Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models

Ann A. Lazar and Gary O. Zerbe
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Ann A. Lazar: University of California, San Francisco
Gary O. Zerbe: University of Colorado, Denver

Journal of Educational and Behavioral Statistics, 2011, vol. 36, issue 6, 699-719

Abstract: Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the “significance region,†or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA), the Johnson-Neyman procedure can be used to determine the significance region; for the hierarchical linear model (HLM), the Miyazaki and Maier (M-M) procedure has been suggested. However, neither procedure can assume nonnormally distributed data. Furthermore, the M-M procedure produces biased (downward) results because it uses the Wald test, does not control the inflated Type I error rate due to multiple testing, and requires implementing multiple software packages to determine the significance region. In this article, we address these limitations by proposing solutions for determining the significance region suitable for generalized linear (mixed) model (GLM or GLMM). These proposed solutions incorporate test statistics that resolve the biased results, control the Type I error rate using Scheffé’s method, and uses a single statistical software package to determine the significance region.

Keywords: generalized estimating equations; hierarchical linear models; longitudinal analysis; multiple testing; simultaneous testing (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:36:y:2011:i:6:p:699-719

DOI: 10.3102/1076998610396889

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