Measures of Association for Cross Classifications III: Approximate Sampling Theory
作者:
LeoA. Goodman,
WilliamH. Kruskal,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1963)
卷期:
Volume 58,
issue 302
页码: 310-364
ISSN:0162-1459
年代: 1963
DOI:10.1080/01621459.1963.10500850
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
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