Abstract
This chapter introduces permutation methods for multiple matched samples, i.e., randomized-blocks designs. Included in this chapter are six example analyses illustrating computation of exact permutation probability values for randomized-blocks designs, calculation of measures of effect size for randomized-blocks designs, the effect of extreme values on conventional and permutation randomized-blocks designs, exact and Monte Carlo permutation procedures for randomized-blocks designs, application of permutation methods to randomized-blocks designs with rank-score data, and analysis of randomized-blocks designs with multivariate data. Included in this chapter are permutation versions of Fisher’s F test for a one-way randomized-blocks design, Friedman’s two-way analysis of variance for ranks, and a permutation-based alternative for the four conventional measures of effect size for randomized-blocks designs: Hays’ \(\hat {\omega }^{2}\), Pearson’s η 2, Cohen’s partial η 2, and Cohen’s f 2.
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Notes
- 1.
Studies such as these that observe the same or matched subjects for many years are often referred to as “panel studies” and require a different statistical approach.
- 2.
Pearson’s η 2 measure of effect size is often erroneously referred to as the “correlation ratio.” Technically, η is the correlation ratio and η 2 is the differentiation ratio [9, p. 137].
- 3.
Emphasis in the original.
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Berry, K.J., Johnston, J.E., Mielke, P.W. (2019). Randomized-Blocks Designs. In: A Primer of Permutation Statistical Methods. Springer, Cham. https://doi.org/10.1007/978-3-030-20933-9_9
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DOI: https://doi.org/10.1007/978-3-030-20933-9_9
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