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George Triest
Connie Silva-Broussard
California Department of Education, Special Education Division's special project...
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Dr. Edward Fergus
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use the chat tool
Getting to the Root Causes of Disproportionate Representation in Special Education: Using Root Cause Tools
Metropolitan Ce...
Objectives
Develop an understanding of the NYU TACD root cause process
Look at disproportionality through in-depth data an...
Part I: Overview of Technical Assistance Work
Technical assistance recipients 
Develop a district team – 25 individuals
Thirteen school districts (2004-2009)
6 pilot di...
Part II: a process for identifying the problem
Identifying Root Causes of Disproportionality: What are the steps…
Step 1: Creating a databook of the problem
Data Analysis Workbook 
http://steinhardt.nyu.edu/metrocenter/index/dataanalysisworkbook.pdf
Conducting an initial analysis of the special education and suspension data
Special Education and suspension Data
Levels of Special Education Data Analysis
Analyzing Special Education and Suspension Data: Data Requirements
In order to analyze special education you need to have ...
Methods of Data Analysis
Three main data tools (calculations) are used to explore special education data: 
Risk Index or C...
Level 1: Overall Risk
Question 1A: What is the overall district classification rate?
Question 1B: What is the overall dist...
Risk Index/Classification Rate
The risk index identifies at what rate, or amount  of risk students of a particular racial/...
Overall Risk
Classification Rate =
Suspension of SWD Rate =
Number SWD divided by 
	Total number of students 
times 100
Cl...
Get ready to calculate your own risk index: Get a calculator
Classification Rate/Risk Index of Black Students across different districts
Calculate the rates
What are the rates of clas...
Classification Rate/Risk Index of Black Students across different districts
Calculate the rates
What are the rates of clas...
Examining your results
What did you notice?
What patterns are emerging and what possible problems are becoming apparent? 
...
So what am I looking for…
Looking at Changing Data
NOTE: You Can’t Fix the Numbers by Fixing the Numbers
Disproportionality is a condition in districts or schools with deep ...
questions? 
use the chat tool
Step 2: going beyond initial databook: Module Series
Identifying Cause: Examining relationship between process, outcomes and context
Root Cause Process Manual
Data Analysis Training Modules
A- Understanding Disproportionality
B-Disproportionality Data Repository (DDR)
C- Analyzing...
Tips Before Embarking on Module Series
Disproportionality is a race-based outcome – facilitators of this work must underst...
Module A: Understanding Disproportionality
Purpose of Module
Provide definitions of disproportionality
Outline intent of I...
Icebreaker - Ms. Sutton’s Dilemma: a need for special education
	Ms. Sutton moves about her fourth grade classroom checkin...
The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education ClassificationsPurpose:          To co...
Homework assignments
Collecting of Special Education Data
Specific focus on race/ethnicity x gender, and academic performa...
Articles
O’Connor and Fernandez (2006) “Race,Class and Disproportionality” http://edr.sagepub.com/content/35/6/6.abstract
...
Common themes to emerge during Module A
Why doesn’t the state and federal government look at poverty as an interacting var...
Module B (Site visit or skip module)  
Support schools in collection of homework.
Site visits are necessary to provide one...
Pre-Module C Preparation
Collect classification and/or discipline referral data prior to session.
Conduct analysis of data...
Module C: Analyzing Referral Process and other indicators
Purpose of Module
Analyze policy, practice and belief data
Begin...
The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education ClassificationsPurpose:          To co...
Homework assignment (Data List Form)
 Collecting data on pre-referral to classification practices. This includes…
Forms us...
Common themes to emerge during Module C
Why doesn’t the state and federal government look at poverty as an interacting var...
Module D: Getting to Root Cause
Purpose of Module
Analyze policy, practice and belief data
Continue conversation regarding...
Common themes to emerge during Module D
Why doesn’t the state and federal government look at poverty as an interacting var...
Module E: Prioritizing and Selecting Root Causes
Purpose of Module
Define root causes of disproportionality based on polic...
questions?
use the chat tool
Part 3:The Caveats of Addressing Disproportionality: What do Districts Struggle with?
Caveat #1: Rate Changes Take Time and Must be Carefully Interpreted
Caveat #2: Districts Can Game the Process
questions?
use the chat tool
Additional Resources
Books	
Articles
Harry, B., & Klingner, J.K. (2006). Why are so many minority students in special ...
Thank you!
