Disproportionality in Special Education Identification and

Vol.77. No. 3, pp. 317-334.©2011 Council for Exceptional Children.

Disproportionality in SpecialEducation Identificationand Placement of EnglishLanguage Learners

AMANDA L. SULLIVANArizona State University

ABSTRACT: This Study explored the extent of disproportionality in the identification and placement

of culturally and linguistically diverse students identified as English language learners in special

education. Descriptive statistics and regression analyses examined patterns and predictors of identi-

fication and placement in special education among English learners throughout the state relative to

their White peers. The results indicate that these students are increasingly likely to he identified as

having learning disahilities or mental retardation, and are less likely to he served in either the least

or most restrictive educational environments relative to their White peers. The author also exam-

ined the infiuence of several district-level factors commonly explored in studies of racial dispropor-

tionality and found that these factors did not evidence similar relationships to the disproportionate

representation of English language learners. The study presents implications for further research

and practice.

Students identified as culturallyand linguistically diverse (CLD)represent an ever increasing per-centage of the U.S. studentpopulation, with English lan-

guage learners (ELLs) comprising the fastestgrowing subgroup (Genesee, Lindholm-Leary,Saunders, Si Christian, 2005). (This article usesthe term "culturally and linguistically diverse,"CLD, to refer to students from racial/ethnicminority groups and linguistic minority groups[i.e., those speaking native languages other than

English]. CLD includes students who are Englishlanguage learners because they represent studentswhose first languages are not English and whohave not yet achieved proficiency in English.)Unfortunately, evidence suggests that studentsidentified as CLD are not receiving the servicesand supports they need to be successful in school(Artiles Si Ortiz, 2002).

Examination of school characteristics andeducational outcomes reveals pervasive disparitiesin resources, opportunities to learn, and attain-ment that disadvantage CLD students relative to

Exceptional Children 3 1 7

their mainstream White peers (Brayboy,Castagno, & Maughan, 2007). (This article refersto students identified as White as the referentgroup for equity comparisons, following therationale of Artiles, Rueda, Salazar, & Higareda[2005], which states: "(a) White students havebeen traditionally used as a comparison group inequity analyses because they are the dominantgroup in society who have not had systematicproblems with access and opportunity issues, (b)White students have been used historically as acontrast group in this literature that facilitatestrend analyses, and (c) White students can beused as a stable contrast group because variouscultural and linguistic groups are compared to thesame group" [p. 289].)

Results are particularly dire for studentsidentified as ELLs, who have among the highestgrade retention and dropout rates of all youth(Duran, 2008). Other societal and systemic fac-tors further shape these students' educationalexperiences; these factors include English-onlylegislation, the availability of language supports,and the widespread shortage of bilingual practi-tioners. Indeed, approximately 30% of all stu-dents identified as ELLs (Planty et al., 2009)reside in states where English-only legislation dic-tates the type and amount of language supportavailable.to students. Such limitations of thesestudents' opportunities to learn can result inundesirable outcomes (e.g., behavioral issues, lowengagement, grade retention, dropout), not theleast of which is (inappropriate) referral to specialeducation (Suárez-Orozco, Roos, & Suárez-Orozco, 2000). The field continues to strugglewith uncertainty regarding how to best provideinstruction and access to English language curric-ula and an unclear role of special education inremediating learning difficulties (Artiles &CKlingner, 2006).

D I S P R O P O R T I O N A L I T Y

I N S P E C I A L E D U C A T I O N

The disproportionate representation of CLD stu-dents in special education is a long-standing issuefirst introduced in the literature more than 40years ago (Dunn, 1968), twice studied by the Na-tional Research Council (Donovan & Cross,

2002; Heller, Holtzman, & Messick, 1982), andfrequently examined in the scholarly literature.Not only does disproportionality exist in specialeducation identification, but in placement deci-sions, disciplinary consequences (Skiba, Michael,Nardo, &C Peterson, 2002), academic perfor-mance, and exiting from special education ser-vices (Blanchett, 2006). For a field built on theprinciple of fairness, formed in the wake oí BrownV. Board of Education, and grounded in therhetoric of the civil rights movement (Blanchett,2006), ongoing disproportionality strongly indi-cates systemic problems of inequity, prejudice,and marginalization within the education system.Studies of disproportionality have generally fo-cused on the high-incidence categories of specificlearning disabilities (SLDs), mild mental retarda-tion (MIMR), emotional disabilities (EDs), and,to a lesser extent, speech-language impairments(SLIs). Together, these four categories constitutemore than 82% of students receiving special edu-cation services (U.S. Department of Education,ED, 2009). Moreover, many researchers have con-cerns about these categories because their defini-tions are vague and inconsistent across contexts;and diagnostic practices differ considerablyamong states, school systems, and individualpractitioners (Klingner et al., 2005).

For afield built on the principle of

fairness. . . and grounded in the

rhetoric of the civil rights movement,

ongoing disproportionality strongly

indicates systemic problems of inequity,

prejudice, and marginalization

within the education system.

The literature on disproportionality has pri-marily consisted of analyses of identification pat-terns for students identified as African American,and, less frequently. Latino, Asian, and NativeAmerican. The ED and MIMR categories consis-tently have included an overrepresentation of stu-dents identified as African American; the learningdisabilities (LD) category has included frequentoverrepresentation of students identified as NativeAmerican; whereas each of the four categories has

Spring 2011

included a national underrepresentation of stu-dents identified as Asian and Latino. However, forAsian and Latino students, some states, districts,and schools have shown overrepresentation, par-ticularly in the LD category. For Latinos espe-cially, patterns of representation can varysubstantially at the local level, leading researchersto call for further studies to examine patterns ofrepresentation (Klingner, Artiles, & Mendez-Bartletta, 2006).

Researchers have examined many factors im-plicated in the disproportionate representation ofracial minority students in special education andhave demonstrated that no one factor alone ex-plajns disproportionality. Instead, predictors ofdisproportionality vary by the group and the dis-abilities studied. Demographic factors (e.g., mi-nority enrollment, proportion of teachers fromminority backgrounds) have historically beenstrong predictors of overrepresentation (e.g., Finn,1982; Parrish, 2002; Serwatka, Deering, &Grant, 1995). Academic variables have generallybeen found to be weak and inconsistent predic-tors of disproportionality (e.g., Hosp & Reschly,2004). Researchers have shown indicators of childpoverty (e.g., proportion of students receiving freeor reduced-price lunch, median community in-come) to be positively related to overrepresenta-tion in some categories (e.g., MIMR for NativeAmerican students) and inversely related in others(e.g., ED, SLD, and SLI for African Americanstudents; Coutinho, Oswald, & Best, 2002;Skiba, Poloni-Staudinger, Simmons, FegginsrAzziz, & Chung, 2005).

THE CASE OF ELLS IN

SPECIAL EDUCATION

Substantial variation exists in the United States inthe rates of special education identification forstudents identified as ELLs, with states reportingfrom zero to 17.3% (Hopstock & Stephenson,2003). The average for all U.S. students is ap-proximately 9% (ED, 2009). Although federalspecial education law now requires states to moni-tor and address racial disproportionality, the pol-icy does not address ELLs. Federal databases (e.g..Office of Civil Rights and the Office of SpecialEducation Programs) only recently began collect-

ing data on identification and placement by lan-guage status even though reporting by racial cate-gory has long been in place. Indeed, few schoolsystems have adequate mechanisms in place tocollect identification, placement, or outcome datafor students identified as ELLs in special educa-tion (Zehler et al., 2003), possibly because report-ing was not mandated; and for many states, ELLsin special education comprise an emerging popu-lation.

