By Jacques A. Hagenaars, Allan L. McCutcheon

This research introduces a number of fresh strategies in latent type research to a much wider viewers of researchers. some of the world's best innovators within the box of latent category research have contributed essays to the amount, every one featuring a key innovation to the elemental LCM and illustrating the way it can end up priceless in events ordinarily encountered in genuine examine.

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Comparing the results presented in Table 5b for models M3 , M4 , and M5 (each of these models having three latent classes) with the corresponding results presented in Table 5a for model M1 (which has two latent classes), we find that models M3 , M4 , and M5 are more parsimonious than model M1 , and that these more parsimonious models fit the observed data essentially as well as the less parsimonious model M1 . ) Indeed, again comparing corresponding results in Tables 5a and 5b, we see that the latent class model M5 in Table 5b turns out to be as parsimonious as the simple null model M0 of mutual independence among the four observed variables (A, B, C, and D), with 11 degrees of freedom corresponding to model M0 and also to model M5 ; and the goodness-of-fit chi-square value Latent Class Analysis 17 A B Figure 2.

ANOTHER EXAMPLE: THE SIMPLE 2 × 2 CROSS-CLASSIFICATION TABLE REVISITED: A NINETEENTH-CENTURY LATENT STRUCTURE AND SOME NEW ALTERNATIVES Let us return now to the analysis of the relationship between two observed variables in the simple context of the 2 × 2 cross-classification table {A, B}, where the observed variables A and B are both dichotomous. In the Introduction, we began with this topic; and, in a comment in that section that pertained to nineteenth-century work on this topic, we included there a reference to the work of C.

Figure 1(c) can be used to describe the situation in which variable S (parental socioeconomic status) and latent variable E (endowment status) reciprocally affect each other (or variables S and E are coincident with each other), and it is variable E that affects what the mental health status is of the individual. 2 Using latent class models, we can examine whether this conditional independence is congruent with the data in Table 1. It may be worthwhile to note here that if variable E (or E ) is viewed as antecedent to variables S and M, as in Figure 1(a), then we could conclude from Figure 1(a) that the observed relationship between S and M has been explained away by variable E (or E ); however, we could also conclude from Figure 1(a) that the observed relationship between S and M has been explained (rather than explained away) by variable E (or E ) – that is, by the relationship between variable E (or E ) and S and the relationship between variable E (or E ) and M.

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