An investigation of the use of standardised leaving certificate performance as a method of estimating pre-morbid intelligence

2020 
BACKGROUND: In cases of brain pathology, current levels of cognition can only be interpreted reliably relative to accurate estimations of pre-morbid functioning. Estimating levels of pre-morbid intelligence is, therefore, a crucial part of neuropsychological evaluation. However, current methods of estimation have proven problematic. OBJECTIVE: To evaluate if standardised leaving certificate (LC) performance can predict intellectual functioning in a healthy cohort. The LC is the senior school examination in the Republic of Ireland, taken by almost 50 000 students annually, with total performance distilled into Central Applications Office points. METHODS: A convenience sample of university students was recruited (n = 51), to provide their LC results and basic demographic information. Participants completed two cognitive tasks assessing current functioning (Vocabulary and Matrix Reasoning (MR) subtests - Wechsler Abbreviated Scale of Intelligence, Second Edition) and a test of pre-morbid intelligence (Spot-the-Word test from the Speed and Capacity of Language Processing). Separately, LC results were standardised relative to the population of test-takers, using a computer application designed specifically for this project. RESULTS: Hierarchical regression analysis revealed that standardised LC performance [F(2,48) = 3.90, p = 0.03] and Spot-the-Word [F(2,47) = 5.88, p = 0.005] significantly predicted current intellect. Crawford & Allen's demographic-based regression formula did not. Furthermore, after controlling for gender, English [F(1,49) = 11.27, p = 0.002] and Irish [F(1,46) = 4.06, p = 0.049) results significantly predicted Vocabulary performance, while Mathematics results significantly predicted MR [F(1,49) = 8.80, p = 0.005]. CONCLUSIONS: These results suggest that standardised LC performance may represent a useful resource for clinicians when estimating pre-morbid intelligence.
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