Despite the scarcity of large-scale data onstudents identified as ELLs in special education,some studies have addressed this issue. In the1980s, the Handicapped Minority Research Insti-tutes in California and Texas reported that Latinochildren of foreign-born parents were more likelyto be identified as disabled, especially whentested in English,, and that the referral and eligi-bility reasons were often related to language pro-ficiency (as cited by Rueda, Artiles, Salazar, &Higareda, 2002). In an analysis of data for 1 1California districts. Rueda and colleagues (2002)found that special education identification of stu-dents identified as ELLs increased beginning infifth grade and became increasingly pronouncedin secondary school. These researchers positedthat the rise was due to decreased language sup-port services as students progressed throughgrades, noting that students with less native lan-guage support were more likely to be served inspecial education. Students identified as ELLswho had lower proficiency in both their nativelanguage and English have also been found tohave the highest rates of identification relative toother students identified as ELLs in select Califor-nia districts (Artiles et al., 2005), with overrepre-sentation noted in the SLD and SLI categoriesand in districts with large ELL populations.

Additionally, results for one urban south-western U.S. district showed that students identi-fied as ELLs were overrepresented in MIMR,SLD, and SLI at rates more than twice that oftheir White peers, and that they were subjected tosubstantially more restrictive placements (Valen-zuela, Copeland, Qi, & Park, 2006). Mostrecently, Samson and Lesaux (2009) found thatELL students were underrepresented in specialeducation in the primary grades but overrepre-sented beginning in tbird grade, consistent withearlier findings. The investigators conjectured that


these patterns were due to lack of services forELLs with disabilities and teacher reluctance torefer ELLs in the primary grades.

A somewhat paradoxical pattern of overrep-resentation and underrepresentation seems toexist in the United States, presumably becauseboth underreferral and overdiagnosis occur be-cause of misunderstanding of the educationalneeds of students identified as ELLs (Case & Tay-lor, 2005), poorly designed language assessments(MacSwan & Rolstad, 2006), and weak psychoe-ducational assessment practices (Figueroa & Neyv-some, 2006). Lack of effective instructionnegatively influences assessment results, which arefurther confounded by the fact that tests designedfor native English speakers may lack reliabilityand validity with students identified as ELLs(Abedi, 2006). For many practitioners, the dis-tinction between emergent English proficiencyand disability can be a difficult one to make(Keller-Allen, 2006); and it is common for lan-guage acquisition to be confused with learningproblems (Artiles & Klingner, 2006). Educationalprofessionals often find it difficult to meet the re-quirements of special education statutes whencompleting cognitive, academic, and behavioralassessments. Such difficulties arise from the lim-ited array of available instruments in most ELLs'native languages, professionals' lack of training inlinguistic and cultural differences, and the short-age of bilingual educators and psychologists(̂ Figueroa & Newsome, 2006).

Another difficulty facing practitioners in dis-tinguishing linguistic difference from disability,particularly a learning disability (LD), is the ten-dency for both students identified as ELLs andstudents identified with LD to perform poorly onacademic tasks with high language demands,which may make ELLs even more vulnerable tomisclassification as having a disability (Abedi,2006). Nonetheless, the large proportion of thesestudents who do struggle academically suggeststhat many of the difficulties are not Ijkely due toeducational disability (Lesaux, 2006). Instead,these students may be inappropriately routed intospecial education as a convenient way to "dosomething" without adequately considering pro-grammatic limitations (e.g., as noted in the early1970s, Mercer, 1973), or when teachers are at aloss about how to provide effective instruction

(Harry & Klingner, 2006). Conversely, studentsmay miss out on needed services and supportswhen educators assume that their difficulties aresolely due to emerging English proficiency (Lim-bos & Ceva, 2001) or when districts only allowfor students to be categorized as either ELL or ashaving a disability, but not as both simultaneously(Lesaux, 2006). The inappropriate identificationof some ELL students has led to high-profile liti-gation regarding discriminatory assessment andplacement (e.g., Diana v. State Board of Educa-tion, 1970, 1973), which in turn has made someschool systems wary of referring students for con-sideration of special education eligibility. Histori-cally, there has been evidence to suggest thatdistricts wait to refer students to special educationbecause of a lack of trained personriel or effectiveprograms for students with disabilities and emerg-ing English proficiency (Campbell, Cersten, &Kolar, 1993). Indeed, the literature suggests thatstudents identified as ELLs begin receiving specialeducation services 2 to 3 years later than the aver-age for students who are English-proficient (Wag-ner, Francis & Morris, 2005), which may beattributable to these factors.

As a result of such delays and difficulties,ELL students are at risk of inappropriate servicesbecause of both misidentification and failure tobe identified. For the former group, the potentialdetrimental effects of special education labels areconcerning; whereas for the latter, the conse-quences of delayed intervention are problematicbecause of the compounded difficulty in over-coming learning problems (Wagner et al., 2005).These issues, coupled with early evidence that stu-dents identified as ELLs make few gains and oftenshow declining performance in special education(Gersten & Woodward, 1994), underscore thenecessity to elucidate this issue and provide soundguidance to educators. This study has examinedpatterns and predictors of special education iden-tification and placement for students identified asELLs in an effort to add to this emerging knowl-edge basé.

THE PRESENT STUDY

Using existing data for students identified as ELLsin special education from a southwestern state.

3 2 O Spring 2011

this study was designed to examine the represen-tation of students identified as ELLs relative totheir White peers over an 8-year period. Thisstudy built on the existing literature by examiningrepresentation by both disability category andtype of placement (i.e., extent of access to generaleducation settings) over time. Further, to shedlight ori the structural factors potentially relatedto patterns of representation, the study examinedthe relationship between the observed patterns ofrepresentation and placement to predictors com-monly studied in the literature on racial dispro-portionality. •

This study contributes to an accumulatingknowledge base concerning the representation ofthis group among students with disabilitiesthrough its use of a sample of identified ELLsfor an entire state. The present analysis allowsfor the study of patterns of representation andplacement—for which our knowledge base is espe-cially limited—at both the aggregated state andlocal education agency levels (referred to through-out as districts) for an entire state. Examining theextent of disproportionality at the various levels ofanalysis is an important first step in understand-ing the forces behind the issue (Bollmer, Bethel,Garrison-Mogren, & Brauen, 2007), and identi-fying potential approaches to correcting disparity.The literature has demonstrated the need to ana-lyze disproportionality at multiple levels (Artileset al., 2005); whereas national aggregates of iden-tification data may suggest that disproportionalityin special education is not an issue for certainpopulations, analyses at the state and district lev-els present a different picture. In particular, state-and district-level analyses of racial disproportion-ality have highlighted the need to examine varia-tions in placement at the local level becauseaggregated analyses can mask important patternsof underrepresentation and overrepresentation.

Four research questions guided this study:

1. To what extent is there a disproportionaterepresentation of students identified as ELLsin special education, focusing on high-inci-dence categories, at the state level over time?

2. To what extent is disproportionality observedat the district level over time?

3. To what extent are students identified asELLs placed in the least restrictive environ-ment at the state and district levels over time?

4. To what extent can one predict disproportion-ate representation of ELLs at the district level,considering certain district characteristics?

METHOD

DATA SOURCE

This study consists of analyses of existing data ondistrict-level general and special education enroll-ment and other data concerning district demo-graphics and resource characteristics availablefrom the state department of education for the1999 to 2006 academic school years to investigatethe extent of disproportionality among studentsidentified as ELLs and the relationships betweendisproportionality and district factors. Annualdata on general and special education enrollmentfor the state and each district were obtained via aresearch agreement with the state.

