Part 1: Detailed findings comparing the identification, placement and performance of Massachusetts students with disabilities with students in the rest of the country Question 1: How do the identification, placement and performance of students with disabilities in Massachusetts compare to students with disabilities across the United States.? Finding 1a – Identification: Massachusetts identifies students as eligible for special education and related services at rates substantially higher than the rest of the country. More than 17% of Kindergarten through 12th grade students in the Commonwealth of Massachusetts are eligible for special education services. This rate of identification for special education service eligibility is the second highest in the country, behind Rhode Island (~19%). In Figure1 (on the following page), we display a comparison of identification rates in Massachusetts and the rest of the United States., by disability category. We interpret Figure 1 as indicating three general patterns. In the first pattern, which applies to categories whose designations have relatively strict and narrow definitions (Vision, Hard of Hearing/Deaf-Blind and Physical Disabilities), rates of identification in Massachusetts look very similar to national averages. In the second pattern, we find disabilities (Intellectual Disabilities, Autism, Emotional Disturbance, Multiple Disabilities and Developmental Delay) where Massachusetts rates of identification, while high relative to the national average, may be more in line with recent research findings (e.g., Bhasin,2006; Rice, 2009, Sheldrick, 2011; 2010) regarding true incidence in the population of school age children. The pattern, which will be the focus of the majority of the analyses presented in this report, is applicable to disability categories whose definitions may be more subject to interpretation (Specific Learning Disability, Communication and Other Health Impairment). Nearly two out of every three Massachusetts students with a disability are identified as belonging in one of these three categories and due to the potentially subjective nature of their diagnosis, rates of identification for these categories may be more sensitive to policy decisions than rates for the more strictly defined categories. Further, we see evidence throughout the commonwealth that indicates that children with similar profiles may fall differentially into one of these three categories, depending on the designation conventions of different school districts. For these reasons, we combine students in the Specific Learning Disability, Communication and Other Health Impairment categories for many of our analyses and we refer to this combined group as the ‘High Incidence’ disability categories. We
Though some level of over-placement of low-income students in special education may be appropriate (Hehir & Gamm, 1999; Donovan & Cross, 2002), the numbers of low-income students being served in ‘High Incidence’ categories exceed what most experts would consider appropriate. Further the generally higher level of segregation among low-income students and students of color suggest that some of these placements may be inappropriate. In addition, the generally weaker MCAS performance for students with disabilities who are educated in substantially separate settings raises serious concerns regarding whether, for these students, being identified as eligible for special education services is beneficial. The data used in this study were all supplied by the state and thus can be used to target school districts that are most in need of intervention. The Department of Elementary and Secondary Education should monitor and intervene in districts in there is inordinate use of substantially separate settings for students with disabilities. Though this report identified problems concerning the use of substantially separate settings to educate low-income students, African American students and Latino students, many school districts also continue to separate large numbers of students across income and racial groups. This is particularly true at the middle and high school level. Although ensuring access to the curriculum should be the over-riding concern when making placement decisions about an individual student with a disability, integration consistently predicts higher performance on the MCAS. Given that there is such variability between districts in the state concerning integration, the Department of Elementary and Secondary Education should intervene in districts that continue to educate higher than average numbers of students in substantially separate settings. Interventions in districts should focus on requiring better practices in general education. Research on over-identification of low-income students and students of color as eligible for special education services situates this problem primarily in general education practices. Most of students are designated as eligible for special education due to problems in academics, particularly reading and/or due to problems concerning behavior. Powerful practices have been developed by researchers and implemented in many schools across the United States that have proven effective in dealing with these issues (these are discussed below). The Massachusetts students who are of most concern in this regard are all students who are eligible for services under Title 1 of the Elementary and Secondary Education Act and IDEA. The Commissioner should consider directing the use of these funds toward this purpose as current authority allows. Promotion of More Effective Practice Though we recommend that the state intervene in districts whose patterns of identification, placement and performance are most problematic, the interventions
Appendix B: Selected Regression Models 1. Taxonomy of selected fitted school-level Ordinary Least Squares regression models predicting district identification rates of students with ‘High Incidence’ disabilities in Massachusetts public school districts, n=298 traditional school districts (model excludes charter schools, vocational/tech schools, or schools serving special education students exclusively). Model 1.1 Model 1.2 Model 1.3 Model 1.4 1.5 Intercept 11.194** (0.156) 8.456** (1.234) 7.309** (1.948) 8.554** (2.924) 9.275** (0.454) District Percent White Students 0.010 (0.011) 0.022 (0.018) 0.021 (0.019) District Percent Students eligible for Free/Reduced Price Lunch (log2) 1.155** (0.297) 1.123** (0.282) 1.096** (0.292) 1.095** (0.261) District Participates in METCO Program 0.055 (0.542) District Percent Students with Limited English Proficiency (square root) 0.210 (0.275) 0.384 (0.301) District Enrollment (log2) -0.255 (0.372) District is ‘Rural’ 0.570 (0.424) 0.609 (0.355) District is ‘Town’ -0.311 (0.661) -0.139 (0.600) District is ‘City’ -0.075 (0.837) -0.232 (0.777) District Percent Elementary Schools -0.005 (0.008) R2 0.000 0.057 0.059 0.072 0.065 N 298 298 298 298 298 dfERROR 297 294 294 289 293 * p<0.05; ** p<0.01 Table 2. Taxonomy of selected fitted school-level Ordinary Least Squares regression models