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http://sed.sagepub.com/content/46/1/49The online version of this article can be foun d at:

DOI: 10.1177/0022466910372137

2012 46: 49 originally published online 13 May 2010J Spec Educ Michael J. Morrier and Kristen L. HessEthnic Differences in Autism Eligibility in the United States Public Schools

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The Journal of Special Education46(1) 49 63 Hammill Institute on Disabilities 2012Reprints and permission: http://www.sagepub.com/journalsPermissions.navDOI: 10.1177/0022466910372137http://journalofspecialeducation.sagepub.com

Ethnic Differences in Autism Eligibilityin the United States Public Schools

Michael J. Morrier 1

and Kristen L. Hess2

AbstractThis study investigates ethnic differences for 295,945 children and youth with an autism eligibility reported to the U.S.Department of Education (USDOE) by 49 states plus the District of Columbia. Data analyses used relative difference, riskindex, and risk ratio (RR). Results indicate that 80% of states report underrepresentation across ethnicities, with Hispanicchildren underrepresented in 95% of states. Use of developmental delay label was significantly related to disproportionaterepresentation for school-age population ( F = 3.291, p = .046). Region of country yielded significant differences in RR forchildren classified as Asian ( F = 3.532, p = .014) and Caucasian ( F = 5.219, p = .002), for Black ( F = 4.355, p = .005) andCaucasian (F = 2.840, p = .038) preschoolers, and for Asian ( F = 5.676, p = .001) and Caucasian ( F = 4.906, p = .002) youth.Policy, training, and programming implications of the data are discussed.

Keywordsautism spectrum disorders, ethnicity, preschoolers, school-aged, disproportionate representation

1Emory University School of Medicine, Atlanta, GA, USA2Atlanta School, Atlanta, GA, USA

Corresponding Author:Michael J. Morrier, Emory Autism Center, Department of Psychiatryand Behavioral Sciences, Emory University School of Medicine,1551 Shoup Court, Atlanta, GA 30322, USAE-mail: [email protected]

or 1 in 110 children (CDC, 2009) to 100 per 10,000 or 1 in100 children (Kogan et al., 2009).

Although individuals with ASD are found in all cultures,ethnicities, and socioeconomic levels (APA, 2000), the ethnic

breakdown of this disability category in the U.S. public schoolsystem is beginning to receive attention in the research

(Dyches, Wilder, Sudweeks, Obiakor, & Algozzine, 2004).Recent legislative mandates for investigating all studentachievement by subgroups (No Child Left Behind [NCLB],2001) have increased attention in this area, which has led tosubsequent increases in funding as well. Previous studies have

been inconclusive in documenting the prevalence of ASDacross ethnic groups. Yeargin-Allsopp and colleagues (2003)analyzed ethnic background as a variable in 3 to 10 yearolds with Autistic Disorder, finding similar prevalence rates

between Whites and Blacks, both at 3.4 per 1,000, and anonsignificant difference in children from other ethnic groups,at 2.9 per 1,000.

More recent data on the ethnic breakdown of childrenwith ASD across 11 states representing each region of the

Autistic Disorder was originally described by Leo Kannerin 1943, yet the USDOE did not recognize this as a separatespecial education eligibility category until 1990 (IndividualsWith Disabilities Education Act [IDEA], 1990). Pervasivedevelopmental disorders, also known as autism spectrumdisorders (ASD), are characterized by deficits in reciprocal

social interactions, communication, and restricted and repeti-tive behaviors and interests (American Psychiatric Associa-tion [APA], 2000). State Departments of Education usuallygroup all ASD subtypes of (a) autistic disorder, (b) Aspergersyndrome, and (c) pervasive developmental disordernototherwise specified under one eligibility categoryautism.Because of reported increases in the prevalence of ASD (Cen-ters for Disease Control and Prevention [CDC], 2007a, 2007b,2009; Kogan et al., 2009), this article investigates racial/ethnic patterns of students identified under the state eligibilitycategory of autism throughout the United States to determineif there are racial/ethnic disparities in identification.

With the fourth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American PsychiatricAssociation, 1994) as well as autism being recognized as aspecial educational eligibility category (Amendments toIDEA, 1997), the prevalence of children identified with amedical diagnosis of ASD has risen considerably (CDC,2007a, 2007b, 2009; Yeargin-Allsopp et al., 2003). Previ-ously, autism was thought to occur in 4 to 5 per 10,000 chil-dren; however, recent prevalence estimates indicate asignificant increase in these diagnoses, from 90 per 10,000

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50 The Journal of Special Education 46(1)

United States (CDC, 2009) found significantly different prevalence rates among non-Hispanic White, non-HispanicBlack, and Hispanic children in 5 of 11 sites (45%) investi-gated. Hispanic children had significantly lower prevalencerates than did non-Hispanic White students in approximately55% of the sites (6 of 11) as well as significantly lower rates

than non-Hispanic Black children in approximately 36%(4 of 11) of the sites. Using an earlier CDC dataset, Mandellet al. (2009) found significant ethnic differences for diagnosisof ASD when compared to children from Caucasian back-grounds (i.e., odds ratio [OR]). These results indicate under-representation for all minority ethnic groups studied: Black(OR = 0.79), Hispanic (OR = 0.76), and other ethnicity (OR = 0.65). These researchers found that this disparity remainedfor children who are Black regardless of IQ and for childrenfrom other ethnicities that had IQs lower than 70.

Using data from the Office of Special Education Programs,Dyches et al. (2004), found that children from African Americanand Asian/Pacific Island backgrounds received special educa-tion services under an autism eligibility at approximatelytwo times the rate of students from American Indian, NativeAlaskan, or Hispanic backgrounds. Morrier, Hess, and Heflin(2008) investigated teacher report of students with ASD forethnic disproportionality in one Southern state. Theseresearchers found that ethnicity played a significant role inwhich students were reported as served under an autism eli-gibility category. Caucasian students were 1.54 times morelikely to be served under an autism eligibility than all otherethnic groups were, and Hispanic students were 99.995 timesless likely to be served under an autism eligibility than allother groups were. Although this sample was small ( n = 226)

and represented one state that historically has had problemswith racial discrimination, the results were similar to thosefound by Williams, Atkins, and Soles (2009), who found thatschool systems did not place students from ethnically diverse

backgrounds under an autism eligibility.Data from Sweden indicate that children from Somali

backgrounds had a three- to four-fold increase in receipt ofa diagnosis of autistic disorder or pervasive developmentaldisordernot otherwise specified than did children from non-Somali backgrounds (Barnevik-Olsson, Gillberg, & Fernell,2008). In this small sample ( n = 17), all children from Somali

