SPSS Training and Education Services

Introduction to AMOS

Course description:

An introduction to structural equation modeling (SEM) using Amos 6.0 and its graphical interactive path modeling tools. The fundamentals of SEM are reviewed, and modern advances in structural modeling and statistical methods are emphasized, demonstrated with practical examples drawn from a variety of application areas, such as customer satisfaction, healthcare, and education.

US Customers - Select this link to begin course registration
Canadian Customers - Select this link to begin course registration

Please note: If you are paying by Check or PO, you will need to download the Public Training or the Academic Training registration form and fax or mail the completed form to SPSS along with your payment.

Subscribers: If you are a Training Subscriber please select "Call Me" as the payment type when preregistering for this course. You will be contacted by a sales representative to verify your subscription — otherwise you will be invoiced for the training.

Duration and Cost:

Costs are for United States courses only. Information about other countries can be found at the worldwide offices page.

Two Days
9:00 a.m. to approximately 4:00/4:30 p.m. each day
$1199

Who Should Attend:

Individuals with an interest in Structural Equation Modeling who are interested in getting up to speed quickly and efficiently on Amos 6.0. The course covers the basics and also advanced topics in SEM.

Prerequisite:

Some experience in multiple regression is strongly recommended. Knowledge of factor analysis would be helpful.

Course Content:

Overview of structural equation modeling
Simple regression models with Amos
Confirmatory factor analysis models
Specifying applied regression, factor analysis, and structural regression models
Tests of model adequacy and fit
Identification problems in factor analysis and structural equation models
Structural equation models with means and intercepts, for trend analysis and exact predictions
Multi-group models, with and without constraints across groups
Amos's efficient modeling approach for incomplete or missing data
Analysis of non-normal data: Applications of the bootstrap method to estimate empirical standard errors and confidence intervals of parameter estimates, and to obtain robust tests of model fit
Bayesian methods of estimation