The psychopathology of everyday art: a quantitative Study - World ...

(ii) The Discriminant analysis technique - Which variables discriminate between
patients and controls?
There were 6 common variables which turned up 3 or more out of 5 times in the
derivation of the function from 5 different partitions of the data set: most of which
variables showed the largest effect sizes identified above. Step 1 used Emotional tone,
which had the largest effect (_=0.92) or green (_=0.89), which had the highest F to enter.
The other variables in order of their appearance were: Form (_=0.88), Space (_= -0.74),
Drawn line (_=-0.56), and Black (n.s.). Painted line, which did not appear, was highly
negatively correlated with Drawn line and so the effect must have been accounted for
with the entry of Drawn line as it is unlikely that the F value was not high enough on
Painted line. It is a mystery to me, given the small effect size, why Black was chosen as
a discriminating variable, unless it was because it took the large effect of Red because of
the high negative correlation. The results for the ANOVA show that controls were not
differentiated on either high or low black from the patients. Red was highly correlated
with intensity, which also wasn't included. On the two occasions Red did turn up, none
of the 6 common variables were consistently absent, so it was probably excluded because
its effect became negligible after accounting for other variables. The middle effects were
not included; Yellow, Brown, Intensity, Painted line, although Drawn line (_=0.57) was
included and Blue (_=0.36), so these low effects were probably due to correlation with
Green, although they all showed F less than 1 when the analysis stopped. The
discrimination set therefore contained most of the variables with high effect sizes and was
generally consistent with the independent t-tests for effect size. The best discrimination
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