SAMPLE

Because this study is a secondary analysis of statedata, limitations are inherent in the sample, selec-tion of variables, and analysis. State statute de-fines a student who is an English learner as "achild who does not speak English or whose nativelanguage is not English, and who is not currentlyable to perform ordinary classroom work in En-glish" (Arizona Revised Statute 15-751, 2007). Ofcourse, students identified as ELLs are a heteroge-neous group characterized by a variety of nativelanguages, cultures, races, countries of origin, lan-guage proficiencies, socioeconomic statuses, edu-cational experiences, and time in the UnitedStates (Artiles et al., 2005; Zehler et al., 2003);but by virtue of the available data, this studytreated the ELL students as one group. The studysample included only those districts reporting en-rollment data for students identified as ELLs. Thetotal number of districts reporting data for ELLseach year showed an increasing trend, with thesample accounting for 72% of the state's total stu-dent population in 1999 and more than 87% in2006. In addition, districts were excluded due tolow enrollment {n < 10) of students identified as


TABLE 1

Demographic Characteristics of Sample

Year

1999

2000

2001

2002

2003

2004

2005

2006

Number

of Districts

119

115

113

154

194

214

219

230

Total

Students

707,214

717,745

837,345

881,823

920,349

976,524

978,102

by Academic Year

ELLs n (%)

104,607 (16.68)

115,944(16.39)

128,077(17.84)

144,504 (17.26)

153,453 (17.40)

143,801 (15.62)

131,078 (13.42)

136,783 (13.98)

White n(%)

307,098 (48.97)

361,817(51.16)

353,568 (49.26)

411,073(49.01)

422,924 (48.96)

435,668 (47.38) •

420,055 (43.02)

436,911 (44.67)

Special

Education n (%)

66,785 (10.65)74,147(10.48)77,317 (10.72)92,347(11.03)

108,266 (12.28)119,801 (13.02)127,720 (13.08)133,804 (13.68)

Note. ELL = English language learner.

ELLs because the risk ratio could not be reliablycalculated for such small cell sizes (BoUmer et al.,2007; Skiba et al, 2005). Table 1 shows the gen-eral demographics of the sample.

The disability categories examined in thisstudy include MIMR, SLD, SLI, and ED, basedon state definitions. As previously noted, these arethe high-incidence categories and include the ma-jority of students identified for special education.The small cell sizes in the other categories (e.g.,deafness, orthopedic impairment, etc.) would notpermit the reliable calculation of tbe risk ratio.Placement categories, defined by the state, in-cluded a designation of the amount of time re-moved from the general education environmentfor special education services; percentage of timeremoved did not include possible time removedto receive language support services. State data forthe 2006 to 2007 academic year provided sevenpredictor variables. The availability of such data,as well as previous research findings on dispropor-tionate representation among racial minority stu-dents had suggested these variables (e.g.,Coutinho et al., 2002; Finn, 1982; Parrish, 2002;Skiba et al., 2005). The variables under investiga-tion included the following:

• District enrollment.

• Proportion of district enrollment that wasELL.

• Proportion of enrollment that was racial/ethnic minority.

• Proportion of students receiving free andreduced-price lunch.

• Proportion of teachers that were minorities.

• Proportion of teachers holding English as asecond language (ESL) certification.

• Student-teacher ratio.

With the exception of the proportion of teachersholding ESL certifications, which is posited to belogically related to the treatment of studentsidentified as ELLs in both general and specialeducation, each variable bas been examined inprevious studies of racial disproportionality andhas been found to be predictive of disparities inidentification.

CONTEXT

This study took place in a southwestern state thatenrolled about I.I million students. Studentsidentified as ELLs constituted approximately16% of enrollment; arid students identified asracial minorities comprised 55% of enrollment,with Latinos represeriting the predominant mi-nority group at 39% of enrollment. Most (91%),of the students identified as ELLs spoke Spanish,compared to 75% nationally (Planty et al., 2009).In addition, more than 44% of all students wereeligible for free or reduced-price lunch, and al-most 8% received special education services. Ofthose students receiving special education, 46%were White, 39% were Latino, 7% were NativeAmerican, 7% were African American, and 1%was Asian/Pacific Islander. Previous research con-

Spring20II

ducted on the state indicated that students identi-fied as African American and Native Americanwere overrepresented in special education and insome of the high-incidence categories, and thatLatino students were somewhat more likely thantheir White peers to be identified as having men-tal retardation (Sullivan, 2007).

In 2000, English-only legislation was passedin the state, requiring that all instruction be pro-vided in English and eventually (i.e., the2008-2009 academic year) requiring that stu-dents identified as ELLs be separated into En-glish-immersion classrooms for 4 hr per day.Before this recent legislation, districts employed avariety of language support models (e.g., dual-lan-guage programs, ESL, immersion programs).Scholarly concern regarding the effect of such leg-islation on students' educational experiences andoutcomes has led to a wave of research in thisstate and others with similar statutes (i.e., Califor-nia and Massachusetts; Olson, 2007; Stritikus o¿García, 2005). Although not the focus of this par-ticular study, this analysis allows for considerationof changes in identification rates following theimplementation of the English-only legislation in2001.

In addition, for much of the duration of thisstudy (i.e., the first 6 years from 1999-2004),methods for determining ELL status variedthroughout the state, as districts selected from avariety of instruments and criteria. In 2004, useof the Stanford English Language ProficiencyTest (Harcourt, 2003) was required; later, in col-laboration with Harcourt Assessment, the statedepartment of education developed its own En-glish-proficiency assessment to be used in allschool districts. As MacSwan and Rolstad (2006)reported, however, such tests generally lacksound theoretical bases, understanding of Span-ish, and strong psychometric properties and mayfail to provide valid information about a person'strue language functioning.

DATA ANALYSLS

Disproportionality refers to "the extent to whichmembership in a given . . . group affects the prob-ability of being placed in a specific disability cate-gory" (Oswald, Coutinho, Best, & Singh, 1999,p. 198). In conjunction with this definition, this

study used the relative risk ratio (RRR) to deter-mine ELLs' relative likelihood of identifica-tion/placement compared to White students. Thisgroup (White) was selected as the referent basedon the rationale provided by Artiles and col-leagues (2005). Thus, the referent group was se-lected based on the situation of this study withinbroader concerns for educational equity, in whichWhite students, as opposed to a category thatmight be called "English-proficient students," areoften the implicit or explicit comparison group.Although this comparison may lead to concernsabout the possible confounding of language andrace, within the state in question, ethnic minoritystudents, particularly those identified as Latino,comprise most of the students identified as ELLs,and more than 91% are Spanish-speaking stu-dents of Mexican origin (Center on EducationPolicy, 2007). These data suggest that the risk ofoverlap in the two groups is small.

The risk ratio is an epidemiological statistic,commonly used in analysis of binary outcomes,and is a measure of effect size commonly em-ployed in medical research. Here, the indicator isused to represent ELLs' risk of identifica-tion/placement in a given category compared toWhite students' risk in the same category. Thisstudy used the term relative risk because the effectof the risk factor (e.g., language status) was evalu-ated relative to some referent group (i.e.. Whitestudents), and was therefore not an absolute indi-cator of risk (Mason, Scott, Chapman, & Tu,2000). A positive risk ratio indicated that ELLstatus was associated with an increased likelihoodof special education identification or placementrelative to the comparison group, whereas a nega-tive ratio indicated a decreased likelihood. Therisk for the referent group served as the baselinelevel of risk that may have resulted from othervariables (Mason et al., 2000) outside of languagestatus. This study investigated ELL status as a fac-tor that might have a substantial effect on overallrates of identification at the population level forstudents.

Within the literature, cutoffs for determiningdisproportionality have included 1.2., 1.5 (Skibaet al., 2005), and 2.0 (Parrish, 2002), whereasstate criteria have ranged from 1.0 to more than4.0 (Sullivan, Kozleski, & Smith, 2008). Consis-tent with recommendations from the field, this


study defined the acceptable range of risk ratios asvalues between 0.80 and 1.20 (Kozleski, 2005;Oswald & Coutinho, n.d). Specific delineationsof overrepresentation and underrepresentationhave varied considerably within the research liter-ature and in state and district practice; thus, thisstudy used these criteria to serve as a meaningfulway to quantify and categorize special educationidentification and placement trends, particularlyconcerning equity.

Correlational analyses and multiple linear re-gressions were used to examine the relationshipsbetween the district-level disproportionality andthe predictors chosen. The study used the relativerisk ratios obtained in the initial phase of theanalysis for identification and placement as thedependent variables in the models. All variableswere standardized to meet the assumptions of in-ferential statistics (Skiba et al., 2005). Significancelevels equal to or less than 0.01 were reportedgiven the number of analyses to reduce the risk ofType I error.

