• Mixture modeling and latent class analysis with training data (9.57)
  In this video, there are three groups. The dataset contains cases that have already been classified, and other cases whose group membership is unknown. Mixture modeling and latent class analysis are used to learn from the already-classified cases and then classify the remaining cases.
• Mixture modeling and latent class analysis without training data (9:15)
  In this video, it is hypothesized that cases fall into three groups, but group membership is unknown for all cases.
• Mixture regression modeling (9:40)
  In this video, two variables are not linearly related, but the sample can be split into two groups in such a way that the variables are linearly related within each group.

You can download the data.

This video shows how to fit a linear growth curve model. (16:38)

• Watch the video.
• Download the data.

This video shows how to estimate indirect effects and test them for significance. (4:56)

• Watch the video.
• Download the data and path diagram.

• Recoding the data (12:09)
• Fitting a factor analysis model (7:45)
• Predictive distributions (estimating unknown data values) (7:20)
• Predictive distributions for factor scores (6:09)
• Imputation (8:26)

This video shows how to fit a regression model using censored data. It is based on Example 32 in the User's Guide. (16:11)

• Watch the video.

The following videos show some uses of Bayesian estimation in Amos. After these videos were made, the Refresh icon was substituted in place of the Snapshot icon .

• Introduction to Bayesian estimation with Amos (10:59)
   
• Improper solutions (4:55). How to avoid negative variance estimates and other types of improper solutions within the Bayesian framework. You can download the files for the example.
   
• Custom estimands (16:55). How to estimate an arbitrary function of the model parameters. This example focuses on the estimation of
  • an indirect effect
  • a direct effect
  • the difference between the indirect effect and the direct effect
  • the probability that the indirect effect is positive
  • the probability that the indirect effect exceeds the direct effect