2
Representation of Minority Students in Special and Gifted Education

Are minority children disproportionately represented in special and gifted education? On the surface, the question is straightforward. How “disproportion” is defined, however, determines whether the question can be answered. If the referent is the population of minority students, then one can simply compare the proportion identified with the proportion in the total student population. If, however, we are asking whether the number identified is in proportion to those whose achievement or behavior indicate a need for special supports, then the question is one for which no database currently exists. In this chapter, we compare the numbers of students of each race/ethnicity identified for special and gifted education with their representation in the student population. The reader should keep in mind, however, that these data cannot tell us about the appropriateness of assignment and, by themselves, they provide a very weak foundation for guiding public policy.

Some researchers have attempted to explain observed differences in placements by race/ethnicity using available data. Special education data at the district level have been analyzed, controlling for sociodemographic characteristics of the district, and conclusions have been drawn about the patterns that emerge. To understand differences in assignment to gifted programs, other data sets that provide information on socioeconomic characteristics of families have been correlated with high achievement data. We address the limitations of these data analyses before turning to conclusions and recommendations.



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Minority Students in Special and Gifted Education 2 Representation of Minority Students in Special and Gifted Education Are minority children disproportionately represented in special and gifted education? On the surface, the question is straightforward. How “disproportion” is defined, however, determines whether the question can be answered. If the referent is the population of minority students, then one can simply compare the proportion identified with the proportion in the total student population. If, however, we are asking whether the number identified is in proportion to those whose achievement or behavior indicate a need for special supports, then the question is one for which no database currently exists. In this chapter, we compare the numbers of students of each race/ethnicity identified for special and gifted education with their representation in the student population. The reader should keep in mind, however, that these data cannot tell us about the appropriateness of assignment and, by themselves, they provide a very weak foundation for guiding public policy. Some researchers have attempted to explain observed differences in placements by race/ethnicity using available data. Special education data at the district level have been analyzed, controlling for sociodemographic characteristics of the district, and conclusions have been drawn about the patterns that emerge. To understand differences in assignment to gifted programs, other data sets that provide information on socioeconomic characteristics of families have been correlated with high achievement data. We address the limitations of these data analyses before turning to conclusions and recommendations.

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Minority Students in Special and Gifted Education FEDERAL DATA SETS Two federal agencies currently report data on enrollment of students in special education programs broken down by racial/ethnic group: the Office of Special Education Programs (OSEP) and the Office for Civil Rights (OCR), both in the U.S. Department of Education. OSEP has reported for over two decades to Congress on the implementation of Public Law 94-142 (and later, the Individuals with Disabilities Education Act [IDEA]), including data on the number of children served under the various disability categories. However, the child count data reported by OSEP were not broken down by racial/ethnic group until the last two reporting periods. OCR, in contrast, has consistently monitored minority representation, but only in the few disability categories with which it is concerned. Until 1994, these included mild mental retardation, emotional disturbance (ED), specific learning disabilities (LD), and speech and language impairments (SLI). Since that time, however, OCR has collected data on the broader category of mental retardation (MR), no longer differentiating “educable mental retardation” and “trainable mental retardation” (in earlier surveys) or among “mild mental retardation,” “moderate mental retardation,” and “severe mental retardation” (in the 1992 survey). In addition, in 1994 OCR discontinued monitoring of speech and language impairments and began monitoring enrollments in programs for gifted and talented students. In federal reporting of data by race/ethnicity, five groups are specified: (1) American Indian/Alaskan Natives, (2) Asian/Pacific Islander, (3) Hispanics, (4) blacks, and (5) non-Hispanic whites. Using the OSEP and OCR datasets one is unable to examine rates for subgroups, such as Puerto Ricans, Cubans, or Mexican Americans as these are all aggregated into a single Hispanic category. And a student can be classified in only one group; “mixed race” is not an option. Disability Categories of Concern in This Report Concern about overrepresentation of certain minority group children in special education has focused almost exclusively on a few disability categories. In the earlier NRC report (National Research Council, 1982) the focus was exclusively on children classified as mildly mentally retarded (MMR), the category at issue in litigation challenging the fairness of intelligence testing as the “reason” behind disproportionately high enrollments of black and Hispanic children in special education programs (Reschly, 1988a). In the years since that report, the focus has broadened to include LD and ED. Concern has been raised as well over the underrepresentation of children from these same minority groups in programs for the gifted and talented. The categories MMR, LD, ED, and gifted and talented are some-

