Amos is written with teaching and consulting applications in mind first. Fully-interactive path diagram input and display options make it easy to discuss and evaluate models with applied researchers and students.
The interface is object oriented and follows the MS Windows standard guidelines for graphical user interfaces. Amos Graphics has an extensive online help system. Anybody used to other Windows programs, such as MathCAD or MS Word, will have little or no trouble getting started with Amos Graphics.
In Amos, the path diagram is the model and the user does not have to manipulate sets of equations or matrices with Greek names. Thus, modeling with Amos is a complete change from the old ways of doing SEM. Amos reads its model specifications only in the form of equations or path diagrams. Even complex models can be drawn out as path diagrams, and at the press of a button (literally) Amos goes ahead and calculates the estimates. The graphics are always in publication quality.
By the same token, and in contrast to LISREL, Amos does not support model specifications in matrix notation.
Mean models, and multi-group models, can be specified with either program. However, it can be done very easily with Amos.
Similarly, bootstrapping and Monte Carlo simulations are very easily set up in Amos, and there are sophisticated output options. For instance, the following three commands will cause amos Amos to produce 1000 bootstrap replications of the current structural model and compute 95% confidence intervals with bias correction:
$bootstrap=1000
! 1000 bootstrap replications
$seed=123489
! pick some seed value for the random number generator
$confidencebc=95
! compute bias-corrected confidence intervals
Analysis of missing data is by full-information maximum likelihood in Amos. The full-information method used by Amos are more efficient in the missing-at-random case. If missingness is not at random, Amos's estimates are generally less biased than those produced by ad-hoc methods as pairwise or listwise deletion.
LISREL 8 excels in ordinal data modeling via polychoric/serial correlations. However, there has been some debate about the asymptotic covariance matrices computed by Prelis 2. Methods for ordinal-categorical data are still subject of ongoing research. While it was clear from early on that the polychoric approach can remove, or largely reduce, bias due to discrete measurement, the asymptotically distribution-free estimation employed by LISREL and EQS is limited to a maximum of 25 observed variables and appears to require formidable sample sizes of at least 2,000-5,000 observations per group.
Lisrel also features instrumental variables (IV) and two-stage least-squares (TSLS) as estimation methods, although in non-standard implementations. Amos does not provide any IV or TSLS estimation methods.
Lisrel 8 allows general polynomial parameter constraints.
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