112015-08-03 14:01:05PeterWatson102013-03-08 10:17:18localhostconverted to 1.6 markup92012-01-24 12:20:07PeterWatson82006-11-08 17:36:45PeterWatson72006-11-08 17:00:02PeterWatson62006-11-08 16:46:40PeterWatson52006-11-08 16:45:41PeterWatson42006-11-08 16:44:57PeterWatson32006-11-08 16:43:33PeterWatson22006-11-08 16:40:19PeterWatson12006-11-08 16:38:05PeterWatson

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 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 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.