Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis

An algorithm is presented for estimating the distribution of a fit measure by drawing bootstrap samples from the model-expected proportions, the so-called nonnaive bootstrap method, from five different data sets, and results show that the asymptotic chi-square distribution is not at all valid for sparse data.

Abstract

When sparse data have to be fitted to a log-linear or latent class model, one cannot use the theoretical chi-square distribution to evaluate model fit, because with sparse data the observed cross-table has too many cells in relation to the number of observations to use a distribution that only holds asymptotically. The choice of a theoretical distribution is also difficult when model-expected frequencies are 0 or when model probabilities are estimated 0 or 1. The authors propose to solve these problems by estimating the distribution of a fit measure, using bootstrap methods. An algorithm is presented for estimating this distribution by drawing bootstrap samples from the model-expected proportions, the so-called nonnaive bootstrap method. For the first time the method is applied to empirical data of varying sparseness, from five different data sets. Results show that the asymptotic chi-square distribution is not at all valid for sparse data.