Fundamentals of Structural Equation Modelling

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.


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. Some participants may want to follow this course by the applied and advanced SEM workshops taught by ACSPRI.


Level 3 - runs over 5 days

Dr Mark Griffin is the Director of ResearchStats, which is a Division of Insight Research Services Associated ( ResearchStats provides training and consulting in statistics for academic audiences. Mark is also an Industry Fellow with the School of Business, University of Queensland, and has established and written training materials for several of their courses in Business Analytics. Mark serves on the Executive Committee for the Statistical Society of Australia, and is Founding Chair of their Section for Business Analytics. Mark is also the Founding Chair of the Business Analytics Special Interest Group within the International Institute of Business Analysis. To date he has presented over 100 two-day and 30 five-day workshops in statistics around Australia.

About this course: 

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:

     (i) all SEM models can be represented visually,

     (ii) a standard notation helps researchers to communicate, and

     (iii) 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.


Course syllabus: 

Day 1

  • Study design and reporting (including Setting goals and objectives, Inclusion and exclusion criteria for participant selection, Data Management and data linkage, Reporting styles including the CONSORT statement, Ethics including privacy and confidentiality)
  • Revision of linear regression, correlation and covariance and how to interpret SPSS output
  • Building regression and path models using an SEM framework
  • General stages of SEM modelling – specification, identification, estimation, testing, and modification


Day 2

  • Reporting SEM research
  • Exploratory factor analysis
  • Confirmatory factor analysis and latent variables


Day 3

  • Tests for mediation and moderation
  • Related groups models and higher order latent factors
  • Multilevel models
  • Latent Growth models


Day 4

  • SEM diagnostics including assessing normality, outliers, linearity, and the presence of missing data


Day 5

  • Time for presenting your own models – for those workshop participants who would like to present, there will be an opportunity for you to draw an SEM model from your research on the board to discuss with the class and get feedback. These models might come from current or future proposed research. This discussion will focus on the arrangement of variables that compose an SEM, rather than on the statistical output produced in fitting such models.
  • There will also be time, particularly at the end of the workshop, for participants to ask questions about fitting SEM models to their own data.


Course format: 

This course may run in a computer lab, or you may be advised to bring your own laptop with specified software.

We will let you know in advance.


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.

Recommended Background: 

Participants must have completed the course Fundamentals of Multiple Regression or an equivalent course at university level and/or have equivalent experience. 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.

Recommended Texts: 

General Reading:

  • Bollen, Kenneth A. (1989). Structural Equations with Latent Variables. New York: John Wiley & Sons.
  • Kline, Rex B. (2005). Principles and Practice of Structural Equation Modeling. (2nd Ed.). New York: Guilford Press.
  • Schumacker, Randall & Lomax, Richard. (2004). A Beginner's Guide to Structural Equation Modeling. (2nd Ed.). Mahwah, N.J.: Lawrence Erlbaum Associates.

Q: Do I have to have any prerequisites to do this course?

A: Yes, you must have completed Fundamentals of Multiple Regression or a University course at the same level


Q: What should I bring to the workshop?

A: Different workshop participants will be at different stages of their research. While we will explore prepared computer exercises with provided datasets during this workshop, some participants might want to bring their own data to this workshop to analyse as well. In addition workshop participants will also have the opportunity to present their own SEM models to the class (where this might involve current or future research). There is no requirement for participants to have their own data or to present their SEM models, but some participants may want to take advantage of this opportunity.


Participant feedback: 


I can now build SEM models and Incorporate in my research and publications. It helped well to further my knowledge of SPSS (Summer 2020)


I came with a 0 zero knowledge of SEM and its application with real data. At the end of the course, I can keep my thoughts towards and work around SEM. (Winter 2019)


Great to learn content/theory and then work through problems with solutions (Winter 2019)


Was great, ample opportunity to practice skills and ask questions (Winter 2018)


The content is focused, straight to what I need, practice also makes it better. The instructor is amazingly supportive and friendly, knowledgeable. (Winter 2017)


Provided a clear and concise explanation of SEM fundamentals along with practical execises which enable us to gain experience in all elements taught within the course. (Winter 2017)


Mark is very helpful. He always answer all the questions. Spend time helping students to do exercising. Thank you so much Mark. You are a great teacher. (Winter 2016)


The fundamentals and ground work covered was exeptionally useful. I feel I now have a very solid understanding of regression, path analysis and SEM. (Summer 2015)



The instructor's bound, book length course notes will serve as the course texts.