backgrounds were found to have a learning disability (i.e.,intellectual disability) and parents from consanguineousmarriages. The genetic make-up of these children may have

been different than those from non-Somali backgrounds.Although no specific genetic marker has been identified

for ASD, it is widely believed that genetics is a large part ofhaving this disorder and can be identified as the cause of theASD in 1 to 2% of children (Rutter, 2005). Genetic studiesof ASD indicate that siblings of children with ASD have higherrates of receiving a diagnosis on the spectrum, with rates from6 to 20% of siblings (Rutter, 2005). Further genetic research

indicates that genetic mutations and abnormalities are higherin families with related parents (i.e., first cousins; Morrowet al., 2008). These data, along with the Barnevik-Olsson et al.(2008) data, indicate that factors other than country or regionof residence may be involved in receiving an ASD diagnosisand that expecting all ethnicities to have equal prevalence

rates for of ASD might not be correct.One reason for the discrepancy in finding children quali-

fied for an autism eligibility may come from differing criteriaused by the medical and educational communities. The medicalcommunity typically used criteria described in the DSM-IV(APA, 2000), whereas educational personnel, namely school

psychologists, use criteria outlined by IDEA (Amendmentsto IDEA, 1997; Individuals With Disabilities EducationImprovement Act, 2004). Parents and educators often equatereceiving a medical diagnosis of ASD with being qualifiedfor an autism eligibility in special education. This confusionoften arises because a child can be found eligible under oneset of criteria but not under the other. Although both criteriarely on behavioral observations to determine qualification,they both still leave room for personal impressions to affectthe final diagnosis and eligibility. The lack of a consistentscreening tool used for determining if a child qualifies foran ASD (Baio, Rice, Wiggins, Morrier, & Hobson, 2009;Rice et al., 2009) may play a role in this lack of consensus.

To increase accurate identification of students with ASDwithin the educational environment, Noland and Gabriels(2004) suggested a model for screening children in publicschools for a possible ASD. This model proposed a multitieredtraining system for school personnel that included (a) an over-view of the legal and clinical issues involved in screening

ASD, (b) a defined training process for school-based person-nel, and (c) an outline for a school-based process for screeningASD. The screening process included training school person-nel on use of the best practice assessments of the AutismDiagnostic Observation Schedule (ADOS; Lord, Rutter,DiLavore, & Risi, 2001) and the Autism Diagnostic Interview(ADI-R; Rutter, LeCouteur, & Lord, 2003). Williams et al.(2009) found that school evaluators were least likely to useADOS or ADI-R, instead relying on more subject observationsusing the Childhood Autism Rating Scale (Schopler, Reichler,DeVellis, & Daly, 1980) or the Gilliam Autism Rating Scale(Gilliam, 1995, 2006). They also found that only 29% ofschool systems used the childs native language of Spanishfor the assessment as required by federal law (IDEA, 1990;Amendments to IDEA, 1997; Individuals With DisabilitiesEducation Improvement Act, 2004). These results wererecently confirmed by CDCs Autism and DevelopmentalDisabilities Monitoring Network, who found that althoughthe use of the ADOS and ADI-R was increasing in four states,the Childhood Autism Rating Scale was still the preferredassessment for autism identification (Baio et al., 2009; Riceet al., 2009).

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Morrier and Hess 51

Although the data indicate that children from ethnicallydiverse backgrounds are underrepresented in receipt of aschool-based autism eligibility (Dyches et al., 2004; Mandellet al., 2009; Morrier et al., 2008), these previous investigationsconcentrated on targeted areas of the United States. This studyinvestigated ethnic disparities in receipt of an autism eligibility

across the United States as well as each individual statesreported data to the Office of Special Education Programs.Other state-specific factors that could be related to this eligi-

bility category were also investigated. Calculations of disproportionality were conducted to investigate the hypotheses ofdisproportionate representation according to ethnicity andage group of students.

MethodSample

State-level data on special education enrollment for the autismeligibility by ethnicity were drawn from the Office of SpecialEducation Programs and made available by Westat (www.ideadata.org) for the 2007-2008 school year. The sampleconsisted of 295,945 children and youth ages 3 to 21 yearsreported as eligible under the autism eligibility. Students rep-resented 49 states and the District of Columbia; data were notavailable for Vermont. Student breakdown by ethnicity was(a) 2,003 American Indian/Native Alaskan; (b) 16,691 Asian/Pacific Islander, (c) 41,075 Black/African American; (d)38,801 Hispanic/Latino; and (e) 195,610 White/Caucasian.The sample included 38,527 3 to 5 year olds (i.e., preschool-ers) and 255,653 6 to 21 year olds (i.e., school-age). Data on

gender distribution were not available. States were able tosuppress data for those ethnic groups for which there werefewer than 5 students reported.

Measures of Disproportionality To calculate disproportionate representation by ethnicity, threerecommended indexes (Westat, 2004) were used: (a) relativedifference, (b) risk index (RI), and (c) risk ratio (RR). Therelative difference calculation provided a comparison of anethnic groups proportion under an autism eligibility comparedto its proportion in the general population. Relative differencecalculations were determined by subtracting the percentageof children with an autism eligibility within a specific ethnicgroup from that ethnic groups percentage in the general popu-lation. The difference between these percentages equals a

positive or negative relative difference. The RI calculates therisk that students from a specific ethnic group had of receivingan autism eligibility based on their proportion in the general

population. This index is an expression of the rate at whicha disability condition (i.e., autism) occurs in a group and isexpressed as a percentage. The RI was calculated by dividing

the number of students from the targeted group under anautism eligibility by the total number of students in that groupin the general population. The RR provided a relative risk forthe students in a specific ethnic group compared to studentsfrom all other ethnic groups served under an autism eligibility.The RR was calculated by dividing the RI from the targeted

group by the RI for all other ethnicities, minus the targetgroup, combined.

ResultsRelative Difference FromPopulation Composition

Table 1 lists the relative difference of receiving an autismeligibility through the USDOE as a whole as well as for eachindividual state. Results indicate that students from AmericanIndian and Hispanic backgrounds had the greatest negativerelative differences when compared to their proportions inthe general populations. Students from Asian backgroundshad the greatest positive relative difference when comparedto the general population. Paired samples t tests indicate thatdifferences of the overall relative difference and the relativedifference for preschool children were statistically signifi-cant for children from Hispanic backgrounds, t (49) = 2.320,

p = .026, and children from Caucasian backgrounds,t (49) = 3.368, p = .001. These data indicate that preschool-aged children classified as Hispanic are consistently under-represented in special education under an autism eligibilityand that those classified as Caucasian are consistently over-represented in this eligibility category. Comparisons of the

overall relative difference and the relative difference forschool-age children were statistically significant for childrenfrom Asian, t (41) = 3.990, p = .001; African American,t (48) = 2.548, p = .014; and Caucasian, t (50) = 2.459,

p = .017, backgrounds. These data indicate that school-agedchildren and youth classified as Asian/Pacific Islander andCaucasian were overrepresented in this category while thoseclassified as African American/Black were underrepresented.Comparisons of the relative difference for preschool versusschool-age children were statistically different for Caucasianchildren only, t (49) = 3.506, p = .001, indicating that moreCaucasian school-aged children are found eligible for thisspecial education category than preschool-aged children.

