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
Instructor: 

Dr Mark Griffin is the Director of Insight Research Services Associated (www.insightrsa.com), and holds Adjunct appointments within the School of Public Health, University of Queensland and the Sydney Medical School, University of Sydney. Mark serves on the Executive Committee for the Statistical Society of Australia, and is Chair of their Section for Business Analytics. Mark also serves as the Asia-Pacific Regional Director for the International Institute of Business Analysis, is Chair of their Business Analytics Special Interest Group, and is an IIBA Endorsed Education Provider. He is currently doing research with the Queensland Ambulance Service analyzing their incident reports, where the QAS visits approximately 700,000 incidents per year. To date he has presented over 80 two-day and 10 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.
FAQ: 

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: 

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)

 

Lots of 1 on 1 time with the instructor was very helpful. (Winter 2016)

 

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)

 

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)

 

 

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)

Program where course next likely to be offered: 
Summer Program 2019
Notes: 

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