Analysis of Nominal Data

By: Reynolds, H.TMaterial type: TextTextSeries: Quantitative applications in the social sciences, no. 07-007Publication details: Beverly Hills, Calif. : Sage Publications, 1984Edition: 2nd edDescription: 85 pagesISBN: 9780803906532; 0803906536 Subject(s): Social sciences -- Statistical methodsDDC classification: 519.5
Contents:
1. Introduction -- Preliminaries -- Analyzing nominal data -- 2. Chi square test -- Interpreting the chi square test -- 3. Measures of association -- Introduction -- Measures of association for 2 x 2 tables -- Measures of association for I x J tables -- Comparing measures of association -- 4. Introduction to multivariate data analysis -- The causal analysis of nominal data -- 5. Conclusions.
Summary: The updated second edition offers expanded discussions of the chi square test of significance and the potential measures of association available for use with categoric data. Reviewing basic techniques in analysis of nominal data, this paper employs survey research data on party identification and ideologies to indicate which measures and tests are most appropriate for particular theoretical concerns. This book serves as an ideal primer for Volume 20, Log-Linear Models.
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1. Introduction --
Preliminaries --
Analyzing nominal data --
2. Chi square test --
Interpreting the chi square test --
3. Measures of association --
Introduction --
Measures of association for 2 x 2 tables --
Measures of association for I x J tables --
Comparing measures of association --
4. Introduction to multivariate data analysis --
The causal analysis of nominal data --
5. Conclusions.

The updated second edition offers expanded discussions of the chi square test of significance and the potential measures of association available for use with categoric data. Reviewing basic techniques in analysis of nominal data, this paper employs survey research data on party identification and ideologies to indicate which measures and tests are most appropriate for particular theoretical concerns. This book serves as an ideal primer for Volume 20, Log-Linear Models.

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