Indicator-coded versus ordinal race/ethnicity variables in the Meyers neuropsychological battery: a proof-of-concept...

Demographic variables such as age, education, sex, and race/ethnicity influence performance on neuropsychological tests. We present a proof-of-concept comparison of two approaches to incorporating race/ethnicity in regression-based norms for the Meyers Neuropsychological Battery (MNB). Linear regression models were developed on a reference dataset (N = 1,840) to predict T-scores from demographic predictors and raw scores, using either the MNB's existing single ordinal race/ethnicity variable or four indicator-coded variables. Models were applied to an independent sample (N = 10,778) to generate predicted scores and correction factors (actual minus predicted T-score). After Benjamini-Hochberg correction across 34 measures, only 3 (Trails B, Token Test, and Boston Naming) showed significant improvement with indicator coding. Delta R-squared analyses indicated that race/ethnicity indicators contributed 0.29% to 0.45% of unique variance. Correction factors for certain test-by-group combinations approached one standard deviation, but bootstrap confidence intervals revealed substantial instability attributable to small minority cell sizes in the development sample (e.g., N = 22 African American). These results suggest that race/ethnicity is a weak overall predictor once other demographics are controlled, though systematic prediction biases may exist for specific measures. The indicator-coded framework can in principle accommodate mixed racial/ethnic backgrounds, though this was not tested here.