Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models

Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This a...

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Bibliographic Details
Published in:Journal of educational and behavioral statistics 2014-06, Vol.39 (3), p.167-179
Main Author: Wagler, Amy E.
Format: Article
Language:English
Subjects:
Asymptotic methods
Bootstrap method
Cluster Grouping
Computation
Confidence interval
Confidence intervals
Error rates
Generalized linear models
Heterogeneous Grouping
Hierarchical Linear Modeling
Interval estimators
Intervals
Logistics
Mathematical independent variables
Modeling
Monte Carlo Methods
Musical intervals
Random variables
Reading
Regression (Statistics)
School campuses
Scores
Simulation
Statistical variance
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Description
Summary:Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for estimating the heterogeneity due to clustering on a scale that is easy to interpret. The performance of the proposed asymptotic intervals andpercentile bootstrap intervals are compared by simulations and in an application.
ISSN:1076-9986
1935-1054
DOI:10.3102/1076998614529159