POWER ANALYSIS ON REPEATED MEASURES DESIGNS

Authors

  • Taylor King Department of Statistics North Dakota State University Fargo, ND 58108
  • Curt Doetkott Department of Statistics North Dakota State University Fargo, ND 58108
  • Rhonda Magel Department of Statistics North Dakota State University Fargo, ND 58108

DOI:

https://doi.org/10.53555/eijms.v4i1.17

Keywords:

Variance Components, Compound Symmetry, First-Order Autoregressive, Toeplitz, Unstructured

Abstract

A simulation study comparing powers of the multivariate analysis (PROC GLM) with using a mixed model (PROC MIXED) using a variety of covariance structures for various treatment, time, and interaction affect sizes in a repeated measures design is conducted.  Type 1 errors are estimated.  Powers are estimated for a variety of covariance structures when the actual covariance structure is AR (1).  It was found that the estimated powers for treatment effect were all very similar with PROC MIXED with the correct covariance structure having the largest estimated powers. When testing for time and interaction effect, it was found when in doubt that it was better to use a simpler covariance structure.  The powers were generally higher in this case than with a more complex covariance structure.

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Published

2017-06-27

Issue

Vol. 3 No. 1 (2017): EPH - International Journal of Mathematics and Statistics (ISSN: 2208-2212)

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Articles

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