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Minority Students in Special and Gifted Education times referred to as the “judgmental” categories because the children so classified typically do not exhibit readily observable distinguishing features, and the authoritative diagnosis of medical professionals, which is common in assessment of many of the low-incidence disabilities, is absent. Categories like visual or auditory impairment may also involve judgment in more marginal cases regarding when the impairment becomes a disability, but the diagnosis of impairment by medical professionals is not called into question. The potential importance of judgment is suggested in the wide variation in placement rates in the judgemental categories across states—variation that is substantially greater than in the low-incidence disability categories. MacMillan and Reschly (1998) found that the ranges of identification rates across states for LD, SLI, MR, and ED were considerable, far greater than one would expect for a given disability. For example, Massachusetts identified 3 times as many children as LD than did Georgia; New Jersey identified 3 times as many children with SLI than did Georgia; Alabama identified 10 times as many children with MR as did New Jersey; and Connecticut identified 41 times as many children with ED as did Mississippi. Inadequacy of Datasets At present, a considerable amount is spent on the data collection efforts of OSEP and OCR, yet the data reported are inadequate for informing policy. While the most fundamental limitation is the absence of data on incidence with which to compare placement rates, the placement numbers by race are themselves problematic. Neither disability status nor ethnicity is measured very precisely (MacMillan and Reschly, 1998). Race/Ethnicity. The imprecision inherent in specifying a child’s race/ ethnicity in these datasets is apparent when one considers that the data are aggregated from the school building to the district to the state to the national level in the OSEP process. For OCR, race/ethnicity is recorded from district records. Any variation in practices for determining race/ethnicity at the school building or district level is obscured when considering state or national figures. One and only one box is checked on the school form, and the person making the decision varies from school district personnel to the child’s parent. The Office of Management and Budget’s Statistical Directive 15 urges that racial and ethnic categories should not be interpreted as scientific or anthropological in nature—yet the datasets summarized here are used in just this way (Hodgkinson, 1995). Phinney (1996) explains that “even within an ethnic group whose members share a relatively precise ethnic label there is tremendous heterogeneity” (p. 919). Variability in

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Minority Students in Special and Gifted Education social class, income, education, generation of immigration, family structure, and geographical region is not captured by the racial/ethnic designation. Phinney goes on to note that “ethnicity cannot be treated like an independent variable that explains an outcome” (p. 924), yet that is precisely what is sought when one examines the bivariate relationship between ethnicity and disability status. In the case of biracial children, the confound is even more severe, as there has not been a “mixed” box for parents to check and a child is forced into one of the extant boxes, suggesting that “everyone in the category belongs completely in that box” (Hodgkinson, 1995:175). Disability Status. In both the OSEP and OCR surveys, the disability status (i.e., the specific disability category) of a child is, in the vast majority of cases, taken directly from school records—that is, the children are “school identified” as qualifying for special education by virtue of qualifying for a specific disability category. In traditional epidemiological studies, the concepts of prevalence (total number of cases at a given point in time) and incidence (number of new cases) are employed. The figures reported in the surveys considered here are assumed to be prevalence figures; however, there is a lack of precision in the school’s ability to detect “true” cases of disability, particularly in the judgmental categories. Stated differently, we do not know what the true prevalence of these conditions would be if specific criteria were applied rigorously in screening the population of children. As a result, there are many false positives and false negatives in identification, introducing error of an unknown size (but known to be substantial, particularly in the LD area) (see Gottlieb et al., 1994; MacMillan et al., 1998a; Shaywitz et al., 1990; Shepard et al., 1983). Comparability across states is difficult, in part, because the states have differing criteria for eligibility. Mercer et al. (1996) surveyed state criteria for defining LD particularly in the method for calculating discrepancy (i.e., standard score discrepancy vs. regressed discrepancy) and the magnitude of the aptitude-achievement discrepancy (e.g., 1 SD, 1.5 SDs) required. Frankenberger and Fronzaglio (1991) and Denning et al. (2000) analyzed state guidelines for defining mental retardation and again reported considerable variability. On the criterion of intellectual level, Denning et al. (2000) reported that 13 states have no IQ cutoff score, Ohio and Pennsylvania use IQ 80 as the cutoff score, while most set the IQ cutoff score at IQ 70 or –2 SDs. Giftedness. The issues plaguing the assignment of disability status also contaminate the collection of data on children identified as gifted and talented. The lack of national legislation governing the definition of, or services for, gifted and talented students has left each state with the pre-

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Minority Students in Special and Gifted Education rogative of defining the construct. Even the definition suggested by the federal government and adopted by many states includes a wide variety of categories, which in some cases overlap and in others are presumably independent. For example, one would expect some overlap between creativity and exceptional performance in the arts, yet there is no reason to expect the same degree of overlap between those who are gifted in a specific academic area (e.g., mathematics) and those gifted in the domain of leadership. The definitions at the federal level and most state levels are also complicated by the phrases “children and youth with outstanding talent who perform or show the potential for performing” and “when compared with others of their age, experience or environment” (U.S. Department of Education, 1993:26, italics added), leaving the interpretation of potential open and making the category of giftedness relative to local school or school system populations. That relativity is rational regarding provision of services: a given student may need special services in a gifted program in a school in which the curriculum is not adequately challenging, but not in a school with a more challenging general curriculum. But a consequence of relativity is that the data on assignment to gifted and talented programs are far more difficult to interpret. Denominators. OSEP requires states to report on the number of children in disability categories by age and (recently) race/ethnicity. They do not collect data on the total number of students (with and without a disability label) by age and race/ethnicity, however. The National Center for Education Statistics (NCES) collects data on student enrollment by race and grade, but not by age. The NCES data can be roughly, but not precisely, paired with the OSEP data. Of particular concern, children who are 6 years old may be in either kindergarten or first grade. They are not likely, however, to be in preschool. NCES provides numbers for preschool-12 enrollment, but these numbers are certainly too high. Subtracting out the preschool children would improve the count, but many states do not provide separate data for preschool, and the lack of uniformity in state counts of preschool children make these numbers hard to estimate reliably. State-to-State Variations The National Association of State Directors of Special Education (1999) reported on a survey of state practices in reporting child count data to the U.S. secretary of education. Findings of the survey illustrate variations and anomalies across states that potentially challenge the reliability and validity of the data reported. Variations in practices in the local education agencies similarly compromise the quality of the data reported to OCR, which gathers data at the district, rather than state, level. Among their findings were

