27:T4f5,

Traditionally, students with learning disabilities (LD) have been identified using an aptitude–achievement discrepancy or response to intervention approach. As profiles of the cognitive deficits of discrepancy-defined students with LD have already been developed using these approaches, these deficits can in turn be used to identify LD using the discrepancy approach as a benchmark for convergent validity. Australian Grade 3 (N = 172) students were administered cognitive processing tests to ascertain whether scores in these tests could accurately allocate students into discrepancy-defined groups using discriminant function analysis. Results showed that 77% to 82% of students could be correctly allocated into LD, low achievement, and regular achievement groups using only measures of phonological processing, rapid naming, and verbal memory. Furthermore, verbal memory deficits were found, along with phonological processing and rapid naming deficits, in students that would be designated as low achieving by the discrepancy method. Because a significant discrepancy or lack of response to intervention is a result of cognitive deficits rather than the other way around, it is argued that LD should be identified via cognitive deficits.

28:T457,

Early and accurate identification of children at risk for reading disabilities (RD) is critical for the prevention of RD within a response to intervention framework. In this study, we investigated the use of universal screening and progress monitoring for the early identification of RD in kindergarten children. A total of 366 children were administered a battery of screening measures at the beginning of kindergarten and progress-monitoring probes across the school year. A subset of children who showed initial risk for RD also received a 26-week Tier 2 intervention. Participants’ achievement in word reading accuracy and/or fluency was assessed at the end of first grade. Results indicated that a screening battery containing measures of letter naming fluency, phonological awareness, rapid naming, or nonword repetition accurately identified good and poor readers at the end of first grade. Findings also showed that children’s response to supplemental and/or classroom instruction measured in terms of growth in letter naming fluency added significantly to the prediction of reading outcomes.

29:T4ee,

This exploratory study identified underlying skill and ability differences among subgroups of adolescent and young adult struggling readers (N = 290) overall and in relation to a fluency-based instructional grouping method. We used principal axis factoring of participants’ scores on 18 measures of reading-related skills and abilities identified in the research literature to identify a smaller set of generally uncorrelated constructs. The four underlying factors of the 18 measures explained 62.7% of the variance. We labeled these factors Encode/Decode (44.5%), Vocabulary (9.5%), Processing Speed (5.2%), and Working Memory (3.5%). Regression analysis demonstrated Working Memory, Encode/Decode, and Vocabulary collectively predicted 45.9% functional reading as measured by the Comprehensive Adult Student Assessment System. Alternatively, when measured by the Test of Adult Basic Education, Vocabulary and Encode/Decode predicted 47.1% of variance in reading. Differences in predictive utility of the factors by fluency group suggest approaches to tailoring instruction for each group. Future research might examine the optimal mix of instructional approaches that support the identified factors.

2a:T4be,

We examine (a) what social ties university students with a history of reading difficulty (RD) report assisting them to achieve their goals, (b) outlets available for developing social ties, (c) resources mobilized within these relationships, and (d) the impact of social ties’ status on academic achievement. Participants were 107 university students with RD who were currently completing or had recently completed a