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Ordered Regression Models: a Tutorial.

Andrew S Fullerton1, Kathryn Freeman Anderson2

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Summary
This summary is machine-generated.

Analyzing ordinal outcomes requires a systematic approach. This study recommends prioritizing flexible ordered models and carefully considering ordinal independent variables for robust social, behavioral, and health science research.

Keywords:
Ordered logitOrdered regression modelsOrdinal variables

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Area of Science:

  • Social Sciences
  • Behavioral Sciences
  • Health Sciences

Background:

  • Ordinal outcomes are prevalent across various scientific disciplines.
  • Current analytical methods lack a universally accepted standard, leading to inconsistent data treatment.
  • Researchers often employ diverse, sometimes arbitrary, recoding strategies for ordinal data.

Purpose of the Study:

  • To advocate for a systematic methodology in analyzing ordinal outcomes.
  • To highlight the utility and flexibility of under-utilized ordered regression models.
  • To address challenges associated with incorporating ordinal independent variables into analyses.

Main Methods:

  • Presentation of a range of ordered regression models.
  • Discussion of theoretical and empirical considerations for model selection.
  • Illustrative analysis using general self-rated health data.

Main Results:

  • Ordered models offer superior flexibility for analyzing ordinal data compared to linear, binary, or count models.
  • Specific guidance is provided for the justifiable inclusion of ordinal independent variables.
  • The empirical example demonstrates practical application of recommended ordered regression techniques.

Conclusions:

  • A standardized, theoretically grounded approach to ordinal data analysis is crucial.
  • Prioritizing ordered models enhances analytical rigor and interpretability.
  • Further research and adoption of systematic methods will improve the analysis of ordinal outcomes in applied research.