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1 Review of Special Education in the Commonwealth of Massachusetts Thomas Hehir and Associates Thomas Hehir, Todd Grindal and Hadas Eidelman Boston, Massachusetts April 2012 Report commissioned by the Massachusetts Department of Elementary and Secondary Education Mitchell D. Chester, Ed.D., Commissioner
3 Review of Special Education in the Commonwealth of Massachusetts Table of Contents Executive Summary Page 1 Introduction Page 3 Methods Page 4 Summary of Key Findings Page 5 Findings Page 5 Part 1: Detailed findings comparing the identification, Page 6 placement and performance of Massachusetts students with disabilities with students in the rest of the country Part 2: Detailed findings regarding rates of special education Page 10 identification in the commonwealth of Massachusetts Part 3: Detailed findings describing the relationship between Page 13 district rates of identification and proficiency on the Massachusetts Comprehensive Assessment System (MCAS) Part 4: Detailed findings regarding the relationship between Page 17 student characteristics and likelihood of being identified as eligible for special education services Part 5: Detailed findings describing the relationship between Page 23 characteristics of students with High Incidence disabilities and the proportion of the school day spent in classrooms with their typically developing peers Part 6: Detailed findings describing the relationship between Page 27 the proportion of the school day a student spent in classrooms with their typically developing peers and scores on the MCAS for students with High Incidence disabilities Recommendations Page 30 References Page 34 Appendix A: Additional Methodological Information Page 36 Appendix B: Selected Regression Models Page 39
4 1 Executive Summary We conducted this study at the behest of Massachusetts Commissioner of Elementary and Secondary Education, Dr. Mitchell D. Chester. The commissioner was concerned about the relatively high numbers of students with disabilities identified in the commonwealth and the degree to which those identification rates were beneficial to students given the high costs associated with special education. For the purposes of this study we primarily examined those disability categories whose determination whether a child is identified as having a disability or not might involve a greater degree of subjectivity and therefore represent likely sources of high rates of special education identification. We performed extensive statistical analyses using the state student databases examining many student and district-level characteristics. We looked at demographic information concerning the populations receiving special education services, the performance of these students on the Massachusetts Comprehensive Assessment System (MCAS) and the National Assessment of Educational Progress (NAEP), the level of integration these students experienced in school (the extent to which they were included in classrooms with their typically developing peers), the variability among districts in serving these students and factors that were associated with identification rates and student performance. Several powerful trends emerged from our analyses: -Massachusetts general education students and students with disabilities score near or at the top of the National Assessment of Educational Progress (NAEP) distribution compared to students in other states. -Massachusetts has the second highest rate of special education identification in the United States. -The percentage of students identified as eligible for special education does not appear to have a consistently positive or negative impact on average district performance on the MCAS. - However, the degree to which students with disabilities are included in classrooms with their non-disabled peers is substantially related to MCAS performance, controlling for a host of relevant variables such as income, race and English language proficiency. - Low-income students are considerably more likely than their counterparts to be deemed eligible for special education services.
5 2 - Low-income students with disabilities and Latino and African American students with disabilities are considerably less likely to be included in general education classes when compared to their White and Asian special education counterparts. - We find evidence that among special education students, lack of integration may be a contributing factor for lower performance on the MCAS. These data paint an uneven picture of special education policy and practice in the commonwealth. On the one hand, many students with disabilities are doing comparatively well in Massachusetts and are experiencing high levels of integration and success. However, there appear to be some disturbing trends among low-income students and students of color. Low-income students are far more likely to be eligible for special education services than other students and once they receive services, are more apt to be educated in separate settings. This is particularly alarming given evidence that separation from the mainstream is associated with poorer standardized test performance for students with disabilities. Though we believe that the findings of this report call for state action, we do not recommend major state policy changes in special education. This report should not be interpreted as a call to dramatically reduce the number of Massachusetts student served through special education across the board. Special education helps to ensure that students with disabilities receive supports and services critical to their academic and lifelong success. Large numbers of students with disabilities the commonwealth are doing well, compared to students with disabilities across the country. However, the substantial variation in district practices and student performance from one school district to another as it relates to low-income students, calls for state action. The Department of Elementary and Secondary Education should be more actively intervening in districts whose identification rates and use of substantially separate educational settings for low-income students and students with disabilities as a whole are substantially higher than average. Further, we believe that the commonwealth should be examining its current efforts under Title 1 of the Elementary and Secondary Education Act and should promote more powerful practices in general education to assure that low-income students receive the support they may need in order to be successful in school. Detailed recommendations are included at the end of this report.
