, 74:97 | Cite as

A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes

  • Daniel J. Bauer
Theory and Methods


When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.


categorical data mixed model multilevel model ordinal binary 


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Copyright information

© The Psychometric Society 2008

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of North CarolinaChapel HillUSA

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