IBM SPSS Amos

IBM® SPSS® Amos gives you the power to easily perform structural equation modeling (SEM). Using SEM, you can quickly create models to test hypotheses and confirm relationships among observed and latent variables – moving beyond regression to gain additional insight. For more than 40 years, organizations of all types have relied on us to increase revenue, outmaneuver competitors, conduct research, and make better decisions. The evaluation period begins on the date that you agree to the terms of the agreement.

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

Importing data
Specify your model
Specify your modelSelect analysis properties
View output
Assess your model's fit

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.

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