Development of an Abbreviated Adult Reading History Questionnaire (ARHQ-Brief) Using a Machine Learning Approach. (September 2022)

• Record Type: Journal Article
• Authors: Feng, Luxi; Hancock, Roeland; Watson, Christa; Bogley, Rian; Miller, Zachary A.; Gorno-Tempini, Maria Luisa; Briggs-Gowan, Margaret J.; Hoeft, Fumiko
• Is Part Of: Journal of learning disabilities. Volume 55:Number 5(2022)
• Page Start: 427
• Page End: 442
• Publication Date: 2022-09
• Subjects: dyslexia -- reading disorder -- Adult Reading History Questionnaire -- reading ability -- adults and children; Learning disabilities -- Periodicals; 371.905
• DOI: 10.1177/00222194211047631
• Languages: English
• ISSNs: 0022-2194

Abstract

Several crucial reasons exist to determine whether an adult has had a reading disorder (RD) and to predict a child's likelihood of developing RD. 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 a subset of ARHQ items (ARHQ-Brief) could be equally effective in assessing adults' reading history as the full ARHQ. 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. Furthermore, 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.