State-by-state variation of the relative differences indicatesa large range for children and youth reported under an autismeligibility. Overall, the state of New York had the largest rela-tive difference for children and youth from American Indian

backgrounds at + 54.75. This indicates that although childrenand youth from this ethnic background represented 0.21% ofthe general population they were represented in special educa-tion under an autism eligibility at 0.58%, yielding overrepre-sentation in this category. For this same group, the state of



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53

T a

b l e 1

. ( c o n

t i n u e

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3 2 1 Y e a r s

3 5 Y e a r s

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a D a t a n o t c a

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b D a t a n o t a v a i l a b

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C h i l d C o u n t .



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Morrier and Hess 55

Table 2. Risk Index (%) for Autism by Ethnicity Across All 50 States

321 Years 35 Years 621 Years

State AI API AA H W AI API AA H W AI API AA H W

United States 0.27 0.50 0.35 0.25 0.42 0.18 0.49 0.27 0.26 0.34 0.29 0.50 0.36 0.25 0.43Alabama 0.30 0.25 0.23 0.11 0.24 0.00 0.30 0.09 0.07 0.14 0.33 0.24 0.25 0.12 0.25Alaska 0.16 0.24 0.16 0.26 0.30 a 0.00 0.12 0.18 0.29 0.19 0.34 0.34Arizona 0.13 0.55 0.43 0.19 0.42 0.06 0.41 0.14 0.08 0.16 0.14 0.58 0.50 0.21 0.46Arkansas 0.25 0.37 0.20 0.14 0.32 0.16 0.09 0.20 0.32 0.47 0.21 0.15 0.34California 0.45 0.64 0.56 0.31 0.60 0.53 0.95 0.70 0.43 0.84 0.43 0.59 0.54 0.28 0.56Colorado 0.21 0.09 0.22 0.11 0.08 0.16 0.23 0.10 0.23Connecticut 0.55 0.45 0.54 0.33 0.52 0.48 0.32 0.47 0.71 0.55 0.55 0.33 0.52Delaware 0.00 0.22 0.37 0.16 0.37 0.00 1.06 0.39 0.21 0.40 0.37 0.15 0.36District 0.00 0.28 0.08 0.00 0.34 0.00 0.27 0.09

of ColumbiaFlorida 0.21 0.38 0.26 0.33 0.29 0.22 0.34 0.22 0.32 0.28 0.21 0.39 0.26 0.33 0.29Georgia 0.12 0.39 0.32 0.18 0.34 0.16 0.08 0.18 0.15 0.47 0.35 0.21 0.37Hawaii 0.42 0.45 0.19 0.12 0.38 0.00 0.51 0.32 0.18 0.40 0.50 0.44 0.17 0.11 0.37Idaho 0.24 0.48 0.24 0.15 0.35 0.10 0.20 0.29 0.62 0.31 0.16 0.38Illinois 0.48 0.44 0.34 0.21 0.41 1.47 0.38 0.27 0.20 0.33 0.31 0.45 0.35 0.21 0.42Indiana 0.33 0.42 0.43 0.24 0.60 0.00 0.29 0.28 0.23 0.37 0.37 0.45 0.46 0.24 0.64Iowa 0.18 0.14 0.28 0.13 0.14 0.00 0.08 0.08 0.21 0.17 0.33 0.14 0.15Kansas 0.33 0.37 0.33 0.15 0.26 0.00 0.16 0.23 0.08 0.16 0.39 0.42 0.35 0.17 0.27Kentucky 0.00 0.34 0.30 0.26 0.00 0.21 0.22 0.43 0.32 0.27Louisiana 0.00 0.29 0.22 0.03 0.22 0.00 0.25 0.17 0.16 0.18 0.30 0.23 0.23Maine 0.59 0.37 0.56 0.49 0.66 0.60 1.07 0.92 0.67 0.45 0.55 0.38 0.62Maryland 0.33 0.56 0.44 0.26 0.45 0.40 0.31 0.30 0.39 0.59 0.46 0.33 0.48Massachusetts 0.22 0.35 0.48 0.38 0.59 0.64 0.65 0.87 0.26 0.42 0.45 0.33 0.54Michigan 0.57 0.44 0.38 0.21 0.50 0.66 0.39 0.35 0.18 0.37 0.56 0.45 0.38 0.21 0.52Minnesota 0.76 0.75 0.94 0.52 0.86 0.39 0.82 0.99 0.44 0.63 0.83 0.73 0.92 0.54 0.90Mississippi 0.00 0.08 0.15 0.08 0.15 0.00 0.42 0.09 0.00 0.12 0.17 0.10 0.15Missouri 0.26 0.45 0.37 0.15 0.36 0.00 0.17 0.16 0.30 0.56 0.41 0.19 0.39Montana 0.09 0.00 0.00 0.07 0.20 0.00 0.00 0.17 0.10 0.08 0.21Nebraska 0.17 0.38 0.33 0.19 0.32 0.39 0.17 0.26 0.21 0.46 0.32 0.19 0.33

