Journal article
Improved tests of independence in singly-ordered two-way contingency tables
Computational statistics & data analysis, Vol.68, pp.339-351
12/2013
DOI: 10.1016/j.csda.2013.06.014
Abstract
A new approach is described for improving statistical tests of independence between two categorical variables R and C, where C is ordinal and R may or may not be ordinal. Common tests of independence that exploit the ordinality of C use a restricted-alternative approach. A different, relaxed-null approach to improving tests of independence is considered. Specifically, the M-moment score test is introduced and shown to be an attractive alternative to well known restricted-alternative tests, such as the row-means Cochran–Mantel–Haenszel test, the Kruskal–Wallis test, and the likelihood-ratio test based on the cumulative-logit row-effects model or the log-linear row-effects model. Unlike these restricted-alternative tests, which are designed to detect location shifts, the M-moment score test is designed to be powerful for detecting shifts in any of the first M conditional moments of C across the values of R. Using multinomial–Poisson homogeneous modeling theory, the M-moment score tests are shown to be computationally and conceptually simple, with an attractive complement consistency property. Results of a simulation study compare the M-moment score test to several other commonly-used tests on the basis of their operating characteristics.
Details
- Title: Subtitle
- Improved tests of independence in singly-ordered two-way contingency tables
- Creators
- Joseph B Lang - Department of Statistics and Actuarial Science, University of Iowa, IA, USAMaria Iannario - Department of Political Sciences, University of Naples Federico II, Naples, Italy
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.68, pp.339-351
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.csda.2013.06.014
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Language
- English
- Date published
- 12/2013
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9983985843602771
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