Dimensional analyses of taxonic data

William M. Grove

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Principal component analysis (PCA) and common factor analysis are often used to model latent data structures. Typically, such analyses assume a single population whose correlation or covariance matrix is modelled. However, data may sometimes be unwittingly sampled from mixed populations containing a taxon (nonarbitrary subpopulation) and its complement class. One derives relations between values of PCA parameters within subpopulations and their values in the mixed population. These results are then extended to factor analysis in mixed populations. As relationships between subpopulation and mixed-population principal components and factors sensitively depend on within-subpopulation structures and between-subpopulation differences, naive interpretation of PCA or factor analytic findings can potentially mislead. Several analyses, better suited to the dimensional analysis of admixture data structures, are presented and compared.

Original languageEnglish (US)
Pages (from-to)617-635
Number of pages19
JournalPsychological reports
Volume101
Issue number2
DOIs
StatePublished - Oct 2007

Fingerprint Dive into the research topics of 'Dimensional analyses of taxonic data'. Together they form a unique fingerprint.

Cite this