Evaluating the robustness of repeated measures analyses: The case of small sample sizes and nonnormal data-Reference-Cited by-同舟云学术

Evaluating the robustness of repeated measures analyses: The case of small sample sizes and nonnormal data

Published:2012-11-25 Issue:3 Volume:45 Page:792-812
ISSN:1554-3528
Container-title:Behavior Research Methods
language:en
Short-container-title:Behav Res

Author:

Oberfeld Daniel,Franke Thomas

Publisher

Springer Science and Business Media LLC

Subject

General Psychology,Psychology (miscellaneous),Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Experimental and Cognitive Psychology

Link

http://link.springer.com/content/pdf/10.3758/s13428-012-0281-2.pdf

Reference106 articles.

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3. Algina, J., & Keselman, H. J. (1998). A power comparison of the Welch–James and improved general approximation tests in the split-plot design. Journal of Educational and Behavioral Statistics, 23, 152–169.

4. Algina, J., & Oshima, T. C. (1994). Type I error rates for Huynh’s general approximation and improved general approximation tests. British Journal of Mathematical and Statistical Psychology, 47, 151–165.

5. Arnau, J., Bono, R., & Vallejo, G. (2009). Analyzing small samples of repeated measures data with the mixed-model adjusted F test. Communications in Statistics: Simulation and Computation, 38, 1083–1103. doi: 10.1080/03610910902785746

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