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.
The 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.
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 will take place in a combination of classroom and computer lab. Participants are also welcome to bring their own laptops.
Dr Mark Griffin is the Director of Insight Social Research & Statistics (https://www.insightrsa.com/industry-social-research). Insight focuses on research methodologies (including survey design and statistics) for public health, monitoring and evaluation for government and non-government organizations, and academic research. Insight has a secondary interest in providing IT services (as a Microsoft Business Partner).
Insight is based at the Gold Coast Health and Knowledge Precinct. The Precinct contains Griffith University Gold Coast, the Gold Coast University Hospital, the Gold Coast Private Hospital, and the Cohort and Lumina tech parks. Insight provides research, consulting, training, and IT support services for clients across the Precinct and for the broader international community.
To date he has presented over 100 two-day and 40 five-day workshops in statistics around Australia
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.