Using Principal Components and Factor Analysis in Animal Behaviour Research: Caveats and Guidelines

Authors


Dr Sergey V. Budaev, Centre for Neuroscience, School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK.
E-mail: sbudaev@gmail.com

Abstract

Principal component (PCA) and factor analysis (FA) are widely used in animal behaviour research. However, many authors automatically follow questionable practices implemented by default in general-purpose statistical software. Worse still, the results of such analyses in research reports typically omit many crucial details which may hamper their evaluation. This article provides simple non-technical guidelines for PCA and FA. A standard for reporting the results of these analyses is suggested. Studies using PCA and FA must report: (1) whether the correlation or covariance matrix was used; (2) sample size, preferably as a footnote to the table of factor loadings; (3) indices of sampling adequacy; (4) how the number of factors was assessed; (5) communalities when sample size is small; (6) details of factor rotation; (7) if factor scores are computed, present determinacy indices; (8) preferably they should publish the original correlation matrix.

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