6 3 Introduction This report presents findings from our comprehensive review of the identification 1, placement 2 and performance 3 of students with disabilities in the Commonwealth of Massachusetts. In the spring of 2011, Thomas Hehir and Associates was retained by the Massachusetts Department of Elementary and Secondary Education to conduct a comprehensive review of special education in the Commonwealth of Massachusetts. Our review focused on the following questions: Question 1: How do the identification, placement and performance of students with disabilities in Massachusetts compare to students with disabilities across the United States? Question 2: What are the characteristics of school districts in Massachusetts that identify a relatively high percentage of students as eligible for special education and how do these characteristics compare to Massachusetts school districts that identify a moderate or relatively low percentage of students as eligible for special education? Question 3: What district-level characteristics or practices are associated with higher district wide performance of students with disabilities on the Massachusetts Comprehensive Assessment System (MCAS)? Question 4: Are some Massachusetts students more likely than other students to be identified as eligible for special education? Question 5: Among students with disabilities, are some students more likely than other students to be educated in classrooms with their typically developing peers? Question 6: What student and district-level characteristics are related to the performance of students with disabilities on the MCAS? 1 Here identification refers to the identification of students as eligible for special education services. 2 Here placement refers to the proportion of the school day that students eligible for special education services spend with their typically developing peers. 3 For this analysis, we will use Massachusetts Comprehensive Assessment System (MCAS) scores as a measure of student academic performance.
7 4 Methods This report is based on quantitative i.e., statistical analyses of district, school and student data and qualitative i.e., interview and focus group data collected through meetings with district and state administrators as well as special educators and advocates. Quantitative data on Massachusetts s students included information on student performance on the Massachusetts Comprehensive Assessment System (MCAS) and student background information from the Student Information Management System (SIMS). Where possible, we added additional school district information to the SIMS and MCAS data by using publicly available data sets, such as the 2000 Census and the National Center for Education Statistics (NCES) Common Core of Data (CCD). We also examined publicly available datasets and reports from the Data Accountability Center ( to provide comparison information on students with disabilities across the country. Although the combination of these data sources provided us with an unprecedented level of information regarding a large number of students with disabilities, it is important to note that they do not permit us to account for district-level factors such as the quality of classroom teaching or child-level factors such as the experience of trauma or housing instability that can be associated with performance on the MCAS. Further, we do not have information on indicators of students social emotional development which we believe to be a critical component of student s lifelong success In this report, the term District refers to traditional, multi-grade, academically-focused school districts only (n=298). We chose to exclude charter schools, vocational districts and separate special education public and private schools from our analysis for the following reasons. First, vocational schools and separate special education public and private schools enroll substantially higher than average percentages of students with disabilities and thus behave quite differently from traditional school districts, which are the intended focus of this analysis. Secondly, since charter schools are each their own district, including them in our analysis would confound schools with districts. Third, since charter schools enroll a substantially lower than average percentage of students with disabilities, they also behave quite differently than traditional school districts and therefore represent a separate substantive area for analysis. Lastly, we are not able to incorporate geographic census data (such as median family income) for these types of non-standard districts, so the models we fit that included such variables would by default exclude such non-traditional districts from the analysis. Additional details describing the specific methods used to determine each of our findings are presented in Appendix A.
