title: An evaluation of bias in k-factor analysis.
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.
authors.affiliation: Département des Sciences biologiques, University du Québec à Montréal, Succ. "A", C.P. 8888, Montréal, Qc, H3C 3P8, Canada.
authors.forename: William L
authors.initials: WL
authors.lastname: Vickery
keywords: Animal populations; Bias; Density dependence; k-factor analysis