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.
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.
Dr Mark Griffin is the Director of Insight Research Services Associated (www.insightrsa.com), where Insight consists of Insight Business Analytics, and Insight Training and Events. Insight Business Analytics providing training and consulting across the areas of business, statistics and IT. Insight Business Analytics is a Business Partner of Microsoft and a member of the IBM's Partner World and Google's Partner Program. Insight Training and Events teaches 30 qualifications from Certificate I to Graduate Diploma level across the areas of business, information technology, health and social services, and civil and environmental engineering.
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, was the Co-Chair of their National Conference in 2021, and is the 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 40 five-day workshops in statistics around Australia
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.
This classroom based course consists of seven parts.
1. A brief history of SEM, including its antecedents (e.g., factor analysis and regression).
2. The fundamental concepts of SEM. This includes the use of path diagrams, the notation that is used to specify SEM models, estimation and identification of SEM models, the assessment of SEM models, the interpretation of parameter estimates and the respecification of models.
3. The specification, estimation and interpretation of common SEM models, including confirmatory factor analysis models and ‘causal’ models with latent variables (i.e., full generalised SEM models).
4. Applications of SEM models (e.g., tests of mediation and moderation, common method and multitrait-multimethod models).
5. Extensions of the basic SEM model (e.g., multisample analysis and multilevel modelling).
6. A demonstration of the software packages (Amos, LISREL).
7. How to write up results from SEM analyses.
This course will take place in a combination of classroom and computer lab. Participants are also welcome to bring their own laptops.
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.
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.
Great introduction to SEM and to a couple of the software packages to use, now understand how SEM can be used and how to go about the process. (Summer 2015)
One on one discussion with participants using own data is helpful as you are applying what you have learnt to your own data. (Summer 2015)
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)
Great to learn the theory and then consolidate it with practical activities. (Winter 2015)
The structure of smaller lectures was great. This content then scaffolded computer activities. (Winter 2015)
The instructor's bound, book length course notes will serve as the course texts.