multiple correlation

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By the method of partial multiple correlations, it is possible to bring out more clearly the relative importance of the different factors.
We used principal-factor analysis (starting with squared multiple correlations as the prior communality estimates) to extract the principal factors from the 17 premenstrual symptoms.
Figure 1(a) presents the adjusted squared multiple correlations for each of the three word reading variables at the three age levels.
Squared multiple correlations were used on items from year 3 to produce initial communality estimates.
Next squared multiple correlations were inserted as communality estimates in the diagonal.
Squared multiple correlations were used for prior communality estimates.
Inclusion of these parameters in the effects model generally improved the squared multiple correlations for structural equations across development (to 39% for ages 8, 10, and 12 years).
Third, we analyzed the relations among measures with multiple correlations.
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