Skip to main content
eScholarship
Open Access Publications from the University of California

UCSF

UC San Francisco Previously Published Works bannerUCSF

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

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.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View