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Minority Students in Special and Gifted Education that some states use categories of disability that differ from the federal categories. The most common difference was for the category of mental retardation. For example, alternative terms are used in several states: significant limited intellectual capacity in Colorado, cognitive delay in Montana, intellectual disabilities in Utah, and cognitively disabled in Wisconsin. Certain states that do not use one of the federal disability categories report zero children in their child count reports: West Virginia and Wisconsin do not use multiple disabilities, and Colorado does not use other health impaired. In Arizona, the category deaf-blind is not used: local education agencies report these cases in the category of multiple disabilities, while cases of deaf-blind children reported in the Arizona count are students identified as deaf-blind by the Arizona School for the Deaf-Blind. Local education agencies in Montana report all disabilities that each student is considered to have and the state education agency, in turn, assigns a single federal disability category to each child. The survey also sought to explain “extreme” counts—that is, child counts for a specific disability or age group that were significantly higher or lower than the national average. Several states pointed to extreme poverty as contributing to high rates of mental retardation, multiple disabilities, and speech and language impairments. Aggressive and successful child find procedures were cited by directors in Maine, Rhode Island, and West Virginia for higher rates. Low child counts in the ED category were attributed to insufficient personnel in Arkansas and Mississippi, while these same states indicated that the stigma associated with the ED label is a contributor to lower counts. States having noncategorical programs vary in how they report child count data. In Pennsylvania and Washington, a child is determined eligible under IDEA and then a disability category is assigned. In Iowa and Massachusetts, “formulas” are used to convert noncategorical counts to disability counts. “In Iowa, percentages for each federal disability category are based on incidence rates from 1986, 87, and 88, before the state became categorical. Massachusetts uses a formula based on disability category estimates from 98 percent of the LEAs, that was updated in 1992” (National Association of State Directors of Special Education, 1999:6). For giftedness, the various states have adopted selected parts of the federal definition or created their own. Coleman and Gallagher (1992) report that 49 states include intelligence or general intellectual ability and achievement in their definitions, 40 states include creativity, 34 states include artistic ability, 28 states incorporate leadership ability, 15 states embrace critical thinking (not included in the federal definition), 26 establish leadership as a domain of giftedness, and 10 states include psychomotor

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Minority Students in Special and Gifted Education abilities, although that category was dropped from the federal definition in 1978. As might be expected, the states have widely varying laws, rules, guidelines, and regulations regarding the identification of gifted students. Some states, for example California, provide standards that are very general and might be considered only as principles to be followed. The California regulations include statements such as “methods and techniques for identification shall generate information as to a pupil’s capacities and needs” (Passow and Rudnitski, 1993). Some states specify instruments to be used, while others go further to define scores required using specific instruments (such as a full-scale score of 130 on the Wechsler scales, 132 on the Stanford-Binet, or 130 on the Kaufman Assessment Battery for Children. Furthermore, some states have specified differing procedures or instruments to be used in identifying students who are considered “disadvantaged—‘economically, culturally, and/or environmentally’” (Passow and Rudnitski, 1993). In the identification of a child as gifted and talented, there are not 50 differing approaches, but innumerable ones. Whether for special or for gifted education, there are clearly sufficient variations and anomalies across states to urge extreme caution in interpreting data. Other Factors Compromising Interpretation of Data A further consideration is that prior to 1997 states could be reimbursed for “up to 12 percent” of their school population under IDEA. While the precise influence of this cutoff point is unknown, it could have served to truncate identification in earlier years once the cap was approached. The quality of the data is potentially further compromised by variability in who reports the data at the local district. Differences in qualifications and familiarity with the district programs (a clerk at the district office vs. the director of special education for the district) could influence the accuracy of the data submitted. In terms of state funding of services for gifted and talented students, Passow and Rudnitski (1993) have documented considerable diversity and complexity. Some services are tied to special education appropriations. In Alabama, funding “shall not exceed the average per pupil appropriation for all exceptional children in each school district, including allowances for teacher units, transportation, and all other aid for exceptional children (Passow and Rudnitski, 1993:64). Florida’s funding formulas for gifted students are tied to “severity” of the giftedness and hence level of placement of the child, with those in homogeneous classrooms qualifying for greater funding. Other states base funding on a percentage of average daily attendance equivalency (e.g., “an amount not to exceed $100 per K-12 pupil for

