Examining accommodation effects for equity by overcoming a methodological challenge of sparse data.
Bundle choice, not just accommodation choice, drives literacy pass rates for students with LD or EBD.
01Research in Context
What this study did
Pei-Cai et al. (2016) looked at bundles of test accommodations for K-12 students with learning or emotional disabilities.
They built a new stats trick to handle tiny data sets. Then they asked which bundles raise the odds of passing state literacy tests.
What they found
Some bundles helped, others did nothing. The mix you pick changes pass rates.
Kids who got the winning combo were more likely to meet the literacy cut score.
How this fits with other research
Chen et al. (2015) also wrestle with sparse data, but in single-case design software. Both papers say missing data is common and you need a plan.
Rao et al. (2017) map UDL for students with intellectual disability. Their review could swallow Pei-Ying’s bundles, since UDL and accommodations aim at the same goal: fair access.
Campos et al. (2017) step back to policy level. They offer logic models; Pei-Ying offers a stats tool. Use both and you can judge accommodations from kid score to state rule.
Why it matters
If you write IEPs or pick testing supports, the bundle matters more than the single tool. Try listing each accommodation in a combo, track pass/fail, and tweak the mix like Pei-Ying did. One small change can flip a kid from fail to pass.
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02At a glance
03Original abstract
BACKGROUND: As accommodation itself is an equity issue in varied contexts in and beyond education (e.g., the provision of assistive technology, extended time, or read-aloud), it is critical to examine the equitability of accommodation policies and practices by examining their effects on student performance. AIMS: This study sought to assess the effectiveness of thirty-one bundled accommodations for students with learning disabilities, emotional or behavioral disorders, or multiple exceptionalities writing a provincial literacy test in Ontario, Canada. METHODS AND PROCEDURES: We employed quantitative methods of log-linear analysis and odds ratio to examine the data. To analyze sparse data, we compared three different adjustment methods to meet this methodological challenge. OUTCOMES AND RESULTS: Our findings suggest that the problems with sparse data can be overcome by an adjustment method. We also found that the likelihood of achieving the provincial standards may differ among students with special needs depending on whether they did or did not receive certain combinations of accommodations for the literacy test. CONCLUSIONS AND IMPLICATIONS: We recommend that education stakeholders review the accommodations that produced significant differential effects to address the concerns regarding whether the test results were interpreted validly and fairly for students with special needs.
Research in developmental disabilities, 2016 · doi:10.1016/j.ridd.2015.12.012