Development and Evaluation of a Linguistically Fair Reading Screener for California's Multilingual Student Population

Early and accurate identification of students who struggle with learning to read is essential for delivering timely and effective interventions; achieving this requires screening tools that not only yield accurate results but also allow for meaningful interpretation of these results in linguistically diverse student populations. In this study, we present the development and evaluation of a universal reading screener, with a particular focus on linguistic fairness for use with Spanish-English multilingual students in the linguistically diverse U.S. State of California. The proposed screening algorithms can accurately predict end-of-year reading risk in English, with sensitivity exceeding 80% in kindergarten and grade 1, and 90% in grade 2. The Spanish algorithm achieves similar sensitivity levels, though kindergarten screening remains challenging. Notably, the inclusion of oral language and expressive vocabulary measures enhances specificity when administered in students' first language, while these same measures may introduce systematic bias when administered in English to students classified as English learners. While very promising, these findings underscore the critical need for developing and validating reading screeners in languages beyond English, as well as creating comprehensive multilingual screening protocols that leverage students' full linguistic repertoires across all their languages.