Improving Equivalent Scores for Clinical Neuropsychology: A new method for regression model selection

Resumen

Equivalent Scores (ES) represent a statistical gold standard to obtain thresholds for clinical inference from normative data. The procedure to obtain ES requires a preliminary mandatory step: to perform a regression model selection to obtain adjusted scores that take into account age, education, and sex. The current article, starting from theoretical considerations, focuses on this step and proposes a new and improved regression model selection method. Results from data simulation show that the newly proposed method outperforms the current one on a wide range of simulation parameters and conditions, leading to better performance in classifying impaired or unimpaired performances, and more precise ES. The article is associated with an online app and R code to allow to easily apply the method to other normative data. This new model selection procedure can be easily incorporated also with other regression-based norm approaches.

Áreas y palabras clave

Cómo citar (APA 7)

Arcara, G. (2024). Improving Equivalent Scores for Clinical Neuropsychology: A new method for regression model selection. https://doi.org/10.31234/osf.io/sw4hu