= Explaining Part (also known as semi-partial) correlations = Try to assess the importance of phonological awareness on predicting Reading Ability which is independent of dysphraxia. You can do this using General Linear Model:univariate. Suppose putting both phonological awareness and dysphraxia as covariates gives Suppose we have Dependent Variable=Reading Ability ||||||||||||<16% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> Source || df || Type III SS || MS || F || p || |||||||||||| Dysphraxia || 1 || 13.59 || 13.59 || 9.44 || .013 || |||||||||||| Phonological Awareness || 1 || 8.45 || 8.45 ||5.87 || .038 || |||||||||||| Error || 9 || 12.96 || 1.44 || || || |||||||||||| Corrected Total || 11 || 35.00 || || || || R-squared=0.630 This tells us that phonological Awareness has a statistically significant influence on reading ability after taking dysphraxia into account (F(1,9)=8.45, p<0.05). Fitting just dysphraxia gives Dependent Variable=Reading Ability ||||||||||||<16% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> Source || df || Type III SS || MS || F || p || |||||||||||| Dysphraxia || 1 || 15.50 || 15.50 || 7.95 || .018 || |||||||||||| Error || 10 || 19.50 || 1.95 || || || |||||||||||| Corrected Total || 11 || 35.00 || || || || R-squared=0.443 Comparing the two R-squareds tells us that phonological awareness accounts for 0.630-0.443 = 0.187 or 18.7% of total variance in reading ability over and above that predicted by dysphraxia. The signed square root of this Sqrt(0.187)=sgn(0.432) is the '''Part correlation''', also called the '''semi-partial correlation''' of phonological awareness adjusted for dysphraxia with reading ability. In other words: The square of the (Part) correlation which relates aspects of phonological awareness, unrelated to dysphraxia, to reading ability is the difference in R-squareds of a model with dysphraxia and phonological awareness and the R-squared of a model with dysphraxia only with reading ability as dependent (outcome) variable. R-squared (or equivalently its signed square root, the part correlation) is often given as a measure of the strength of an association between one or more predictor variables of interest, adjusted for other confounding predictors, with an outcome. Since this is a regression term R-squared can also be used to describe analysis of (co)variance.