Abstract
One aspect of statistical consulting is assessing a clients needs. Sometimes the need for simplicity beclouds the information contained in the experiment. As an example, an experiment was performed as a multivariate study with repeated measures, yet the client preferred numerous univariate analyses that ignored time. The challenge was to show how a more sophisticated analysis provided additional insight into the biological process. Various covariance structures were employed to illustrate the usefulness of progressively more complex analyses. Multivariate methods were performed to utilize the correlation among variables to illuminate biological concepts. To complicate the whole process, an additional problem occurred where extreme variability among experimental units within treatment groups led to the identification and clumping of homogeneous units to increase precision. Power studies were performed to determine required sample sizes to minimize potential error reoccurrences in future trials.
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Recommended Citation
Milliken, April J. and Parkhurst, Anne M.
(1993).
"OVERCOMING RESISTANCE TO MULTIVARIATE ANALYSIS OVER TIME,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1384
OVERCOMING RESISTANCE TO MULTIVARIATE ANALYSIS OVER TIME
One aspect of statistical consulting is assessing a clients needs. Sometimes the need for simplicity beclouds the information contained in the experiment. As an example, an experiment was performed as a multivariate study with repeated measures, yet the client preferred numerous univariate analyses that ignored time. The challenge was to show how a more sophisticated analysis provided additional insight into the biological process. Various covariance structures were employed to illustrate the usefulness of progressively more complex analyses. Multivariate methods were performed to utilize the correlation among variables to illuminate biological concepts. To complicate the whole process, an additional problem occurred where extreme variability among experimental units within treatment groups led to the identification and clumping of homogeneous units to increase precision. Power studies were performed to determine required sample sizes to minimize potential error reoccurrences in future trials.
