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An evaluation of bias ink-factor analysis

โœ Scribed by William L. Vickery

Publisher
Springer-Verlag
Year
1991
Tongue
English
Weight
573 KB
Volume
85
Category
Article
ISSN
0029-8549

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โœฆ Synopsis

Randomization and simulation are used to detect bias in k-factor analysis. In nine previously published data sets there is strong evidence of bias. This may result from either non-independence of observations or the arithmetic relationship used to estimate k-factors, which can generate "spurious correlations". Randomization can be used to test for density dependence without bias. This procedure confirms the existence of densitydependent effects in 8 of the 9 populations and 11 of the 16 k-factors previously thought to have density-dependent effects.

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