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A generalized estimating equations approach to mixed-effects ordinal probit models.

Timothy R Johnson1, Jee-Seon Kim

  • 1Department of Statistics, University of Idaho, Moscow, ID 83844-1104, USA. trjohns@uidaho.edu

The British Journal of Mathematical and Statistical Psychology
|October 30, 2004
PubMed
Summary

Generalized estimating equations offer a computationally efficient and robust alternative for analyzing clustered ordinal data in behavioral and educational research, overcoming limitations of traditional mixed-effects models.

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Area of Science:

  • Statistics
  • Behavioral Science
  • Educational Research

Background:

  • Clustered ordinal responses are common in behavioral and educational research.
  • Mixed-effects ordinal probit models are frequently used but computationally intensive.
  • Likelihood-based inference can be inconsistent under model misspecification.

Purpose of the Study:

  • To propose an alternative inferential approach for mixed-effects ordinal probit models.
  • To develop methods that are computationally less demanding than maximum likelihood estimation.
  • To enhance robustness against model misspecification, especially in longitudinal data.

Main Methods:

  • Utilizing generalized estimating equations (GEE).
  • Specifying systems of estimating equations for mixed-effects ordinal probit models.
  • Comparing GEE-based inference with likelihood-based methods.

Main Results:

  • The proposed GEE approach avoids computationally burdensome maximum likelihood estimation.
  • Inferences derived from GEE are robust to certain model misspecifications.
  • This robustness is particularly beneficial for handling serial effects in longitudinal data.

Conclusions:

  • Generalized estimating equations provide a computationally efficient alternative for analyzing clustered ordinal data.
  • The GEE method offers robust statistical inference, improving upon traditional approaches.
  • This approach is valuable for behavioral and educational researchers dealing with complex data structures.