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Updated: Feb 3, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A genetic algorithm to find optimal reading test word subsets for estimating full-scale IQ.

Ian van der Linde1,2, Peter Bright2,3

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A genetic algorithm efficiently identifies optimal word subsets from the National Adult Reading Test to predict IQ scores. This method shortens the test and improves premorbid cognitive function estimates.

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Area of Science:

  • Neuropsychology
  • Cognitive Science
  • Psychometrics

Background:

  • Clinical neuropsychology commonly uses paper-based tests to estimate cognitive abilities in neurological patients.
  • Premorbid abilities are estimated using 'hold' tests that measure preserved cognitive functions.
  • Word reading tests, like the National Adult Reading Test, are often used as hold tests.

Purpose of the Study:

  • To develop a genetic algorithm to identify optimal word subsets from the National Adult Reading Test.
  • To minimize prediction error when estimating IQ scores using the Wechsler Adult Intelligence Scale Fourth Edition.
  • To improve the efficiency and accuracy of premorbid cognitive function estimation.

Main Methods:

  • A genetic algorithm was developed and applied to a dataset of 92 neurologically healthy participants.
  • The algorithm identified optimal word subsets from the National Adult Reading Test.
  • Prediction accuracy was evaluated using jackknifing and leave-one-out cross-validation.

Main Results:

  • The algorithm identified subsets of 17-20 words that minimized mean prediction error.
  • The optimized subsets explained a higher proportion of variance in IQ scores (r2 = 0.61) compared to using all 50 words (r2 = 0.46).
  • The test could potentially be reduced by up to 66% in length.

Conclusions:

  • The genetic algorithm effectively optimizes word subsets for predicting cognitive ability.
  • This approach can significantly shorten tests, reduce patient burden, and improve the accuracy of premorbid ability estimates.
  • The general method can be applied to optimize relationships between any two psychological tests and develop new predictive instruments.