Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis

Rolf Langeheine, Jeroen Pannekoek and Frank van de Pol
Additional contact information
Rolf Langeheine: University of Kiel, Germany
Jeroen Pannekoek: Statistics Netherlands
Frank van de Pol: Statistics Netherlands

Sociological Methods & Research, 1996, vol. 24, issue 4, 492-516

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.

Date: 1996
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0049124196024004004 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:24:y:1996:i:4:p:492-516

DOI: 10.1177/0049124196024004004

Access Statistics for this article

More articles in Sociological Methods & Research
Bibliographic data for series maintained by SAGE Publications ().