Nevada 0.38 0.53 0.44 0.23 0.48 0.55 0.26 0.63 0.46 0.65 0.42 0.22 0.44New Hampshire 0.00 0.11 0.26 0.17 0.42 0.00 0.49 0.37 0.33 0.20 0.43New Jersey 0.41 0.48 0.40 0.30 0.50 0.23 0.18 0.33 0.47 0.59 0.43 0.32 0.53New Mexico 0.09 0.30 0.14 0.11 0.29 0.07 0.09 0.32 0.09 0.38 0.17 0.12 0.28New York 0.56 0.29 0.37 0.28 0.40 1.03 0.17 0.23 0.22 0.28 0.51 0.31 0.40 0.29 0.41North Carolina 0.13 0.37 0.39 0.16 0.37 0.11 0.39 0.25 0.10 0.30 0.13 0.36 0.41 0.18 0.38North Dakota 0.11 0.34 0.64 0.20 0.27 0.24 0.00 0.19 0.08 0.41 0.77 0.24 0.29Ohio 0.27 0.30 0.28 0.16 0.35 0.06 0.06 0.31 0.37 0.31 0.20 0.41Oklahoma 0.27 0.27 0.21 0.08 0.22 0.10 0.09 0.06 0.29 0.32 0.23 0.10 0.25Oregon 1.05 0.81 0.84 0.41 0.84 0.45 0.88 0.64 0.39 0.76 1.15 0.79 0.88 0.42 0.86Pennsylvania 0.69 0.48 0.44 0.33 0.48 1.65 0.63 0.60 0.45 0.60 0.50 0.45 0.42 0.30 0.46Rhode Island 0.31 0.27 0.40 0.22 0.59 0.38 0.31 0.54 0.38 0.32 0.40 0.20 0.60South Carolina 0.27 0.15 0.22 0.21 0.16 0.20 0.28 0.14 0.23South Dakota 0.21 0.18 0.15 0.31 0.21 0.91 0.28 0.21 0.19 0.31Tennessee 0.13 0.28 0.23 0.16 0.28 0.18 0.15 0.24 0.15 0.35 0.24 0.17 0.28Texas 0.36 0.57 0.36 0.23 0.41 0.18 0.48 0.27 0.19 0.28 0.39 0.59 0.38 0.24 0.43Utah 0.18 0.24 0.34 0.12 0.35 0.00 0.05 0.21 0.21 0.29 0.43 0.14 0.38Vermont b

Virginia 0.39 0.59 0.39 0.29 0.38 0.00 0.49 0.21 0.16 0.25 0.45 0.62 0.42 0.33 0.41Washington 0.30 0.49 0.38 0.15 0.41 0.13 0.39 0.21 0.09 0.28 0.33 0.51 0.41 0.17 0.43West Virginia 0.00 0.36 0.26 0.09 0.23 0.00 0.00 0.07 0.00 0.42 0.31 0.11 0.25Wisconsin 0.58 0.39 0.36 0.25 0.46 0.43 0.28 0.29 0.16 0.32 0.61 0.41 0.37 0.27 0.48Wyoming 0.00 0.35 0.10 0.32 0.00 0.00 0.23 0.44 0.12 0.34

Abbreviations: AI = American Indian/Native Alaskan; API = Asian/Pacific Islander; AA = Black (non-Hispanic); H = Hispanic/Latino; W = White(non-Hispanic).aData not calculated because n = 0 or suppressed by state.bData not available at time of Child Count.



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Table 3. Risk Ratio for Autism by Ethnicity Across All 50 States

321 Years 35 Years 621 Years

State AI API AA H W AI API AA H W AI API AA H W

United States 0.72 1.34 0.91 0.60 1.09 0.56 1.58 0.83 0.78 1.19 0.74 1.31 0.93 0.61 1.32Alabama 1.30 1.10 1.00 0.44 0.91 0.00 2.47 0.67 0.56 1.49 1.35 0.97 1.03 0.48 1.04Alaska 0.55 0.93 0.59 0.98 1.44 a 0.00 0.56 0.98 0.63 1.18 1.48Arizona 0.40 1.79 1.42 0.46 1.61 0.44 3.44 1.12 0.50 1.62 0.39 1.68 1.45 0.48 1.92Arkansas 0.91 1.33 0.68 0.46 1.35 0.91 0.51 1.54 1.05 1.56 0.66 0.49 1.61California 0.96 1.46 1.20 0.49 1.28 0.83 1.59 1.11 0.51 1.56 1.00 1.42 1.26 0.50 1.54Colorado 1.20 0.45 1.76 0.88 0.53 2.16 1.25 0.45 2.13Connecticut 1.12 0.92 1.11 0.61 1.03 1.14 0.72 1.43 1.42 1.11 1.12 0.62 1.19Delaware 0.00 0.64 1.10 0.43 0.98 0.00 2.86 0.98 0.50 1.02 1.14 0.43 1.24District 0.00 5.22 0.28 0.00 0.00 4.34 0.40

of ColumbiaFlorida 0.71 1.30 0.84 1.11 0.81 0.77 1.23 0.75 1.22 1.00 0.70 1.32 0.87 1.15 0.96Georgia 0.39 1.22 1.02 0.51 0.93 1.09 0.50 1.36 0.42 1.37 1.01 0.58 1.10Hawaii 1.12 1.57 0.46 0.26 0.86 0.00 1.60 0.77 0.39 0.95 1.37 1.58 0.44 0.26 1.02Idaho 0.75 1.51 0.76 0.43 1.56 0.51 2.73 0.83 1.79 0.89 0.43 1.79Illinois 1.33 1.24 0.92 0.50 1.18 5.04 1.33 0.91 0.61 1.33 0.83 1.23 0.93 0.52 1.43Indiana 0.59 0.76 0.76 0.41 1.35 0.00 0.85 0.80 0.66 1.41 0.64 0.76 0.76 0.39 1.65Iowa 1.19 0.96 1.93 0.87 0.66 1.07 1.34 1.09 2.18 0.88 0.73Kansas 1.30 1.47 1.35 0.56 0.89 0.00 1.05 1.58 0.51 1.20 1.45 1.56 1.33 0.59 1.04Kentucky 1.30 1.16 0.94 1.03 1.61 1.58 1.19 1.07Louisiana 1.35 1.04 0.11 0.92 1.42 0.95 0.92 1.07 1.35 1.06 1.09Maine 0.90 0.56 0.85 0.75 1.13 0.66 1.21 1.64 1.10 0.73 0.89 0.61 1.28Maryland 0.76 1.29 0.99 0.54 0.90 1.48 1.16 1.20 0.84 1.28 0.99 0.69 1.05Massachusetts 0.40 0.62 0.86 0.66 1.22 0.83 0.83 1.75 0.51 0.82 0.87 0.64 1.41Michigan 1.25 0.95 0.79 0.42 1.21 1.86 1.10 0.98 0.49 1.15 1.18 0.94 0.77 0.43 1.46Minnesota 0.90 0.89 1.12 0.59 0.96 0.59 1.28 1.58 0.66 0.89 0.95 0.83 1.06 0.60 1.20Mississippi 0.51 1.08 0.50 0.85 4.24 0.77 0.00 1.32 1.12 0.62 0.98Missouri 0.73 1.28 1.06 0.42 0.91 0.00 1.15 1.45 0.77 1.45 1.06 0.48 1.05Montana 0.45 0.00 0.38 2.63 0.00 0.52 0.43 2.44Nebraska 0.57 1.25 1.09 0.58 1.05 1.65 0.67 1.27 0.66 1.50 1.01 0.58 1.28

