Structural equation modeling (sometimes called path analysis) can help you gain additional insight into causal models and explore the interaction effects and pathways between variables.
Quickly build graphical models using IBM SPSS Amos software’s simple drag-and-drop drawing tools. With the latest release, nonprogrammers can easily specify a model without drawing a path diagram by entering the model into a spreadsheet-like table that can be modified.
And after 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.
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.
Create a model based on nonnumerical 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 data set 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 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.