A genetic algorithm to find optimal reading test word subsets for estimating full-scale IQ
- Submitting institution
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Anglia Ruskin University Higher Education Corporation
- Unit of assessment
- 12 - Engineering
- Output identifier
- 328
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0205754
- Title of journal
- PLOS ONE
- Article number
- ARTN e0205754
- First page
- e0205754
- Volume
- 13
- Issue
- 10
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The findings demonstrate that more than one half of the widely used NART clinical test is redundant and provide critical data that enhances research that has led to the recent restandardisation of a commonly used clinical test.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -