Factors contributing to disproportionate minority representation in special education

The disproportionate representation of minority students in specialeducationhas been a constant and consistent concern for nearly four decades (Klingner et al. , 2005). Currently, there are disproportionate numbers of minority students who are referred, assessed, identified, classified, and placed in programs for students with disabilities. The issue of disproportionate representation for minority students has been and continues to be an incessant dilemma that has detrimental effects on the educational opportunities and outcomes for this specific group of students.

Concerns about disproportionate representation are focused on the ” judgmental” categories of special education (learning disabilities, emotional disturbance, and mental retardation), those disabilities usually identified after the child starts school and by school personnel rather than a medical professional. Children identified with these disabilities usually do not exhibit any obvious discernible features, yet they are still considered to have internal deficits that affect their learning and/ or behavior (Klingner, et al. , 2005).

One of the earlier discussions on disproportionality (Heller, Holtzman, & Messick, 1982) noted that it cannot be assumed that ethnic disproportionality in special education is a problem, since it could be that certain groups of students require special education services in greater amounts than those from other ethnic categories. The panel declared that ” the adequacy and appropriateness” of all phases of the special education process as well as the outcome had to be determined before recognizing disproportionality as a problem.

The existence of bias or inappropriate practice at any phase of the process was the feature that would determine that disproportionality was indeed problematic. In its 26th Annual Report to Congress, the U. S. Department of Education (2005) presented the percentage of students ages 6 through 21 who were served under IDEA by disability and race/ethnicity. Specific learning disability was the largest disability category for all racial/ethnic groups.

Hipic/Latino children represented the largest racial/ethnic group (58. 3%) diagnosed with a specific learning disability. American Indian/Alaskan Native students were the next highest group (55. 3%) identified with a specific learning disability. The highest percentage of minority students served under IDEA for mental retardation was African American/Black (16. 8%). Mental retardation can be defined as sub-average intellectual functioning that concurrently exists with deficits in adaptive behavior.

Similarly, African American/Black students also represented the highest percentage of children served under the category of emotionally disturbed students (11. 3%) (U. S. Department of Education, 2005). It is critical to note that the existence of this problem has been repeatedly documented by the U. S. Department of Education’s Office forCivil Rights(OCR), in that it has confirmed the overrepresentation of minority students in special education programs with data for the past thirty years.

In addition, the issue of determining whether students of different racial or ethnic groups are disproportionately identified for special education has been a longstanding public and professional concern; however, the causes of disproportionality and factors contributing to this problem are not clearly understood (Donovan & Cross, 2002). Thus, this paper aims to examine factors contributing to disproportionality of minority in special education. Firstly, the paper examines historical background of disproportionality problem. Finally, some conclusive remarks are presented.

History of the Disproportionality Problem The problem of overrepresentation of minority students in special education is not a new phenomenon and has been a serious concern for the last 40 years. Dunn (1968) cited statistics from the U. S. Office of Education and concluded ” about 60% to 80% of students in mild mental retardation classes were from low status backgrounds, including African Americans, American Indians, Mexicans, and Puerto Rican Americans; those from nonstandard English speaking, disorganized and inadequate homes; and children from nonmiddle class environments” (p.

6). Mercer (1973) documented disproportionate representation of minority students in special education classes in Riverside, California. She concluded that the percentage of African Americans placed in special classes for students diagnosed with mental retardation was three times their percentage in the population. In a similar manner, the percentage ofMexicanAmerican students placed in classes for mentally retarded children was four times their percentage in the population (Mercer, 1973). The overrepresentation problem has also been documented over time through court cases.

Two notable cases, Diana v. State Board of Education (1970) and Larry P. v. Riles (1971) dealt with issues related to assessment bias, disproportionate placement, and the long-term consequences of special education placement (Donovan & Cross, 2002). Larry P. v. Riles (1979) was a class action suit on behalf of African American/Black students in the San Francisco Public School System who were placed in mildly mentally retarded education programs. At that time African American/Black students represented 28.

5% of the district’s student enrollment, but 67% of the students in mildly mentally retarded programs were African American/Black. The plaintiffs argued that intelligence tests were culturally biased. The court ruled that intelligence tests were culturally biased against African American/Black students and that reliance on biased instruments was related to the overrepresentation problem. Overrepresentation of certain ethnic minority groups, such as African Americans/Blacks or Hipics/Latinos, in certain special education environments is a serious problem for a number of reasons.

Heller, Holtzman, and Messick (1982) stated that disproportionality is a problem if children are invalidly placed in programs for mentally retarded children, if they are unduly exposed to the likelihood of such placement by virtue of having received poor regular education, or if the quality andacademicrelevance of the special instruction blocks students’ educational progress, including decreasing the likelihood of their return to the regular education classroom. In their review of the literature, Hosp and Reschly (2003) discussed three main reasons why disproportionate representation in special education is problematic.