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Minority Students in Special and Gifted Education up to 5 percent of the district’s or consortium’s K-12 membership”). Some states reimburse only for the salary and benefits of the instructional personnel who provide services exclusively to gifted and talented students. In combination, these influences may have greater influence on the total number of students identified as gifted and talented than on the educational needs of students. Efforts to collect reliable and valid data on gifted and talented enrollments have been stymied in various ways. Since federal efforts are limited to OCR data collection, the organization of state directors of gifted programs has attempted to collect data by surveying its membership. In their latest survey, only 43 states responded and data from many of the other states were incomplete. The conclusion of this group is that it is “quite apparent that complete, reliable data about gifted student education within states are not readily available” and “comprehensive information about gifted education throughout the United States is most difficult to produce” (Council of State Directors of Programs for the Gifted, 1999:9). REVIEW OF THE DATA Despite the limitations of the data, they are useful in some important respects. They provide an indicator of school placement rates in various categories of disability over time. While any individual figure may be imprecise, consistent patterns over time are informative. The numbers may indicate more about the variation in state and local practice than about differences in student populations, but this variation is of interest for policy monitoring. Comparison of placement rates for different racial/ethnic groups can appear quite different in magnitude when different indices are used to present identical data. One can report that in 1998 1.45 percent of black students, and 0.91 percent of white students were labeled ED, or one can report that black students were 17 percent of the student population, but 27 percent of the ED population. The underlying numbers are the same, but the impression is somewhat different. We present the OCR and OSEP data using three different indices, each of which communicates disproportion somewhat differently. Calculations Risk Index The risk index (RI) is calculated by dividing the number of students in a given racial or ethnic category (e.g., Hispanic) served in a given disability category (e.g., LD) by the total enrollment for that racial or ethnic group in

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Minority Students in Special and Gifted Education the school population. Hence, the “risk index” reveals the percentage of all students of a given racial/ethnic group identified in a given disability category. The 1998 risk index of 6.02 for white students in the LD category reveals that just over 6 percent of all white students were labeled LD. Odds Ratio The odds ratio (OR) divides the risk index of one racial/ethnic group (e.g., black) by the risk index of another racial/ethnic group (e.g., white) and thereby provides a comparative index of risk. All odds ratios reported here are relative to white students. If the risk index is identical for a particular minority group and white students, the odds ratio will equal 1.0. Odds ratios greater than 1.0 indicate that the minority group students are at greater risk for identification, while odds ratios of less than 1.0 indicate that they are less at risk. The 1998 LD odds ratio for American Indian/Alaskan Natives is 1.24, revealing that they have a 24 percent greater likelihood of being assigned to LD than whites. This is obtained by dividing the American Indian/Alaskan Native risk index (7.45) by the white risk index (6.02). It should be noted, however, that the odds ratio does not reveal the absolute rate at which children from a given racial/ethnic group are identified by the schools in various disability categories. Composition Index The composition index (CI) is calculated by dividing the number of students of a given racial or ethnic group enrolled in a particular disability category by the total number of students (summed across all five racial/ ethnic groups) enrolled in that same disability category. It therefore reflects the proportion of all children served under a given disability category who are members of a given racial/ethnic group. Note that the sum of composition indices for the five racial/ethnic groups will total 100 percent. The composition index does not control for the baseline enrollment of a given racial/ethnic group. Therefore, knowing that 53 percent of all MR students in a given state are white is not immediately interpretable without knowing the percentage of the total enrollment that is white. If, in a hypothetical state, whites constituted 85 percent of the total enrollment of students, one might conclude that whites are underrepresented in the MR category. Conversely, if whites constituted only 15 percent of the total enrollment, a very different conclusion would be warranted. We introduce this term because a variation of the composition index was used extensively in court cases concerned with overrepresentation. In Larry P. v. Riles (1972, 1974, 1979, 1984, 1986), for example, the plaintiffs presented the following figures to document overrepresentation of black students in the mild MR category: whereas 25 percent of the total mild MR enrollments were black, black students constituted only 10 percent of the California school enrollments.

