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The primary objective of this study was to validate the Pain Experience Interview using the discriminant validation procedure of group differences.
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Furthermore, a diagnostic algorithm was developed using the discriminant functions with a leave-one-out method of cross-validation.
Before using the discriminant function method, we need to specify the type of discriminant function.
We used the discriminant analysis package available in MATLAB Statistics Toolbox.
Known-groups validation [ 28, 29] was used to assess the discriminant validity of the questionnaire, i.e. the ability of the questionnaire to distinguish between subgroups known to differ on key socio demographic or clinical variables.
Using the proposed discriminant cluster selection and soft labeling method, two common and four discriminant clusters are obtained.
Expected cross validation index (ECVI) is useful to determine the discriminant validity.
Subsequent to a training procedure through leave-one-out cross-validation, we fitted a discriminant (linear discriminant analysis (LDA)) equation using the four most discriminant probesets.
The first 2 discriminant functions give 97% of the total discrimination using all the discriminant functions, which is sufficient to discriminate among the 5 groups.
The discriminant validity of the WPS-RA was assessed using the known-groups validation method.
In the context of the present study it was possible to test the discriminant validity of the "SmoCess-GP" instrument as one form of construct validation [ 32].
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