The Role of Extended Time and Item Content on a High‐Stakes Mathematics Test

The premise of a great deal of current research guiding policy development has been that accommodations are the catalyst for student performance differences. Rather than accepting this premise, two studies were conducted to investigate the influence of extended time and content knowledge on the performance of ninth‐grade students who took a statewide mathematics test with and without accommodations. Each study involved 1,250 accommodated students (extended time only) with learning disabilities and 1,250 nonaccommodated students demonstrating no disabilities. In Study One, a standard differential item functioning (DIF) analysis illustrated that the usual approach to studying the effects of accommodations contributes little to our understanding of the reason for performance differences across students. Next, a mixture item response theory DIF model was used to explore the most likely cause(s) for performance differences across the population. The results from both studies suggest that students for whom items were functioning differently were not accurately characterized by their accommodation status but rather by their content knowledge. That is, knowing students' accommodation status (i.e., accommodated or nonaccommodated) contributed little to understanding why accommodated and nonaccommodated students differed in their test performance. Rather, the data would suggest that a more likely explanation is that mathematics competency differentiated the groups of student learners regardless of their accommodation and/or reading levels.     

Effects of Linking Methods on Detection of DIF

Studies of differential item functioning under item response theory require that item parameter estimates be placed on the same metric before comparisons can be made. The present study compared the effects of three methods for linking metrics: a weighted mean and sigma method (WMS); the test characteristic curve method (TCC); and the minimum chi-square method (MCS), on detection of differential item functioning. Both iterative and noniterative linking procedures were compared for each method. Results indicated that detection of differentially functioning items following linking via the test characteristic curve method gave the most accurate results when the sample size was small. When the sample size was large, results for the three linking methods were essentially the same. Iterative linking provided an improvement in detection of differentially functioning items over noniterative linking particularly with the .05 alpha level. The weighted mean and sigma method showed greater improvement with iterative linking than either the test characteristic curve or minimum chi-square method.