R ESU LTS

PATTERNS OE ELL IDENTIFICATION

IN SPECIAL EDUCATION

This study addressed the question regarding theextent of state-level disproportionality in specialeducation for students identified as ELLs by ex-amining identification patterns for the aggregatesample for each year. Table 2 shows these results,as well as information on district-level patterns ofunderrepresentation and overrepresentation, asindicated by the relative risk ratio criteriadescribed previously. The results showed that atthe state level, students identified as ELLs wereincreasingly overrepresented in special educationand in each of the high-incidence categories ofSLD, SLI, and MIMR, as other studies of ELLrepresentation' in special education have shown.Overrepresentation was highest in SLD andMIMR, where the 2006 risk ratios reached 1.82and 1.63, respectively. A high degree of under-representation was persistent for ED.

Both underrepresentation and overrepresen-tation were common in many categories at thedistrict level, although, consistent with rising risk

ratios at the aggregate level, districts were increas-ingly less likely to evidence underrepresentation,as shown by the decreasing proportion of districtswith risk ratios less than 0.80 across most of thecategories examined (see Table 2). The resultsindicated an increasing frequency of overrepresen-tation in special education generally and in thespecific disability categories of SLD and SLI.Despite increasing state-level risk ratios forMIMR identification, the proportion of districtswith overrepresentation in this category decreasedafter 1999 and was less than most of the cate-gories examined, suggesting that the identificationpractices among a small proportion of districtswith high relative risk strongly affects statewiderates of identification.

PATTERNS OE ELL PLACEMENT

IN SPECIAL EDUCATION

The majority of students identified as ELLs whoreceive special education services, approximately51%, spent at least 80% of their time in generaleducation settings. Though patterns varied acrossthe state and district levels (see Table 3), the datasuggested that students identified as ELLs wereless likely to be placed in the very least restrictiveenvironment compared to White students. (Thisleast restrictive placement entailed no removalfrom the general education classroom to receivespecial education services.) However, they werealso less likely than their White peers to be re-moved for most of the school day. Students iden-tified as ELLs were increasingly representedamong those who spent at least part of their dayin separate settings (e.g., resource rooms), thougha downward trend has occurred in the relative riskof removal for the majority of the school day. Thetrend toward increasing placement in special edu-cation settings such as resource rooms is concern-ing given that most special education teacherslack adequate training to work with this popula-tion (Baca & Cervantes, 2004).

PREDICTORS OE DISPROPORTIONALITY

To evaluate how well district characteristics pre-dicted disproportionality in special education, thestudy included multiple regression analyses ineach of the high-incidence disability categoriesand in each of the placement categories. Variance

3 2 4 Spring 2011

TABLE 2

Patterns of State and District Disproportionality in Disability Ldentification: State-Level RelativeRisk Ratios and Percentage of Districts With Relative Risk Ratios Indicative of Overrepresentationand Underrepresentation

RRR and SpecialEducation Category

Special EducationState RRR% Districts <0.80% Districts > 1.20

SLDState RRR% Districts <0.80% Districts > 1.20

MIMRState RRR% Districts <0.80% Districts > 1.20

SLI •

State RRR% Districts <0.80% Districts > 1.20

EDState RRR% Districts <0.80% Districts > 1.20

1999

0.7752.1031.93

1.3042.86

36.97

1.2416.8125.20

0.6936.9714.29

0.1536.130

2000

0.84

57.3926.96

1.01

38.0533.63

1.22

19.3329.57

0.7434.7815.04

0.1836.52

0

2001

0.7753.1023.01

0.9938.9443.36

1.0619.4720.35

0.64

40.7114.16

0.1837.170

2002

0.90

50.6525.97

1.2127.9241.56

1.1915.5818.18

0.9528.5718.83

0.2127.920

2003

0.946.3928.87

1.3529.3845.36

1.3310.3120.10

0.9823.32

19.59

. 0.21

27.830

2004

1.0536.9242.06

1.5322.9052.34

1.4934.1121.50 .

1.1420.01

27.57

0.2226.64

0

2005

. 1.1328.77 •

46.57

1.7221.0058.90

1.42

31.0522.83

1.22

30.5933.33

0.10

23.290.43

2006

1.1927.3942.31

1.8220.0056.96

1.6337.8316.96

1.3030.3032.61

0.1224.78

0

Note. RRR = relative risk ratio of overrepresentation or underrepresentation; SLD = specific learning disability;MIMR = mild mental retardation; SLI = speech-language impairment; ED = emotional disability.

inflation factors (VIFs) were calculated for eachpredictor to evaluate multicoUinearity; all VIFswere of acceptable values (< 4), indicating thatmulticoUinearity was not a concern (Cohen,Cohen, West, & Aiken, 2003). As previous re-search has demonstrated (e.g., Oswald et al.,1999, 2001; Skiba et al., 2005), the predictors ofdisproportionality varied by the categories studied(see Table 4). As discussed previously, this studyincluded certain predictors because previous re-search had shown these factors to be related to theoverrepresentation of racial/ethnic minorities inspecial education, but the linear models were gen-erally weak with small effect sizes and inconsistentpredictors of the identification and placement ofstudents identified as ELLs.

The predictive power of district characteris-tics examined in this study was strongest for over-

all special education identification, F{J, 134) =3.58, jO < .001, and placement with minimal re-moval (i.e., less than 2 1 % of the school day) fromthe general education environment, F{7, 135) =2.59,/» < .01. Districts with higher proportions ofstudents identified as ELLs were less likely to havedisproportionality in special education generally,SLD, or SLI, which differs from earlier researchthat showed this to be predictive of overrepresen-tation (Artiles et al , 2005; Finn, 1982), but con-firms the findings of Zehler and colleagues(2003). Districts with high proportions of teach-ers with ESL certification were more likely toplace students identified as ELLs in the leastrestrictive environment. Additionally, poverty, asindicated by the proportion of students receivingfree or reduced-price lunch, was inversely relatedto disproportionality in SLI.


TABLE 3

Patterns of State and Districts Disproportionality in Special Education Placements^: State-Level Relative

Risk Ratios and Percentage of Districts With Relative Risk Ratios Indicative of Overrepresentation and

Underrepresentation

Placement Category 1999 2000 2001 2002 2003 2004 2005 2006

General class with supplemental aids/servicesState RRR 1.06 1.02% Districts <0.80 35.29 39.13% Districts > 1.20 9.24 6.09

Outside general class less than 21% of the dayState RRR 0.96 0.95% Districts <0.80 43.70 48.70% Districts > 1.20 18.49 27.83

Outside general class for at least 21% but not more than 60%

State RRR 1.17 1.19% Districts <0.80 23.37 19.13% Districts > 1.20 46.22 59.91

Outside general class for more than 60% of the dayState RRR 0.93 0.97% Districts <0.80 38.67 32.17% Districts > 1.20 23.53 31.30

0.6245.13

2.65

0.9342.4824.78

than 60%1.34

18.5851.33

0.8738.0523.01

0.7135.06

1.95

1.02

32.4724.68

of the day1.41

20.7844.81

0.6051.309.74

0.5534.02

3.09

1.04

28.8727.32

1.4225.2641.75

0.6143.30

7.73

0.5228.04

3.27

1.0329.44

19.63

1.46

15.8947.66

0.5941.59

6.07

0.7731.51

3.56

1.0423.88

20.55

1.4816.4461.78

0.6048.40

4.11

0.8028.26

3.91

1.0221.3018.70

1.4915.2239.13

0.6044.7813.48

Note. RRR = relative risk ratio of overrepresentation or underrepresentation."Placements indicate time removed from general education ro special education settings. Time spent outside ofgeneral education settings for separate language supports is not included.

