Measuring Growth for Students With Disabilities: Differences in Disability Type, Services, and Accommodations

• In Event: Alternate Assessments: Looking Back, Looking Forward, Looking Around

Abstract

As a follow-up to our study comparing growth for students with and without disabilities (Paek & Domaleski, 2011), we analyzed two states’ assessment data to identify patterns of growth for students with disabilities (SWD), and see what patterns may exist by disability type, special education services, and accommodations.
Research has shown that although SWD begin at a significantly lower level of achievement, with targeted instruction they can learn on the same trajectory as typical learners (Bottge, et al., 2007; Catts, et al., 2008; Ding & Davison, 2005; Landerl & Kölle, 2008; Morgan, et al., 2009). Other research shows that growth may differ in students with speech impairments compared with students with language impairments (Puranik, et al., 2008). This and other research indicates that it may be important to analyze learning growth by disability category (Caffrey & Fuchs, 2007) or by specific skill deficits (Chong & Siegel, 2008).
We examined two states’ large-scale assessment data from the Spring 2006 to Spring 2009 administrations (four years) for all SWD with available data for the general and alternate assessments, looking at an elementary cohort (grades 3 through 6) and a secondary cohort (grades 6 through 9).
Because growth analyses involve multiple years, it was important to identify which students were included in each year. We described the number and percentage of SWD by disability category, which state assessment was taken each year, and types of accommodation(s), in addition to gender, ethnicity, assessment content area, grade level, and disability category. We compared this information with what is known about the prevalence and characteristics nationally for SWD, confirming findings that students who were black, poor, and male were over-represented for receiving services.
Next, we analyzed year-to-year gain scores. Gain scores are calculated by simply taking the difference of student scores at time t from the scores at time t-1. To account for scale differences, we transformed the assessment metrics to a z-score and examined changes in standard deviation units. This process was useful for identifying patterns to illuminate the most promising growth trajectories. We used the previous year’s assessment data to account for prior learning/achievement, so we are controlling for this variable when looking at the average growth across years.
Growth is much more pronounced for students with lower initial scores. While there is more variability based on disability type, the mean gains in growth are less pronounced, especially for students with cognitive impairments. Analyses across mathematics, other years, grade levels, and across both states, result in patterns similar to these. These results provide an indication of typical growth, while the spread of performance encompassed in the standard error range provides a lens through which to examine atypically high and low growth.
Educational experts are beginning to understand how SWD learn and what instructional methods provide improved access to the general curriculum. Our work will contribute to a research-based understanding of growth and provide a more complete basis for expectations of growth for students with disabilities.

Authors

• Pamela L. Paek, National Center for the Improvement of Educational Assessment, Inc.

• Christopher Domaleski, National Center for the Improvement of Educational Assessment