This course was formerly known as Applied Structural Equation Modelling
This course is designed as an introductory course in the use of both AMOS and/or Mplus software packages to estimate basic structural equation models.
(N.B. Although the instructor will teach and use both packages throughout the course, students may choose to work with just one of the packages or they may choose to work with both.)
This course is being held in-person at the University of Melbourne.
Mr Philip Holmes-Smith (OAM) is the Director of School Research, Evaluation and Measurements Services (SREAMS), an independent educational research consultancy business. His research, evaluation and measurement interests lie in the areas of teacher effectiveness and school improvement, accountability models and benchmarking, improving the quality of teaching, using student performance data to inform teaching, and large-scale achievement testing programs. He is an experienced teacher of social science research methods and is a regular instructor at the ACSPRI programs. He also regularly teaches Structural Equation Modeling (SEM) and Multi-Level Analysis (MLA) at various universities around Australia.
Structural Equation Modeling (SEM) is used widely by researchers in a diverse array of fields to find and test complex relationships amongst observed variables that measure latent (unobserved) variables and amongst the latent variables themselves. SEM subsumes other analytical techniques (such as factor analysis, regression analysis and path analysis) into one omnibus approach to modeling relationships amongst observed and latent variables. This course is designed as an introductory SEM course aimed at introducing participants to the basic concept of SEM and to a range of structural equation models using AMOS and/or Mplus software to estimate the model parameters.
Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.
The target audience for this course is post graduate students, academic staff and other researchers needing to learn the basic concept of SEM and how to run structural equation models using either the AMOS and/or Mplus software.
Day 1
Introduction to SEM. Factor analysis and regression. Introduction to AMOS and Mplus. On Day 1 we will learn that SEM is a combination of Factor Analysis and Regression Analysis. Therefore, we will do a revision of factor analysis and regression analysis and discuss their relevance to SEM. Participants will be introduced to the AMOS and/or Mplus programs including how to draw AMOS diagrams, how to write Mplus syntax, how to run models and how to review output.
Day 2 & 3
Fundamentals of SEM and the eight steps to SEM. On Day 2 & 3 we will begin with the fundamentals of SEM including the advantages of SEM over conventional analytical techniques, the fundamentals underlying SEM and an overview of the eight basic steps to SEM before elaborating on each of these eight steps, namely: Step 1 - model conceptualisation, Step 2 & 3- path diagram construction and model specification using the AMOS graphical interface and/or Mplus Syntax, Step 4 - model identification, Step 5 - parameter estimation, Step 6 - assessing model fit, Step 7 - model re-specification, and Step 8 - model cross-validation. Participants will continue to learn the AMOS graphical interface, Mplus Syntax, and how to review AMOS and Mplus output.
Day 4
Basic SEM models. On Day 4, we will look at the three basic types of structural equation models, namely: (i) causal models for directly observed variables (regression and path analysis); (ii) one-factor congeneric measurement models, multi-factor confirmatory factor analysis (CFA) and second order CFA; and (iii) full structural equation models with latent variables (including models with mediating variables).
Day 5
AM - Problems in SEM. We will begin Day 5 dealing with problem data and difficult models including topics such as the treatment of missing data, treatment of non-continuous variables, treatment of outliers; treatment of non-normal data and small samples, constraining parameters, non-positive definite matrices, negative error variances, unidentified and inadmissible models and recognising equivalent models.
PM - Personal Research. Finally, the course provides an opportunity for participants to work on their own research problems with the instructor’s assistance. Therefore, participants are encouraged to bring a data set and/or research problem with them.
You will need to bring your own laptop with specified software (eiether AMOS or Mplus).
You are encouraged to bring a data set and/or research problem with you.
Participants must have completed an introductory course in statistics (or have equivalent experience).
Familiarity with multiple regression and factor analysis is highly desirable, as is experience with a statistical data analysis package such as IBM SPSS, SAS or Stata. However, it is assumed that participants have had little or no experience with either AMOS or Mplus.
The instructor's bound, book length course notes will serve as the course text.
Other references include:
- Arbuckle, James L. (1983-2022). IBM SPSS AMOS 29 User’s Guide. IBM Corp.
- Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén.
- Byrne, Barbara M. (2016). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. (3rd Ed.) New York: Routledge Academic.
- Byrne, Barbara M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. New York: Routledge Academic.
- Kline, Rex B. (2023). Principles and Practice of Structural Equation Modeling (5th Ed.). New York: Guilford Press.
Q: Do I have to have any prerequisites to do this course?
A: Yes, see recommended background section for details.
The course I take is mainly on Structural Equation Modelling and how to use SPSS AMOS and I feel it is really helpful. Phil has given us really good practices and clear explanation.
The course was extremely useful and I would recommend it to anyone interested in SEM
The course content was relevant and useful for my research. Phil did an excellent job in reviewing simple concepts first and then introducing more complex material to the course in a systematic way.
I got a lot more out of this course than I had expected. I entered the course thinking I would be getting a new skill out of it, but even by lunchtime on the Monday, I was applying the concepts to my own data and realising how useful SEM will be in my work.
The theory and concepts were clearly explained including with examples then we got on the computers and learned ”how”. It was brilliant.
Yes I am ready to use this tool now. I am confident I can use it and solve problems.
The instructor's bound, book length course notes will serve as the course texts.