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Example 33
Posterior Predictive Distributions
When you think of estimation, you normally think of estimating model parameters or
some function of the model parameters such as a standardized regression weight or an
indirect effect. However, there are other unknown quantities in the present analysis.
Each entry in the data table on p. 490 represents a numeric value that is either unknown
or partially known. For example, Person 1 did not respond to item2, so we can only
guess at (estimate) that person’s score on the underlying numeric variable. On the other
hand, it seems like we ought to be able to make a fairly educated guess about the
underlying numeric value, considering that we know how the person responded to the
other items, and that we can also make use of the assumption that the model is correct.