Analysis of Ordered Categorical Data: Two Score-Independent Approaches
Article first published online: 11 FEB 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1276–1279, December 2008
How to Cite
Zheng, G. (2008), Analysis of Ordered Categorical Data: Two Score-Independent Approaches. Biometrics, 64: 1276–1279. doi: 10.1111/j.1541-0420.2008.00992.x
- Issue published online: 24 NOV 2008
- Article first published online: 11 FEB 2008
- Received November 2007. Revised November 2007. Accepted November 2007.
- Ordered categorical data;
- Parametric bootstrap;
- Robust test;
Summary A trend test is often employed to analyze ordered categorical data, in which a set of increasing scores is assigned a priori. There is a drawback in this approach, because how to choose a set of scores is not clear. There have been debates on which scores should be used (e.g., Graubard and Korn, 1987, Biometrics 43, 471–476; Ivanova and Berger, 2001, Biometrics 57, 567–570; Senn, 2007, Biometrics 63, 296–298). Conflicting conclusions are often obtained with different sets of scores. Two approaches, which have been applied to genetic case–control studies, are appealing for ordered categorical data, because they take into account the natural order in the data, are score independent, and not contingent on asymptotic theory. These two approaches are applied to a prospective study for detecting association between maternal drinking and congenital malformations.