D I S C U S S I O N

The disproportionate representation of CLD stu-dents in special education has been a persistentproblem, but limited research exists pertaining tostudents identified as ELLs. This study examinedpatterns and predictors of special education iden-tification and placement of this group. The resultssuggest that students identified as ELLs are in-creasingly overrepresented in special education inthis state. Whereas generalizability may be limitedgiven certain contextual factors (e.g., state demo-graphics and English-only legislation), this studyextends the findings of previous research thatfound disproportionality among ELLs in selectdistricts (Artiles et al., 2005; Valenzuela et al.,2006) and in special education generally (Samson& Lesaux, 2009). It also highlights the impor-tance of analyzing data at multiple levels and sug-gests the need for further research.

Researchers have often conceptualized dis-proportionality along racial lines, with importantpolicy implications (e.g., federal requirements for

state monitoring); however, issues of ELLs aretypically absent from these conversations. Thepresent results highlight the importance of con-sidering not only race, but also language, in thediscourse regarding special education equity.Whereas placements of ELLs in special educationare often predicated on an understanding of thecase of Latino students, the data presented herepoint to the need to look at ELL status specifi-cally. Although researchers have not identifieddisproportionality among Latino students asproblematic nationally, or in this particular state,disproportionality is problematic in some states,because of frequent underrepresentation. The pre-sent data for students identified as ELLs present adifferent picture, with overrepresentation demon-strated in the high-incidence categories of SLD,MIMR, and SLI as indicated by elevated risk ra-tios at the state level (e.g., 1.82, 1.63, and 1.30 in2006, respectively) and in 20% to 37% of dis-tricts, even though overall special education iden-tification did not indicate elevated risk for any of

3 2 6 Spring 2011

TABLE 4

Predictors of Identification and Placement for Special Education: Correlations (r) and StandardizedCoefficients (b)

All Disabilities SLD SLI MIMR ED

Predictor R

District sizeStudent-teacher ratio

% Teachers ESL-eert.

% Teaehers CLD

% ELL students

% minority students

% free lunch

Adjusted R^

P

-0.13-0.16

0.06

-0.08

-0.21*

-0.14

0.07

0.

0.

-0.02-0.06

0.15

0.09

-0.38

-0.26

0.10

11

12

0.12-0.12

0.03

-0.10

-0.14*

-0.13

0.07

0

-0.03

0.10

0.02

-0.22

-0.28

0.14

0.05

0.05

• 0.11

0.09

-0.12

-0.09

-0.19

-0.15

-0.29

0.03

0.05

-0.06

0.07

* -0.10

0.10

* -0.31

0.05

0.05

0.25

0.16

-0.01

-0.01

-0.06

-0.03

-0.16

* 0.22

0.10

0.10

0.20

0.190.21

0.18

0.03

0.03

0.01

0.30*

0

-0.07

-0.10

-0.16

-0.12

0.04

0.32

0.07

0.24

-0.01

-0.40

0.10

0.01

0,,01 .

General Class Removed <21 % Removed21%-60% Removed >60%

B

Distriet sizeStudent-teaeher ratio

% Teachers ESL-eert.

% Teachers CLD

% ELL students

% minority students

% free luneh

Adjusted R^

f

-0.10

0.29

0.09

0.02

0.05

0.15

0.19

-0.07

0.33

-0.01

-0.20

-0.18

0.31

0.22

0.09

0.10

0.01

-0.06

0.30*

-0.04

-0.05

-0.05

-0.03

0.08

0.09

0.01

-0.06

0.36

0.04

-0.10

-0.11

-0.01

0.05-0.01

-0.01

.-0.06

-0.01

-0.02

0.04

0.090.02

-0.01

-0.10

-0.05

-0.04

0.19

-0.03

0

0.09

0.12

-0.04

0.13

0.06

0.08

-0.06

0.04

0.08

-0.050.14

0.06

0.08

-0.20

0

0

Note. SLD = speeifie learning disability; SLI = speeeh-language impairment; MIMR = mild mental retardation; ED= emotional disability; ESL-eert. = English-as-a-seeond-language eertified; CLD = eulturally and linguistieally di-verse; ELL = English language learner. The eategory "free lunch" includes students qualifying for free orreduced-priee luneh.

the years examined at the state level. In the SLIcategory in particular, students identified as ELLswent from beirig 30% less likely to be identifiedin 1999 to 30% more likely in 2006. Moreover,in the SLD and MIMR categories, students wentfrom being 24% to 30% more likely to be identi-fied in 1999 to 82% and 63% more likely in2006, respectively. Of note is that students identi-fied as ELLs were rarely identified as ED. Severalscholars have noted the low rate of ED identifica-tion for all students relative to the incidence ofchildhood psychiatric disorders (Osher et al.,2004) and called for greater attention to emo-tional and behavioral difficulties in schools. Theseresults differ from earlier research, which found

students identified as ELLs to be overrepresentedin all of the high-incidence categories, includingED (Valenzuela et al., 2006), although this re-search examined identification in a single districtonly.

These results also suggest that the district fac-tors predicting disproportionality of ELLs differfrom those predicting disproportionality of racialminority students. Whereas research on the struc-tural factors that predict racial disproportionalityhave suggested the importance of student andteacher demographics and resource availability(e.g., human resources, teacher training, andcommunity poverty), these factors were generallyweak predictors of disproportionality among

Exceptional Children

ELLs, pointing to the need to explore other fac-tors that may be related to this problem.

CONTEXT AND LIMITATIONS

This study, coupled with emerging research thatindicates that many ELLs may be inappropriatelyidentified for special education without adequateconsideration of disability eligibility criteria or theinfluence of cultural, linguistic, and experientialfactors (e.g., Liu, Ortiz, Wilkirison, Robertson, &Kushner, 2008; Wilkinson, Ortiz, Robertson, &Kushner, 2006), is a cause for concern. However,one limitation of this type of work is thatalthough it is illuminating to examine disparitiesin patterns of identification and placement, theseanalyses do not address whether the identificationand services provided are valid in general wheredisproportionality is observed, or where parity isdemonstrated (i.e., risk ratios in the acceptablerange). It is inappropriate to assume that dispro-portionality alone, or the lack thereof, is indica-tive of the adequacy of the practices occurring ineither general or special education. Ultimately, itis the validity of the practices that is the criterionfor determining the appropriateness of identifica-tion and the services provided to any student orgroup of students (Rueda & Windmueüer, 2006).Disproportionality is problematic because of thepossibility that students are receiving inappropri-ate labels and services. If the validity of educa-tional decisions can be ensured, relative risk ofidentification for special education eligibilitywould be less of a concern because the assump-tion that students were receiving inappropriateservices would be bypassed.

A primary limitation of this study is its re-liance on district data from a state department ofeducation, which includes aggregation of dataacross schools and districts and inconsistent re-porting, and does not allow a more fine-grainedanalysis (e.g., disentangling country-of-origin, lin-guistic status, and race). In addition, state anddistrict instruments used to identify ELLs variedthroughout the years sampled, and it is notknown if this infiuenced special education identi-fication by influencing the students identified asELLs. The sample included only public districtsreporting enrollment data for both students idenrtified as ELLs and those identified as White,

which was not rriandated by the state. Conse-quently, there was some degree of variation in thedistricts included in the sample from year to year,and the sample only captured a proportion of alldistricts and students in the state, ranging from72% in 1999 to 87% in 2006. Risk ratios couldbe calculated only for districts in which studentsidentified as White were identified in the targetcategory because their risk served as the denorni-nator in the risk ratio. Such risk ratios were notobtained for every district. For instance, the over-all proportion of districts identifying any studentsas ED is low compared to the other high-inci-dence categories, and eyen fewer identify studentsidentified as ELLs as ED. It is possible that thedistricts that failed to report this data may havediffered in some substantive way from the dis-tricts reporting.

If the validity of educational

decisions can be ensured, relative risk

of identification for special education

eligibility would be less of a concern

because the assumption that students

were receiving inappropriate

services would be bypassed.