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Minority Students in Special and Gifted Education Organization of Our Review In the following sections we summarize data from OCR and OSEP on the relative participation of students from the five racial/ethnic groups in the various disability categories. Because the two data-collection efforts are separate, we look at the indices from both. While there are some differences between them, the discrepancies are small: the two datasets tell a very similar story. We begin by considering the three categories monitored by both OCR and OSEP (MR, LD, and ED), then report on the gifted and talented data monitored by OCR but not by OSEP. Finally, we report on the remaining disability categories recognized under IDEA and monitored only by OSEP. For each category, we first present data on risk indices and odds ratios for 1998, then we show patterns over time. The time trends rely on the OCR data, as OSEP has only recently required that child count data be broken out by racial/ethnic group. Mental Retardation Recent Surveys The most recent survey data available from OCR are for 1998, and for OSEP, 1999. For comparison purposes, we use indices calculated from 1998 data from both sources. The 1998 OCR survey using national projections (see Table 2-1) reveals that black students are most at risk for identification as MR (RI = 2.64 percent) with American Indian/Alaskan Natives the next highest (RI = 1.28 percent), followed by whites (RI = 1.18 percent). Hispanic students are at considerably less risk (RI = 0.92 percent) with Asian/Pacific Islander lower still (RI = 0.64 percent). The same pattern is evident in the 1998 OSEP data, although the risk indices based on actual child counts vary slightly. Comparing these rates for the four racial/ethnic groups with that of white students reveals that black students are more than twice as likely to be identified as mentally retarded (OCR OR = 2.24; OSEP OR = 2.35), with American Indian/Alaskan Natives being identified at about the same rate as whites (OCR OR = 1.09; OSEP OR = 1.07). Both Hispanics (OCR OR = 0.78; OSEP OR = 0.87) and Asian/Pacific Islander (OCR OR = 0.54; OSEP OR = 0.51) are considerably less at risk than are whites for identification as MR. The composition index for the racial/ethnic groups suggests that whites constitute approximately 54 percent of the total MR enrollments (compared with 63 percent of the student population), while blacks account for 33 percent of the MR enrollments but only 17 percent of the student population.

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Minority Students in Special and Gifted Education TABLE 2-1 Indices of Placement for Mental Retardation by Race/ Ethnicity: 1998 OCR and OSEP Data   Risk Index Odds Ratio Composition Index Characteristic OCR OSEP OCR OSEP OCR OSEP American Indian/Alaskan Native 1.28 1.20 1.09 1.07 1.04 1.03 Asian/Pacific Islander 0.64 0.57 0.54 0.51 1.90 1.67 Black 2.64 2.63 2.24 2.35 33.04 33.51 Hispanic 0.92 0.98 0.78 0.87 10.04 10.89 White 1.18 1.12     53.97 52.89 Total 1.37 1.32     100.00 100.00 NOTES: OCR placement and membership data are taken from the Fall 1998 Elementary and Secondary School Civil Rights Compliance Report, National Projections. OSEP data are taken from the 1998-1999 Child Count, and the indices were calculated using total enrollment data for K-12 from the U.S. Department of Education, National Center for Education Statistics, Common Core of Data, School Universe Study, 1998-1999, compiled by Mark Glander, National Education Data Resource Center. Trends Over Time Over the past 25 years, there has been a substantial reduction in the rate at which students are classified as mentally retarded by the schools (MacMillan et al., 1996c). Examination of OCR survey national projections over time for MR (Table 2-2) suggests that rates for black children have consistently been higher than rates for other racial/ethnic groups. From a high of over 4 percent of black children identified as MR in 1976, the risk index shows a gradual decline until it reaches 2.23 percent in 1998. These data are also displayed in Figure 2-1. Extremely low risk is evidenced for Asian/Pacific Islander students across surveys, staying very close to 0.50 percent. Rates for whites consistently fell between 1 and 1.3 percent. Slightly higher risk indices were recorded for American Indian/Alaskan Native children; however, the index never exceeded 2 percent for this group. For Hispanic students the identification rate has fallen, and since the 1992 survey it has been below 1 percent. In 1997 it was half the rate of 1974. The trends in odds ratios in the bottom half of Table 2-2 provide another description of the same story. The ratios for Asian/Pacific Islander have been steady and relatively low—well under half the rate for white students in all but one year. For American Indian/Alaskan Natives, one notes a considerable decline in the odds ratios. In 1974, American Indian/ Alaskan Native students were more than half again as likely to be classified

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Minority Students in Special and Gifted Education TABLE 2-20 1994 Average NAEP Reading and History Scores for 12th Graders, by Race/Ethnicity and Parent Education Level   1994 NAEP Reading Test Score Averages by Parent Education Level 1994 NAEP History Test Score Averages by Parent Education Level Race/Ethnicity Less than High School Degree Graduated from High School Some Post-Secondary Education Graduated from College Less than High School Degree Graduated from College Some Post-Secondary Education Graduated from College White 274 283 294 302 271 281 291 300 Black 258 258 271 272 251 258 269 273 Hispanic 260 265 279 283 256 264 277 277 White–Black= 16 25 23 30 20 23 22 27 White–Hispanic= 14 17 15 19 14 17 14 23 NOTE: Differences in white and black scores and in white and Hispanic scores were calculated before rounding. SOURCES: Campbell et al. (1996) and Beatty et al. (1996), compiled by Miller (2000).

