Filter
Top Products
2
Chapter 1
Structural equation modeling (SEM) is sometimes thought of as esoteric and difficult
to learn and use. This is incorrect. Indeed, the growing importance of SEM in data
analysis is largely due to its ease of use. SEM opens the door for nonstatisticians to
solve estimation and hypothesis testing problems that once would have required the
services of a specialist.
IBM SPSS Amos was originally designed as a tool for teaching this powerful and
fundamentally simple method. For this reason, every effort was made to see that it is
easy to use. Amos integrates an easy-to-use graphical interface with an advanced
computing engine for SEM. The publication-quality path diagrams of Amos provide a
clear representation of models for students and fellow researchers. The numeric
methods implemented in Amos are among the most effective and reliable available.
Featured Methods
Amos provides the following methods for estimating structural equation models:
Maximum likelihood
Unweighted least squares
Generalized least squares
Browne’s asymptotically distribution-free criterion
Scale-free least squares
Bayesian estimation
IBM SPSS Amos goes well beyond the usual capabilities found in other structural
equation modeling programs. When confronted with missing data, Amos performs
state-of-the-art estimation by full information maximum likelihood instead of relying
on ad-hoc methods like listwise or pairwise deletion, or mean imputation. The program
can analyze data from several populations at once. It can also estimate means for
exogenous variables and intercepts in regression equations.
The program makes bootstrapped standard errors and confidence intervals available
for all parameter estimates, effect estimates, sample means, variances, covariances,
and correlations. It also implements percentile intervals and bias-corrected percentile
intervals (Stine, 1989), as well as Bollen and Stine’s (1992) bootstrap approach to
model testing.
Multiple models can be fitted in a single analysis. Amos examines every pair of
models in which one model can be obtained by placing restrictions on the parameters
of the other. The program reports several statistics appropriate for comparing such