Such reasons include negative effects of labeling, segregation of placement, and presumed ineffectiveness of special education (Hosp & Reschly, 2003). For example, labeling a child handicapped has been found to reduce teachers’ expectancy for the child to succeed (Dunn, 1968). Furthermore, removing a child from regular education to special education may contribute significantly to feelings of inferiority and problems with acceptance by peers (Dunn, 1968). Factors Contributing to Disproportionate Representation

Several variables related to the disproportionate representation of some students in special education have been documented in the literature. Specifically, research has examined possible reasons that might explain the overrepresentation of minorities in special education including bias in the referral process, bias in assessment tools, English Language Proficiency of a child, poverty, gender, race, and poor academic achievement (Hosp & Reschly, 2004). Referral process One factor that may contribute to disproportionality involves the referral process for special education.

Referral for assessment has been cited as an important predictor of subsequent special education placement (Ysseldyke, Vanderwood, & Shriner, 1997). Ysseldyke et al. (1997) found that 90% to 92% of referred students are tested and 70% to 74% of tested students were then declared eligible for special education placement. Thus, if a child is referred to a multidisciplinary team for special education eligibility consideration, there is a high probability that the student will be identified with a disability and be placed in a special education program.

One possible explanation for overidentification of students for special education may be due to the information processing strategies of multidisciplinary team decision makers, which can cause the presence of a confirmatory bias (O’Reilly et al. , 1989). Confirmatory bias can occur when the multidisciplinary team decision makers have a tendency to gather information that confirms a hypothesis that is being tested and ignores contradictory information. This can result in special education decisions that are not based on objective evidence and lead to inappropriate placements.

Thus, if teachers are biased in their referral decisions, psychologists may confirm already flawed judgments (Podwell & Soodak, 1993). In terms of demographics, research has shown that teachers over refer African American, Hipic/Latino, and American Indian/Alaskan Native males to programs for students with learning disabilities, emotional/behavioral problems, speech and language disorders, and mild cognitive disorders in comparison to female and White middle class students (Grossman, 1995).

The majority of teachers nationwide are White; therefore cultural misunderstanding of expectations may affect referral rates of non-White students (Hosp & Reschly, 2003). For example, differences between students and teachers in learning style, verbal style, social style, and cultural information has been suggested to account for some proportion of minority students poor school performance which may lead to referral and subsequent placement in special education (Harry, 1994). Assessment procedures Overrepresentation could also be a result of bias in the assessment procedures used to identify students for special education.

Assessment procedures that are not suited to the diverse characteristics of the students evaluated may result in misleading and biased information about their academic abilities (Grossman, 1995). For example, unfamiliarity with the assessment process may invalidate results. Grossman (1995) cited research that African American/Black and Hipic/Latino children may be more anxious during assessments to the point that theanxietyinterferes with performance because they are not familiar with the assessment process.

Today, more minority children continue to be placed into special education on the basis of intelligence tests, even though it has been suggested that intelligence instruments as a basis for placement are imperfect and unfair (Agbenyega & Jiggetts, 1999). The validity and reliability of intelligence tests has been discussed when such measures are used with minority students (Reschly & Grimes, 2002). Intelligence tests are valid only if they measure a student’s ability to learn and to predict how the student will function in specific learning situations (Grossman, 1995).

Furthermore, since the reliability of many assessments is established by studying their use with White students, they may be unreliable when used with non-White students (Grossman, 1995). It has been asserted that intelligence tests reflect the cultural knowledge base and cognitive and linguistic orientations of their creators (Harry, Klinger, Sturges, & Moore, 2002). Intelligence tests have been criticized as being culturally, socially, and racially biased because they reflect White, middle class values and experiences and therefore, are not applicable to minority children (Agbenyega & Jiggetts, 1999).

It has been suggested that traditional measures of intelligence are not biased if certain standards are met. For example, standards should require a rigorous implementation of procedural safeguards in the referral, classification, and placement process; implementation of multifactored assessments designed to identify specific educational needs by a group of professionals; and programming and placement decisions made by a team that included professionals and parents (Reschly & Grimes, 2002).

Nevertheless, there are concerns about using intelligence measures like the Wechsler scales in making identification decisions. Such concerns include that different environments provide different preparation for the test, the narrow item content of the test, and negative outcomes associated with the use of intelligence tests (Reschly & Grimes, 2002). Therefore, the use of more culturally fair measures of cognitive processing, such as the CAS, may be an important step in addressing the disproportionality problem in special education.

English Language Proficiency Since most standardized tests are administered in English and normed on proficient English speakers, a linguistic bias may also invalidate the results of an evaluation if the assessment is conducted in a language that the student is not proficient in. For example, English Language Learners have been found to perform lower than proficient English speakers on standardized assessments in reading, science, andmathematics(Abedi, 2002).

