Bootstrapping goodness-of-fit measures in categorical data analysis  

Oleh:

Langeheine, Rolf ; Pol, Frank Van De ; Pannekoek, Jeroen

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 24 no. 04 (May 1996), page 492-516.

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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 nonnative 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.

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