NDLI: Incorporating variance uncertainty into a power analysis of monitoring designs

Content Provider	Springer Nature Link
Author	Sims, Michelle Elston, David A. Harris, Michael P. Wanless, Sarah
Copyright Year	2007
Abstract	Power calculations usually assume that the components of the population variance are known, but it is frequently the case that they are estimated using data from a pilot study. Imprecision in the estimates is then ignored and a single value for power is generated. We present a method that incorporates the error in the estimates of any number of variance components into the power calculations. We show that, by sampling values for the variance components from the residual likelihood function of the pilot data, our method can approximate the distribution of powers expected given the uncertainty in the variance components. Alternative summary measures of power can then be derived: we strongly recommend treating a minimum acceptable power as a quality standard and summarizing power in terms of the probability that this quality standard is attained. The method is illustrated by application to counts of common guillemots (Uria aalge) on the Isle of May in Scotland to assess the power of detecting long-term trends in abundance using a model for random variation with seven parameters.
Starting Page	236
Ending Page	249
Page Count	14
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	12
Issue Number	2
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Guillemot Mixed model Monte Carlo Quality standard Residual likelihood Seabird Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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