Dassanayake TL, Hewawasam C, Baminiwatta A, Ariyasinghe DI. Regression-based, demographically adjusted norms for Victoria Stroop Test, Digit Span, and Verbal Fluency for Sri Lankan adults.
Clin Neuropsychol 2021;
35:S32-S49. [PMID:
34499569 DOI:
10.1080/13854046.2021.1973109]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVE
Lack of test norms is a major limitation in neuropsychological assessment in many non-English-speaking countries. Our objective was to generate sex-, age-, and education-adjusted, regression-based norms for three pen-and-paper-based executive function/working memory tests, viz. Victoria Stroop Test (VST), Digit Span, and Verbal Fluency, for Sinhala-Speaking Sri Lankan adults.
METHOD
Six-hundred and six healthy, community-living, Sinhala-speaking adults (273 men) aged 19-83 years completed VST, Digit Span (forward and backward), and Phonemic (Sinhala phonemes "N," "P," and "S") and Category (animals and vegetables) Fluency tests. We conducted multiple linear regression analyses with sex, age, and years of education fitted as predictors to model the test outcome variables.
RESULTS
Younger age was associated with better performance in all tests except in Phonemic Fluency. Longer education was associated with better performance in all outcomes except VST errors and interference. Women had a significant but small advantage over men in VST Neutral and Color Word tests, and Phonemic Fluency. We report regression equations to predict norms for the test performance measures based on sex, age, and years of education; and the test variances accounted by these factors.
CONCLUSIONS
We propose demographically adjusted norms for VST, Digit Span, and Verbal Fluency for Sinhala-speaking Sri Lankans aged 19-83 years, and supplement the regression formulae with a calculator that produces predicted and standard scores. These norms would help in interpreting the results of clinical samples in the future, taking into account the variability introduced by sex, age, and education.
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