A note on interobserver error in multivariate analyses of populations

Abstract

Abstract The effect of interobserver error on a principal components analysis of a small sample of human crania is examined. A comparison of individual specimen scores for components is made to find rotated principal components which identify interobserver error. The individual variables which load highly on such components are then tested for interobserver error univariately. Multivariate components which must identify interobserver error contain no high loadings for variables which demonstrate interobserver error in the univariate case. Principal component analysis, in defining new component variables, extracts such error in an easily identified way which makes comparison of samples measured by more than one anthropometrist more reliable.

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