Eddie Fergus
edward.fergus@nyu.edu
Metropolitan Center for Urban EducationNew York University
www.steinhardt.nyu.edu/metrocenter
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Getting to the Root Causes of Disproportionate Representation in Special Education: Using Root Cause Tools

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Dr. Edward Fergus introduces Root Cause tools for identifying and addressing disproportional representation in special education.

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Getting to the Root Causes of Disproportionate Representation in Special Education: Using Root Cause Tools

  1. 1. Welcome! Please While Others Join the Meeting Call-In 1-866-244-8528 Enter Pin 303385 and press # Today You Will Need a calculator Connie
  2. 2. George Triest Connie Silva-Broussard California Department of Education, Special Education Division's special project, State Performance Plan Technical Assistance Project (SPPTAP) is funded through a contract with the Napa County Office of Education. SPPTAP is funded from federal funds, (State Grants #H027A080116A) provided from the U.S. Department of Education Part B of the Individuals with Disabilities Education Act (IDEA). Opinions expressed herein are those of the authors and do not necessarily represent the position of the U.S. Department of Education.
  3. 3. Connie Use Chat to Ask Questions Type message in box on (lower right corner) Click into box, type message, press enter Test Chat Now
  4. 4. Connie Housekeeping This event is being recorded Follow-up Survey
  5. 5. Connie Dr. Edward Fergus
  6. 6. Webinar Tips Ask questions along the way – use the chat box on the right to pose any questions. use the chat tool
  7. 7. Getting to the Root Causes of Disproportionate Representation in Special Education: Using Root Cause Tools Metropolitan Center for Urban Education http://education.nyu.edu/metrocenter/
  8. 8. Objectives Develop an understanding of the NYU TACD root cause process Look at disproportionality through in-depth data analysis Methods of data analysis Levels of data analysis
  9. 9. Part I: Overview of Technical Assistance Work
  10. 10. Technical assistance recipients Develop a district team – 25 individuals Thirteen school districts (2004-2009) 6 pilot districts (2004-2009) 1 rural, 4 suburban, and 1 urban school district 7 SPP districts (2007-2009) 1 rural and 6 suburban Sixteen school districts (2009-2014) School districts will receive two years of TA services Current districts: 3 large urban school districts and 13 suburban school districts Ten regional teams (2004-2014) Comprised of New York State Education Department funded technical assistance providers. Focused professional development for at-risk districts
  11. 11. Part II: a process for identifying the problem
  12. 12. Identifying Root Causes of Disproportionality: What are the steps…
  13. 13. Step 1: Creating a databook of the problem
  14. 14. Data Analysis Workbook http://steinhardt.nyu.edu/metrocenter/index/dataanalysisworkbook.pdf
  15. 15. Conducting an initial analysis of the special education and suspension data Special Education and suspension Data
  16. 16. Levels of Special Education Data Analysis
  17. 17. Analyzing Special Education and Suspension Data: Data Requirements In order to analyze special education you need to have the following data District enrollment by race and gender Special education enrollment by race and gender, classification, and placement It is critical the general and special education enrollment data reflect the same school years; a lack in consistency prevents appropriate analysis
  18. 18. Methods of Data Analysis Three main data tools (calculations) are used to explore special education data: Risk Index or Classification Rate Composition Index Relative Risk Ratio
  19. 19. Level 1: Overall Risk Question 1A: What is the overall district classification rate? Question 1B: What is the overall district suspension of SWD rate?
  20. 20. Risk Index/Classification Rate The risk index identifies at what rate, or amount of risk students of a particular racial/ethnic group are falling into a particular category What is the rate in which Black students are classified disabled? What is the rate in which Black students with disabilities are suspended? What is the rate in which Latino students are receiving A’s and B’s? What is the rate in which low-income students are in honors and/or AP courses?
  21. 21. Overall Risk Classification Rate = Suspension of SWD Rate = Number SWD divided by Total number of students times 100 Classification Rate = 500÷ 5000 x 100 = 10% Number SWD suspended for more than 10 days divided by total number of SWD times 100 Suspension Rate = _______÷_______ x 100
  22. 22. Get ready to calculate your own risk index: Get a calculator
  23. 23. Classification Rate/Risk Index of Black Students across different districts Calculate the rates What are the rates of classification?
  24. 24. Classification Rate/Risk Index of Black Students across different districts Calculate the rates What are the rates of classification?
  25. 25. Examining your results What did you notice? What patterns are emerging and what possible problems are becoming apparent? Critical Analysis What are the possible explanations for your findings?
