Regression Equations in Clinical Neuropsychology: An Evaluation of Statistical Methods for Comparing Predicted and Obtained Scores

Abstract

Regression equations are widely used in clinical neuropsychology, particularly as an alternative to conventional normative data. In neuropsychological applications the most common method of making inferences concerning the difference between an individual's test score and the score predicted by a regression equation is to multiply the standard error of estimate by an appropriate value of z to form confidence limits around the predicted score. The technically correct method is to calculate the standard error of a new individual Y and multiply it by the value of t corresponding to the desired limits (e.g., 90% or 95%). These two methods are compared in data sets generated to be broadly representative of data sets used in clinical neuropsychology. The former method produces confidence limits which are narrower than the true confidence limits and fail to reflect the fact that limits become wider as scores on the predictor deviate from the mean. However, for many of the example data sets studied, the differences between the two methods were trivial, thereby providing reassurance for those who use the former (technically incorrect) method. Despite this, it would be preferable to use the correct method particularly with equations derived from samples with modest Ns, and for individuals with extreme scores on the predictor variable(s). To facilitate use of the correct method a computer program is made available for clinical practice.