Measures of Association for Cross Classifications

Measures of Association for Cross Classifications
Author: L. A. Goodman
Publisher: Springer Science & Business Media
Total Pages: 156
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461299950

In 1954, prior to the era of modem high speed computers, Leo A. Goodman and William H. Kruskal published the fmt of a series of four landmark papers on measures of association for cross classifications. By describing each of several cross classifications using one or more interpretable measures, they aimed to guide other investigators in the use of sensible data summaries. Because of their clarity of exposition, and their thoughtful statistical approach to such a complex problem, the guidance in this paper is as useful and important today as it was on its publication 25 years ago. in a cross-classification by a single number inevita Summarizing association bly loses information. Only by the thoughtful choice of a measure of association can one hope to lose only the less important information and thus arrive at a satisfactory data summary. The series of four papers reprinted here serve as an outstanding guide to the choice of such measures and their use.

Nonparametric Measures of Association

Nonparametric Measures of Association
Author: Jean Dickinson Gibbons
Publisher: SAGE
Total Pages: 108
Release: 1993-02-25
Genre: Reference
ISBN: 9780803946644

Aimed at helping the researcher select the most appropriate measure of association for two or more variables, the author clearly describes such techniques as Spearman's rho, Kendall's tau, Goodman and Kruskals' gamma and Somer's d and carefully explains the calculation procedures as well as the substantive meaning of each measure.

Measures of Association

Measures of Association
Author: Albert M. Liebetrau
Publisher: SAGE
Total Pages: 100
Release: 1983-04
Genre: Reference
ISBN: 9780803919747

Clearly reviews the properties of important contemporary measures of association and correlation. Liebetrau devotes full chapters to measures for nominal, ordinal, and continuous (interval) data, paying special attention to the sampling distributions needed to determine levels of significance and confidence intervals. Valuable discussions also focus on the relationships between various measures, the sampling properties of their estimators and the comparative advantages and disadvantages of different approaches.