Background: Norming neuropsychological tests and standardizing their raw scores are needed to draw objective clinical judgments on clients’ neuropsychological profile. The Equivalent Score (ES) method is a regression-based normative/standardization technique that relies on the non-parametric identification of the observations corresponding to the outer and inner tolerance limits (oTL; iTL) — to derive a cut-off, as well as to between-ES thresholds — to mark the passage across different levels of ability. However, identifying these observations is still a time-consuming, “manual” procedure. This work aimed at providing practitioners with a user-friendly code that helps compute TLs and ES thresholds. Methods: R language and RStudio environment were adopted. A function for identifying the observations corresponding to both TLs by exploiting Beta distribution features was implemented. A code for identifying the observations corresponding to ES thresholds according to a z-deviate-based approach is also provided. Results: An exhaustive paradigm of usage of both the aforementioned function and script has been carried out. A user-friendly, online applet is provided for the calculation of both TLs and ESs thresholds. A brief summary of the regression-based procedure preceding the identification of TLs and ESs threshold is also given (along with an R script implementing these steps). Discussion: The present work provides with a software solution to the calculation of TLs and ES thresholds for norming/standardizing neuropsychological tests. These software can help reduce both the subjectivity and the error rate when applying the ES method, as well as simplify and expedite its implementation.

Aiello, E., Depaoli, E. (2022). Norms and standardizations in neuropsychology via equivalent scores: software solutions and practical guides. NEUROLOGICAL SCIENCES, 43(2), 961-966 [10.1007/s10072-021-05374-0].

Norms and standardizations in neuropsychology via equivalent scores: software solutions and practical guides

Aiello E. N.
;
2022

Abstract

Background: Norming neuropsychological tests and standardizing their raw scores are needed to draw objective clinical judgments on clients’ neuropsychological profile. The Equivalent Score (ES) method is a regression-based normative/standardization technique that relies on the non-parametric identification of the observations corresponding to the outer and inner tolerance limits (oTL; iTL) — to derive a cut-off, as well as to between-ES thresholds — to mark the passage across different levels of ability. However, identifying these observations is still a time-consuming, “manual” procedure. This work aimed at providing practitioners with a user-friendly code that helps compute TLs and ES thresholds. Methods: R language and RStudio environment were adopted. A function for identifying the observations corresponding to both TLs by exploiting Beta distribution features was implemented. A code for identifying the observations corresponding to ES thresholds according to a z-deviate-based approach is also provided. Results: An exhaustive paradigm of usage of both the aforementioned function and script has been carried out. A user-friendly, online applet is provided for the calculation of both TLs and ESs thresholds. A brief summary of the regression-based procedure preceding the identification of TLs and ESs threshold is also given (along with an R script implementing these steps). Discussion: The present work provides with a software solution to the calculation of TLs and ES thresholds for norming/standardizing neuropsychological tests. These software can help reduce both the subjectivity and the error rate when applying the ES method, as well as simplify and expedite its implementation.
Articolo in rivista - Articolo scientifico
Equivalent score; Neuropsychological assessment; Normative data; Psychometrics; R; Tolerance limits;
English
17-giu-2021
2022
43
2
961
966
none
Aiello, E., Depaoli, E. (2022). Norms and standardizations in neuropsychology via equivalent scores: software solutions and practical guides. NEUROLOGICAL SCIENCES, 43(2), 961-966 [10.1007/s10072-021-05374-0].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/321598
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