8 5 Summary of Key Findings Some of the key findings we discuss in this report, tied to their respective research questions, include: Summary of Findings for Question 1: Overall, Massachusetts s schools identify a higher percentage of students as having a disability than do schools across the country. Massachusetts s students with disabilities compare favorably to students across the country in terms of their academic achievement and the degree to which they are included with their typically developing peers. Summary of Findings for Question 2: Though some wealthy districts have high rates of special education identification, overall high-income districts are not primarily responsible for the commonwealth s relatively high identification rates statewide. Rather, districts with higher percentages of low-income students, on average, identify a higher percentage of their students as disabled than do districts with low percentages of low-income students. Summary of Findings for Question 3: At the district level, the performance of students with disabilities while consistently lower on average typically parallels the performance of general education students; such that in districts where general education students have higher MCAS scores, students with disabilities also, on average, earn higher scores on the MCAS. Summary of Findings for Question 4: Low-income students in Massachusetts are more likely to be identified as eligible for special education services than are students who are not low-income. This is particularly true for low-income students who attend school in high-income districts. Meanwhile, among students with limited English proficiency, there are dramatic differences in the rates of disability identification between students whose home language is Spanish and students whose home language is not Spanish. Summary of Findings for Question 5: Among students with disabilities, lowincome students, African American students and Latino students are consistently more likely to be educated in classrooms away from their typically developing peers than are middle- or high-income students or students who are not African American or Latino. Summary of Findings for Question 6: Holding constant other student and districtlevel characteristics associated with MCAS performance, students with disabilities who spend more time being educated with their typically developing peers, on average, earn higher scores on the MCAS than students who spend much of their time in substantially-separate, non-mainstream classes.
9 6 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 12 th 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; Pizur- Barnekow, 2010) regarding true incidence in the population of school age children. The third 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). 4 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. 4 The Neurological Impairment designation does not clearly fall into any of the three patterns we describe here. In part because of the very small proportion of Massachusetts children represented in this category and in part due to variability in the use of this designation in the state, we do not include this category in most of the analyses presented in this report.
10 7 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 will discuss in detail later in this report evidence of overrepresentation of the High Incidence disability categories among Massachusetts students and because of their policy relevance, we focus the majority of analyses discussed in this report on this subset of children. Throughout this report, any reference to the High Incidence disability categories therefore refers to the combined group of children who are identified as having a Specific Learning Disability, Communication Impairment, or Other Health Impairment. Figure 1. Percentage of students identified with specific disabilities in the United States and Massachusetts. Finding 1b Placement: Students with disabilities in Massachusetts are educated in classrooms with their typically developing peers at rates that are the same as or higher than rates for students across the country. The majority of students with disabilities in Massachusetts (approximately 63 out of every hundred students with disabilities) spend at least 80% of their school day in classrooms with their typically developing peers. This compares favorably with figures for the rest of the country, where approximately 58 out of every hundred students with disabilities are included to this extent. This relationship between
11 8 rates of inclusion in Massachusetts and the rest of the country is relatively consistent across disability categories. We see this as a positive finding. Numerous studies of students across the country indicate that students with disabilities who spend more time with their typically developing peers outperform similar students who are educated in less integrated settings on measures of numerous social, academic and post-school success (Wagner et al, 2003; Hehir, 2005). Later in this report, we will discuss the relationship between inclusion with typically developing peers and academic performance for Massachusetts students with disabilities specifically. Finding 1c Performance: Massachusetts students with disabilities, on average, score higher than students with disabilities across the country on the National Assessment of Educational Progress (NAEP). In 2009, students with disabilities in Massachusetts scored at or near the top of the national distribution on the NAEP English and Mathematics tests. This finding was generally consistent across grade levels. While this is laudable, we urge the reader to interpret this finding with caution. As we will discuss below, we have reason to believe that the phenomenon of higher rates of disability identification in Massachusetts creates a pool of students with disabilities whose challenges are potentially less severe than in other states. These students therefore are more likely to perform well on average on measures of academic achievement than students with disabilities in states that identify a relatively smaller percentage of students as eligible for special education and related services. Question 1: Summary Massachusetts s students are identified as eligible for special education in the categories of Vision, Hard of Hearing/Deaf-Blind and Physical Disabilities at rates similar to students across the country. Massachusetts s students are identified as eligible for special education in the categories of Intellectual Disabilities, Autism, Emotional Disturbance, Multiple Disabilities and Developmental Delay at rates higher than students across the country, but we believe, based on findings from other research, that these higher rates may represent a more accurate reflection of true population incidence. Massachusetts s students may be inappropriately overrepresented in the disability categories of Specific Learning Disability, Communication and Other Health Impairment.
