Download Applied Latent Class Analysis by Jacques A. Hagenaars, Allan L. McCutcheon PDF

By Jacques A. Hagenaars, Allan L. McCutcheon

This learn introduces numerous contemporary options in latent type research to a much wider viewers of researchers. the various world's prime innovators within the box of latent type research have contributed essays to the quantity, each one proposing a key innovation to the elemental LCM and illustrating the way it can end up precious in occasions more often than not encountered in genuine study.

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We can also use models of this kind in the study of the relationships among a set of unobserved (or latent) dichotomous or polytomous variables in the situation in which there are observed dichotomous or polytomous variables that can be viewed as indicators or markers for the unobserved (or latent) variables. ) 2. THE LATENT CLASS MODEL Now let us consider the latent class model in the situation in which variable A is an observed (or manifest) dichotomous or polytomous variable having I classes (i = 1, 2, .

S. Peirce (1884) on the analysis of the 2 × 2 table in the context of measuring the success of predictions. We shall now show how Peirce’s work can be interpreted as an example of the early use of a latent class model in the analysis of a 2 × 2 table; and we shall also show how the introduction of latent class models in this context can lead to some additional simple measures that are different from the one proposed by Peirce. With respect to the prediction of, say, tornadoes, where the dichotomous row variable A pertains to tornado prediction (predict “tornado” or predict “no tornado”), and the dichotomous column variable B pertains to tornado occurrence (tornado occurs or no tornado occurs), Peirce pointed out that the observed results in the 2 × 2 cross-classification table could be viewed as having been obtained by using an infallible predictor a proportion θ of the time, and a completely ignorant predictor the remaining proportion 1 − θ of the time.

Models Applied to the Cross-Classified Data in Table 4 Chi-Square Model No. 39 16 Leo A. Goodman Table 5b. More Parsimonious Models Applied to the Cross-Classified Data in Table 4 Chi-Square Model Latent class Latent class Latent class Latent class M2 M3 M4 M5 No. 72 data in Table 4; and (b) the simple latent class model M1 that has two latent classes fits the observed data very well indeed. 72), with the corresponding reduction in degrees of freedom from 11 to 6. As we noted earlier, in addition to the results presented in Table 5a for the latent class model M1 , which has two latent classes, we also presented there the corresponding results obtained when the latent class model M2 , which has three latent classes, is applied to Table 4.

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