Regression-based developmental models exemplified for Wisconsin Card Sorting Test parameters: Statistics and software for individual predictions

Abstract

The prediction of an individual's score is relevant in clinical research and requires normative data and a statistical rationale. In the case of developmental research the latter is typically a set of descriptive statistics (e.g., standard scores) for a set of age groups. Here we illustrate a multiple regression approach with a set of 345 Wisconsin Card Sorting Test (WCST) data obtained from subjects aged 6 to 26 years. We modeled linear and curvilinear age effects for each of the 11 WCST variables and, based on this, determined confidence limits for the expected value (mean) and the prediction of individual scores. In these multiple regression models, which accounted for 2% to 26% of the variance, curvilinear age effects clearly dominated linear ones, suggesting the features under scrutiny to be negatively accelerated functions of age. Finally, we developed a statistics program that can be used to apply multiple regression models for individual predictions that are based on normative data with up to 7 predictor variables. We discuss the conditions of applicability of the approach, compare it with the conventional standard score approach, discuss its cognitive-developmental implications, and outline the applicability in applied research and practising.