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Prospective evaluation of designs for analysis of variance without knowledge of effect sizes

Prospective evaluation of designs for analysis of variance without knowledge of effect sizes
Prospective evaluation of designs for analysis of variance without knowledge of effect sizes
Estimation of design power requires knowledge of treatment effect size and error variance, which are often unavailable for ecological studies. In the absence of prior information on these parameters, investigators can compare an alternative to a reference design for the same treatment(s) in terms of its precision at equal sensitivity. This measure of relative performance calculates the fractional error variance allowed of the alternative for it to just match the power of the reference. Although first suggested as a design tool in the 1950s, it has received little analysis and no uptake by environmental scientists or ecologists. We calibrate relative performance against the better known criterion of relative efficiency, in order to reveal its unique advantage in controlling sensitivity when considering the precision of estimates. The two measures differ strongly for designs with low replication. For any given design, relative performance at least doubles with each doubling of effective sample size. We show that relative performance is robustly approximated by the ratio of reference to alternative ? ? quantiles of the F F distribution, multiplied by the ratio of alternative to reference effective sample sizes. The proxy is easy to calculate, and consistent with exact measures. Approximate or exact measurement of relative performance serves a useful purpose in enumerating trade-offs between error variance and error degrees of freedom when considering whether to block random variation or to sample from a more or less restricted domain
1352-8505
239-261
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Davey, Andrew J.H.
86f3030f-9c68-4708-9fa0-508c5361cf98
Dixon, Philip M.
4c966f02-98eb-40b2-907b-2c95f64a8636
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Davey, Andrew J.H.
86f3030f-9c68-4708-9fa0-508c5361cf98
Dixon, Philip M.
4c966f02-98eb-40b2-907b-2c95f64a8636

Doncaster, C. Patrick, Davey, Andrew J.H. and Dixon, Philip M. (2014) Prospective evaluation of designs for analysis of variance without knowledge of effect sizes. Environmental and Ecological Statistics, 21, 239-261. (doi:10.1007/s10651-013-0253-4).

Record type: Article

Abstract

Estimation of design power requires knowledge of treatment effect size and error variance, which are often unavailable for ecological studies. In the absence of prior information on these parameters, investigators can compare an alternative to a reference design for the same treatment(s) in terms of its precision at equal sensitivity. This measure of relative performance calculates the fractional error variance allowed of the alternative for it to just match the power of the reference. Although first suggested as a design tool in the 1950s, it has received little analysis and no uptake by environmental scientists or ecologists. We calibrate relative performance against the better known criterion of relative efficiency, in order to reveal its unique advantage in controlling sensitivity when considering the precision of estimates. The two measures differ strongly for designs with low replication. For any given design, relative performance at least doubles with each doubling of effective sample size. We show that relative performance is robustly approximated by the ratio of reference to alternative ? ? quantiles of the F F distribution, multiplied by the ratio of alternative to reference effective sample sizes. The proxy is easy to calculate, and consistent with exact measures. Approximate or exact measurement of relative performance serves a useful purpose in enumerating trade-offs between error variance and error degrees of freedom when considering whether to block random variation or to sample from a more or less restricted domain

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More information

Published date: 2014
Organisations: Centre for Biological Sciences

Identifiers

Local EPrints ID: 356863
URI: http://eprints.soton.ac.uk/id/eprint/356863
ISSN: 1352-8505
PURE UUID: 505a351b-f913-4c08-ac9f-2a626591e011
ORCID for C. Patrick Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

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Date deposited: 16 Sep 2013 10:23
Last modified: 20 Jul 2019 01:18

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