In:
Journal of Educational and Behavioral Statistics, American Educational Research Association (AERA), Vol. 36, No. 6 ( 2011-12), p. 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.
Type of Medium:
Online Resource
ISSN:
1076-9986
,
1935-1054
DOI:
10.3102/1076998610396889
Language:
English
Publisher:
American Educational Research Association (AERA)
Publication Date:
2011
detail.hit.zdb_id:
1225314-5
detail.hit.zdb_id:
2174169-4
SSG:
5,3
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