SPSS Amos

IBM SPSS Amos builds models that more realistically reflect complex relationships because any numeric variable, whether observed (such as non-experimental data from a survey) or latent (such as satisfaction and loyalty) can be used to predict any other numeric variable.

Its rich, visual framework lets you to easily compare, confirm and refine models.

Quickly build graphical models using SPSS Amos’ simple drag-and-drop drawing tools. Models that used to take days to create are just minutes away from completion. And once the model is finished, simply click your mouse and assess your model’s fit. Then make any modifications and print a presentation-quality graphic of your final model.

SPSS Amos 20 introduces a
non-graphical method to specify
models.
Click on the image to enlarge it.A non-graphical, programmatic approach, introduced with SPSS Amos 20, improves accessibility for those who can benefit by specifying models directly. Its scripting capabilities improve the productivity of users who need to run large, complicated models, and make it easy to generate many similar models that differ slightly.

Its approach to multivariate analysis encompasses and extends standard methods – including regression, factor analysis, correlation and analysis of variance.

Bayesian analysis enables you to apply your subject-area expertise or business insight to improve estimates by specifying an informative prior distribution. Markov chain Monte Carlo (MCMC) is the underlying computational method for Bayesian estimation. The MCMC algorithm is fast and the MCMC tuning parameter can be adjusted automatically.

Perform estimation with ordered categorical and censored data Create a model based on non-numerical data without having to assign numerical scores to the data. Or work with censored data without having to make assumptions other than the assumption of normality. You can also impute numerical values for ordered-categorical and censored data. The resulting dataset can be used as input to programs that require complete numerical data.

Choose from three data imputation methods: regression, stochastic regression, or Bayesian. Use regression imputation to create a single completed dataset. Use stochastic regression imputation or Bayesian imputation to create multiple imputed datasets. You can also impute missing values or latent variable scores.

1. Select a data file

Input data from a variety of file formats (IBM® SPSS® Statistics, Micosoft® Excel, text files, or many others). Select grouping variables and group values. IBM SPSS Amos also accepts data in a matrix format if you’ve computed a correlation or covariance matrix.

2. Specify your model

Use drag-and-drop drawing tools to quickly specify your path diagram model. Click on objects in the path diagram to edit values, such as variable names and parameter values. Or simply drag variable names from the variable list to the object in the path diagram to specify variables in your model.

3. Select analysis properties

Select the analysis properties you wish to examine, such as standardized estimates of parameters or squared multiple correlations. Constrain parameters for more precise models by directly specifying path coefficients.

4. View output

IBM SPSS Amos output provides standardized or un-standardized estimates of covariances and regression weights as well as a variety of model fit measures. Hotlinks in the help system link to explanations of the analysis in plain English.

5. Assess your model's fit

Make any modifications to your model and print publication-quality output.

We recommend complementing the rich functionality of IBM SPSS Amos by using it with IBM SPSS Statistics Base.