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The latent class model is a powerful unsupervised clustering algorithm forcategorical data. Many statistics exist to test the fit of the latent classmodel. However, traditional methods to evaluate those fit statistics are notalways useful. Asymptotic distributions are not always known, and empiricalreference distributions can be very time consuming to obtain. In this paper wepropose a fast resampling scheme with which any type of model fit can beassessed. We illustrate it here on the latent class model, but the methodologycan be applied in any situation.  The principle behind the lazy bootstrap method is to specify a statisticwhich captures the characteristics of the data that a model should capturecorrectly. If those characteristics in the observed data and in model-generateddata are very different we can assume that the model could not have producedthe observed data. With this method we achieve the flexibility of tests fromthe Bayesian framework, while only needing maximum likelihood estimates. Weprovide a step-wise algorithm with which the fit of a model can be assessedbased on the characteristics we as researcher find important. In a Monte Carlostudy we show that the method has very low type I errors, for all illustratedstatistics. Power to reject a model depended largely on the type of statisticthat was used and on sample size. We applied the method to an empirical dataset on clinical subgroups with risk of Myocardial infarction and compared theresults directly to the parametric bootstrap. The results of our method werehighly similar to those obtained by the parametric bootstrap, while therequired computations differed three orders of magnitude in favour of ourmethod.

The Lazy Bootstrap. A Fast Resampling Method for Evaluating Latent Class Model Fit