IBM® SPSS® Amos™ 28

Amos performs mixture modeling. Mixture modeling is appropriate when you have a model that is incorrect for an entire population, but where the population can be divided into subgroups in such a way that the model is correct in each subgroup. Mixture modeling is discussed in the context of structural equation modeling by Arminger, Stein & Wittenberg (1999), Hoshino (2001), Lee (2007, Chapter 11), Loken (2004), Vermunt & Magidson (2005), and Zhu & Lee (2001), among others.

Any model can be used in mixture modeling. Example 34 and Example 35 use a saturated model. These examples also demonstrate the fitting of latent structure analysis models, which require the observed variables to be independent (uncorrelated for multivariate normal variables). Example 36 employs a regression model. Factor analysis models have also been used in mixture modeling (Lubke & Muthén, 2005).

Mixture modeling is often known as latent class analysis. In the terminology of Lazarsfeld (Lazarsfeld & Henry, 1968), the term latent class analysis is reserved for the variant of latent structure analysis in which all variables are categorical. Amos does not perform that type of latent class analysis.