  26. 26. So what am I looking for…
  27. 27. Looking at Changing Data
  28. 28. NOTE: You Can’t Fix the Numbers by Fixing the Numbers Disproportionality is a condition in districts or schools with deep seeded root causes. In order to help districts and schools address disproportionality, additional data should also be collected.
  29. 29. questions? use the chat tool
  30. 30. Step 2: going beyond initial databook: Module Series
  31. 31. Identifying Cause: Examining relationship between process, outcomes and context
  32. 32. Root Cause Process Manual
  33. 33. Data Analysis Training Modules A- Understanding Disproportionality B-Disproportionality Data Repository (DDR) C- Analyzing Referral Process and other indicators D- Getting to Root Cause E- Root Cause Identification, Report and Service Plan
  34. 34. Tips Before Embarking on Module Series Disproportionality is a race-based outcome – facilitators of this work must understand the complexities of how race intersects with the schooling process. Disproportionality generally exists due to gaps in the following policies and practices: fidelity of evaluation process, pre-referral interventions (fidelity and appropriateness), core instructional program (fidelity and appropriateness). Disproportionality also exists due to vulnerability experienced by racial/ethnic minority populations
  35. 35. Module A: Understanding Disproportionality Purpose of Module Provide definitions of disproportionality Outline intent of IDEA Outline disproportionality as a national, state and local issue Outline disproportionality as a race-based problem Content Definitions of disproportionality (federal, state and research) Long-term effects of disproportionality on racial/ethnic minority and low-income groups Methods of calculating disproportionality Activities Icebreaker: what do we know? Critical Questions: what should be asked at each step in the referral process? Data Analysis Workbook: what is the nature of our problem? Homework Data List form – collecting classification and discipline data Read research article on poverty, race and disproportionality
  36. 36. Icebreaker - Ms. Sutton’s Dilemma: a need for special education Ms. Sutton moves about her fourth grade classroom checking to see which of her students continues to have difficulty with the newly introduced math process of long division. Suddenly, a loud crash draws her attention away from helping students to the commotion in the center of the room. Fallen desks and papers cover the floor. Andy stands in the middle of the havoc. Ms. Sutton breathes deeply. She thinks “When will somebody do something for this child? After all, his test scores show he has difficulty with reading and mathematics. Hasn’t this child struggled long enough to be considered for special education? Can’t the special education classes in this school give him more attention than he can possibly get in a general education class of 30 students?” When Andy engages in class discussions on topics he enjoys, his comments and contributions reflect his regular viewing of educational programs on TV, but his overall performance is low. Ms. Sutton desperately wants to help him, but what are her options? Determined not to let him fail, Ms. Sutton decides to refer him for a special education evaluation. She sees this as her only option to get help for him. From: Truth in Labeling: Disproportionality in Special Education
  37. 37. The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education ClassificationsPurpose: To consider the path taken by a student who is classified as having a disability. Directions: Please discuss this student’s journey through the referral and classification process, and write down the key policies, practices, and beliefs that may affect or determine the student’s outcome at each of the steps below
  38. 38. Homework assignments Collecting of Special Education Data Specific focus on race/ethnicity x gender, and academic performance levels of classified students Number of students referred and number of students referred and classified Collecting of Suspension Data Specific focus on race/ethnicity x gender, and academic performance levels of classified students Number of students referred and number of students referred and suspended Read articles on interaction of race/ethnicity, poverty, community conditions, and educational practice
  39. 39. Articles O’Connor and Fernandez (2006) “Race,Class and Disproportionality” http://edr.sagepub.com/content/35/6/6.abstract Skiba, Michael, and Nardo (2000) “The Color of Discipline” http://www.indiana.edu/~safeschl/cod.pdf
  40. 40. Common themes to emerge during Module A Why doesn’t the state and federal government look at poverty as an interacting variable in causing disproportionality? Gaps in practices and policies of pre-referral to referral process. Are we racist or biased as individuals and/or a system? District policies need to be examined more carefully because some may encourage disproportionality. For example, designating some buildings with self-contained classrooms and others with inclusion and co-teaching as the pedagogical approach.
  41. 41. Module B (Site visit or skip module) Support schools in collection of homework. Site visits are necessary to provide one-on-one understanding of what information to collect All data should reflect one complete academic year – for example, number of students referred to a pre-referral intervention team should reflect all students referred between September and May.
