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from information theory Normally, the most commonly used significance test when analyzing latent trait models, that is, the likelihood ratio test, is not appropriate for testing the fit of latent class models This is often because the likelihood ratio statistic does not always have an asymptotic 2 distribution Rost (1990, 1991, 2004) suggests three such indices: Akaike s information criterion (AIC; Akaike, 1973), the best information criterion (BIC; Schwarz, 1978), and the consistent Akaike s information criterion (CAIC; Bozdogan, 1987) The BIC is superior to AIC when the number of items is large and pattern frequencies are small (Rost, 2004) The CAIC, in turn, is recommended for its robustness (Bozdogan, 1987) Both CAIC and BIC additionally include sample size, unlike AIC The use of the CAIC index is frequently recommended (Read & Cressie, 1988).

Further research was undertaken with dogs differing in temperament, one type being extremely excitable, the other type extremely inhibitable The results led Pavlov to conclude that experimental neurosis develops only in animals of extreme types, and the symptoms of neurosis differ in the two types and relate to the characteristics of the animals different nervous organizations Coincidentally, a violent storm ooded Petrograd, and the dogs had to swim from the kennels to the laboratory After this experience some of them displayed disturbed behavior similar to the experimental neuroses obtained in the laboratory Pavlov concluded that a development of a chronic pathological state of the hemispheres can occur from rst a con ict between excitation and inhibition which the cortex nds itself unable to resolve; second the action of extremely powerful and unusual stimuli (Pavlov, 1957, p 318).

One solution applies when controlling model validity: The smaller the indices (AIC, BIC, or CAIC), the better the fit between the model solution and the data Because there is no formal decision rule for a test model based on significance tests, it is necessary to include criteria of empirical validity and simplicity when deciding on a test model However, the particular strength of these indices is that they use the same information ( likelihoods and estimated model parameters) as the likelihood-quotient tests One advantage of these indices is that it is still possible to compare models with each other even when there is no hierarchic relation between them, as, for example, between latent class and Rasch models (Read & Cressie, 1988; Rost, 2004) With the help of information indices, the mixed Rasch model (Rost, 1990) can be used as a model test of person homogeneity in latent trait models.

Pavlov linked the phenomena seen in laboratory studies of animals with those seen in human psychopathology in a manner that was to in uence experimental psychopathology for several decades to come Much later, Howard Liddell at Cornell University investigated the adaptive behavior of sheep and goats in which the thyroid had been removed Using a mild shock, Liddell was interested in how long the animal would require to make a conditioned anticipatory response Both animals with and without thyroid glands learned this quite readily Because of time pressure to complete his experiments, he increased the number of trials at each testing session This had the unexpected effect of producing an experimental neurosis in certain sheep They showed excitement, alarm, struggled to escape, showed rapid heart rate and breathing, micturation.

However, in this case, groups are formed not on the basis of manifest criteria (as in Andersen s, 1973, CLR test) but on the basis of latent classes This is the strictest conceivable criterion, because maximum person heterogeneity exists between the classes Accordingly, Rost and von Davier (1995) have been able to show that a test of person homogeneity based on the mixed Rasch model is more sensitive than Andersen s CLR test (1973) The weakness of information indices is, first, that they are sample-dependent In other words, an increasing number of cases can lead to an inflation of the number of classes, and thus to overparameterizations Nonetheless, this weakness is also found in the inferential statistics mentioned previously (McDonald & Marsh, 1990) Second, no rational criterion (such as significance limits) is available.

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