Normative Data Estimation in Neuropsychological Tests: A Systematic Review

神经心理学测试中常模数据估计:系统性综述

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Abstract

OBJECTIVE: To quantify the evolution, impact, and importance of normative data (ND) calculation by identifying trends in the research literature and what approaches need improvement. METHODS: A PRISMA-guideline systematic review was performed on literature from 2000 to 2022 in PubMed, Pub-Psych, and Web of Science. Inclusion criteria included scientific articles about ND in neuropsychological tests with clear data analysis, published in any country, and written in English or Spanish. Cross-sectional and longitudinal studies were included. Bibliometric analysis was used to examine the growth, productivity, journal dispersion, and impact of the topic. VOSViewer compared keyword co-occurrence networks between 1952-1999 and 2000-2022. RESULTS: Four hundred twelve articles met inclusion and exclusion criteria. The most studied predictors were age, education, and sex. There were a greater number of studies/projects focusing on adults than children. The Verbal Fluency Test (12.7%) was the most studied test, and the most frequently used variable selection strategy was linear regression (49.5%). Regression-based approaches were widely used, whereas the traditional approach was still used. ND were presented mostly in percentiles (44.2%). Bibliometrics showed exponential growth in publications. Three journals (2.41%) were in the Core Zone. VOSViewer results showed small nodes, long distances, and four ND-related topics from 1952 to 1999, and there were larger nodes with short connections from 2000 to 2022, indicating topic spread. CONCLUSIONS: Future studies should be conducted on children's ND, and alternative statistical methods should be used over the widely used regression approaches to address limitations and support growth of the field.

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