Nevada 0.98 1.39 1.13 0.46 1.29 1.28 0.48 2.35 1.24 1.80 1.12 0.48 1.44New Hampshire 0.26 0.64 0.41 2.05 1.44 0.81 0.49 2.50New Jersey 0.92 1.08 0.86 0.59 1.14 0.90 0.66 1.99 0.98 1.23 0.87 0.62 1.31New Mexico 0.47 1.80 0.84 0.49 2.16 0.40 0.39 3.90 0.48 2.24 1.00 0.52 2.33New York 1.55 0.78 1.02 0.70 1.04 4.05 0.65 0.89 0.83 1.29 1.33 0.81 1.05 0.73 1.22North Carolina 0.36 1.05 1.13 0.42 0.96 0.42 1.52 0.97 0.35 1.44 0.35 0.98 1.15 0.47 1.11North Dakota 0.38 1.27 2.45 0.74 1.26 1.17 0.00 0.28 1.48 2.86 0.86 1.34Ohio 0.80 0.90 0.80 0.47 1.15 1.22 1.24 0.80 0.96 0.79 0.51 1.36Oklahoma 1.29 1.26 0.97 0.36 1.03 1.57 1.52 1.13 1.25 1.33 0.94 0.41 1.16Oregon 1.36 1.05 1.08 0.48 1.24 0.65 1.30 0.93 0.51 1.53 1.46 1.00 1.11 0.48 1.49Pennsylvania 1.48 1.02 0.94 0.67 0.98 2.80 1.08 1.01 0.74 1.06 1.12 1.00 0.92 0.66 1.20Rhode Island 0.62 0.53 0.78 0.38 1.80 0.82 0.62 1.89 0.75 0.62 0.78 0.35 2.19South Carolina 1.29 0.59 0.75 1.14 0.79 1.02 1.32 0.59 0.88South Dakota 0.72 0.62 0.53 1.53 0.69 3.53 1.25 0.71 0.65 1.72Tennessee 0.48 1.08 0.87 0.59 1.03 0.80 0.69 1.47 0.54 1.29 0.88 0.61 1.20Texas 1.08 1.77 1.11 0.54 1.21 0.74 2.05 1.12 0.67 1.26 1.12 1.74 1.11 0.55 1.50Utah 0.56 0.76 1.08 0.34 1.86 0.00 0.24 5.91 0.60 0.85 1.25 0.36 2.09Vermont b

Virginia 1.00 1.57 1.00 0.70 0.81 0.00 2.15 0.84 0.64 1.05 1.09 1.52 1.03 0.78 0.96Washington 0.79 1.34 0.99 0.37 1.20 0.53 1.66 0.85 0.32 1.53 0.82 1.31 1.02 0.39 1.41West Virginia 0.00 1.58 1.14 0.41 0.84 0.00 .00 0.00 1.67 1.23 0.43 0.94Wisconsin 1.34 0.89 0.81 0.54 1.15 1.41 0.91 0.97 0.52 1.28 1.34 0.90 0.79 0.57 1.36Wyoming 1.23 0.32 2.71 0.00 1.46 0.37 2.75

Note: Italicized values = under-representation; bolded values = over-representation based on cut-off of 0.67/1.50. AI = American Indian/NativeAlaskan; API= Asian/Pacific Islander; AA = Black (non-Hispanic); H = Hispanic/Latino; W = White (non-Hispanic).aData not calculated because n = 0 or suppressed by state.bData not available at time of Child Count.



10/16


11/16

58

T a

b l e 4

. M e a n s , S

t a n d a r

d D e v i a t i o n s , a

n d 9 5 % C o n f

i d e n c e

I n t e r v a l s

( C I ) f o r R

i s k R a t i o

C a l c u l a t i o n s

b y U s e o f

D e v e

l o p m e n t a

l D e l a y (

D D ) L a b e l

3 2 1 Y e a r s

3 5 Y e a r s

6 2 1 Y e a r s

U s e o

f D D L a b e l

A I

A P I

A A

H

W

A I

A P I

A A

H

W

A I

A P I

A A

H

W

N o

D D l a b e l

M S D

9 5 % C I

1 . 0 1

0 . 1 7

0 . 7 5

1 . 2 7

1 . 2 7

0 . 4 2

0 . 6 1

1 . 9 4

1 . 2 6

0 . 4 8

0 . 5 0

2 . 0 2

0 . 5 9

0 . 1 9

0 . 2 9

0 . 8 9

1 . 0 7

0 . 2 8

0 . 6 2

1 . 5 2

0 . 5 2

0 . 4 6

0 . 6

1 1 . 6 6

1 . 8 2

0 . 3 3

1 . 1 1

4 . 7 5

1 . 1 5

0 . 0 6

1 . 0 1

1 . 3 0

0 . 7 5

0 . 2 8

0 . 0 4

1 . 4 6

1 . 3 5

0 . 1 8

0 . 9 0

1 . 8 1

1 . 0 7

0 . 2 3

0 . 7 1

1 . 4 3

1 . 3 0

0 . 3 5

0 . 7 5

1 . 8 6

1 . 3 4 0 . 5 9 0 . 3 9 2 . 2 8

0 . 6 1

0 . 1 8

0 . 3 3

0 . 9 0

1 . 2 8

0 . 3 8

0 . 6 8

1 . 8 8

D D 0 - 9 y e a r s

M S D

9 5 % C I

0 . 6 7

0 . 4 8

0 . 4 9

0 . 8 5

1 . 0 5

0 . 4 4

0 . 8 8

1 . 2 2

1 . 1 6

0 . 8 5

0 . 8 4

1 . 4 9

0 . 4 8

0 . 2 1

0 . 4 0

0 . 5 6

1 . 1 2

0 . 3 9

0 . 9 6

1 . 2 6

0 . 5 7

0 . 8 2

0 . 1 7

0 . 9 6

1 . 5 6

0 . 9 3

1 . 0 5

2 . 0 7

0 . 9 8

0 . 4 1

0 . 8 1

1 . 1 5

0 . 5 4

0 . 2 2

0 . 4 5 0 . 6

4

1 . 6 3

1 . 0 9

1 . 1 8

2 . 0 8

0 . 8 4

0 . 4 1

0 . 6 7

1 . 0 1

1 . 2 8

0 . 3 5

1 . 1 4

1 . 4 2

1 . 1 7 0 . 7 3 0 . 8 9 1 . 4 5

0 . 5 7

0 . 1 8

0 . 5 0

0 . 6 4

1 . 2 8

0 . 4 3

1 . 1 2

1 . 4 5

D D 0 - 6 y e a r s

M S D

9 5 % C I

0 . 6 7

0 . 4 9

0 . 4 2

0 . 9 3

0 . 8 3

0 . 5 9

0 . 5 2

1 . 1 3

0 . 9 2

0 . 3 2

0 . 7 6

1 . 0 8

0 . 5 2

0 . 1 9

0 . 4 2

0 . 6 1

1 . 4 4

0 . 5 5

1 . 1 5

1 . 7 2

1 . 2 9

1 . 8 8

0 . 1 6

2 . 7 4

1 . 1 7

1 . 1 8

0 . 3 2

2 . 0 1

0 . 9 6

0 . 7 6

0 . 5 5

1 . 3 7

0 . 6 8

0 . 2 6

0 . 5 3 0 .

