A first course in infinitesimal calculus by Daniel A. Murray

By Daniel A. Murray

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23. 17 to obtain new estimates of each of the probabilities. There are two methods that can be used to halt this iterative process of estimation and reestimation of probabilities. First, the analyst can determine how many iterations he or she is willing to complete. The disadvantage of this approach is that the estimates may still be changing substantially from iteration to iteration. 17 can be used to test the fit of the obtained estimates to the originally observed data. There are three points that need to be made regarding the estimation of conditional and latent class probabilities.

25 no longer reflects the number of nonredundant model parameters to be estimated. 25 by the number of nonredundant restrictions imposed. We will explore models with restrictions more thoroughly below, but first we examine latent class models without restrictions. 3. Estimating Latent Categorical Variables As we noted in the previous two chapters, the most general form of latent class analysis permits us to characterize a nominal level latent variable from a crosstabulation of two or more ordinal or nominal level observed variables.

05 alpha level, we would conclude that the observed variables are not interrelated, and thus no latent variable is needed since there is no relationship between the manifest variables that requires explanation. 67, 35df). The next hypothesis tests the model that there are two classes of respondents, the ideal and the skeptics, as described above. 05 alpha level. 59, 16df). 2 indicates that there is a loss of only 6, not 7, degrees of freedom between the two-and three-class models. This occurs because of a special instance in which one of the conditional probabilities is found to equal zero.

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