However, the performance gap between English Language Learners and proficient English speakers was greatest in content areas that required a higher language demand (i. e. , reading) and less or almost non-existent in content areas that required less language demand (i. e. , mathematic computation). The linguistic complexity of the actual test items, unrelated to the content being assessed, may be a reason for poorer performance for English Language Learners on standardized tests.

Thus, these tests may function more as an English proficiency test than a test of ability or achievement (Abedi, 2002). This problem can impact special education identification and placement because there are very few school psychologists who are bilingual and are able to provide linguistically appropriate assessment services in the school (Grossman, 1995). The consideration of English Language Proficiency is important since it is estimated that more than 3. 5 million children in U. S. schools have Limited English Proficiency (U.

S. Census Bureau, 2003). However, there has been limited research on the representation of English Language Learners in special education programs since state departments of education often do not gather data about language proficiency of students in special education. Artiles et al. (2005) assessed the magnitude of disproportionate representation of English Language Learners in 11 urban California school districts. Placement patterns at the elementary level indicated an absence of overrepresentation in special education.

However, overrepresentation of English Language Learners was detected at the end of elementary school (4th grade) and continued through the high school years. Children who demonstrated limited proficiency in their native language as well as in English had the highest rate of identification in high incidence special education categories (i. e. , learning disability). Impact of gender Other variables have also been examined to evaluate their influence on the disproportionate representation of minority students in special education.

For example, recent research has looked at gender as a predictor of special education placement (Coutinho & Oswald, 2005; Coutinho et al. , 2002). In general, gender disproportionality has been found to exist in special education with male students more likely to be overrepresented in special education. Boys are about twice as likely as girls to be identified with a learning disability and almost three and half times more likely to be identified with a serious emotional disturbance (Coutinho & Oswald, 2005). Coutinho et al.

(2002) investigated the extent of disproportionality among students with a learning disability and described the relationship between ethnicity, gender, and socio-demographic variables (i. e. , poverty indicators, Limited English Proficiency status, parent education, etc. ). The findings showed an association between ethnicity, gender and the odds of being identified as a student with a learning disability. White, African American/Black, American Indian/Alaskan Native, and Hipic/Latino males were all at least twice as likely as White females to receive special education.

African American/Black and Hipic/Latino females were essentially as likely as White females to receive special education. American Indian/Alaskan Native females were more likely than White females to receive special education. Asian males and females were both less likely than White females to receive special education services (Coutinho et al. , 2002). In addition to student characteristics, results indicated that socio-demographic characteristics were important in determining the likelihood of being identified as learning disabled.

However, the impact of the socio-demographic characteristics was found to be different for each gender-ethnicity group. For example, increased poverty was associated with increased identification of a learning disability for African American/Black, Hipic/Latino, and male Asian students. For White and American Indian/Alaskan Native students, increased poverty was associated with lower identification rates (Coutinho et al. , 2002). Economic, demographic, and achievement factors Oswald et al.

(1999) examined the influence of economic and demographic variables on the identification of minority students for special education. The study examined the extent U. S. school districts displayed patterns of disproportionate presentation in the identification of minority students as mildly mentally retarded (MMR) and serious emotional disturbance (SED). It also analyzed the extent disproportionate representation at the district level is predicted by other district characteristics including school characteristics and demographic and economic factors (Oswald et al. , 1999).

Oswald et al. (1999) used predictor variables that included median housing value, median income, percentage of children below the poverty level, percentage of adults in the community with a 12th grade education or less, percentage of children enrolled in school who are considered ” at risk” (i. e. , from a single parent home, below the poverty level, and where the mother does not have a high school diploma or GED); and percentage of children who were Limited English Proficient. Race of the student was classified as African American/Black or non-African American/non-Black.

Results indicated that African American/Black students were 2. 4 times more likely to be identified as MMR and approximately 1. 5 times more likely to be identified as SED than non-African American/Black students. Additionally, environmental and demographic variables were found to be significant predictors of identification of students as MMR or SED. For example, as poverty and drop-out levels increased, more African Americans/Blacks were identified as MMR, but less were classified as SED.

Furthermore, there was a disproportionate number of African American/Black students classified as SED in wealthier communities. It was suggested that these results may indicate that wealthier communities are more tolerant of cognitive impairments and less tolerant of emotionally challenging behaviors of African Americans/Blacks (Oswald et al. , 1999). Poor academic achievement is often a reason for referral and eventual placement in special education.

Hosp and Reschly (2004) hypothesized that differences in academic achievement between racial and ethnic groups may lead to differences in referral and subsequent placement rates for special education. These researchers expanded on findings of Oswald et al. (1999) and included achievement variables with demographic and economic predictor variables of White, African American, Latino, Asian/Pacific Islander, and American Indian/Alaskan Native ethnic/racial groups in an effort to better understand disproportionate representation of minorities in special education.

