期刊
PSYCHOMETRIKA
卷 74, 期 1, 页码 97-105出版社
SPRINGER
DOI: 10.1007/s11336-008-9080-1
关键词
categorical data; mixed model; multilevel model; ordinal; binary
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.
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