12 9 Massachusetts s students with disabilities are educated in classrooms with their typically developing peers at slightly higher rates compared to students with disabilities across the country. Massachusetts s students with disabilities, on average, outperform students with disabilities across the country on the National Assessment of Educational Progress (NAEP).
13 10 Part 2: Detailed findings regarding rates of special education identification in the Commonwealth of Massachusetts Question 2: What are the characteristics of school districts in Massachusetts that identify a relatively high percentage of students as eligible for special education and how do these characteristics compare to Massachusetts school districts that identify a moderate or relatively low percentage of students as eligible for special education? Finding 2a Variation in Identification Rates Within and Between Districts: Rates of special education identification vary substantially within and between Massachusetts school districts. Rates of special education identification vary substantially across Massachusetts s school districts, 5 ranging from a low of approximately 9% to a high of 29% of a district s students identified as eligible for special education and related services. While this does not change the overall finding that the state demonstrates a higher rate of students with disabilities than most of the rest of the country, it does indicate that this higher rate is not evident in all districts. The map below displays the variation in rates of identification of students with high incidence disabilities for school districts across the state. Figure 2: Displaying variation in the percentage of students in a district identified with high incidence disabilities (Specific Learning Disability, Communication Impairment, or Other Health Impairment). n=275 school districts. 6 5 See methods section (p. 6 above) for details on how we identified districts for these analyses. 6 In cases where there were overlapping geographic boundaries (e.g. an area in which several small school districts serving elementary school students occupy the same geographic space as one larger district that supports high schools students), we display the larger geographic unit.
14 11 The variability in identification between specific disability categories from district to district is also notable. For example, we found one district with the highest rates of identification for Specific Learning Disability in the commonwealth (17.7%) and the lowest rates of identification for the Communication category (0%). This same district, meanwhile, had an average identification rate for the Health category (<1%). In another district, we found the commonwealth s lowest rate of identification for Specific Learning Disabilities (<1%), one of the highest rates of identification in the Communication category (7.4%) and an above average rate of identification in the Health category (1.7%). While these districts represent extreme examples, this high degree of variation in the use of these three disability categories was evident across the commonwealth. Through focus groups with advocates and state special education officials, we learned that local school districts are given substantial flexibility in their interpretation of these specific disability categories. In some cases, this appears to result in children with the same underlying issues receiving different disability labels in different school districts. This represents a potential impediment to the effective delivery of services to children and also presents challenges for our analyses. Based on what appears to be the relative diagnostic subjectivity for certain disability categories across the Commonwealth, we combined as described in Part 1 of this report children in the Specific Learning Disability, Health and Communication disability categories into one High Incidence disability category in some of the analyses reported below. Finding 2b Low-Income Students and Special Education: Districts that have a larger percentage of low-income students, on average, identify a larger percentage of their students in the High Incidence disability categories. As we discussed our work with colleagues who are familiar with special education in the commonwealth, we have repeatedly encountered a narrative that attributes the high rates of special education identification in Massachusetts to high-resource parents seeking to secure advantages (for example, untimed or otherwise accommodated standardized exams) for their children through gaining eligibility for special education. However, in our examination of district level data, we find little evidence that this is the case. Districts with higher median family incomes do not identify a higher percentage of their students as eligible for special education and related services. In fact, counter to the narrative that district wealth might drive high rates of special education identification; we found that districts with a larger percentage of lowincome 7 students, on average, identified a larger percentage of their students into 7 In this report, a student is considered low-income if he or she was eligible to receive a free or reduced priced lunch.
15 12 the High Incidence special education categories. 8 In this portion of our analysis, we fit a series of statistical models in which we predicted district-level percentage of students identified in the High Incidence disability categories, controlling for the effect of other relevant district characteristics, 9 and we found that every doubling of district percentage of free or reduced price lunch students (for example, from 5% to 10%, or 12% to 24%, etc.) was associated with nearly a one percentage point difference in the district percentage of students identified as having a Specific Learning Disability, Other Health Impairment, or Communication Impairment designation. Question 2: Summary Rates of special education identification vary substantially within and between Massachusetts s school districts. Though some wealthy districts have high rates of special education identification, overall high-income districts are not primarily responsible for the relatively high identification rates in the state. Districts that have a larger percentage of low-income students, on average, identify a larger percentage of their students in the High Incidence disability categories. 8 Unless otherwise stated, throughout this report the term High Incidence will refer to students in the Specific Learning Disabilitiy, Health, or Communication disability categories. 9 In these analyses we controlled for whether the district was classified by the National Center of Education Statistics (NCES) as rural, city, suburban or town (see for more information); the percentage of white students in a district; the percentage of students who demonstrated limited English proficiency in a district; the percentage of a district s students who are in elementary school; and whether the district participated in the METCO program. We found all of these potential predictors of rates of High Incidence identification to be trivial and non-significant.
