Abstract
Multivariate permutation tests have advantages over conventional methods in analyzing repeated measures designs. The tests are exact for all sample sizes regardless of the underlying population distribution from which the observations are selected. More importantly the tests do not require a priori assumptions about the form of the correlation structure, obviating the need to check Huynh-Feldt conditions. An example is given of how a multivariate permutation test may be conducted in a context frequently encountered in agricultural research. The SAS program corresponding to this example is also given.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Higgins, James J. and Noble, William
(1993).
"A PERMUTATION TEST FOR A REPEATED MEASURES DESIGN,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1386
A PERMUTATION TEST FOR A REPEATED MEASURES DESIGN
Multivariate permutation tests have advantages over conventional methods in analyzing repeated measures designs. The tests are exact for all sample sizes regardless of the underlying population distribution from which the observations are selected. More importantly the tests do not require a priori assumptions about the form of the correlation structure, obviating the need to check Huynh-Feldt conditions. An example is given of how a multivariate permutation test may be conducted in a context frequently encountered in agricultural research. The SAS program corresponding to this example is also given.