These factors may be related to tbe variabilityin risk ratios in some way and may lead to an in-accurate representation of the relationships be-tween the outcomes and predictors examinedhere. Because these data are reflective of a singlestate, generalizability rriay be limited by character-istics of the region and the influence of state poli-cies (e.g., English-only legislation). Here, it ispossible that the state's English-only legislationmight have affected special education rates forstudents identified as ELLs; such a possibility issuggested by the year-to-year changes iri relativerisk although causality cannot be shown. Giventbat nearly a third of students identified as ELLsare affected by such policies, it is critical for thisresearch to take place. As noted in earlier work(e.g., Zehler et al., 2003), considerable variabilityexists in special education identification for thispopulation.

3 2 8 Spring 2011

Although this study could not eliminate thepossibility of overlap between White and ELLstudents, it is probably negligible given the racialand linguistic makeup of the state's ELL popula-tion. Considering that the majority of the state'sELLs speak Spanish and are reported to be ofMexican nationality, most are also likely to beethnic minorities; and only a small percentagewould be classified as both ELL and White. Inthe interest of using White as the comparisongroup, following the rationale of Artiles and col-leagues (2005) and because the assumptions be-hirid the discourse arourid disproportionaterepresentation of culturally and linguistically di-verse students are grounded in implicit arid ex-plicit comparisons to White students' educationalopportunities and outcomes (Artiles & Bal,2008), this limitation was deemed reasonable andconsistent with the theoretical grounding of theresearch.

Nevertheless, the state's educational system,with its high proportion of students identified asELLs and its unique English-only policy, providesa rich context for the study of disproportionalityfor this group. Moreover, earljer studies examinedonly select districts in a few states (Artiles et al.,2005; Valenzuela et al., 2006), whereas this studyexamines patterns across multiple districts in astate. This study is an important next step in theresearch given that this type of work is con-strained by the fact that many educational agen-cies do not have the data management systems inplace to support such research, and ELL statuscahnot be further disaggregated by race/ethhicity,native language, language proficiency levels, levelof support, and other factors, to better under-stand variations in the educational experiences ofstudents ideritified as ELLs. There is a need to ex-amine how specific characteristics (e.g., languageproficiency) and experiences (e.g., enrollment inbilingual versus English-only programming) in-fiuence patterns of special education identifica-tion, which was impossible with the present data.

IMPLICATIONS EOR RESEARCH

AND PRACTICE

Tbe Need to Examine Patterns at MultipleLevels. This study is important given the limitedresearch on this particular topic, and it empha-

sizes the need' for future studies regarding treat-ment and outcomes for students identified asELLs in special education. The need for futureanalyses of representation at the various levels(e.g., national, state, district, school) is under-scored by the results, as patterns differ from onelevel to another. Analysis at only one level can ob-scure or overlook problematic trends at others. Inthis study, overrepresentation in special educationdid not appear to be particularly problematic atthe state level, but district-level analysis showedthat an increasingly large proportion of districtshad risk ratios indicative of disparity. What ismore, state-level ñgui-es can mask the muchhigher risk ratios in many districts throughout thestate. Ediicational administrators should analyzeidentification and placement data at all levels oftheir systems, categories, and placement options,and for students identified as ELLs, as well asracial minorities, to identify potential problemswithin general and special education. Such analy-sis is not mandated, but it can provide valuabledata to inform consideration of systemic or pi'o-grammatic capacity to meet the educational needsof CLD students.

Examination of disproportionality data iscritical because it allows researchers and practi-tioners to establish baselines and methods formonitoring progress in efforts geared towards re-ducing disparity, in addition to supporting sys-tems change efforts (Skiba et al., 2008). Theprimary method used in this study, the relativerisk ratio, is common in the disproportionality lit-erature and in state practices for meeting the re-quirements of special education law (Sullivan etal., 2008). Comparability in the methodologyused in different contexts and at various levels ofanalysis will allow for comparison of findings,something that has been difficult because of therange of calculations used in the past. Educatorsshould select methods and criteria that allow forearnest consideration of equity concerns involvedin disproportionality.

Consideration of the Multitude of Factors Influ-encing Special Education Patterns. Given the highproportion of ELLs who struggle academically, itis unlikely that most have educational disabilities;instead, it is necessary to effectively distinguishthe sources of students' difficulties by examiningthe interaction among structural forces, learning


conditions, and learner characteristics (Lesaux,2006). It is necessary to investigate the extent towhich language support, preservice training, andprofessional development contribute to identifica-tion and placement and patterns of dispropor-tionality at various levels of educational systems,given the potential effect of these factors on stu-dents' educational experiences.

For instance, Keller-Allen (2006) reportedthat ELL overrepresentation was associated withdecreased language support, as noted by Artilesand colleagues (2005). These findings support theassumption that special education may be inap-propriately used to remedy the decreased supportcreated by the lack of language programming, inthat increasing relative risk was found followingthe passage of English-only legislation. Given thatmost teachers feel unprepared to teach ELLs (Na-tional Center for Educational Statistics, 2000),such a trend is not surprising. The transition frombilingual programs to general education classes isespecially problematic for students identified asELLs because they are likely to be taught by inex-perienced teachers, are prone to experience de-clines in academic performance, and are morelikely to be referred to special education (Case &Taylor, 2005; Harry & Klingner, 2006). Educa-tors must be vigilant against using special educa-tion as a fall-back option when appropriatelanguage support, instruction, and curriculum arenot provided; such use of services is not the intentof special education and can be detrimental to thestudents. Instead, educators need to explore pro-grammatic changes.

Further research is needed to understand theiinplications of educational policies, such as En-glish-only educational policies and federal legisla-tion, for the special education identification andplacement of students identified as ELLs. In par-tictilar, how do such policies affect school climateand the decision-making processes and outcomesthat result in special education eligibility? Doesthe restricted range of educational options for stu-dents identified as ELLs conti-ibùte to spécial edu-cation referrals? How do the assumptions aboutlanguage acquisition and learning inherent in thelegislation contribute to the potential misidentifi-cation of ELLs as having a disability through themediation of instructional, referral, and assess-ment decisions?

Additional analyses are needed to understandthe factors that contribute i:o patterns of dispro-portionality. More sophisticated methodologiesand research designs are needed to understand therelationships and interactions among student fac-toi-s, practices, local contextual and structural fac-tors, and systemic factors as they contribute todisparate opportunities for and treatment of stu-dents identified as ELLs, and how this contributesto inappropriate identification and placement inspecial education. Disproportionality is a corriplexproblem that must be approached as such. Givenits basis in long-standing inequity, and its roots incultuiral and institutional practices, the multidi-mensionality of this problem is to be expected.Research must attehd to the factors in general ed-ucation systems that contribute to disproportion-ality, as it is not a problem inherent in the spécialeducation system, but rather it is a product of ed-ucation as a broader cultural practice.

The findings in this study, as well as the liter-ature documenting flaws in thé referral and iden-tification processes (e.g., Harry & Klingner,2006), ihdicate a pressing need to examine profes-sional practices affecting these students. Policiesshould ensure that all students have access to cul-turally and linguistically appropriate pedagogyand curricula that support their academic devel-opment, including access to evidence-based prac-tices in instruction, intervention, and assessment(Osher et al., 2004). To the extent that this is nothappening, the repercussions for disproportional-ity must be explored.

It will be beneficial to examine the potentialrelationships between various facets of universalpreventative efforts, instruction, curriculum, pre-referral interventions, psychoeducational evalua-tions, and eligibility determination processes asthey relate to patterns of overrepresentation andunderrepresentation for students identified asELLs in special education, particularly given in-creased emphasis on response to interventionmodels as a means of reducing disproportionality(e.g., Lesaux, 2006; Lirian-Thompsoh, Cirino, &Vaughn, 2007). Researchers have yet to explorethe influence bf systemic factors, such as qualityof curriculum and instruction, availability of pro-gramming and resources, and teacher training, asthey may relate to differential rates of referral and

3 3 O Spring 20II

identification for special education (Skiba et al.,2008).