16 13 Part 3: Detailed findings describing the relationship between district rates of identification and proficiency on the Massachusetts Comprehensive Assessment System (MCAS) Question 3: What district-level characteristics or practices are associated with higher district wide performance of students with disabilities on the Massachusetts Comprehensive Assessment System (MCAS)? In the previous section of this report, where we discussed findings related to Question 2, we discussed the fact that some school districts identify higher numbers of children as eligible for special education than other districts. In this section, we explore this further, looking not only at rates of identification but also at aggregate student performance on the MCAS. Given the crucial importance of MCAS performance on school attainment (most notably, high school graduation), the MCAS performance of students with disabilities, as well as the relationship between rates of identification and students MCAS scores, are indispensable to this analysis. Specifically, we examine here whether on average, district characteristics, including districts percentage of students identified in the High Incidence disability category are meaningfully associated with district average performance on the MCAS. Finding 3a Districts with High Rates of MCAS Proficiency for General Education Students tend to also have High Rates of MCAS Proficiency for Students with Disabilities: The performance of a school district s general education students is, on average, the strongest district-level predictor of that district s special education students performance on the MCAS. General education students consistently outperform students with disabilities on the MCAS. For example, on average, approximately 78% of a district's general education students score proficient or higher on the English language MCAS, while only 38% of a district s students in the High Incidence disability category do the same. That said, the performance of a district s general education students and students with disabilities are closely related, although average scores for students with disabilities are consistently lower. We conducted Ordinary Least Squares regression analyses and found that, controlling for relevant district-level characteristics, 10 a one-percentage-point difference in the percentage of general education students scoring proficient or higher on the English language MCAS (for example, 78% vs. 79%; or 85% vs. 86%) is associated with an approximately onepercentage-point difference in the percentage of students with disabilities with a High Incidence disability designation scoring proficient or higher on the same exam (for example, 38% vs. 39%; or 45% vs. 46%). Put another way, districts with higher- 10 In these regression models we controlled for the percentage of students in the district with a High Incidence designation, the percentage of students in the district who were White, district enrollment and district urbanicity. Control variables that were trivial and/or non-significant were excluded from the final model (see Appendix B, Table 2 for an example).
17 14 than-average rates of general education students scoring proficient or higher on the MCAS are also likely to have higher-than-average rates of students with disabilities scoring proficient or higher. Figure 3. District-level percentage of Students with Disabilities scoring Proficient or Advanced on the 8 th grade MCAS math assessment predicted by district-level percentage of General Education students scoring Proficient or Advanced on the same test, controlling for districts percentage of students with a High Incidence disability, percentage of White students, district enrollment and district urbanicity (n=297 traditional school districts). Finding 3b Rates of High Incidence Disability Identification and MCAS Scores: Students in the High Incidence disability categories in districts that identify a higher percentage of children as disabled do not demonstrate meaningfully higher scores on the MCAS. 11 We fit a series of multilevel regression models to assess the relationship between the percentage of children in a district identified in the High Incidence category and those students scores on the MCAS. The purpose of these models was to explore whether students in High Incidence catagories in high-identifying districts scored notably lower or higher than students in High Incidence categories in lowidentifying districts, taking into account other relevant student- and district-level factors See appendix B, tables 2 & 3, for results from sample multi-level regression analyses in support of this finding. 12 We conducted these analyses separately for students in grades 4, 8 and 10. We excluded students enrolled in vocational or technical schools, charter schools or out-of-district placements. In these analyses we controlled for student race (African American, Latino, Asian, American Indian and Other race, with White as the reference category); whether the child has a High Incidence disability or is eligible for special education in a different disability category; the extent to which a child spent
