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Modelling ordered categorical data: recent advances and future challenges
Author(s) -
Agresti Alan
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990915/30)18:17/18<2191::aid-sim249>3.0.co;2-m
Subject(s) - categorical variable , ordinal data , computer science , sample size determination , data science , ordinal regression , sample (material) , cluster analysis , data mining , econometrics , statistics , artificial intelligence , machine learning , mathematics , chemistry , chromatography
This article summarizes recent advances in the modelling of ordered categorical (ordinal) response variables. We begin by reviewing some models for ordinal data introduced in the literature in the past 25 years. We then survey recent extensions of these models and related methodology for special types of applications, such as for repeated measurement and other forms of clustering. We also survey other aspects of ordinal modelling, such as small‐sample analyses, power and sample size considerations, and availability of software. Throughout, we suggest problem areas for future research and we highlight challenges for statisticians who deal with ordinal data. Copyright © 1999 John Wiley & Sons, Ltd.