sources of disproportionality in special education: tracking minority representation through the...

Sources of Disproportionality in Special Education: Tracking Minority Representation through the Referral-to-Eligibility Process

Ashley Gibb M. Karega Rausch Russell Skiba Indiana Disproportionality ProjectIndiana University

National Center for Culturally Responsive Educational SystemsFebruary 17, 2006

Overview History Rationale Referral-to-Eligibility Ratio Preliminary Data Challenges in Assessing the

Referral Process

The Indiana Disproportionality Project (IDP) Collaboration of IDOE and Center for

Evaluation and Education Policy at Indiana University Document status of minority

disproportionality in Indiana Use that information to guide change

planning

Project History and Timeline Phase I (1999-2000):

Developing Measures of Disproportionality Phase II (2000-2001):

Understanding What Contributes to Special Ed. Disproportionality

Phase III (2002-Present): Addressing Disproportionality in Local

School Corporations and Addressing Key Research Questions

Findings: Years One and Two Statewide: African American most

severe Mild Mental Disability 3.29 x more Emotional Disturbance 2.38 x more Moderate MD 1.91 x more Communication Disorder 35% less Learning Disabled 6% less

AA underrepresented in LRE Disproportionality not uniformly

distributed

Beyond the Numbers: Where Does It Come From and What Should We Do?To remediate we first have to

understand Literature review of causes – e.g.

National Research Council, Harvard Civil Rights Project

IDP Qualitative Study LEAD Projects in ten corporations

How Do We Measure Progress? Conversation in district

How do we monitor progress? The problem of short term change in

disproportionality.

Solution: Examine representation at various points in the decision-making process

Exploration of Referral to Eligibility

Rationale

The Contribution of the Special Ed. Process NRC (2002) unable to draw firm conclusion High percentage of students referred are

placed (Algozzine, Ysseldyke, & Christensen, 1983)

Referral most important judgment made in assigning students to disability programs (Ysseldyke & Algozzine, 1983)

Teachers quickly form inaccurate impressions, especially of black males (Irvine, 1990)

The Referral-to-

Eligibility Ratio

Referral-to-Eligibility Ratio (RER)

Referral for Assistance Referral to General Education

Intervention Referral to Psychoeducational

Assessment Special Education Placement

Questions to be Addressed Where in the referral to eligibility

process is disproportionality occurring?

How do we know we are making a difference in disproportionality?

Are our specific general education interventions working?

Data Tracking Process Collecting data from administrators

directly working with pre-referral intervention teams or from central office personnel on Excel form

Data at 4 points in the special education decision making process

Analysis of students within and across these stages

How Do We Know there is Disproportionality?

Composition Index Indicates the representation of a group at a particular stage Example: 100 students are referred for assistance and 25

are Hispanic, the composition is 25% Risk Index

Indicates the risk of a group being represented at a particular stage

Example: 100 African American students attend a school and 10 are assessed for services, risk would be 10%

Relative Risk The ratio of risk for one group compared to all other groups Example: Risk of assessment for African Americans is 10%

and all other students is 5%, then the relative risk for African Americans is 2.0

Calculation Considerations Risk relative to all other students

or one group of students (e.g., white)

Numbers contingent on previous step, or population as a whole

Look at all students going through process, or just initial referrals, re-evaluations, etc.

School District Example

Sample District: King Community School Corporation Diverse, Urban District Wide Use of Pre-Referral

Intervention Form varies widely among schools

Follow students through this sample district to understand the calculations and process

A. Student Population CompositionRacial Category

Students in Participating Schools

Composition of Population by Race

Total 5,171African American 2,903 56.1%White 1061 20.5%Hispanic 606 11.7%Multi-Racial 446 8.6%Asian 150 2.9%American Indian 5 0.1%

Population GraphKing Community School Corporation: Racial Make Up of Student Population

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

African American White Hispanic Multi-Racial Asian American Indian

Racial Group

Perc

enta

ge

Student Population

B. Students Referred for AssistanceRacial Category

A. Students in Participating Schools


B. Number Referred for Assistance

Composition of Referrals by Race

Relative Risk of Referral for Assistance

Total 5,171 356African American 2,903 56.1% 245 68.8% 1.72White 1061 20.5% 63 17.7% 0.83Hispanic 606 11.7% 22 6.2% 0.50Multi-Racial 446 8.6% 23 6.5% 0.73Asian 150 2.9% 2 0.6% 0.19American Indian 5 0.1% 1 0.3% 2.91

Population & Referrals for Assistance

King Community School Corporation: Racial Make Up of Referrals for Assistance

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%


Racial Group

Perc

enta

ge

Student PopulationReferrals for Assistance

C. Students Referred to GEIRacial Category



C. Number Referred to GEI

Composition of GEI Referrals by Race

Relative Risk of Referral to GEI

Total 5,171 343African American 2,903 56.1% 238 69.4% 1.77White 1061 20.5% 59 17.2% 0.80Hispanic 606 11.7% 21 6.1% 0.49Multi-Racial 446 8.6% 23 6.7% 0.76Asian 150 2.9% 1 0.3% 0.10American Indian 5 0.1% 1 0.3% 3.10

