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


Dr Sergey V. Budaev, Centre for Neuroscience, School of Life Sciences, University of Sussex, Brighton BN1 9QG, UK.


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.