In sum, this study highlighted the need forfuture research examining the disproportionalityof students identified as ELLs. The persistent dis-proportionate representation of students who areCLD in special education is a complex problemthat is related to the construction of difference,educational opportunity, and local context of pol-icy and practice. For many students, special edu-cation identification and placement is appropriateand indeed necessary; however, for others, it maybe the result of factors outside the student andunrelated to the presence of disability per se. Evi-dence of disproportionality should be treated asindicative of underlying problems across andwithin multiple levels of the system (Rueda &Windmueller, 2006). Where data reveal poten-tially problematic patterns of identification andplacement, researchers should consider contextualand systemic factors as potentially contributing tothe misidentification of students.

R E F E R E N C E S

Abedi, J. (2006). Psychometric issues in the ELL assess-ment and special education eligihility. Teachers CollegeRecord 108, 2282-2303. doi: 10.1111/j.1467-9620.2006.00782.x

Arizona Revised Statute, 15-751 (2007).

Artiles, A. J., & Bal., A. (2008). The next generation ofdisproportionaliry research: Toward a comparativemodel in the study of equity in ability differences. 77?̂Journal of Special Education, 42, 4-14.

Artiles, A. J., & Klingner, J. K. (2006). Forging aknowledge hase on English language learners with spe-cial needs: Theoretical, population, and technicalissues. Teachers College Record, 108, 2187-2194. doi:10.1111/j. 1467-9620.2006.00778.X

Artiles, A. J., & Ortiz, A. (Eds.). (2002). Englishlanguage learners with special needs: Identification, place-

ment, and instruction. Washington, DC: Center for Ap-plied Linguistics, doi: 10.1177/002221948401701014

Artiles, A. J., Rueda, R., Salazar, J. J., & Higareda, I.(2005). Within-group diversity in minority dispropor-tionate representation: English language learners inurban school districts. Exceptional Children, 71,283-300.

Baca, L., Si Cervantes, H. (2004). The bilingual specialeducation interface. Columbus, OH: Merrill.

Blanchett, W. (2006). Disproportionate representationof African American students in special education: Ac-knowledging the role of White privilege and racism.Educational Researcher, 35, 24-28. doi: 10.3102/0013189X035006024

Bollmer, J., Bethel, J., Garrison-Mogren, R., & Brauen,M. (2007). Using the risk ratio to assess racial/ethnicdisproportionality in special education at the school-district level./o«r«a/o/^5/)í«fl/fi^«c'flí/o«, 4l, 186—198.doi: 10.1177/00224669070410030401

Brayboy, B. M. J., Castagno, A. E., & Maughan, E.(2007). Equality and justice for all? Examining race ineducation scholarship. Review of Research in Education,31, 159-194. doi: 10.3102/0091732X07300045

Camphell, J., Gersten, R., & Kolar, C. (1993). Qualityof instruction provided to language minority students withlearning disabilities: Five findings from microethnogra-phies (Technical Report 93-5). Eugene,'OR: EugeneResearch Institute.

Case, R. E., & Taylor, S. S. (2005). Language differ-ence or learning disability? Answers from a linguisticperspective. The Clearinghouse, 78, 127-130.

Center on Education Policy. (2007). Caught in the mid-dle: Arizona's English language learners and the highschool exit exam. Washington, DC: Author.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S.(2003). Applied multiple regression/correlation analysis forthe behavioral sciences (3rd ed.). Mahwah, NJ: LawrenceErlbaum.

Coutinho, M. J., Oswald, D. P, & Best, A. M. (2002).The influence of .sociodemographic and gender on thedisproportionate identification of minority students ashaving learning disabilities. Remedial and Special Edu-cation, 23, 49-59. doi: 10.1177/074193250202300107

Diana v. California State Board of Education. Civ. Act.No. C-70-37 (N.D. Cal., 1970, 1973).

Donovan, M. S., & Cross, C. T. (Eds.). (2002). Minor-ity students in special andgified education. Washington,DC: National Academies Press.

Dunn, L. M. (1968). Special education for the mildlyretarded: Is much of it justifiable? Exceptional Children,35, 5-22.

Duran, R. P. (2008). Assessing English-language learn-ers' achievement. Review of Research in Education, 32,292-327. doi: 10.3102/0091732X07309372

Figueroa, R. A., & Newsome, P. (2006). The diagnosisof LD in English learners: Is it nondiscriminatory?Journal of Learning Disabilities, 39, 206-214.doi: 10.1177/00222194060390030201


Finn, J. D. (1982). Patterns in special education place-ment as revealed by the OCR surveys. In K. A. Heller,W. H. Holtzman, Si S. Messiek (Eds.), Placing childrenin special education: A strategy for equity (pp.322-381).Washington, DC: National Aeademy Ptess.

Genesee, F., Lindholm-Leary, K., Saunders, W, SiChristian, D. (2005). English language learners in U.S.sehools: An overview of researeh findings. Journal ofEducation for Students Placed at Risk, 10, 363-385.doi: 10.1207/sl5327671esprl004_2

Gersten, R., Si Woodward, J. (1994). The language-minority student and special education: Issues, trends,and paradoxes. Exceptional Children, 60, 310-322.

Hareourt. (2003). Stanford English Language ProficiencyTest. San Antonio: Author.

Harry, B., Si Klingner, J. K. (2006). Why are so manyminority students in special education? Understandingrace and disability in schools. New York, NY: TeaehersCollege Press.

Heller, K. A.,'Holtzman, W. H., Si Messiek, S. (Eds.).(1982). Placing children in special education: A strategyfor equity. Washington, DC: National Aeademy Press.

Hopstoek, P J., Si Stephenson, T. D. (2003). Analysisof Office for Civil Rights data related to LEP students.Washington, DC: Development Assoeiates.

Hosp, J. L., Si Resehly, D. J. (2004). Disproportionaterepresentation of minority students in speeial eduea-tion: Aeademie, démographie, and eeonomie predie-tots. Exceptional Children, 70, 185-199.

Keller-Allen, C. (2006). English language learners withdisabilities: Identification and other state policies andissues. Alexandria, VA: Project Forum.

Klingner, J. K., Artiles, A. J., Kozleski, E., Harry, B.,Zion, S., Täte, W, . . . Riley, D. (2005). Addressing thedisproportionate representation of eulturally and lin-guistieally diverse students in speeial edueation througheulturally responsive edueational systems. EducationPolicy Analysis Archives, 73(38). Retrieved fromhttp://epaa.asu.edu/epaa/vl3n38.

Klingner, J. K., Artiles, A. J., Si Mendez-Barletta, L.(2006). English language learners who struggle withreading: Language acquisition or LD? Journal ofLearning Disabilities, 39, 108-128. doi: 10.1177/00222194060390020101

Kozleski, E. (2005, March). The disproportionate repre-sentation of culturally and linguistically diverse students inspecial education. Paper presented at the Offiee of Spe-eial Edueation Programs' Annual Leadership Confer-enee, Washington, DC.

Lesaux, N. K. (2006). Building eonsensus: Future di-reetioh for researeh on English language learners at risk

for learning disabilities. Teachers College Record, 108,2406-2438. doi: 10.1111/j.l467-9620.2006.00787.x

Limbos, M. M., Si Geva, E. (2001). Accuracy ofteacher assessments of second-language students at riskfor reading disability. Journal of Learning Disabilities,34, 136-151. doi: 10.1177/002221940103400204

Linan-Thompson, S., Cirino, P. T., Si Vaughn, S.(2007). Determining English language learners'response to intervention: Questions and some answers.Learning Disabilities Quarterly, 30, 185-195.

