Predicting normative data in healthy individuals on the computerized wisconsin card sorting test using regression models

Abstract

BACKGROUND: Computerized neuropsychological tests provide advantages to clinicians with cost, administration, and time. However, studies have pointed out performance differences between manual and computerized versions of some neuropsychological tests. One of these is the Wisconsin Card Sorting Test (WCST). Due to the performance difference, the normative data of manual tests cannot be used for their computerized versions. Therefore, normative data searches are needed for computerized versions. OBJECTIVE: This study aimed to determine the norm values of WCST-CV in a healthy sample. METHODS: 422 healthy adults aged 18–78 participated in this study. WCST-CVsub-scores are modeled by Regression Analysis based on Age and Education level to generate normative data. Among the 13 WCST scores, the regression models for WCST 2, WCST 3, WCST 4, WCST 10, and WCST 11 are significant. WCST 2, WCST 4, and WCST 11 scores are estimated with Ordinary Least Squares (OLS). However, WCST 3 and WCST 10 scores are estimated with Weighted Least Squares (WLS) due to the violation of the homoscedasticity assumption. RESULTS: The regression results show that p-values calculated from error increase as age and education level increase. CONCLUSION: As a result of our research, norm values between 18–78 years of age were produced using RA. It was determined that gender was not significant for any sub-score. Therefore, only age and education level from socio-demographic variables were included in the model.