# Measures of association for cross classifications

**Leo A. Goodman****William Kruskal**

CiteWeb id: 20120000107

CiteWeb score: 2451

When populations are cross-classified with respect to two or more classifications or polytomies, questions often arise about the degree of association existing between the several polytomies. Most of the traditional measures or indices of association are based upon the standard chi-square statistic or on an assumption of underlying joint normality. In this paper a number of alternative measures are considered, almost all based upon a probabilistic model for activity to which the cross-classification may typically lead. Only the case in which the population is completely known is considered, so no question of sampling or measurement error appears. We hope, however, to publish before long some approximate distributions for sample estimators of the measures we propose, and approximate tests of hypotheses. Our major theme is that the measures of association used by an empirical investigator should not be blindly chosen because of tradition and convention only, although these factors may properly be g...

**Measures of association for cross classifications**" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 1000. Additionally, the publicaiton "

**Measures of association for cross classifications**" is placed in the Top 1000 among other scientific works published in 2012.

- www.jstor.org/stable/2281536
- amstat.tandfonline.com/doi/pdf/10.1080/01621459.1954.10501231
- web.abo.fi/fak/mnf/mate/jc/statistik1/GoodmanKruskalGamma.pdf
- www.jstor.org/pss/2281536
- amstat.tandfonline.com/doi/abs/10.1080/01621459.1954.10501231
- www.tandfonline.com/doi/abs/10.1080/01621459.1954.10501231?tab=permissions
- onlinelibrary.wiley.com/doi/10.2307/3315307/pdf
- ci.nii.ac.jp/ncid/BA0035411X
- dx.doi.org/10.1080/01621459.1954.10501231
- link.springer.com/chapter/10.1007/978-1-4612-9995-0_1

Leo A. Goodman, William Kruskal,

Measures of association for cross classifications(2012)## HTML code:

## Wiki code: