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Modelling ordered categorical data: recent advances and future challenges.

A Agresti1

  • 1Department of Statistics, University of Florida, Gainesville, Florida 32611-8545, USA. aa@stat.ufl.edu

Statistics in Medicine
|September 4, 1999
PubMed
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This review covers recent progress in modeling ordered categorical data, also known as ordinal data. It highlights advancements, special applications, and future research directions for statistical modeling of ordinal variables.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Ordered categorical (ordinal) data are common in various scientific fields.
  • Traditional statistical models may not fully capture the nuances of ordinal variables.
  • A need exists for advanced modeling techniques to analyze ordinal data effectively.

Purpose of the Study:

  • To summarize recent advancements in the statistical modeling of ordinal response variables.
  • To review established and novel models for ordinal data analysis.
  • To identify challenges and future research directions in ordinal data modeling.

Main Methods:

  • Literature review of ordinal data models from the past 25 years.
  • Survey of extensions for specific applications like repeated measurements and clustered data.

Related Experiment Videos

  • Examination of related methodologies including small-sample analyses and power considerations.

    • Main Results:

    • Comprehensive overview of established and emerging models for ordinal data.
    • Discussion of methodological extensions for complex data structures (e.g., repeated measures, clustering).
    • Identification of practical aspects such as software availability and sample size planning.

    Conclusions:

    • Significant progress has been made in ordinal data modeling.
    • Further research is needed in areas like small-sample inference and complex data structures.
    • Statisticians face ongoing challenges in effectively analyzing ordinal data.