A method of factor analysis for shape coordinates. Issue 2 (2nd August 2017)

• Record Type: Journal Article
• Authors: Bookstein, Fred L.
• Is Part Of: American journal of physical anthropology. Volume 164:Issue 2(2017)
• Page Start: 221
• Page End: 245
• Publication Date: 2017-08-02
• Subjects: BE–PwV plot -- geometric morphometrics -- principal components -- shape coordinates -- varimax factor rotation; Physical anthropology -- Periodicals; Anthropology -- Periodicals; Anthropologie physique -- Périodiques; 599.9
• DOI: 10.1002/ajpa.23277
• Languages: English
• ISSNs: 0002-9483

Abstract

Abstract: Currently the most common reporting style for a geometric morphometric (GMM) analysis of anthropological data begins with the principal components of the shape coordinates to which the original landmark data have been converted. But this focus often frustrates the organismal biologist, mainly because principal component analysis (PCA) is not aimed at scientific interpretability of the loading patterns actually uncovered. The difficulty of making biological sense of a PCA is heightened by aspects of the shape coordinate setting that further diverge from our intuitive expectations of how morphometric measurements ought to combine. More than 50 years ago one of our sister disciplines, psychometrics, managed to build an algorithmic route from principal component analysis to scientific understanding via the toolkit generally known as factor analysis . This article introduces a modification of one standard factor‐analysis approach, Henry Kaiser's varimax rotation of 1958, that accommodates two of the major differences between the GMM context and the psychometric context for these approaches: the coexistence of "general" and "special" factors of form as adumbrated by Sewall Wright, and the typical loglinearity of partial warp variance as a function of bending energy. I briefly explain the history of principal components in biometrics and the contrast with factor analysis, introduce the modified varimax algorithm I am recommending, and work three examples that are reanalyses of previously published cranial data sets. A closing discussion emphasizes the desirability of superseding PCA by algorithms aimed at anthropological understanding rather than classification or ordination.