Liu, Y, Ortiz, A. A., Wilkinson, C. Y, Robertson, P,Si Kushner, M. 1. (2008). From early childhood specialeducation to special education resource rooms: Identifi-cation, assessment, and eligibility determinations forEnglish language learners with reading-related disabili-ties. Assessment for Effective Intervention, 33, \77-l87.doi: 10.1177/1534508407313247MaeSwan, J., Si Rolstad, K. (2006). How languageprofieieney tests mislead us about ability: Implieationsfor English language learner plaeement in special edu-eation. Teachers College Record, 108, 2304-2328.doi: 10.1111/J.1467-9620.2006.00783.X

Mason, C. A., Seott, K. G., Chapman, D. A., &c Tu, S.(2000). A review of some individual- and eommunity-level effeet size indiees foi- the study of risk faetors forehild and adoleseent development. Educational andPsychological Measurement, 60, 385-410. doi: 10.1177/00131640021970619

Mereer, J. R, (1973). Labeling the mentally retarded.Berkeley, CA: University of California Press.

National Center for Educational Statisties. (2000). Fastresponse survey system: Survey on professional developmentand training in U.S. public schools, 1999-2000. Wash-ington, DC: Author.

Olson, K. (2007). Lost opportunities to learn: The ef-feets of edueation poliey on primary language instrue-tion for English learners. Linguistics and Education, 18,121-141. doi: 10.1016/j.linged.2007.07.001

Osher, D., Cartledge, G., Oswald, D., Suthedand, K.S., Ardles, A. J., Si Goudnho, M. (2004). Cultural andlinguistie eompeteney and disproportionate representa-tion. In R. B. Rutherford, M. M. Quinn, Si S. R.Mathur (Eds.), Handbook of research in emotional andbehavioral disorders (pp. 54—77). New York, NY: Guil-ford Press.

Oswald, D. P, Si Coutinho, M. J. (n.d.). Why itmatters: What is disproportionate representation? Re-trieved from http://www.ealstat.org/podeasts/pdfs/disproportionate_rep.pdf

Oswald, D. P, Coutinho, M. J., Best, A. L., SiNguyen, N. (2001). Impaet of soeiodemographic

3 3 2 Spring 2011

characteristics on the identification rates of minoritystudents as having mental retardation. Mental Retarda-tion, 39, ?>5\-'5G7.

Oswald, D. P, Coutinho, M. J., Best, A. M., & Singh,N. N. (1999). Ethnic representation in special educa-tion: The influence of school-related economic and de-mographic variables. The journal of Special Education,32, 194-206. doi: 10.1177/002246699903200401

Parrish, T. (2002). Racial disparities in the identifica-tion, funding, and provision of special education. In D.J. Losen & G. Orfield (Eds.), Racial inequity in specialeducation (pp. 15-38). Cambridge, MA: Harvard Edu-cation Press.

Planty, M., Hussar, W, Snyder, T., Kena, G., KewalRa-mani. A., Kemp, J., . . . Dinkes, R. (2009). The condi-tion of education 2009 (NCES 2009-081). Washington,DC: National Center for Education Statistics, Instituteof Education Sciences, U.S. Department of Education..

Rueda, R., Artiles, A. J., Salazar, J., 0¿ Higareda, I.(2002). An analysis of special education as a responseto the diminished academic achievement of Chi-cano/Latino students: An update. In R. R. Valencia(Ed.), Chicano school failure and success: Past, present,and future (2nd ed., pp. 149-169). New York, NY:RoudedgeFalmer.

Rueda, R, & Windmueller, M. P (2006). English lan-guage learners, LD, and overrepresentation: A multi-level analysis, journal of Learning Disabilities, 39,99-107. doi: 10.1177/00222194060390020801

Samson, J. F., & Lesaux, N. K. (2009). Language-minority learners in special education: Rates andpredictors of identification for services, journalof Learning Disabilities, 42, 148-162. doi: 10.1177/0022219408326221

Serwatka, T. S., Peering, S., & Grant, P (1995). Dis-proportionate representation of African Americans inemotionally handicapped classes, journal of AfiicanAmerican Studies, 25, 492-506.

Skiba, R. J., Michael, R. S., Nardo, A. C , & Peterson,R. L. (2002). The color of discipline: Sources ofracial and gender disproportionality in schoolpunishment. Urban Review, 34, 317—342. doi: 10.1023/A: 1021320817372

Skiba, R. J., Poloni-Staudinger, L., Simmons, A. B.,Feggins-Azziz, R., & Chung, C. (2005). Unprovenlinks: Can poverty explain ethnic disproportionality inspecial education? journal of Special Education, 39,130-144.

Skiba, R. J., Simmons, A. B., Ritter, S., Gibb, A. C ,Rauch, M. K., Cuadrado, J., & Chung, C. (2008).Achieving equity in' special education: History, status.

and current challenges. Exceptional Children, 74,

264-288. doi: 10.1177/00224669050390030101

Stritikus, T. T.,&; Garcia, E. (2005). Revisiting thebilingual debate from the perspectives of parents: Pol-icy, practice, and matches or mismatches. Educational

Policy, 19, 729-744. doi: 10.1177/0895904805278068

Suárez-Orozco, M., Roos, P. M., & Suárez-Orozco, C.(2000). Culture, education, and legal perspective onimmigration: Implications for school reform. In J. P.Heubert (Ed.), Law and school reform: Six strategies forpromoting educational equity (pp. 160-204). NewHaven, CT: Yale University Press.

Sullivan, A. (2007, August 17). Factors related to minor-ity disproportionality in special education. Paper pre-sented at the American Psychological AssociationAnnual Convention, San Francisco, CA.

Sullivan, A., Kozleski, E. B., & Smith, A. (2008,March). Understanding the current context of minoritydisproportionality in special education: Federal response,state activities, and implications for technical assistance.Paper presented at the American Educational ResearchAssociation Annual Meeting, New York, NY.

U.S. Department of Education. (2009). 28th annualreport to Congress on the implementation of the Individu-als with Disabilities Education Act. Washington, DC:Office of Special Education Programs.

Valenzuela, J. S., Copeland, S. R., Qi, C. H., & Park,M. (2006). Examining educational equity: Revisitingthe disproporrionate representation of minority studentsin special education. Exceptional Children, 72, 425—441.

Wagner, R, Francis, D. J., & Morris, R. D. (2005).Identifying English language learners with learningdisabilities: Challenges and possible approaches. Learn-ing Disabilities Research & Practice, 20, 6—15.doi: 10.1111/j.1540-5826.2005.00115.x

Wilkinson, C. Y., Ortiz, A. A., Robertson, P M.,& Kushner, M. I. (2006). English language learnerswith reading-related LD: Linking data from multiplesources to make eligibility determinations, journalof Learning Disabilities, 39, 129-141. doi: 10.1177/00222194060390020201

Zehler, A., Fleischman, H., Hopstock, P. , Stephenson,T, Pendzick, M., Ô£ Sapru, S. (2003). Descriptive studyof services to LEP students and LEP students with disabil-ities: Policy report: Summary of findings related to LEPand SPED-LEP students. Washington, DC: Develop-ment Associates.


" ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ " " " • " " " " " ' " ^ Address correspondence concerning this article to

ABOUT THE AUTHOR Amanda L. SuUivan, P.O. Box 871811, Tempe,AZ 85287-1811 (e-mail: Amanda.L.SuUivan

AMANDA L. SULLIVAN (AHzona CEC), Assis- @asu.edu).tant Professor, School Psychology Program, Divi-sion of Educational Leadership & Innovation, ^^^^"'^^ ' " ^ ' " ' " " ' ^ ^^'°^'' ^009; acceptedMary Lou Fulton Institute & Graduate School ofEducation, Arizona State University, Tempe.

The author acknowledges the support of theAmerican Educational Research AssociationMinority Dissertation Fellowship. The Associa-tion's endorsement of the ideas expressed in thisarticle should not be inferred.

3 3 4 Spring 2011

Copyright of Exceptional Children is the property of Council for Exceptional Children and its content may not

be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written

permission. However, users may print, download, or email articles for individual use.

Documents

Disproportionality in Special Education Identification and