18 15 Examining student-level raw scores on the MCAS, we found that consistently, although students in the High Incidence disability categories do appear to score somewhat higher on the MCAS in high-identifying districts than in low-identifying districts, the difference in High Incidence scores between low- and high-identifying districts was not substantial. The difference in average score between High Incidence students in a district identifying 5% of their students as having a High Incidence disability (placing that district s rate of identification at the 5 th percentile for the state) and High Incidence students in a district identifying 20% of their students as having a High Incidence disability (representing the 95 th percentile for identification rates in the state) was about 1.8 raw points on the 8 th grade math and English assessments. Looking at MCAS performance categories, we also examined the combined percentages of students (special and general education) who scored Proficient or Advanced on the MCAS and once again we did not see a clear trend regarding the relationship between district rates of special education identification and overall student performance. That said, we did find some evidence in uncontrolled analyses, that students in highly inclusive districts, on average, earn higher marks than students in districts that include a relatively low percentage of students with disabilities. We take the finding regarding test scores and rates of identification to be sensible, given the reasonable likelihood that students with High Incidence disabilities in high identifying districts are likely to be, on average, less severely disabled than students with High Incidence disabilities in low identifying districts. We therefore conclude that the difference in average scores is not only small but also likely driven by the ability levels of more mildly disabled students rather than by district rates of identification per se. We examine this relationship in more detail in Part 6 of this report their school day in classrooms with their typically developing peers (inclusiveness); child gender; child eligibility for free or reduced-priced lunch; whether the child s school district participates in the METCO program; district median family income (from 2000 census data); overall inclusiveness of the district; percentage of LEP students in the district and percentage of a district s students who receive free or reduced-priced lunches. Models also included two-way statistical interactions between: child inclusiveness and special education category ( High Incidence vs. a different disability category);and child special education category ( High Incidence vs. a different disability category)and the percentage of students in the district identified as having a High Incidence disability. We tested a number of other main effects and interactions and found them to be non-significant.
19 16 Question 3: Summary The performance of a school district s general education students is, on average, the strongest predictor of the performance of a district s special education students. Students in the High Incidence disability categories in districts that identify a higher percentage of children as disabled do not demonstrate meaningfully higher scores on the MCAS.
20 17 Part 4: Detailed findings describing the relationship between student characteristics and likelihood of being identified as eligible for special education services Question 4: Are some students more likely than other students to be identified as eligible for special education? Finding 4a Student Race and Special Education: Despite the fact that African American and Latino students are identified as eligible for special education at slightly higher rates than White students, this disparity virtually disappears when we consider other relevant district and student characteristics including socio-economic status, which appears to be the main driver of the race differential. Approximately 22% of African American and 21% of Latino students in Massachusetts are students with disabilities. This is higher than the rates of disability identification for White students, which are approximately 17%, as well as for Asian students, which are approximately 9%. While the differential between rates for African American and Latino students and rates for White and Asian students might appear substantial at first, we see a different picture when we control for other student and district-level characteristics. We fit a series of multilevel logistic regression models to predict the probability that a child would receive a High Incidence designation 13 while taking into account a variety of other relevant factors. These analyses indicated that the difference in the odds that an African American or Latino student compared to a White or Asian student would be identified as having one of these disabilities were quite small. For example, the fitted odds ratio that an African American student compared to a White student would be identified as having a High Incidence disability were The magnitude of this differential is not only trivial, but also substantially smaller than the magnitude of differentials found in other areas of the country, where we and other researchers have observed large and systematic patterns of race-based overrepresentation (Hehir et al., 2010; Losen & Orfield, 2002). 13 These models (School Districts n=299; Children n=468,158) control for student race (African American, Latino, Asian, American Indian and Other race with White as the reference category); child gender; child eligibility for free or reduced-priced lunch; whether the child s school district participates in the METCO program; percentage of White students in the district; and log 2 of percentage of a district s students who receive free or reduced-priced lunches. Models also included two-way statistical interactions between: student race and district percentage of white students; student race and district METCO status; and child eligibility for free or reduced-priced lunch and log 2 of percentage of a district s students who receive free or reduced-priced lunches.
