*Easily perform structural equation modeling*

IBM® SPSS® Amos enables you to specify, estimate, assess and present models to show hypothesized relationships among variables. The software lets you build models more accurately than with standard multivariate statistics techniques. Users can choose either the graphical user interface or non-graphical, programmatic interface.

SPSS Amos allows you to build attitudinal and behavioral models that reflect complex relationships. The software:

**Provides structural equation modeling (SEM)**—that is easy to use and lets you easily compare, confirm and refine models.**Uses Bayesian analysis**—to improve estimates of model parameters.**Offers various data imputation methods**—to create different data sets.

## Screenshots of SPSS Amos

## Drag-and-drop interface

The SPSS Amos drag-and-drop user interface allows you to easily build a structural equation model in a visual manner.

## 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.

## 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.

## View output

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.

## Assess your model's fit

Make any modifications to your model and print publication-quality output. We recommend complementing the rich functionality of SPSS Amos by using it with IBM SPSS Statistics Base.

## Provides SEM

- Quickly build graphical models using drag-and-drop drawing and editing tools.
- Create models that realistically reflect complex relationships.
- Use any numeric value, whether observed or latent, to predict any other numeric value.
- Use non-graphical scripting capabilities to run large, complicated models quickly and to generate similar models that differ slightly.
- Take advantage of multivariate analysis to extend standard methods such as regression, factor analysis, correlation and analysis of variance.

## Uses Bayesian analysis

- Improve estimates by specifying an informative prior distribution.
- Take advantage of the underlying Markov chain Monte Carlo (MCMC) computational method, which is fast and can be adjusted automatically.
- Perform estimation with ordered categorical and censored data.
- Specify user-defined estimands using a simplified technique.
- Create models based on non-numerical data without having to assign numerical scores to the data.
- Work with censored data without having to make assumptions other than normality.

## Offers various data imputation methods

- Use regression imputation to create a single, completed data set.
- Use stochastic regression imputation or Bayesian imputation to create multiple imputed data sets.
- You can also impute missing values or latent variable scores.

### SPSS Amos resources

- Data sheet: IBM SPSS Amos (1MB)
Build attitudinal and behavioral models that reflect complex relationships.

### Trial software: IBM SPSS Amos

- Trial software: IBM SPSS Amos
Download this software for a no-cost 14-day evaluation.

- White paper: Structural Equation Modeling with IBM SPSS Amos
A methodology for predicting behavioral intentions in the services sector.

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Priority Code: Analytics