TREATMENT OF DEVELOPMENTAL READING DISABILITIES

描述（改编自申请人摘要）：本项目

DESCRIPTION (Adapted from the Applicant's Abstract): This project evaluates the effectiveness of four theoretically-motivated treatment packages for children with developmental, language-based reading disabilities (RD). Treatments are based upon increasing, converging evidence that developmental reading problems are primarily due to two core linguistic deficits (phonological awareness; retrieval/access speed) that are highly specific and relatively independent, and a more general metacognitive control problem. Three different types of treatment modules (PHAB/DI-Phonological Analysis and Blending; WIST-Word Identification Strategies; RAVE-O Retrieval Rate and Accuracy, Vocabulary Elaboration and Orthography) and a contrast/control module (CSS, Classroom Survival Skills) will be combined into four different experimental treatment packages based on a multidimensional model of RD (PHAB/DI + CSS; PHAB/DI + WIST; PHAB/DI + RAVE-O; PHAB/DI + WIST + RAVE- O) and compared to each other and to a Math Treatment Control Group (CSS + MATH). All treatment packages will be of equal length, similar format, and independently monitored for integrity. For each of the five treatment/control groups, 64 children will be evaluated (total=320). Each sample will be generated using IQ level, socioeconomic status, and race (African-American Black/Caucasian) in a factorial design that permits evaluation of these factors in relation to treatment outcomes. This will result in half of the children involved in the treatments being African-American, and half coming from lower socioeconomic classes, two understudied RD groups. The sampling strategy is key to evaluating differential remedial outcomes based on a child's individual characteristics, including co-morbidity,different subtype classification, other definitions of RD and RD typologies. Three treatment sites (Atlanta, Boston, Toronto) will be utilized to increase generalizability of results. Both repeated measure designs and learning (growth) curve modeling techniques will be used to evaluate treatment outcomes in relation to individual ability characteristics, as measured by standardized and experimental measures.