21 18 Finding 4b Student Socioeconomic Status and Special Education: Low-income students are nearly twice as likely as students who are not low-income to be identified as having a High Incidence designation. This is particularly true for low-income students in relatively high-income school districts. Approximately 23% of low-income students in Massachusetts are students with disabilities, compared to about 15% of students who are not low-income. This disparity in rates persists after we take into account the potential effect of relevant student and district characteristics 14 on the likelihood of being identified as eligible for services. We fit a series of multilevel logistic regression models and found strong evidence that low-income students are disproportionately identified as eligible for special education. In these controlled models, we found that the odds that a lowincome student would receive a High Incidence disability designation are 1.95 times the odds for a student who is not low-income. We also find that the disproportion in odds between low-income and non-lowincome students differs as a function of district-level percentage of low-income students. In Figure 4 (below) we display an example of this relationship. Controlling for relevant student and district characteristics we find that the odds that a lowincome elementary school student in a district that enrolls a large percentage of lowincome students (approximately 45%) will receive a Specific Learning Disability designation are 1.86 times the odds for a non-low-income student. When we compare low-income and non-low-income students in districts that enroll a small percentage of low-income students (approximately 4%), we see that the corresponding fitted odds ratio is substantially higher, approximately These findings are drawn from the same models discussed in Finding 4a (above).
22 19 Figure 4. Representation of fitted odds ratios that a low-income student (eligible for Free/Reduced Price Lunch) will be identified as having a Specific Learning Disability compared to a student who is not low-income in districts with high (45%) and low (4%) proportions of low-income children, 15 controlling for student-level factors (gender, race and LEP status) and district-level factors (percentage of White students in a district and whether the district participates in the METCO program). 16,17 Finding 4c Students with Limited English Proficiency and Special Education: Students in Massachusetts with limited English proficiency (LEP) as a whole are eligible to receive special education and related services at rates similar to their English proficient peers. Limited English proficient and English proficient students in Massachusetts, on average, are designated as eligible to receive special education and related services at similar rates. The table below displays rates of eligibility for special education for LEP and non-lep students. With the exception of the categories of Intellectual and Communication disabilities, LEP students are relatively equitably represented when compared to English proficient students. 18 This contrasts with previous work in other parts of the U.S., where we have found consistent evidence of systematic over- 15 These rates of low-income student represent the 90 th and 10 th percentiles in the state. 16 All other student- and district-level factors in the dataset were not meaningfully related to the probability of a student s eligibility for special education services. 17 See appendix B, Table 1, for results from sample multi-level logistic regression analyses in support of this finding. 18 These patterns hold when examined using multi-level logistic regression controlling for relevant student and district level characteristics.
23 20 representation of LEP students in numerous special education categories (Hehir et al., 2010; Hehir & Mosqueda, 2007). Figure 5. Percentages of Massachusetts English Proficient students and Limited English proficient students identified in each disability category (does not include Deafness, Vision, or Deaf/Blind categories). Finding 4d Home Language of LEP Students (Spanish vs. Other Languages) and Special Education: Within the LEP student subgroup, we find dramatic differences in special education identification between LEP students whose home language is Spanish and LEP students whose home language is not Spanish. Though rates of special education identification are similar between LEP and non- LEP students overall, there are meaningful differences in identification rates for LEP students who speak Spanish as a first language and those whose first language is not Spanish. Spanish speakers account for approximately 55% of all Massachusetts LEP students, but they represent approximately 75% of LEP students who are eligible for special education services. When we explored this issue statistically(through multi-level logistic regression analyses), we found that the fitted odds that an LEP student whose first language is Spanish will be identified as eligible for special education services are quite similar to the odds for an English proficient student. LEP students who speak a first language other than Spanish, on the other hand, exhibit odds that are nearly half those of non-lep students in the state. As the figures below indicate, this means that while LEP students whose first language is Spanish do not demonstrate systematic over- or under-identification in special