8 4

1 . 5 7

0 . 4 1

1 . 3 4

1 . 8 1

0 . 8 4

0 . 4 0

0 . 6 1

1 . 0 6

1 . 2 0

0 . 4 2

0 . 9 4

1 . 4 7

1 . 0 4 0 . 2 5 0 . 9 0 1 . 1 7

0 . 5 3

0 . 1 9

0 . 4 4

0 . 6 3

1 . 6 3

0 . 5 1

1 . 3 7

1 . 8 9

A b b r e v

i a t i o n s : A

I = A

m e r

i c a n

I n d i a n /

N a t

i v e A l a s

k a n ;

A P I =

A s i a n /

P a c i f

i c I s l a n

d e r ;

A A = B

l a c k ( n o n - H

i s p a n

i c ) ; H = H

i s p a n

i c / L a t i n o ;

W = W

h i t e

( n o n - H

i s p a n

i c ) .



12/16

59

T a

b l e 5

. M e a n s , S

t a n d a r

d D e v i a t i o n s , a

n d 9 5 % C o n f

i d e n c e

I n t e r v a l s

( C I ) f o r R

i s k R a t i o

C a l c u l a t i o n s

b y R e g i o n o f

C o u n t r y

3 2 1 Y e a r s

3 5 Y e a r s

6 2 1 Y e a r s

R e g i o n o f

C o u n t r y

A I

A P I

A A

H

W

A I

A P I

A A

H

W

A I

A P I

A A

H

W

N o r t h e a s t

M S D

9 5 % C I

0 . 7 1

0 . 5 6

0 . 3 3

1 . 0 8

0 . 7 0

0 . 3 7

0 . 4 5

0 . 9 5

1 . 3 1

1 . 3 0

0 . 4 3

2 . 1 8

0 . 5 2

0 . 2 1

0 . 3 8

0 . 6 6

1 . 1 4 0 . 4 6 0 . 8 3 1 . 4 5

1 . 3 7

1 . 9 3

1 . 0

2 3 . 7 6

1 . 5 0

0 . 8 3

0 . 4 7

2 . 5 4

0 . 9 3

0 . 1 6

0 . 8 1

1 . 0 6

0 . 7 6

0 . 2 1

0 . 5 9

0 . 9 4

1 . 4 8

0 . 3 6

1 . 2 0

1 . 7 5

0 . 9 0

0 . 4 4

0 . 5 6

1 . 2 3

0 . 9 5

0 . 2 4

0 . 7 5

1 . 1 5

1 . 2

5

1 . 0

3

0 . 5 6

1 . 9

5

0 . 5 8

0 . 1 2

0 . 5 0

0 . 6 7

1 . 3 6

0 . 5 6

0 . 9 9

1 . 7 4

M i d d l e

W e s t

M S D

9 5 % C I

0 . 9 3

0 . 3 4

0 . 7 1

1 . 1 4

0 . 9 9

0 . 3 8

0 . 7 5

1 . 2 3

1 . 1 4

0 . 5 4

0 . 8 0

1 . 4 8

0 . 5 5

0 . 1 4

0 . 4 7

0 . 6 4

1 . 1 1 0 . 2 3 0 . 9 6 1 . 2 6

1 . 0 7

1 . 5 4

0 . 0

3 2 . 1 8

0 . 9 3

0 . 4 5

0 . 5 2

1 . 3 4

1 . 4 4

0 . 8 0

0 . 8 7

2 . 0 1

0 . 6 5

0 . 1 8

0 . 5 0

0 . 8 0

1 . 2 5

0 . 1 6

1 . 1 4

1 . 3 6

0 . 9 1

0 . 3 5

0 . 6 9

1 . 1 4

1 . 1 5

0 . 3 0

0 . 9 5

1 . 3 5

1 . 2

3

0 . 6

8

0 . 7 8

1 . 6

9

0 . 5 9

0 . 1 5

0 . 4 9

0 . 6 8

1 . 3 0

0 . 2 7

1 . 1 3

1 . 4 8

S o u t

h e a s t

M S D

9 5 % C I

0 . 4 3

0 . 4 6

0 . 1 4

0 . 7 2

1 . 1 2

0 . 4 5

0 . 8 3

1 . 4 0

1 . 0 2

0 . 1 6

0 . 9 2

1 . 1 3

0 . 4 9

0 . 2 8

0 . 3 1

0 . 6 6

0 . 9 2 0 . 1 5 0 . 8 3 1 . 0 2

0 . 1 5

0 . 2 9

0 . 0 9

0 . 3 9

1 . 8 6

1 . 3 1

0 . 6 5

3 . 0 7

0 . 9 0

0 . 1 5

0 . 8 0

1 . 0 0

0 . 6 2

0 . 3 3

0 . 3 8

0 . 8 5

1 . 3 1

0 . 2 3

1 . 1 5

1 . 4 6

0 . 6 9

0 . 4 5

0 . 3 1

1 . 0 6

1 . 3 6

0 . 2 4

1 . 1 9

1 . 5 3

1 . 0

4

0 . 1

8

0 . 9 3

1 . 1

6

0 . 6 2

0 . 2 1

0 . 4 7

0 . 7 7

1 . 0 8

0 . 1 9

0 . 9 6

1 . 2 0

S o u t

h w e s t

M S D

9 5 % C I

0 . 8 1

0 . 4 4

0 . 1 1

1 . 5 2

1 . 6 6

0 . 2 6

1 . 2 4

2 . 0 7

1 . 0 8

0 . 2 5

0 . 6 9

1 . 4 8

0 . 4 6

0 . 0 7

0 . 3 4

0 . 5 8

1 . 5 0 0 . 5 0 0 . 7 1 2 . 3 0

0 . 7 9

0 . 5 4

0 . 0 8

1 . 6 6

2 . 7 5

0 . 9 8

6 . 0

9

1 1 . 5

8

1 . 2 5

0 . 2 3

0 . 6 8

1 . 8 3

0 . 5 2

0 . 1 4

0 . 1 7

0 . 8 7

1 . 9 8

1 . 3 0

0 . 0 9

4 . 0 4

1 . 7 5

0 . 3 7

1 . 1 6

2 . 3 4

1 . 1 3

0 . 2 3

0 . 7 6

1 . 4 9

0 . 4

9

0 . 0

6

0 . 3 8

0 . 5

9

1 . 7 3

0 . 5 1

0 . 9 2

2 . 5 3

1 . 7 5

0 . 3 7

1 . 1 6

2 . 3 4

W e s t

M S D

9 5 % C I

0 . 6 8

0 . 4 3

0 . 4 0

0 . 9 7

0 . 9 1

0 . 6 4

0 . 4 8

1 . 3 4

0 . 8 8

0 . 3 1

0 . 6 2

1 . 1 5

0 . 4 5

0 . 1 9

0 . 3 2

0 . 5 8

1 . 6 2 0 . 5 9 1 . 2 2 2 . 0 2

0 . 5 0

0 . 3 6

0 . 0 7

1 . 0 7

1 . 0 3

0 . 8 0

0 . 1 8

1 . 8 7

0 . 5 8

0 . 5 2

0 . 2 1

0 . 9 6

0 . 4 4

0 . 1 1

0 . 3 5

0 . 5 3

2 . 3 4

1 . 5 5

1 . 0 5

3 . 6 4

1 . 3 4

0 . 3 7

1 . 0 3

1 . 6 5

1 . 0 4

0 . 3 1

0 . 8 2

1 . 2 7

0 . 4 9

0 . 2 4

0 . 3 2

0 . 6 5

1 . 7 8

0 . 5 2

1 . 4 3

2 . 1 2

1 . 3 4

0 . 3 7

1 . 0 3

1 . 6 5

A b b r e v

i a t i o n s : A

I = A

m e r

i c a n

I n d i a n /

N a t

i v e A l a s

k a n ;

A P I =

A s i a n /

P a c i f

i c I s l a n

d e r ;

A A = B

l a c k ( n o n - H

i s p a n

i c ) ; H = H

i s p a n

i c / L a t i n o ;

W = W

h i t e

( n o n - H

i s p a n

i c ) .



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( M = 1.75). School-aged children from Caucasian back-grounds were significantly less eligible under an autism labelin the Southeast ( M = 1.08) than in the West ( M = 1.78).

Discussion

The purpose of this study was to investigate ethnic differencesin children and youth receiving special education serves underan autism eligibility as reported by individual states to theUSDOE during the 2007-2008 school year. Additional factorsof region of country and a states use of a developmentaldelay label for children under age 9 were also investigated.Results from this study indicate that ethnic disparities arefound in receipt of this eligibility label. The main findingindicates that 80% of states report underrepresentation ofchildren and youth from all ethnicities under an autism eli-gibility. Ethnic enrollment in special education under anautism eligibility proportionally in line with the general popu-lation percentages occurs in only 15 to 20% of states reportingdata. Children from Hispanic backgrounds are especiallyunderrepresented in this eligibility category, with this dispar-ity occurring in almost 95% of states; only 5% of states enrollchildren from Hispanic backgrounds proportionally to their

