Solutions for determining the significance region using the Johnson-Neyman type procedure in generalized linear (mixed) models

Journal of Educational and Behavioral Statistics

Volumn 36, Issue 6, 2011, Pages 699-719

  Lazar, Ann A ^a   Zerbe, Gary O ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF COLORADO   (United States)

Author keywords

generalized estimating equations; hierarchical linear models; longitudinal analysis; multiple testing; simultaneous testing

Indexed keywords

EID: 82455167338     PISSN: 10769986     EISSN: None     Source Type: Journal    
DOI: 10.3102/1076998610396889     Document Type: Article

References (23)

1. Coleman J.,Hoffer T.,Kilgore S.High school achievement: Public, Catholic, and other schools compared. New York, NY: Basic Books; 1982:.
2. Dempster A. P.,Rubin D. B.,Tsutakawa R. K.Estimation in covariance components model.Journal of the American Statistical Association. 1981;76:341-353.
3. Hunka S.Identifying regions of significance in ANCOVA problems having nonhomogeneous regressions.British Journal of Mathematical and Statistical Psychology. 1995;48:161-188.
4. Hunka S.,Leighton J.Defining Johnson-Neyman regions of significance in the three-covariate ANCOVA using Mathematica.Journal of Education and Behavioral Statistics. 1997;22:361-387.
5. Johnson P. O.,Fay L. C.The Johnson-Neyman technique, its theory and application.Psychometrika. 1950;15:349-367.
6. Johnson P. O.,Neyman J.Tests of certain linear hypothesis and their application to some educational problems.Statistical Research Memoirs. 1936;1:57-93.
7. Kenward M. G.,Roger J. H.Small sample inference for fixed effects from restricted maximum likelihood.Biometrics. 1997;53:993-997.
8. Littell R. C.,Milliken G. A.,Stroup W. W.,Wolfinger R. D.,Schabenberger O.SAS for mixed models second edition. Cary, NC: SAS Institute; 2006:756.
9. McCullagh P.,Nelder J. A.Generalized linear models. 1989). Generalized linear models2nd ed.).London: Chapman and Hall; 1989:.
10. McCulloch C. E.,Searle S. R.Generalized, linear and mixed models. New York, NY: John Wiley; 2001:.
11. Miyazaki Y.,Maier K. S.Johnson-Neyman type technique in hierarchical linear models.Journal of Education and Behavioral Statistics. 2005;30:233-259.
12. Molenberghs G.,Verbeke G.Meaningful statistical model formulations for repeated measures.Statistica Sinica. 2004;14:989-1020.
13. Molenberghs G.,Verbeke G.Models for discrete longitudinal data. New York: Springer; 2005:41.
14. Potthoff R. F.On the Johnson-Neyman technique and some extensions thereof.Psychometrika. 1964;29:241-256.
15. Raudenbush S. W.,Bryk A. S.,Cheong Y. F.,Congdon R.HLM5-Hierarchical linear and nonlinear modeling. Chicago, IL: Scientific Software International; 2000:.
16. Raudenbush S. W.,Bryk A. S.Hierarchical linear models: Applications and data analysis methods. 2002). Hierarchical linear models: Applications and data analysis methods2nd ed.).Newbury Park, CA: Sage; 2002:.
17. Schaalje B. G.,McBride J. B.,Fellingham G. W.Approximation to distributions of test statistics in complex mixed linear models using SAS Proc MIXED. Proceedings of the Twenty-Sixth Annual SAS Users Group International Conference; 2001SAS Institute; 2001.
18. Scheffé HThe analysis of variance. New York, NY: Wiley; 1959:.
19. Snedecor W. G.,Cochran W. G.Statistical methods. 1989). Statistical methods8th ed.).Ames: Iowa State University Press; 1989:.
20. Steffens F. E.On comparing two simple linear regression lines.South African Statistical Journal. 1968;2:33-53.
21. Steffens F. E.On confidence sets for the ratio of two normal means.South African Statistical Journal. 1971;5:105-113.
22. Zerbe G. O.On Fieller's theorem and the general linear model.The American Statistician. 1978;32:103-105.
23. Zerbe G. O.,Archer P. G.,Banchero N.,Lechner A. J.On comparing regression lines with unequal slopes.American Journal of Physiology: Regulatory, Integrative and Comparative Physiology. 1982;242:178-180.