24 21 education categories, LEP students whose first language is not Spanish appear to be identified as eligible for special education services at dramatically lower rates. Figure 6. Representation of fitted odds ratios that a student with Limited English Proficiency (LEP) will be identified as eligible for special education services compared to a non-lep student for LEP students whose home language is Spanish and LEP students whose home language is not Spanish 19, controlling for student-level factors (gender, race and eligibility for free/reduced price lunch) and district-level factors (percentage of students eligible for free/reduced price lunch). 20 The category of intellectual disabilities represents an exception to the overall pattern of equitable- or under-representation of LEP students as eligible for special education services. Massachusetts Limited English Proficient students are identified as having an intellectual disability at more than double the rate of English proficient students. Although this differs from the overall pattern for LEP versus non-lep students, the relationship between LEP students whose first language is Spanish versus those whose first language is not Spanish holds consistently in the intellectual disability category. Rates of eligibility for special education services related to intellectual disability vary sharply between Spanish speaking and non- Spanish speaking LEP students. While approximately 1% of English proficient students are identified as having an intellectual disability, the rate for non-spanish speaking LEP students is 1.5% and the rate for Spanish speaking LEP students is substantially higher at nearly 6%. Controlling for relevant student and district characteristics 21 we find that the odds that a Spanish speaking LEP student will 19 These results come from a model including only elementary school students, although the pattern was consistent for students in middle and high schools as well. 20 All other student- and district-level factors in the dataset were not meaningfully related to the probability of a student s eligibility for special education services. 21 In these models we control for student gender, low-income status, district percentage of lowincome students, district METCO participation, the interaction between student LEP status and district percentage of low-income students and the interaction between student low-income status and district percentage of low-income students (n=307,193 children, nested in n=252 school districts).
25 22 receive a designation of intellectual disability are nearly double (1.87 times) the odds that an English Proficient student will receive the same designation. Non- Spanish speaking LEP students by contrast are identified at rates similar to non-lep students. Question 4: Summary While African American and Latino students are identified as eligible for special education services at slightly higher rates than White students, this differential virtually disappears when we control for SES and other district and student characteristics. Low-income students are nearly twice as likely as students who are not lowincome to be identified as having a High Incidence designation. This is particularly true for low-income students in relatively wealthy school districts. Students in Massachusetts with limited English proficiency (LEP) as a group are eligible to receive special education and related services at rates similar to their English proficient peers. Within the LEP student subgroup, we found dramatic differences in special education identification between LEP students whose first language was Spanish and LEP student whose first language was not Spanish.
26 23 Part 5: Detailed findings describing the relationship between characteristics of students with High Incidence disabilities and the proportion of the school day spent in classrooms with their typically developing peers Question 5: Among students with disabilities, are some students more likely than other students to be educated in classrooms with their typically developing peers? Like students across the country, Massachusetts students with different disability designations are educated in classrooms with their typically developing peers at different rates. For example, students with disabilities in the High Incidence categories are included with their typically developing peers at higher rates than students with disabilities in other special education categories. Within the High Incidence category, students with Communication disabilities are included with their typically developing peers at a higher rate than students designated as having a Specific Learning Disability or Other Health Impairment. Although students with disabilities in Massachusetts are, overall, educated in inclusive settings at higher rates than similar students across the United States, we found that the degree to which students with disabilities are educated in classrooms with their typically developing peers varies substantially from district to district and student to student. For example, in some districts, more than 3 out of 10 students with disabilities are in substantially separate settings (spending between 0-40% of their days with typically developing peers), while in other districts this figure is less than 1 out of 10. There were also student-level characteristics that were associated with the extent to which students with disabilities spent their school day in classrooms with their typically developing peers. These characteristics tended to be consistent across grades and here we present as an example, the subgroup of students in the state who were in the 10 th grade in who were identified as eligible for special education in one of the High Incidence catagories. There were 7, th grade students with disabilities in High Incidence disability categories in traditional school districts across the commonwealth in the school year. 677 (approximately 9%) of these 10 th grade High Incidence students spent less than 40% of their day with their typically developing peers. Of the 2,543 of these High Incidence students who qualified for free or reduced price lunch, 409 (or 14%) were separated from the mainstream classroom to this same degree. Breaking this group down further, of the th grade High Incidence students who were low-income and African American, 103 (or 16%) were educated in these substantially separate settings. And finally, within that group, looking only at males, more than 18% of low-income, 10 th grade African American male High Incidence students were educated in substantially separate classrooms away from their typically developing peers.
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