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Minority Students in Special and Gifted Education The second important pattern in the data...is that, despite the tendency across the racial/ethnic groups for average scores to rise with parent education level, there are, nevertheless, large differences in average scores among the racial/ethnic groups at each parent education level. Indeed, there is a tendency for the gaps in average scores to be larger at high parent education levels than at low parent education levels. In any case, for both tests, black 12th graders with parents with college degrees had average scores that were about the same as for white students with no parent with a high school diploma. And Hispanics with parents with college degrees had average scores close to those of white students who had parents with a high school degree (pp. 14-15). Data presented in his paper (see Table 2-20) were interpreted to support several important conclusions and generalizations about the pattern of minority underrepresentation among high-achieving students: The overall underrepresentation of several racial/ethnic minority groups among top students relative to the white majority is very extensive and long-standing. This limited minority presence among top students is found using virtually all traditional measures of academic achievement, including school grades, standardized test scores, and class rank. Extensive underrepresentation is present at all levels of the educational system, beginning in kindergarten. The limited presence of several minority groups among high-achieving students cuts across social class lines, that is, substantial minority-majority achievement gaps exist at all social class levels as measured by parent education and family income (Miller, 2000:1). As with the special education analyses, data correlations cannot begin to suggest why the achievement distributions differ. Rather, they describe a situation in which available measures of student achievement place a smaller proportion of non-Asian minority children in the upper range from which gifted students are likely to be drawn. CONCLUSIONS AND RECOMMENDATIONS The national datasets provided by OCR and OSEP provide a snapshot of the relative participation in special education categories of children in different racial/ethnic groups. An important caveat that we have emphasized is that the figures aggregated at a national level obscure variations at the state and local levels and do not permit examination of other factors (e.g., social class, exposure to risk factors) that correlate with race and

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Minority Students in Special and Gifted Education ethnicity. In addition, we have noted that weaknesses in the data temper the confidence with which conclusions can be drawn. Nevertheless, these two large datasets suggest that both black and American Indian/Alaskan Native children are at heightened risk for identification as having mental retardation and emotional disturbance. In the most recent surveys, black children are twice as likely as their white counterparts to be classified as MR and half again as likely to be classified as ED. American Indian/Alaskan Native students are also at slightly higher risk than white students for being identified as MR and LD. Conversely, Asian/Pacific Islander students are least likely to be classified MR, LD, or ED. Clearly, the LD category subsumes the vast majority of children classified into one of the judgmental high-incidence categories. The recent surveys find that over 6 percent of the children in all racial/ethnic groups, except Asian/Pacific Islander, are served in the category of LD. American Indian/Alaskan Native students are somewhat more at risk for identification as LD. Despite the high rates of assignment to the LD category, since the rate of participation of black and Hispanic students approximates that of white students overall, the issue of overrepresentation has generally not been raised. The picture from the gifted and talented data is a mirror image of what is seen for mental retardation. The 1998 OCR survey reveals a rather high rate of participation of students in gifted programs: 6.20 percent of the nation’s students are projected to be participating. Asian/Pacific Islander students are clearly the most likely to participate, with far lower placement rates for blacks and Hispanics. There continues to be higher participation in the high-incidence disability categories for males. The greatest gender disparity in identification rates is found in the ED category (80-percent male), followed by LD (70-percent male) and MR (60-percent male). Using the OCR surveys over time permits some examination of how participation by children in the racial/ethnic groups in certain disability categories has changed (see Table 2-21). Of the four categories considered (MR, LD, ED, and gifted and talented), only mental retardation shows a reduction in the percentage of children served between the mid-1970s and 1998. Between the mid-1970s and 1998 the only category in which risk for identification fell is mental retardation. The racial/ethnic group in which there is the largest reduction is black students (–1.08 percent), while there has been a very slight increase for Asian/Pacific Islander students (0.19 percent). For the learning disability category, there has been a dramatic and uniform increase in the risk for identification. There are substantially more

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Minority Students in Special and Gifted Education TABLE 2-21 Changes in Participation Rates in Judgmental Categories by Ethnic Group (Percentage)   AmI/A A/PI Hispanic Black White Total Mental Retardation 1974 1.94 0.45 1.50 3.72 1.19 1.58 1998 1.28 0.64 0.92 2.64 1.18 1.37   -0.65 0.19 -0.58 -1.08 -0.01 -0.21 Learning Disabilities 1974 1.60 0.52 1.29 1.03 1.24 1.21 1998 7.45 2.23 6.44 6.49 6.02 6.02   5.86 1.70 5.15 5.46 4.78 4.82 Emotionally Disturbed 1976 0.29 0.08 0.25 0.42 0.26 0.28 1998 1.03 0.26 0.55 1.45 0.91 0.93   0.74 0.18 0.30 1.03 0.66 0.65 Gifted and Talented 1976 0.42 2.26 0.40 0.47 1.05 0.93 1998 4.86 9.98 3.57 3.04 7.47 6.20   4.44 7.72 3.17 2.57 6.42 5.27 black and Hispanic students served as LD than are served in MR and ED combined, although placement in this category shows no disproportion for those groups. For ED there has been an increase for all groups; however, the increase is far more modest than what has occurred in the LD category. The increase in placement rates (“risk”) for gifted and talented programs is greater than for any of the three judgmental disability categories—5.27 percent across racial/ethnic groups; however, the increase for black and Hispanic students is substantially less than for the other racial/ethnic groups. In the next few chapters we look at potential explanations for the patterns suggested by the data. But for the reasons described in this chapter, these data are a weak foundation on which to build public policy. The committee urges that policy decisions utilizing these datasets explicitly recognize the tenuous nature of the data. Our recommendations with respect to data collection (DC) are directed at two goals: one is to improve the existing data collection process designed for monitoring program participation and civil rights compliance, and the other is to expand the collection of data to allow for research that would improve understanding of nonnormative achievement and behavior, as well as responses to intervention. Currently there is considerable redundancy in the reporting requirements placed on schools by the Office for Civil Rights and the Office of Special Education Programs. In response to the Paper-