Population and Referrals to GEI

King Community School Corporation: Racial Make Up of Referrals to GEI

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

African American White Hispanic Multi-Racial Asian

Racial Group

Perc

enta

ge

Student PopulationReferrals to GEI

D. Students Referred for AssessmentRacial Category



D. Number Referred for Assessment

Composition of Asment Referrals by Race

Relative Risk of Asmnt

Total 5,171 187African American

2,903 56.1% 121 64.7% 1.43

White 1061 20.5% 42 22.5% 1.12Hispanic 606 11.7% 10 5.3% 0.43Multi-Racial 446 8.6% 11 5.9% 0.66Asian 150 2.9% 2 1.1% 0.36American Indian 5 0.1% 1 0.5% 5.55

Population and Referrals for Assessment

King Community School Corporation: Racial Make Up of Referrals for Assessment

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%


Student PopulationReferrals for Assessment

E. Students Eligible for Special Education

Racial Category



E. Student found Eligible

Composition of Students Eligible by Race

Relative Risk of Eligibility

Total 5,171 109African American

2,903 56.1% 68 62.4% 1.30

White 1061 20.5% 26 23.9% 1.21Hispanic 606 11.7% 5 4.6% 0.36Multi-Racial 446 8.6% 8 7.3% 0.84Asian 150 2.9% 1 0.9% 0.31American Indian 5 0.1% 1 0.9% 9.57

Population and EligibilityKing Community School Corporation: Racial Make Up of Students Found Eligible

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%


Racial Group

Perc

enta

ge

Student PopulationStudents found Eligible

Analysis of RRR’s I. Incidence rate: Student found eligible from

total population (eligible/population) II. Assessment hit rate: Students found

eligible from those assessed (eligible/tested) III. Process outcomes: Students found eligible

from those referred (eligible/referred) IV. Process contributions: Compare III with

referral RRR (difference in RRR between initial referral and outcome of process)

I. Incidence Rate:Students Eligible from Population (E/A)Racial Category

A. Total Students in Participating Schools

E. Number Eligible

Percent Eligible of Student Population

Relative Risk of Eligibility from Population

Total 5,171 109 2.1%African American

2,903 68 2.3% 1.30

White 1,061 26 2.5% 1.21Hispanic 606 5 0.8% 0.36Multi-Racial

446 8 1.8% 0.84

Asian 150 1 0.7% 0.31American Indian

5 1 20.0% 9.57

II. Assessment Hit Rate (E/D)Racial Category

D. Number of Students Assessed

E. Number Eligible

Percent Eligible of Those Assessed

Relative Risk of Eligibility from Assessment

Total 187 109 58.3%African American

121 68 56.2% 0.90

White 42 26 61.9% 1.08Hispanic 10 5 50.0% 0.85Multi-Racial

11 8 72.7% 1.27


1 1 100.0% 3.29

III. Process Outcomes:Students Eligible from Referred (E/B)Racial Category

B. Number of Students Assessed

E. Number Eligible

Percent Eligible of Those Referred

Relative Risk of Eligibility from Referral for Assistance

Total 356 109 30.6%African American

245 68 27.8% 0.75

White 63 26 41.3% 1.46Hispanic 22 5 22.7% 0.73Multi-Racial

23 8 34.8% 1.15


1 1 100.0% 3.29

IV. Relative Risk Ratio (RRR) Through the Referral to Eligibility Process

Racial Category

Referred for Assistance RRR

Referred to GEI RRR

Referred for Assessment RRR

Eligibility Decision RRR

African American

1.72 1.77 1.43 1.30

White 0.83 0.80 1.12 1.21Hispanic 0.50 0.49 0.43 0.36Multi-Racial

0.73 0.76 0.66 0.84Asian 0.19 0.10 0.36 0.31

RER Process Graph

King Community School Corporation: Relative Risk through the Referral to Eligibility Process

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

Referred for Assistance Referred to GEI Referred for Assessment Eligibility Decision

Stage in Process

Rel

ativ

e R

isk African American

WhiteHispanicMulti-RacialAsian

School Level Data (Trends in RRR)Race Referred

for Assistance

Referred to GEI

Referred for Assessment

Eligibility

School IAfrican American 1.99 2.14 1.33 3.10White 0.66 0.61 1.24 0.53Hispanic 0.62 0.62 0.68 0.90School IIAfrican American 3.15 2.11 1.77 1.36White 0.57 0.57 0.84 0.93Hispanic 0.66 0.69 0.77 0.72

General Conclusions

Within the Process Compare contribution of each stage

to representation of group Compare one group’s

representation at specific stage to representation of other groups

Investigate different outcomes Assessment hit rate, Process

outcomes, Incidence rate

Schools & District Comparisons Which schools are contributing to

over/under representation? How do the schools’ numbers

compare to the district as a whole? How does the process differ across

schools? Leads to questions about the

contextual factors not necessary captured in data form

Challenges in Assessing the

Referral Process

Issues Encountered Calculations based on Small

Numbers

Nature of the Beast

Logistical Challenges

Approaches to Addressing Challenges LEAD Project: Culture

Competence

Technical support

Build in-house systems and ownership

Contact Information Ashley Gibb, Russ Skiba, Karega Rausch

Center for Evaluation and Education Policy509 E. Third St. Bloomington, IN [email protected]@[email protected]

IDP Website: http://ceep.indiana.edu/ieo/idp/index.shtml

mailto:[email protected]



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