The CPRS-93, (Conners, 1989) is a widely administered, well validated questionnaire used to characterize behaviour of a child and compare it to levels of appropriate normative groups. Norms are based on samples of children aged 6 to 14 years. It is a widely used instrument for clinical and research applications with children. Originally designed to help identify hyperactive children, several decades of research has shown it to be a useful measure of a spectrum of childhood behaviour abnormalities. It has been validated in many countries, including Australia, and is among the most widely used questionnaires for diagnosis of childhood psychiatric disturbances in the world. It shows adequate reliability and validity.
The 93 items are rated with four responses (not at all, just a little, pretty much, very much). Responses are coded 1, 2, 3 or 4. The CPRS-93 includes the subscales Conduct Disorder, Anxious-Shy, Restless-Disorganized, Learning Problem, Psychosomatic, Obsessive Compulsive, Antisocial and Hyperactive-Immature. It also gives a score on a Hyperactivity Index. The latter construct is based on ten items distributed throughout the questionnaire. It was developed to provide an easily measured, empirical assessment of the extent to which the child performs behaviours which are usually considered indicative of a disorder with a hyperkinetic component. It is automatically scored from each form of the questionnaire. The Hyperactivity-Immature subscale is not synonymous with the Hyperactivity Index.
After computer scoring of questionnaires completed in a paper and pencil format, the scale total scores are converted to T-scores automatically and plotted. A computer generated narrative interprets the meaning of the score profiles using narrative rules developed by the scale's author (see Appendix H). T-scores used in the CPRS-93 are linear T-scores which do not transform actual distributions of the variables in any way. While each variable has been standardised and transformed to have a mean of 50 and a standard deviation of 10, the distributions of the subscale scores do not change. Variables which are not normally distributed in the raw data will continue to be non-normally distributed after the transformation. Each scale with have the same mean and standard deviation. An interpreter can therefore compare the subscale scores.
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