UC Riverside

Reading is the most valuable skill children must master early in schooling. Unfortunately, many students struggle to read and may be identified as having a Reading Disability (RD). In this dissertation, I explored the usefulness of the Response-to-Intervention (RtI) framework for identifying children with RD by examining the use of 1st and 2nd grade reading measures and responsiveness criteria for predicting RD in 3rd grade. Data were derived from a longitudinal RtI project executed in low-income, high-poverty schools in Southern California. Participants attended one of five schools from 1st to 3rd grade and had access to a high-quality Tier II intervention during their attendance. I used logistic regression to identify reading measures most useful for predicting RD in word reading/fluency (WR-F) and comprehension/vocabulary (C-V) separately; I then paired intervention responsiveness criteria with significant predictors and explored RD classification accuracy using 2x2 contingency tables. Model-based results generally yielded superior classification accuracy compared to single-measure predictors of RD; however, 1st grade word identification and 2nd grade oral reading fluency showed promise as isolated measures for predicting RD in WR-F. Model-based predictions were required to obtain adequate classification accuracy for RD in C-V. While the former finding is promising for early identification of those students in need of more intensive instruction in lexical or fluency-based skills, the latter finding reaffirms literature attesting to the complexity of RD in comprehension and difficulty of predicting such deficits using early measures of reading, which principally assess word reading skill. Models and classification analyses replicated well with an independent sample, thus enhancing confidence in study results. Practical implications and need for future research are discussed.