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Even without missing data, there are some serious limitations associated with goodness-of- t assessment in some LCA and almost all LTA models The limitations fall in two areas First, goodness-of- t testing relies upon the idea that the distribution of the G2 is approximated reasonably well by the chi-square distribution However, when the ratio N/k becomes small, where N is the sample size and k is the number of cells in the contingency table, the distribution of the G2 deviates more and more substantially from the chi-square Unfortunately, under these circumstances the true distribution of the G2 is not known, rendering it of limited utility for model selection This applies to all contingency table models, and is particularly a problem in latent class models with large numbers of indicators Second, the standard hypothesis testing approach using the G2 difference test can be used to compare only nested models.

Calculate the rst 3 bits of F (X ) = 0.F1 F2 . . . when X = 1010111 . . . . How many bits of X does this specify 13.4 Arithmetic coding. transition matrix

The modules are still there, but you view and edit them in the Visual Basic Editor. Press Alt+F11 to toggle between the VBE and Excel.

However, many important research questions addressed using latent class methods call for comparing models that are not strictly nested An alternative to the standard approach to goodness-of- t testing is badly needed in order to overcome these two limitations Bayesian methods may provide the needed alternative Rubin (1984; Rubin & Stern, 1994) describes a general procedure for model monitoring based on the posterior predictive check distribution of any test statistic This is an empirical, simulation-based procedure in which the observed value of the test statistic is compared with its posterior predictive check distribution under the null model to determine whether the data are consistent with the null model The p value indicates the probability of a result more extreme than the data under the posterior predictive check distribution of the test statistic.

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This is an appropriate method for comparing two models when no single parameter, but instead a set of parameters distinguishes them For example, the posterior predictive check distribution can be used to test the validity of a veclass model versus a six-class model The rst step is to draw a set of parameter values from their joint posterior distribution using a Markov chain sampling approach, in particular a data augmentation algorithm The second step is to create a data set the same size as the original using random draws from appropriate distributions with the chosen parameter values The t of the model to this imputed data set is assessed, and this process is repeated many times to obtain a distribution of the t statistic This is a very promising but complex approach, and widely available software for this procedure is not yet available.

Alfons Salden (Telematica Instituut) Jukka T Salo (Nokia) Stephan Steglich (Fraunhofer FOKUS) 4 Editors Authors

Hoijtink (1998; Hoijtink & Molenaar, 1997) presents several examples in which the ts of constrained latent class models are assessed by applying the posterior predictive check distribution to several goodness-of- t statistics A closely related area in which more work is needed is statistical power for testing the t of complex latent class and.

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