Regression equations, using as predictors demographic variables and scores on tests resistant to the effects of brain injury, are often used to calculate predicted test scores on tests used in neuropsychological assessment. The discrepancy between a predicted and an obtained score can be used in the diagnosis of cognitive deficits resulting from brain damage. The aim of the present study was to develop regression equations that would allow the prediction of scores on tests sensitive to executive deficits and to apply these to data from persons with brain injuries. A total of 100 persons from the community were tested and the resultant equations were used to predict the scores of 40 persons with traumatic brain injuries. For those equations with multiple correlations in the order of .50, the numbers of persons classified using either the equations or the published normative tables were comparable. Procedures for determining the abnormality of an individual's test score discrepancy described by Crawford and Howell (1998), based on the output from multiple regression procedures, are discussed. Where normative data from a local sample are available, use of regression equations to determine the abnormality of a discrepancy can provide a useful means of validating conclusions based on the application of US or UK norms.
In clinical practice, neuro-psychologists often need to determine whether a test score obtained from a person with neurological damage differs from what might have been expected if no brain injury had occurred. One means of achieving this is to estimate a client's expected score with a regression equation, using as predictors demographic variables such as age or socio-economic status, other test scores obtained either concurrently or pre-injury, or some combination of both. The standardised difference between the expected and obtained scores can then be used as an index of acquired cognitive impairment. In effect, regression equations are being used in this situation as an alternative to consulting conventional normative tables (Crawford & Howell, 1998).
Most manuals of neuropsychological tests contain tables of norms, often stratified by age and, in some instances by education levels, that allow evaluation of a client's obtained score in terms of a distribution of scores from a representative sample of healthy individuals. An abnormal score is one that lies at the extreme of the distribution. Although normative tables are a useful method of conveying information about expected scores, there can be advantages to developing regression equations. For example, the size of the sample is not so significant for regression-based norms, provided it is sufficiently large and diverse enough to provide stable correlations between the test and predictors. This is a significant advantage where local norms are being constructed and the resources for collecting a large and representative sample may not be available. For example, in New Zealand it may often be more cost effective to develop regression equations as an aid to test interpretation than to renorm neuro-psychological tests standardised in the United Kingdom or the United States. There are other advantages to using regression equations. The predicted or expected score is based on continuous variables rather than grouped variables, that is, on the client's actual age, for example, rather than on the age band in which they happen to fall. Thus the use of regression equations to estimate test scores can be more precise than referring to tables, and where more than one variable is used to stratify the sample, less cumbersome. As Crawford and Howell (1998) have observed, applying regression equations means that "... an individual's predicted score reflects his/her particular combination of demographic characteristics. Such an approach is in keeping with the emphasis placed on individual versus normative comparison standards in neuropsychological assessment (p. …