percentage in the general population. These data may indicatesystemic problems with identifying this group of childrenand youth within the autism spectrum, which is especiallytroubling considering this is the fastest growing populationin the United States (U.S. Census Bureau, 2001). This isconsistent with other data indicating that Hispanic childrenare underrepresented in special education as a whole (Parrish,2002) as well as in autism spectrum disorders using both the

medial model (Baio et al., 2009; CDC, 2007a, 2007b, 2009;Mandell et al., 2009; Rice et al., 2009; Williams et al; 2009;Yeargin-Allsopp et al., 2003) and the educational model(Dyches et al., 2004; Morrier et al., 2008).

Autism was once considered a low-incidence disabilityeligibility category (C. Rice, personal communication,October 28, 2009), even though medical professionals andschool psychologists are able to determine a childs eligibilityfor educational purposes. Although medical professionalsand school psychologists use differing criteria to determineautism (i.e., DSM-IV-TR vs. Individuals With DisabilitiesEducation Improvement Act, 2004), both use behavioralobservations and some element of personal perception andindividual impression to come to the final call of autism.The results of this study support previous findings that ethnicdisproportionality does occur within this eligibility category(Dyches et al., 2004; Mandell et al., 2009; Morrier et al.,2008). With the reported increases in prevalence of ASDs(CDC, 2007b, 2009; Kogan et al., 2009), it is important foreducators to examine the effects of ethnicity on their deter-mination of an autism eligibility. Although receipt of a medicaldiagnosis of a pervasive developmental disorder (APA, 2000)

differs from an educational eligibility of autism (IndividualsWith Disabilities Education Improvement Act, 2004), educa-tors must pay careful attention to the ways in which theinterface of ethnicity and assessment plays into determininga child eligible for special education. These differences couldexplain variations in autism identification in ethnically

diverse students and require further investigation.Examination of the data reported to the USDOE indicates

that state variability within the data reported is great. Indi-vidual state differences in the proportion of ethnically diversechildren vary from underrepresentation to overrepresentation.This could be due to the manner in which individual statesdetermine eligibility under autism. The federal special educa-tion legislation provides a minimum guideline for states tofollow, although states are able to interpret these guidelinesdifferently. Although beyond the scope of this research, theindividual eligibility criteria within each state may play arole in the results found here. For example, the IndividualsWith Disabilities Education Improvement Act (2004) givesstates the option of using a DD label for children until their9th birthday. At that time, departments of special educationmust use one of the 12 remaining categories to continuespecial education support. Because not all states have electedto use this eligibility category, that requires them to classifychildren under the remaining 12 categories, resulting in diag-nostic substitution. This may be especially true for childrenand youth who are later diagnosed with Asperger syndromeor pervasive developmental disordernot otherwise specified

but receive a special educational eligibility of learning dis-ability or emotional or behavioral disturbance.

Although not examined in this investigation, the personnel

involved in and the assessments used by school systems may play a large role in the results presented here. Previous researchindicates that a variety of school personnel with different

backgrounds rely on the Childhood Autism Rating Scale todetermine an autism eligibility (Baio et al., 2009; Rice et al.,2009), which may result in school assessment teams missingsome of the more subtle signs associated with ASD that can

be picked up by use of the ADOS or a parent interview (i.e.,ADI-R), neither of which are tied to the DSM-IV-TR criteriafor pervasive developmental disorders. Considered the goldstandard for receiving a medical diagnosis of ASD, the ADOSand ADI-R require a considerable amount of training to admin-ister, score, and interpret them correctly, whereas the Child-hood Autism Rating Scale is relatively easy and quick toadminister and score. The degree to which autism specialistsand autism-specific services are available to families andeducators may also play a role in the likelihood of qualifyingchildren under an autism eligibility because many regions ofthe country have few individuals trained to make this distinc-tion and/or places to access best practice placements (NationalResearch Council, 2001). Further investigations into the typeof professionals and assessments used to identify students



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Morrier and Hess 61

from ethnically diverse backgrounds in public schoolsand the relationship to disproportionate representation iswarranted.