In their study, demographic predictors included the base rates of various ethnic backgrounds (base rate of White students, base rate of African American/Black students, base rate of Hipic/Latino students, base rate of Asian/Pacific Islander students, base rate of American Indian/Alaskan Native students) in the district, percentage of students in the community who were Limited English Proficient, and base rate of students in the district identified who had a disability.

Economic predictors included median house value of the community, median household income, percentage of adults in community who have a 12th grade education or less, and percentage of children who are considered at risk. Academic predictors included percentage of each ethnic group who achieved mastery in criterion reading and criterion math. Results indicated that economic, demographic and academic blocks of variables contributed to the prediction of disproportionate representation.

Specifically, Hosp and Reschly (2004) found that the block of economic variables was the strongest predictor for mental retardation diagnosis, the demographic block of variables was the strongest predictor for emotional disturbance, and the academic block of variables was a strong predictor for a learning disability diagnosis. For African American/Black, Hipic/Latino, and American Indian/Alaskan Native students, the demographic variables were the strongest predictors for emotional disturbance and learning disability.

It is not possible to comment on the influence of each individual variable in its ability to predict special education eligibility since related variables were grouped together as broader blocks of variables. An ecological framework How bias in the referral process, bias in assessment techniques, English Language Proficiency, gender, socioeconomic status, and demographics contribute to the disproportionate representation of minority students in special education may be better understood by considering an ecological developmental framework.

According to Bronfenbrenner (1992), understanding human development requires a consideration of the interaction of diverse characteristics of a child and of theenvironmentin which the child is embedded. Bronfenbrenner (1992) suggests a hierarchy of ecological contexts that effect human development that move from the most proximal to the most remote. The first of these contexts is the microsystem, which has the most power to influence the course of development for the child (Bronfenbrenner & Crouter, 1983).

Bronfenbrenner (1992) defined a microsystem as ” a pattern of activities, roles, and interpersonal relations experienced by the developing person in a given setting with particular physical and material features, and containing other persons with distinctive characteristics of temperament, personality, and systems of belief’ (p. 227). Such settings include the home, school, or peer group. The microsystem considers the immediate environment a child develops in, as well as the characteristics of all other people (i. e. , parents, teachers, siblings, etc. ) also in that environment.

The characteristics of other people in the child’s environment might include demographic features (age, race/ethnicity, and sex), cognitive abilities and skills, or temperaments/personalities. For example, a child whose parents only speak a language other than English may have difficulty acquiring English, which may put them at risk for lower achievement scores and a higher probability of being referred for special education services. Children interact with their teachers daily. Personalities and belief systems of theteachermay impact how he/she views the child and therefore, influences how the teacher interacts with the child.

A teacher who lacks cultural awareness may inappropriately refer a child to the multidisciplinary team. Thus, the microsystem may be the most important ecological level to consider when analyzing factors that relate to the overrepresentation of specific racial/ethnic groups because it considers not only the characteristics of the environment a child develops in, but the characteristics of those who interact with that child. The most distal ecological system is the macrosystem. The macrosystem is the largest ecological level and is typically defined by region, societies, cultural values, and governmental and economic institutions.

The macrosystem involves the belief systems prevailing in the world. At this level, the individual has little control over the factors related to disproportionality, but it is important to understand the intent of the laws governing special education programs and the national goal to decrease the overrepresentation of minority groups in special education. Conclusion Disproportionate representation is a complex phenomenon without a definite etiology or solution. As a result of this complexity, consensus has yet to be reached regarding the actual causes of this persistent dilemma.

What is clear is that a variety of educational, sociocultural, socioeconomic, and teacher and school-related factors appear to contribute to the disproportionate representation of minority students in special education programs. Current literature on overrepresentation of certain students in special education focuses more on the general patterns of disproportionality rather than examining if there are specific variables related to special education representation (Donovan & Cross, 2002). Thus, the causes of the disproportionate representation of minorities in special education are not clearly understood.

Therefore, it is important to consider structural and community level variables to provide a framework for analyzing the problem. Indirectly these variables affect the prescribed evaluation procedures, parental involvement in the special education process, and the availability of alternative programs (Turnbull & Turabull, 2001). Bronfenbrenner’s (1992) expanded ecological paradigm provides a conceptual framework for examining and understanding the factors that influence disproportionate representation of minorities in special education.

An ecological approach considers the context within which individuals function and that influence individuals. In using Bronfenbrenner’s framework, the larger issue of disproportionality should consider the combined influences of various contexts a child is a member, including the locality (type of neighborhood and resources), the organization (school factors such as performance, racial/ethnicity composition, teacher: student ratios, or discipline), the microsystem (familycharacteristics such as size, socioeconomic status, and education level), and the individual (unique characteristics of the child)