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Analysis of Ordered Categorical Data: Two Score‐Independent Approaches
Author(s) -
Zheng Gang
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.00992.x
Subject(s) - categorical variable , computer science , statistics , mathematics
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.