is the Unit Co-coordinator for the Monash Masters of Health Data Analytics course. Joanna has taught advanced statistical methods for many years at universities and for ACSPRI.

About this course: 

This Master-class is designed for participants with an introductory-level understanding of the statistical methods of regression analysis and exploratory factor analysis. Participants will experience hands-on SEM examples and have the ability to build their own Stata SEM models.


This workshop is targeted at those researchers who wish to expand their understanding of this highly powerful technique. SEM has been utilised in many areas of research from psychology to medicine.

Course syllabus: 

Day 1: Fundamentals of SEM

  • Background to SEM
  • Discussion of the advantages of SEM over conventional analytical techniques
  • Understanding of the fundamentals underlying SEM; model conceptualisation, path diagrams, model specification and when should they be used
  • Introduction to Stata SEM notation and diagrams
  • Introduction to confirmatory factor analysis using Stata


Day 2: Working with SEM Models

  • Introduction to path analysis using Stata
  • SEM models using Stata
  • Multigroup SEM and invariance testing
  • Brief introduction to more complex SEM models
  • Stata postestimation tests, predictions and goodness of fit statistics
Course format: 

This workshop will take place online. Participants will need to have Stata preloaded.

If you don't have a copy of Stata, we will be able to organise a trial copy for the course, please let us know in advance.

Recommended Background: 

Participants need to have completed the ACSPRI Course Data Analysis using Stata, or have the equivalent experience with Stata.


This course assumes that participants have familiarity with the Stata command language and a sufficient understanding of statistics to be able to comprehend the material covered in the course outline, such as a basic grounding in regression and exploratory factor analysis.

Recommended Texts: 

Course notes will be supplied.


No specific references are suggested but participants might find the following text useful:

  • Bollen KA. 1989. Structural equations with latent variables. New York: John Wiley.
  • Schumacker RE & Lomax RG. 2010. A Beginner’s Guide to Structural Equation Modeling. Mahwah, New Jersey: Lawrence Erlbaum.
  • A Acock, 2013,Discovering Structural Equation Modeling Using Stata, Revised Edition, A Stata Press Publication.
Supported by: 

Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.