Comparative ethometrics: Congruence of different multivariate analyses applied to the same ethological data

Five statistically appropriate multivariate analyses were applied to the same data on burrowing in the sea hare Aplysia brasiliana to: (1) identify homogeneous subject-related subgroups within a heterogeneous sample, and (2) compare the extent of congruency among the analyses in terms of the number of extracted subgroups and each subject's placement within the subgroups. Raw scores from 32 subjects on ten burrowing parameters were origin-corrected, standardized to z-scores, and normalized in order to facilitate comparisons among the analyses. One to five identified subgroups were extracted which indicated sensitivity differences to sampling variability among the methods. These results suggested that selecting a biologically interpretable analysis represents the subjective aspect of quantitative data treatment. Q-factor analysis (three subgroups) and linear typal analysis (four subgroups) yielded the most biologically interpretable subgroups for these data. Multidimensional scaling (one group) and principal-components analysis (two subgroups) tended to "lump" subjects, while simple distance-function cluster analysis (five subgroups) tended to "split" subjects into additional groups. As a diagonistic tool, multivariate analyses provide insight into underlying dimensions of individual variation and help generate testable hypotheses for guiding future research.