1 June 2006 Transformations of Count Data for Tests of Interaction in Factorial and Split-Plot Experiments
Mark E. Payton, Scott J. Richter, Kristopher L. Giles, Tom A. Royer
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In applied entomological experiments, when the response is a count-type variable, certain transformation remedies such as the square root, logarithm (log), or rank transformation are often used to normalize data before analysis of variance. In this study, we examine the usefulness of these transformations by reanalyzing field-collected data from a split-plot experiment and by performing a more comprehensive simulation study of factorial and split-plot experiments. For field-collected data, significant interactions were dependent upon the type of transformation. For the simulation study, Poisson distributed errors were used for a 2 by 2 factorial arrangement, in both randomized complete block and split-plot settings. Various sizes of main effects were induced, and type I error rates and powers of the tests for interaction were examined for the raw response values, log-, square root-, and rank-transformed responses. The aligned rank transformation also was investigated because it has been shown to perform well in testing interactions in factorial arrangements. We found that for testing interactions, the untransformed response and the aligned rank response performed best (preserved nominal type I error rates), whereas the other transformations had inflated error rates when main effects were present. No evaluations of the tests for main effects or simple effects have been conducted. Potentially these transformations will still be necessary when performing these tests.

Mark E. Payton, Scott J. Richter, Kristopher L. Giles, and Tom A. Royer "Transformations of Count Data for Tests of Interaction in Factorial and Split-Plot Experiments," Journal of Economic Entomology 99(3), 1002-1006, (1 June 2006). https://doi.org/10.1603/0022-0493-99.3.1002
Received: 17 October 2005; Accepted: 1 January 2006; Published: 1 June 2006

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data transformation
experimental analyses
factorial arrangements
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