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Principal Component Analysis (PCA) table of the full dataset on N = 23 variables, showing correlation coefficients for variables loaded on the 3 PCs extracted from the analysis of data from 27 participants.

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posted on 26.09.2016, 17:40 by Deanna Gigliotti, Jeff R. S. Leiter, Peter B. MacDonald, Jason Peeler, Judy E. Anderson

In the row for each variable, numbers indicate the strength of correlation of that variable with the eigenvector of each PC. When the absolute value of correlation coefficients was ≥ 0.3, they were considered important (bold font) in defining the PC. Variables loaded on PCs 1–3 below, appear in the PCA plot (Fig 4A) with an asterisk (*) to indicate they also project upward (since PC3 is perpendicular to axes for PCs 1 and 2).