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Minority Students in Special and Gifted Education work Reduction Act of 1995 (44 U.S.C. Chapter 35), the Department of Education provided states with the option in 2001 of consolidated data collection on students with disabilities (Federal Register: March 8, 2001 (Vol. 66, Number 46)), an option that few states have chosen. While the efforts of the two agencies within the Department of Education to consolidate the collection are commendable, the committee believes a reexamination of survey design in the interest both of providing more reliable indicators, and of facilitating reporting from the perspective of local education agencies and states, is warranted. Recommendation DC.1: The committee recommends that the Department of Education conduct a single, well-designed data collection effort to monitor both the number of children receiving services through the Individuals with Disabilities Education Act and the characteristics of those children of concern to civil rights enforcement efforts. Whether data collection responsibility is given to either of these offices, the National Center for Education Statistics, or some other entity, the shift in responsibility would require supporting changes: Data collected should meet all requirements for effective OCR monitoring, including disaggregated data by district and state, and they should be accessed easily by OCR and OSEP. This would require data collection to accommodate OCR’s monitoring of data on assignment to gifted and talented programs and on limited English proficiency not currently collected by OSEP. The definitions in this category should allow for the distinction between “gifted” and “talented” to the extent that students are being served in different types of programs. In the reauthorization of IDEA, statutory authority should be given to those responsible for data collection to collect child count data for disability category by racial/ethnic group by gender for both special education and gifted and talented placements as well as by state and local district levels. The committee urges the federal agency reporting on special education enrollments by racial/ethnic group do so by reporting risk indices—the proportion of a given racial/ethnic group’s enrollment in the general school population that is enrolled in a given disability category. In order to accomplish this goal, steps must be taken to coordinate reporting child counts by age, currently done in the OSEP reporting by disability category, for ages 3-21, with the NCES Common Core of Data, which reports by grade level. This would remedy the current situation in which it is impossible to align the ages 3-5 and the 18-21 child count by OSEP with any meaningful count of the total population.

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Minority Students in Special and Gifted Education The committee also urges that the Office for Civil Rights monitor the impact of education reform initiatives, such as high-stakes testing programs, to ensure that implementation of these initiatives does not exacerbate minority representation problems in special or gifted education. While a more careful data collection effort of the sort outlined above would improve the understanding of who is being assigned to special education and gifted and talented programs, it would do little to further understanding of the reasons for placement, the appropriateness of placement (or nonplacement), the services provided, or the consequences that ensue. Moreover, the variation observed from one state to the next serves as a reminder that in special education or gifted and talented programs, we refer to practices that differ dramatically from one location to the next. While special education may be a set of well-targeted specialized classroom supports for children in need in one school, it may be a dead-end program in others—a last resort for teachers who can no longer work with a student. The data are not available to tell which it is, in which schools, and for which students. And while the data are poor with respect to special education, the data on gifted and talented students are even worse. Recommendation DC.2: The committee recommends that a national advisory panel be convened to design the collection of nationally representative longitudinal data that would allow for more informed study of minority disproportion in special education and gifted and talented programs. The panel should include scholars in special education research as well as researchers experienced in national longitudinal data collection and analysts in a variety of allied fields, including anthropology, psychology, and sociology. The panel should assess the cost of collecting data that could answer the following questions: What antecedents to special education placement are associated with students’ assignment to special education services? Antecedents studied should include, but not be limited to: race (self-identified and school-identified), gender, and other socioeconomic and social background factors, and school factors, such as class size, teacher experience and preparation, instructional strategies, and school and classroom resources. How do schools differ in their categorization of students, and are these differences associated with differences in students’ access to special education services? Are students who present with the same researcher-identified condition treated differently in different schools and, if so, what policy, resource, and individual-level factors are associated with these differences in treatment? What is the incidence of students who have the same research-iden-

