Applied Structural Equation Modelling using AMOS

This course is currently full. You can join the waiting list below in case there are any cancellations. If the waiting list reaches a sufficient length, there is a chance a second offering of this course will be added to a different week of the 2013 Summer Program.
 
This course is designed as an applied introduction to the use of the Amos software for estimating structural equation models. Structural equation modelling (SEM) is used widely by researchers in a diverse array of fields to find and test complex relationships amongst observed (measured) variables and latent (unobserved) variables and amongst the latent variables themselves. SEM subsumes other analytical techniques such as regression, path analysis, factor analysis, and canonical correlation. This course is designed as an introductory, applied course in the use of the Amos software to run structural equation models. 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 course is divided into five parts.
 
Part I: Fundamentals of SEM. Topics include a revision of factor analysis and regression analysis and their relevance to SEM; the advantages of SEM over conventional analytical techniques; the fundamentals underlying SEM; path diagrams, model specification and notation for structural equation models; model identification; parameter estimation; assessing model fit; model re-specification and model cross-validation. Throughout this part of the course participants will be introduced to the Amos software with a particular emphasis on the Amos Graphics, including how to run models and how to review output.
 
Part II: Basic Models. This part of the course looks at the three basic types of structural equation models, namely: causal models for directly observed variables (regression and path analysis); measurement models, confirmatory factor analysis (CFA) and second-order CFA; and full structural models with latent variables including models with mediating variables.
 
Part III: Common Problems in SEM. This part of the course deals with problem data and difficult models including topics such as missing data, small samples, ordinal and/or dichotomous variables, non-normal data, constraining parameters, non-positive definite matrices, negative error variances, unidentified and inadmissible models and recognising equivalent models.
 
Part IV: Introduction to Advanced Topics. This part of the course gives an introduction to the topics covered in the advanced SEM course including the testing of model and parameter invariance across groups (multi-group analysis), tests of indirect effects using bootstrapping, and latent growth models.
 
Part V: 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