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Squared multiple correlations for endogenous variables indicated substantial variance in engagement and commitment accounted for by their respective predictors.
The standardized factor weights, as well as the items' squared multiple correlations for the one-factor solution are presented in Fig. 1.
The analyses produced multiple correlations for each variable, as there are several combinations of decision options.
The squared multiple correlations for the child scales ranged from 0.62 to 0.87 and were therefore within an acceptable range.
Multiple correlations for a pair of genes from different datasets were then combined to produce a final correlation value for a specific gene pair.
The analysis of the APSA structure and content was begun with an initial exploratory factor analysis (EFA) [ 19, 20] consisting of a principal factor analysis with squared multiple correlations for the prior communality estimates, based on a common factor model.
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Using MATLAB, pair-wise nonparametric correlation coefficients (Kendall tau), and a coefficient of nonparametric multiple correlation for all replicates (Kendall's W), were calculated for regions with >50X coverage in at least one replicate (n = 10,968 regions; p = 7 replicates).
Kendall's W, a coefficient of nonparametric multiple correlation for all replicates, was estimated at 0.16 (p<0.01), indicating a small but statistically significant relationship may exist between genome position and high representation in the sequencing library.
The multiple correlation for these variables was 0.187.
The squared multiple correlation for the UFP regression models was 0.96.
All associations remained significant after adjustment for multiple correlations and for smoking.
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