1 / 41

Ashley Gibb M. Karega Rausch Russell Skiba Indiana Disproportionality Project

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

hasana
Télécharger la présentation

Ashley Gibb M. Karega Rausch Russell Skiba Indiana Disproportionality Project

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Overview • History • Rationale • Referral-to-Eligibility Ratio • Preliminary Data • Challenges in Assessing the Referral Process

  3. 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

  4. 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

  5. Findings: Years One and Two • Statewide: African American most severe • Mild Mental Disability3.29 x more • Emotional Disturbance 2.38 x more • Moderate MD1.91 x more • Communication Disorder 35% less • Learning Disabled 6% less • AA underrepresented in LRE • Disproportionality not uniformly distributed

  6. 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

  7. 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

  8. Rationale

  9. 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)

  10. The Referral-to-Eligibility Ratio

  11. Referral-to-Eligibility Ratio (RER) • Referral for Assistance • Referral to General Education Intervention • Referral to Psychoeducational Assessment • Special Education Placement

  12. 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?

  13. 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

  14. 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

  15. 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.

  16. School District Example

  17. 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

  18. A. Student Population Composition

  19. Population Graph

  20. B. Students Referred for Assistance

  21. Population & Referrals for Assistance

  22. C. Students Referred to GEI

  23. Population and Referrals to GEI

  24. D. Students Referred for Assessment

  25. Population and Referrals for Assessment

  26. E. Students Eligible for Special Education

  27. Population and Eligibility

  28. 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)

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

  30. II. Assessment Hit Rate (E/D)

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

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

  33. RER Process Graph

  34. School Level Data (Trends in RRR)

  35. General Conclusions

  36. 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

  37. 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

  38. Challenges in Assessing the Referral Process

  39. Issues Encountered • Calculations based on Small Numbers • Nature of the Beast • Logistical Challenges

  40. Approaches to Addressing Challenges • LEAD Project: Culture Competence • Technical support • Build in-house systems and ownership

  41. Contact Information • Ashley Gibb, Russ Skiba, Karega Rausch Center for Evaluation and Education Policy 509 E. Third St. Bloomington, IN 47401 812-855-4438 acgibb@indiana.edu skiba@indiana.edu marausch@indiana.edu • IDP Website: http://ceep.indiana.edu/ieo/idp/index.shtml

More Related