ERIC Number: EJ829940
Record Type: Journal
Publication Date: 2009-Mar
Abstractor: As Provided
Reference Count: 0
A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes
Bauer, Daniel J.
Psychometrika, v74 n1 p97-105 Mar 2009
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.
Descriptors: Goodness of Fit, Computation, Models, Predictor Variables, Mathematical Models, Correlation, Longitudinal Studies, Research Methodology, Evaluation Methods, Psychometrics
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Authoring Institution: N/A