An evaluation of bias in k-factor analysis
1Département des Sciences biologiques, University du Québec à Montréal, Succ. "A", C.P. 8888, Montréal, Qc, H3C 3P8, Canada.
Abstract:
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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