Sources of Disproportionality in Special Education: Tracking Minority Representation through the Referral-to-Eligibility Process Ashley Gibb M. Karega Rausch Russell Skiba Indiana Disproportionality Project Indiana University National Center for Culturally Responsive Educational Systems February 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 ( ): Developing Measures of Disproportionality  Phase II ( ): 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 formExcel 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 Composition Racial CategoryStudents in Participating Schools Composition of Population by Race Total 5,171 African American 2, % White % Hispanic % Multi-Racial % Asian % American Indian 50.1%

Population Graph

B. Students Referred for Assistance Racial Category A. Students in Participating Schools Composition of Population by Race B. Number Referred for Assistance Composition of Referrals by Race Relative Risk of Referral for Assistance Total 5, African American 2, % %1.72 White %6317.7%0.83 Hispanic %226.2%0.50 Multi-Racial %236.5%0.73 Asian %20.6%0.19 American Indian 50.1%10.3%2.91

Population & Referrals for Assistance

C. Students Referred to GEI Racial Category A. Students in Participating Schools Composition of Population by Race C. Number Referred to GEI Composition of GEI Referrals by Race Relative Risk of Referral to GEI Total 5, African American 2, % %1.77 White %5917.2%0.80 Hispanic %216.1%0.49 Multi-Racial %236.7%0.76 Asian %10.3%0.10 American Indian 50.1%10.3%3.10

Population and Referrals to GEI

D. Students Referred for Assessment Racial Category A. Students in Participating Schools Composition of Population by Race D. Number Referred for Assessment Composition of Asment Referrals by Race Relative Risk of Asmnt Total 5, African American 2, % %1.43 White %4222.5%1.12 Hispanic %105.3%0.43 Multi-Racial %115.9%0.66 Asian %21.1%0.36 American Indian 50.1%10.5%5.55

Population and Referrals for Assessment

E. Students Eligible for Special Education Racial Category A. Students in Participating Schools Composition of Population by Race E. Student found Eligible Composition of Students Eligible by Race Relative Risk of Eligibility Total 5, African American 2, %6862.4%1.30 White %2623.9%1.21 Hispanic %54.6%0.36 Multi-Racial %87.3%0.84 Asian %10.9%0.31 American Indian 50.1%10.9%9.57

Population and Eligibility

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, % African American 2, %1.30 White 1, %1.21 Hispanic %0.36 Multi-Racial %0.84 Asian %0.31 American Indian %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 % African American %0.90 White %1.08 Hispanic %0.85 Multi-Racial %1.27 Asian %0.86 American Indian %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 % African American %0.75 White %1.46 Hispanic %0.73 Multi-Racial %1.15 Asian %1.64 American Indian %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 White Hispanic Multi-Racial Asian

RER Process Graph

School Level Data (Trends in RRR) RaceReferred for Assistance Referred to GEI Referred for Assessment Eligibility School I African American White Hispanic School II African American White Hispanic

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 Policy 509 E. Third St. Bloomington, IN  IDP Website: