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James L. Arbuckle, "Amos 18 User's Guide"

Amos is short for Analysis of MOment Structures. It implements the general approach to data analysis known as structural equation modeling (SEM), also known as analysis of covariance structures, or causal modeling. This approach includes, as special cases, many well-known conventional techniques, including the general linear model and common factor analysis.

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.

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.

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

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, as well as 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 models. It provides a test of univariate normality for each observed variable as well as a test of multivariate normality and attempts to detect outliers.

Amos accepts a path diagram as a model specification and displays parameter estimates graphically on a path diagram. Path diagrams used for model specification and those that display parameter estimates are of presentation quality. They can be printed directly or imported into other applications such as word processors, desktop publishing programs, and general-purpose graphics programs.

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