getting to the root causes of disproportionate representation in special education: using root cause...
TRANSCRIPT
Call-In1-866-244-8528
Enter Pin 303385 and press #
Please While Others Join the MeetingWelcome!
Today You Will Need
a calculator
Connie
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.
Connie
Use Chat to Ask QuestionsType message in box on (lower right corner)
Click into box, type message, press enter
Test Chat Now
Connie
HousekeepingThis event is being recorded
Follow-up Survey
Dr. Edward Fergus
Connie
Webinar Tips• Ask questions along the way – use the chat box on
the right to pose any questions.
use the chat tool
Getting to the Root Causes of Disproportionate Representation in Special Education: Using Root Cause Tools
Metropolitan Center for Urban Educationhttp://education.nyu.edu/metrocenter/
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
PART I: OVERVIEW OF TECHNICAL ASSISTANCE WORK
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
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
SPECIAL EDUCATION AND SUSPENSION DATA
Conducting an initial analysis of the 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 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
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
Level 1: Overall RiskQuestion 1A: What is the overall district classification rate?
Question 1B: What is the overall district suspension of SWD rate?
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?
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 SWDtimes 100
• Suspension Rate = _______÷_______ x 100
GET READY TO CALCULATE YOUR OWN RISK INDEX: GET A CALCULATOR
Classification Rate/Risk Index of Black Students across different districts
District 1 District 2 District 3 District 4
(A) District Enrollment 557 246 1880 2831
(B) Number of Students with
Disabilities 78 65 326 396
Classification Rate
B divided by A X
100 =
Calculate the rates
What are the rates of classification?
Classification Rate/Risk Index of Black Students across different districts
District 1 District 2 District 3 District 4
District Enrollment 557 246 1880 2831
Number of Students with
Disabilities 78 65 326 396
Classification Rate 14.00% 26.42% 17.34% 13.99%
Calculate the rates
What are the rates of classification?
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?
SO WHAT AM I LOOKING FOR…
Looking at Changing Data
Risk IndexRelative Risk
Ratio Interpretation Explanation
1 Increases Increases Disproportionality has increased
The rise in the risk index indicates that the group’s risk has increased overall. Furthermore, the increase in the relative risk ratio indicates that the group’s risk has increased relative to all other students.
2 Increases Decreases Disproportionality has increased
The rise in the risk index indicates that the group’s risk has increased overall. Thus, the decrease in the relative risk is most likely due to the increased risk of another group.
3 Decreases Increases Disproportionality has decreased
The reduction in the risk index indicates that the group’s risk has decreased overall. The increase in the relative risk indicates that the risk of all other students has decreased to a greater extent.
4 Decreases Decreases Disproportionality had decreased
The reduction in the risk index indicates that the group’s risk has decreased overall, which is further evidenced by the decrease in the relative risk.
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.
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 Referral Process and other indicators
•D- Getting to Root Cause
•E- Root Cause Identification, Report and Service Plan
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
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
• 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?
• 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
• Homework– Data List form – collecting
classification and discipline data
– Read research article on poverty, race and disproportionality
Icebreaker - Ms. Sutton’s Dilemma: a need for special educationMs. 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
The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education Classifications
Purpose: 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
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
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
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.
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.
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
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
• 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?
• Content– Definitions of how poverty
and race impact school practice and student outcomes
– Understanding outcomes of school and district practices, policies and beliefs
• Homework– Data List form – collect
policy and practice data at building and district level
– Conduct NCCRESt survey
The Policies, Practices, and Beliefs Along the Way –Referrals and Special Education Classifications
Purpose: 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
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
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?
• Gaps in practices and
policies of pre-referral to
referral process.
• 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.
• 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.
Module D: Getting to Root CausePurpose of Module
– Analyze policy, practice and belief data
– Continue conversation regarding relationship of poverty, race and school practice
– Hypothesis root causes
Activities– Critical Analysis Worksheet :
what gaps in practices, policies and beliefs are present?
– Culturally Responsive Survey: Are our practices responsive to our populations?
Content– Understanding outcomes of
school and district practices, policies and beliefs
Homework– Research article
bibliography – select 1-2 articles for jigsaw conversation
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?
• Gaps in practices and
policies of pre-referral to
referral process. And its
vitally important that we fix
them.
• 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.
• How do we start having these conversations at the building level?
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
Activities– Mapping Root Causes:
where is the nature of our problem?
Content– Understanding research on
disproportionality and its root causes
Homework– Outline preliminary root
causes
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
2005-06 2007-086%
8%
10%
12%
14%
16%
18%
20%
Classification Rate
Black
Latino
White
School Year
Cla
ssifi
catio
n R
ate
2005-06 2007-080.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
Relative Risk
Black
Latino
White
School Year
Rel
ativ
e R
isk
Caveat #2: Districts Can Game the Process
2003-04 2007-0810%
11%
12%
13%
14%
15%
16%
17%
18%
Classification Rate
Black
Latino
White
School Year
Cla
ssifi
catio
n R
ate
2003-04 2007-080.5
0.7
0.9
1.1
1.3
1.5
1.7
Relative Risk
Black
Latino
White
School Year
Rel
ativ
e R
isk
QUESTIONS?
use the chat tool
Additional ResourcesBooks• 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.
Articles• 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
Metropolitan Center for Urban EducationNew York University
www.steinhardt.nyu.edu/metrocenter