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Measures of Association for Cross Classifications III: Approximate Sampling Theory

  • Leo A. Goodman
  • William H. Kruskal
Part of the Springer Series in Statistics book series (SSS)

Abstract

The population measures of association for cross classifications, discussed in the authors’ prior publications, have sample analogues that are approximately normally distributed for large samples. (Some qualifications and restrictions are necessary.) These large sample normal distributions with their associated standard errors, are derived for various measures of association and various methods of sampling. It is explained how the large sample normality may be used to test hypotheses about the measures and about differences between them, and to construct corresponding confidence intervals. Numerical results are given about the adequacy of the large sample normal approximations. In order to facilitate extension of the large sample results to other measures of association, and to other modes of sampling, than those treated here, the basic manipulative tools of large sample theory are explained and illustrated.

Keywords

Maximum Likelihood Estimator Asymptotic Distribution Asymptotic Approximation Asymptotic Variance Multivariate Normal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York 1979

Authors and Affiliations

  • Leo A. Goodman
    • 1
  • William H. Kruskal
    • 1
  1. 1.University of ChicagoUSA

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