Information from Noise: Measuring Dyslexia Risk Using Rasch-like Matrix Factorization with a Procedure for Equating Instruments

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 12; p. 1580
• Main Authors: Moulton, Mark H, Eide, Brock L
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.11.2023
• Subjects: Age groups; Algorithms; alternating least squares; Calibration; Cognitive load; Dyslexia; Educational evaluation; Entropy (Information theory); Factorization; Information theory; matrix factorization; Measurement techniques; Measuring instruments; Noise measurement; Phonology; Quantitative psychology; Rasch model; Reading; Risk analysis; Risk factors; Spelling; Standard error; test; alternating least squares; matrix factorization; test; Rasch model; dyslexia
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25121580

This study examines the psychometric properties of a screening protocol for dyslexia and demonstrates a special form of matrix factorization called Nous based on the Alternating Least Squares algorithm. Dyslexia presents an intrinsically multidimensional complex of cognitive loads. By building and enforcing a common 6-dimensional space, Nous extracts a multidimensional signal for each person and item from test data that increases the Shannon entropy of the dataset while at the same time being constrained to meet the special objectivity requirements of the Rasch model. The resulting Dyslexia Risk Scale (DRS) yields linear equal-interval measures that are comparable regardless of the subset of items taken by the examinee. Each measure and cell estimate is accompanied by an efficiently calculated standard error. By incorporating examinee age into the calibration process, the DRS can be generalized to all age groups to allow the tracking of individual dyslexia risk over time. The methodology was implemented using a 2019 calibration sample of 828 persons aged 7 to 82 with varying degrees of dyslexia risk. The analysis yielded high reliability (0.95) and excellent receiver operating characteristics (AUC = 0.96). The analysis is accompanied by a discussion of the information-theoretic properties of matrix factorization.