Variations in autism eligibility rates between ethnic groupscould have been influenced by the professional conductingthe eligibility evaluation. States that are more rural could have

less exposure to professionals who are knowledgeable in best practices for diagnosing autism (e.g., ADOS, and ADI-R) aswell as ethnic differences in behavioral symptomology associ-ated with autism (e.g., cultural variations in childrens useof eye contact with adults). The effects of the availability ofqualified autism professionals in the school systems was avariable that could not be investigated in this dataset andshould be investigated further to see how that influencesdisproportionate representation of ethnically diverse studentsunder an autism eligibility.

LimitationsThere are several limitations to this study. First, federal guide-lines allow states to suppress data categories that contain fouror fewer children to protect child privacy (M. Brauen, personalcommunication, October 18, 2007). Data suppression andaggregation of data did not allow investigations of within- oracross-state factors such as large numbers of diverse learners,urban versus rural status of district, and large versus smallindividual district size; nor did they allow for investigationof specific child factors such as gender, socioeconomic status,linguistic diversity, within-group ethnic diversity, or immigra-tion status. Although some states data were limited, the largesample size ( n = 295,945) provided sufficient power to reduce

Type I and Type II errors, making results interpretable andreliable.

A second limitation is the lack of data provided on theexact ethnic origin of children in the same as well as othermedical conditions (e.g., genetic mutations) that could have

played a role in these results. Assuming that prevalence ratesfor ASD are not equal among all ethnic groups (Barnevik-Olsson et al., 2008), the differences between rates would beexpected. Although previous research indicates that ethnicitydoes play a role in underrepresentation and overrepresentationof ethnically diverse children with ASD (CDC, 2007b, 2009;Mandell et al., 2009), without knowing specific medical his-tories of the children (e.g., genetic abnormalities, seizures,etc.), these data should be taken with caution because thereare some instances where the cause of the ASD can be deter-mined (Morrow et al., 2008; Rutter, 2005). Regardless, thisinvestigation centered on the rates of receiving an autismeligibility as reported by the state school system; the causeof the ASD was not under investigation.

A third limitation is the use of state special education datareported as an aggregate, which allowed for gross analysesof the data provided. The use of aggregated data diminished

the ability to track children according to all variables underconsideration. For example, the researchers could not reporton the exact number of Black, 4-year-old males in the Stateof Virginia. These gross aggregate analyses may mask truewithin- and across-state differences and variability (e.g., dis-trict level factors) in disproportionate representation data for

this eligibility category.A fourth limitation is that the investigators could not con-

firm the autism eligibility nor determine if a specific medicaldiagnosis within the autism spectrum was made prior to eli-gibility determination. Without direct confirmation of eli-gibility requirements, some children may be placed underan autism eligibility that do not necessary meet eligibilitycriteria, although this limitation is also minimized by thelarge sample size.

A final limitation is that eligibility requirements differedacross the states evaluated. Federal legislation providesrequirements for determining special education eligibility forautism (Individuals With Disabilities Education ImprovementAct, 2004). Individual states are then allowed to determinetheir own autism eligibility requirements as long as the stan-dards do not go below federal guidelines. This allows eachstate to qualify children under an autism eligibility in a uniquemanner (Danaher, 2004; Mller & Markowitz, 2004). Forexample, children qualifying for an autism eligibility inGeorgia may not qualify for such an eligibility in Tennessee.

Future Research SuggestionsThe data reported in this investigation should be viewed asa first look at disproportionate representation in receipt of an

autism eligibility in public school systems. It will be importantto conduct future studies using variables that were not includedin this s tudy. Analyses using modeling approaches (e.g.,hierarchical linear modeling, structural equation modeling)may benefit this area by examining factors not related to thestatewide data available to these researchers. Areas needingfurther study include impact of socioeconomic status oneligibility, trends of disproportionate representation overtime, especially pre-NCLB and post-NCLB (2001) mandates,and urban versus rural residence.

The role of disproportionate representation in autism is arelatively new area of research; therefore, time trends should

be further investigated. It would be especially important tosee how federal mandates such as Individuals With DisabilitiesEducation Improvement Act (2004) and NCLB (2001) influ-ence not only the rates over time but the educational place-ments received by children with an autism eligibility. Theseanalyses would assist in determining whether placement is afactor that is considered when determining eligibility for spe-cial education. Calculations of pre-IDEA/NCLB rates and

post-IDEA/NCLB rates would show how these federal man-dates are being implemented in public school systems. Trends



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over time would also allow for child cohortspecific calcula-tions to be monitored as children progress through the educa-tion system (Bollmer et al., 2007). For example, tracking 4 yearolds in 2006, who are 5 years old in 2007 and 6 years old in2008, would assist with seeing if positive or negative trendsare occurring. It would also assist with answering how many

new children are being identified under an autism eligibilityover time. Investigation into how disproportionate representa-tion has been influenced by district level mandates (Bollmeret al., 2007) would also be important to minimize the biasincluded in aggregated data. Looking at disproportionaterepresentation figures in urban versus rural districts mayallow for closer inspection of what is occurring within eachstate individually.

Finally, investigating how overrepresentation and under-representation of ethnically diverse children and youth underan autism eligibility as compared to the overall special educa-tion population should be investigated. This investigationwould assist in determining if disproportionate representationis specific to the autism eligibility or if it is more of a systemic

problem within the special education system as a whole.Investigations into trends by state, region of country, use ofthe DD eligibility category, and childrens linguistic back-grounds would assist with this determination.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respectto the authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research and/or

authorship of this article.

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About the Authors

Michael J. Morrier , PhD, BCBA-D, is an assistant director for program evaluation and research at the Emory Autism Center inthe Department of Psychiatry and Behavioral Sciences at EmoryUniversity School of Medicine. His current interests include earlydiagnosis of autism spectrum disorders, peer-related social behav-iors, and disproportionate representation of ethnically diverse chil-dren with disabilities.

Kristen L. Hess , PhD, teaches at the Atlanta School in Atlanta,

Georgia. Her interests include stress in individuals with autismspectrum disorders (ASD), best practices for children and youthwith ASD, and personal preparation for teaching children withASD.
http://sed.sagepub.com/

j spec educ 2012 morrier 49 63

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