PT  - JOURNAL ARTICLE
AU  - Jason D. Yeatman
AU  - Kenny An Tang
AU  - Patrick M. Donnelly
AU  - Maya Yablonski
AU  - Mahalakshmi Ramamurthy
AU  - Iliana I. Karipidis
AU  - Sendy Caffarra
AU  - Megumi E. Takada
AU  - Klint Kanopka
AU  - Michal Ben-Shachar
AU  - Benjamin W. Domingue
TI  - Rapid Online Assessment of Reading Ability
AID  - 10.1101/2020.07.30.229658
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.07.30.229658
4099  - http://biorxiv.org/content/early/2021/01/11/2020.07.30.229658.short
4100  - http://biorxiv.org/content/early/2021/01/11/2020.07.30.229658.full
AB  - An accurate model of the factors that contribute to individual differences in reading ability depends on data collection in large, diverse and representative samples of research participants. However, that is rarely feasible due to the constraints imposed by standardized measures of reading ability which require test administration by trained clinicians or researchers. Here we explore whether a simple, two-alternative forced choice, time limited lexical decision task (LDT), self-delivered through the web-browser, can serve as an accurate and reliable measure of reading ability. We found that performance on the LDT is highly correlated with scores on standardized measures of reading ability such as the Woodcock-Johnson Letter Word Identification test (r = 0.91, disattenuated r = 0.94). Importantly, the LDT reading ability measure is highly reliable (r = 0.97). After optimizing the list of words and pseudowords based on item response theory, we found that a short experiment with 76 trials (2-3 minutes) provides a reliable (r = 0.95) measure of reading ability. Thus, the self-administered, Rapid Online Assessment of Reading ability (ROAR) developed here overcomes the constraints of resource-intensive, in-person reading assessment, and provides an efficient and automated tool for effective online research into the mechanisms of reading (dis)ability.Competing Interest StatementThe authors have declared no competing interest.