Abstract
Statement of Problem
Over seven million students received special education-related services under IDEA from 2021 to 2022 (National Center for Educational Statistics [NCES], 2023a). These seven million students are spread across 13 eligibility categories, with the largest number of students placed within the category of specific learning disability (U.S. Department of Education, 2021). When assessing a student for a specific learning disability (SLD), two of the three most prominent approaches utilize standardized cognitive assessment. These two SLD identification models are referred to in this research as simple discrepancy and simple patterns of strengths and weaknesses (PSW). While multiple sources of data are considered during the assessment of SLD, previous research suggests practitioners using the simple discrepancy and simple PWS models over-rely on data obtained from standardized cognitive assessments (Maki & Adams, 2020). Furthermore, eligibility determinations may vary depending on the SLD identification model used (Maki & Adams, 2020; Proctor & Prevatt, 2003). This inconsistency in eligibility determinations poses many harmful implications to both the student and may subject the school district to potential litigation for denial of a free and appropriate public education, or not placing the student in the least restrictive environment. Due to the importance practitioners placed on standardized cognitive assessments and the potential for varying SLD eligibility determinations, the researchers felt it was necessary to analyze how often the determinations between the simple discrepancy and simple PSW models align when examining the same data set.
Sources of Data
This study used preexisting psychoeducational report data obtained from the Sacramento State Center for Counseling and Diagnostic Services (CCDS) clinic. The profiles of 10 school-age students previously evaluated at the CCDS clinic were analyzed to determine whether the student would qualify for a SLD in each corresponding model.
Conclusions Reached
Across the ten profiles analyzed using both identification models, the two models agreed or made consistent determinations 70% of the time. Of the 70% agreement between the two models, 60% (n = 6) comprised participants who did not meet SLD identification criteria under either model. On the contrary, when the profile may have been indicative of SLD, the rate of agreement was only 10% (n = 1). The overall number of participants who qualified under the simple discrepancy model was 20% (n =2), while 30% (n = 3) qualified under the simple PSW model. This suggests that students may be more likely to be eligible for special education services when assessed using simple PSW rather than simple discrepancy. The lack of determination consistency amongst these SLD identification models suggests there is a potential for students within special education who are not in the least restrictive environment, or conversely, students who did not qualify and consequently are not receiving a free and appropriate public education. The lack of accurate and consistent approaches to SLD identification has tremendously harmful implications for both students and school districts alike.