Development of an Abbreviated Adult Reading History Questionnaire (ARHQ-Brief) Using a Machine Learning Approach
| Autor: | Zachary A. Miller, Roeland Hancock, Luxi Feng, Rian Bogley, Margaret J. Briggs-Gowan, Christa Watson, Maria Luisa Gorno-Tempini, Fumiko Hoeft |
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| Rok vydání: | 2023 |
| Předmět: |
Adult
Health (social science) media_common.quotation_subject Sample (statistics) Machine learning computer.software_genre Education Dyslexia Machine Learning Cognition Lasso (statistics) Reading (process) Surveys and Questionnaires medicine Humans Child media_common business.industry Variance (accounting) medicine.disease General Health Professions Artificial intelligence Psychology business computer |
| Zdroj: | Journal of learning disabilities. 55(5) |
| ISSN: | 1538-4780 |
| Popis: | Several crucial reasons exist to identify whether an adult has had reading disorder (RD) and to predict a child’s likelihood of developing RD, which is known to be primarily genetically transmitted. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child, and that their children may develop RD. This study focused on whether using a subset of ARHQ items (ARHQ-brief) could be equally effective and efficient in assessing adults’ reading history. We used a machine learning approach, lasso (known as L1 regularization), and identified 6 of 23 items that resulted in the ARHQ-brief. Data from 97 adults and 47 children were included. With the ARHQ-brief, we report a threshold of 0.323 as suitable to identify past likelihood of RD in adults with a sensitivity of 72.4% and a specificity of 81.5%. Comparison of predictive performances between ARHQ-brief and the full ARHQ showed that ARHQ-brief explained an additional 10-35.2% of the variance in adult and child reading. Further, we validated ARHQ-brief’s superior ability to predict reading ability using an independent sample of 28 children. We close by discussing limitations and future directions. |
| Databáze: | OpenAIRE |
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