  42. 42. Pre-Module C Preparation Collect classification and/or discipline referral data prior to session. Conduct analysis of data by various subgroups – e.g., race/ethnicity, gender, academic performance levels, etc. Review articles
  43. 43. Module C: Analyzing Referral Process and other indicators Purpose of Module Analyze policy, practice and belief data Begin conversation regarding relationship of poverty, race and school practice Content Definitions of how poverty and race impact school practice and student outcomes Understanding outcomes of school and district practices, policies and beliefs Activities Critical Questions: what actions are taken at each step in the referral process? Examine Referral and Records review data: what is the nature of our problem? Community Context data: who’s living in our community? Homework Data List form – collect policy and practice data at building and district level Conduct NCCRESt survey
  44. 44. The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education ClassificationsPurpose: To consider the path taken by a student who is classified as having a disability. Directions: Please discuss this student’s journey through the referral and classification process, and write down the key policies, practices, and beliefs that may affect or determine the student’s outcome at each of the steps below
  45. 45. Homework assignment (Data List Form) Collecting data on pre-referral to classification practices. This includes… Forms used to refer student to a bldg level intervention/problem-solving team Notes from team meetings, specifically goals established to intervene Number of students referred in academic year by race/ethnicity, gender, academic performance level, grade level, etc. List of common interventions provided by team Collecting data on disciplinary practices including office referrals, in-school suspension, and suspension patterns. Forms used to refer student for disciplinary action Notes from team/individual meeting regarding behavior List of common interventions provides by team or individual for behavior issues
  46. 46. Common themes to emerge during Module C Why doesn’t the state and federal government look at poverty as an interacting variable in causing disproportionality? Our system can’t work unless we define and expect the same cultural values. We’re not explicit about the school cultural values and vulnerable populations are penalized for it. Gaps in practices and policies of pre-referral to referral process. District policies need to be examined more carefully because some may encourage disproportionality. For example, designating some buildings with self-contained classrooms and others with inclusion and co-teaching as the pedagogical approach.
  47. 47. Module D: Getting to Root Cause Purpose of Module Analyze policy, practice and belief data Continue conversation regarding relationship of poverty, race and school practice Hypothesis root causes Content Understanding outcomes of school and district practices, policies and beliefs Activities Critical Analysis Worksheet : what gaps in practices, policies and beliefs are present? Culturally Responsive Survey: Are our practices responsive to our populations? Homework Research article bibliography – select 1-2 articles for jigsaw conversation
  48. 48. Common themes to emerge during Module D Why doesn’t the state and federal government look at poverty as an interacting variable in causing disproportionality? Our system can’t work unless we define and expect the same cultural values. We’re not explicit about the school cultural values and vulnerable populations are penalized for it. Gaps in practices and policies of pre-referral to referral process. And its vitally important that we fix them. How do we start having these conversations at the building level?
  49. 49. Module E: Prioritizing and Selecting Root Causes Purpose of Module Define root causes of disproportionality based on policy, practice and belief data Continue conversation regarding relationship of poverty, race and school practice Content Understanding research on disproportionality and its root causes Activities Mapping Root Causes: where is the nature of our problem? Homework Outline preliminary root causes
  50. 50. questions? use the chat tool
  51. 51. Part 3:The Caveats of Addressing Disproportionality: What do Districts Struggle with?
  52. 52. Caveat #1: Rate Changes Take Time and Must be Carefully Interpreted
  53. 53. Caveat #2: Districts Can Game the Process
  54. 54. questions? use the chat tool
  55. 55. Additional Resources Books Articles Harry, B., & Klingner, J.K. (2006). Why are so many minority students in special education? Understanding race and disability in schools. New York: Teachers College Press. Losen, D. & Orfield, G. (2002) Racial Inequity in Special Education. Harvard Education Press. Klingner, J. K., Artiles, A. J., Kozleski, E., Harry, B., Zion, S., Tate, W., Durán, G. Z., & Riley, D. (2005). Addressing the disproportionate representation of culturally and linguistically diverse students in special education through culturally responsive educational systems. Education Policy Analysis Archives, 13(38). Retrieved [June 22, 2007] from http://epaa.asu.edu/epaa/v13n38/. Skiba, R. J., Poloni-Staudinger, L, Simmons, A. B., Feggins-Azziz, L. R., & Chung, C. (2005). Unproven links: Can poverty explain ethnic disproportionality in special education?. The Journal of Special Education, 39(3), 130-144. National Education Association “Truth in Labeling” http://www.nccrest.org/Exemplars/Disporportionality_Truth_In_Labeling.pdf
  56. 56. Thank you! Eddie Fergus edward.fergus@nyu.edu
  57. 57. Metropolitan Center for Urban EducationNew York University www.steinhardt.nyu.edu/metrocenter

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