IBM® SPSS® Amos is powerful structural equation modeling (SEM) software that enables you to support your research and theories by extending standard multivariate analysis methods, including regression, factor analysis, correlation, and analysis of variance. In SPSS Amos, you can specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables. The latest release enables you to specify user-defined estimates using a simplified technique. You can use a non-graphical, programmatic method to specify models. SPSS Amos also provides a way for non-programmers to specify a model easily without drawing the path diagram.
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 the 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.
Provides SEM (Structural Equation Modeling)
♦ 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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