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

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size (_=0.36) which contrasts wildly with the medium-large effect from the DAPA<br />

(_=0.74); 7 other separate categories were encompassed under the DAPA emotional tone<br />

category: three categories, those <strong>of</strong> themes, reality and complexity which seemed very<br />

close in meaning but were measured separately by most studies, showed negligible effect;<br />

quality showed a low effect; energy and composition, medium effect. Only content as<br />

defined by counts <strong>of</strong> specific psychiatric indicators showed a high effect comparable to<br />

the general category <strong>of</strong> emotional tone. <strong>The</strong> other high effect from the literature review,<br />

body details was not included in the DAPA, there was a medium-high effect on control<br />

which was not included in the DAPA test and neither was detail in other areas (which<br />

achieved a small effect).<br />

<strong>The</strong> deletion <strong>of</strong> negligible effects from the literature review set <strong>of</strong> categories gave<br />

a subset <strong>of</strong> 8 significant areas <strong>of</strong> measurement <strong>of</strong> drawing with a medium effect size<br />

(_=0.57), and is still lower than that <strong>of</strong> the DAPA, but it only covers two DAPA<br />

categories and was relatively overloaded on content. <strong>The</strong> DAPA test therefore seems<br />

more sensitive to different areas <strong>of</strong> measurement <strong>of</strong> a painting and is potentially a more<br />

effective discrimination tool. However, the t-test does not take account <strong>of</strong><br />

interrelationships between variables, nor the number <strong>of</strong> tests performed 338 so this<br />

analysis is limited to a theoretical comparison <strong>of</strong> independent effect <strong>of</strong> the variables with<br />

that produced in Chapter 2 and is not an appropriate measure to predict practical<br />

discriminations.<br />

338 We could use the Bonferroni correction here, but it merely confuses the data and the problem <strong>of</strong><br />

correlation <strong>of</strong> the variables remains and it is better to use the following analysis.<br />

316

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