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Minority Students in Special and Gifted Education tified conditions but are never referred for special education assessment? And is referral to special education assessment associated with severity of the researcher-identified condition or some other factors? If students who present with the same researcher-identified condition are treated differently, how is access or lack of access to a variety of special education services associated with later levels of cognitive achievement and behavioral adjustment? The data would have improved value if the following additional information were included: how long the family has lived in the United States; birth country of students, their parents, and their grandparents; language proficiency (in both English and native language); education level of parents; level of acculturation; and experiences with literacy artifacts and practices. Analysis for this report of the effect of race/ethnicity on special education placement or outcomes was made more difficult because many research studies did not specify the racial/ethnic composition of the sample or had too few minority children to measure effects by race/ethnicity. The committee urges that research funded by the Department of Education using these or other data require the careful description of samples as well as differential effects, to the extent feasible, by race, ethnicity, limited English proficiency, socioeconomic status, and gender. APPENDIX 2-A TABLE 2-A1 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Black Students in the Category of Mental Retardation State RI CI White RI for State Highest RI Arizona 5.62% 43.11% 0.69% Alabama 5.58% 62.93% 1.90% Iowa 4.92% 6.67% 2.58% Nebraska 4.30% 11.46% 1.95% Highest CI District of Columbia 1.21% 95.36% 0.13% Mississippi 2.38% 78.45% 0.63% Georgia 3.44% 64.19% 1.28% Alabama 5.58% 62.93% 1.90%

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Minority Students in Special and Gifted Education TABLE 2-A2 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Hispanic Students in the Category of Mental Retardation State RI CI White RI for State Highest RI Nebraska 2.43% 5.52% 1.95% Iowa 2.19% 2.08% 2.58% Ohio 2.16% 1.43% 2.34% Hawaii 2.06% 3.44% 6.78% Highest CI New Mexico 0.81% 51.97% 0.56% California 0.50% 43.50% 0.40% Arizona 1.02% 34.94% 0.69% Texas 0.72% 34.85% 0.60% TABLE 2-A3 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Black Students in the Category of Learning Disabilities State RI CI White RI for State Highest RI Delaware 11.84% 43.72% 6.75% New Mexico 9.99% 3.06% 6.83% Nevada 9.61% 14.92% 6.19% Alabama 9.47% 30.63% 5.79% Highest CI District of Columbia 5.34% 90.97% 3.94% Mississippi 6.83% 56.76% 5.15% Louisiana 6.65% 53.83% 4.26% South Carolina 4.96% 43.29% 5.03%

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Minority Students in Special and Gifted Education TABLE 2-A4 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Hispanic Students in the Category of Learning Disability State RI CI White RI for State Highest RI Delaware 8.88% 3.35% 6.75% New York 8.41% 21.42% 6.63% New Mexico 8.21% 52.79% 6.83%   13.84% 6.89% 7.37% Highest CI New Mexico 8.21% 52.79% 6.83% California 5.27% 41.89% 4.92% Texas 6.82% 36.62% 6.69% Arizona 5.40% 31.80% 4.40% TABLE 2-A5 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Black Students in the Category of Emotional Disturbance State RI CI White RI for State Highest RI Minnesota 3.64% 10.39% 1.84% Iowa 3.49% 12.54% 0.92% Nebraska 2.81% 19.79% 0.71% Highest CI District of Columbia 0.97% 97.54% 0.19% Louisiana 1.00% 59.65% 0.51% South Carolina 0.93% 55.67% 0.58% North Carolina 1.25% 50.92% 0.53% TABLE 2-A6 1997 Comparison of States by Highest Risk (RI) and Composition (CI) Indices for Hispanic Students in the Category of Emotional Disturbance State RI CI White RI for State Highest RI Vermont 2.38% 0.70% 1.40% Minnesota 1.76% 2.16% 1.84% Hawaii 1.53% 3.29% 1.52% Highest CI New Mexico 0.85% 43.68% 1.10% Texas 0.67% 26.15% 1.09% New York 1.48% 21.26% 0.74% California 0.08% 17.40% 0.24%

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Minority Students in Special and Gifted Education TABLE 2-A7 1997 Comparison of States by Lowest Risk (RI) and Composition (CI) Indices for Black Students in the Category of Gifted and Talented State RI CI White RI for State Lowest RI Massachusetts 0.39% 3.94% 0.83% New Hampshire 0.39% 0.28% 1.34% Louisiana 0.72% 12.31% 3.81% Kansas 0.80% 2.31% 3.37% Lowest CI North Dakota 2.14% 0.39% 4.80% Idaho 1.99% 0.42% 2.97% Montana 6.42% 0.52% 6.57% Wyoming 2.75% 0.52% 3.75% TABLE 2-A8 1997 Comparison of States by Lowest Risk (RI) and Composition (CI) Indices for Hispanic Students in the Category of Gifted and Talented State RI CI White RI for State Lowest RI New Hampshire 0.25% 0.27% 1.34% Massachusetts 0.50% 5.62% 0.83% New York 0.66% 3.74% 5.04% Kansas 0.72% 1.59% 3.37% Lowest CI West Virginia 0.77% 0.11% 2.31% Maine 0.97% 0.14% 3.25% New Hampshire 0.25% 0.27% 1.34% Mississippi 2.92% 0.29% 7.70%

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