National PBIS Leadership Forum

National PBIS Leadership Forum
October 27 & 28, 2016 A4 – Using Data to Enhance Equity Leader Presenter: Bert Eliason Exemplars: Jennifer Rollenhagen Key Words: Equity, Applied Evaluation, Training

Presenters Bert Eliason, Ed.D. Jennifer Rollenhagen, Ed.S.
PBIS Applications Training Team Educational and Community Supports – College of Education, University of Oregon Jennifer Rollenhagen, Ed.S. Measurement Evaluation Specialist Michigan’s Integrated Behavior & Learning Support Initiative, MIBLSI

Using Data to Enhance Equity
Data-driven Conversations on Equity & Disproportionality Bert Eliason, PBIS Applications Jennifer Rollenhagen, MIBLSI PBIS Leadership Forum October 27, 2016 Session A4

Organizing and Analyzing the Data Putting It Into Practice
Session Intentions Goal Use a problem-solving model focused on the use of data to address discipline disproportionality Resources Data Guide for School Teams (pbis.org, 2014) SWIS Ethnicity Report (pbisapps.org) Four-step problem-solving model Organizing and Analyzing the Data Data Sources Common Metrics Drill Down Process Putting It Into Practice Michigan School Example Lessons Learned

Maximizing Your Session Participation
When Working In Your Team Consider 4 questions: Where are we in our implementation? What do I hope to learn? What did I learn? What will I do with what I learned?

Where are you in the implementation process
Where are you in the implementation process? Adapted from Fixsen & Blase, 2005 We think we know what we need so we are planning to move forward (evidence-based) Exploration & Adoption Let’s make sure we’re ready to implement (capacity infrastructure) Installation Let’s give it a try & evaluate (demonstration) Initial Implementation That worked, let’s do it for real and implement all tiers across all schools (investment) Let’s make it our way of doing business & sustain implementation (institutionalized use) Full Implementation

Leadership Team Action Planning Worksheets: Steps
Self-Assessment: Accomplishments & Priorities Leadership Team Action Planning Worksheet Session Assignments & Notes: High Priorities Team Member Note-Taking Worksheet Action Planning: Enhancements & Improvements

PBIS Applications PBISApps.org houses tools designed to support evidence-based practices for creating a positive, safe, and effective learning environment for children and adults.

PBIS.org - National Resource Center
is the national resource center for materials and information related to the implementation of PBIS with fidelity. School

PBIS.org - National Resource Center
is the national resource center for materials and information related to the implementation of PBIS with fidelity. School Equity & PBIS

5-point Multicomponent Approach to Reduce Disproportionality in Schools
Collect, Use, and Report Disaggregated Discipline Data Effective Instruction School-wide PBIS Policies with Accountability for Disciplinary Equity Teach Neutralizing Routines for Vulnerable Decision Points

The Data Guide Identified:
Revision coming soon! The Data Guide Identified: Data Needed for Investigating Disproportionality Four-step Problem Solving Model Metrics to Use to Monitor Disproportionality Bias in Decision Making – Explicit vs Implicit Vulnerable Decision Points

Data Sources: What is Necessary?
From the Data Guide Data Sources: What is Necessary? Required features: Consistent data collection Discipline referrals (ODRs) Suspensions/expulsions Identified by student race/ethnicity for disaggregation Total school enrollment by race/ethnicity Instantaneous access for school teams—not just district teams Capability to: Disaggregate ODRs and patterns by race/ethnicity Calculate risk indices and risk ratios by race/ethnicity Who here has a school data system that includes all of these features?

Data Sources: What is Recommended?
From the Data Guide Data Sources: What is Recommended? Standardized referral forms with the 5W’s Who, what, when, where, why Clear definitions of problem behaviors Clear guidance in discipline procedures Office vs. staff-managed Report generation On demand Disaggregated by race/ethnicity Automatic calculation of disproportionality

Schoolwide Information System
SWIS Demo Data

SWIS Demo Data

SWIS – Ethnicity Reports

SWIS Ethnicity Reports
Each report will have the option of showing the Graph, the Data Table, or Information.

Upcoming Data Guide Revision
We will likely recommend an additional metric. Relative metric – Risk Index Risk Ratio Composite metrics – Students with Referrals Total Referrals Absolute metric – Measure a distance from something ODRs/100 Students/Day vs. National Median 1 2 3 4

1. Problem Identification
From the Data Guide Problem Solving Model 2. Problem Analysis 3. Plan Implementation 4. Plan Evaluation 1. Problem Identification Is there a problem? Why is it happening? You also need for your school’s PBIS team to have a problem-solving model… it doesn’t need to be overly complicated, this one has 4 basic steps: Is the plan working? What should be done?

Step 1: Problem Identification
Use valid & reliable metrics. Quantify the difference between current outcomes and goals. This is the performance gap! 1. Problem Identification Is there a problem?

Step 1: Problem Identification For disproportionality Quantify outcomes across racial/ethnic subgroups Compare differences Specified group vs. Comparator group Subgroup vs. White subgroup Subgroup vs. All Other students Native vs. All Non-Native students 1. Problem Identification Is there a problem? Multiple metrics are recommended! IDEA Data Center, 2014

# Students with Referrals % Students with Referrals
Step 1: Problem Identification Common Metrics – Risk Index Percent of a group at risk for a certain outcome % Receiving an Office Discipline Referral (ODR) % Receiving a suspension or expulsion Calculate the Risk Index? Divide At Risk / Total number How? Calculate by hand Create a spreadsheet Use automated program Use a Risk Ratio Calculator SWIS Ethnicity Report Referral Risk Index Table # Students Enrolled # Students with Referrals % Students with Referrals Risk Index Native 6 3 50.00% 0.50 Asian 7 2 28.57% 0.29 Black 65 47 72.31% 0.72 Latino 100 40 40.00% 0.40 Pacific 4 0.00% 0.00 White 300 103 34.33% 0.34 Multi-Racial 22 Total 504 195

Common Metrics – Risk Ratio
Step 1: Problem Identification Common Metrics – Risk Ratio Free Risk Ratio Calculator from Wisconsin RtI Center

Step 1: Problem Identification Common Metrics – Risk Index Percent of a group at risk for a certain outcome % Receiving an Office Discipline Referral (ODR) SWIS Ethnicity Report Referral Risk Index Table # Students Enrolled # Students with Referrals % Students with Referrals Risk Index Native 6 3 50.00% 0.50 Asian 7 2 28.57% 0.29 Black 65 47 72.31% 0.72 Latino 100 40 40.00% 0.40 Pacific 4 0.00% 0.00 White 300 103 34.33% 0.34 Multi-Racial 22 Total 504 195 Native students with 1 or more ODRs Native students enrolled 3 6 = 50% = = 50.00% ----- Meeting Notes (3/12/15 09:07) ----- Touched by the hand Percent that is at risk of receiving the outcome Asian students with 1 or more ODRs Asian students enrolled 2 7 = 29% = = 28.57% Black students with 1 or more ODRs Black students enrolled 47 65 = 72% = = 72.31%

SWIS Ethnicity Reports -
Automatically calculates the risk of receiving a referral for the various racial/ethnic subgroups enrolled in the school.

> 1.0 is overrepresentation < 1.0 is underrepresentation
Step 1: Problem Identification Common Metrics – Risk Ratio Risk index of one group divided by the risk index of a comparison group How do we calculate Risk Ratio? Divide 1.0 is equal risk > 1.0 is overrepresentation < 1.0 is underrepresentation Risk Index of Specified Group Risk Index of Comparison Group ----- Meeting Notes (3/12/15 09:07) ----- How many times more likely that one group is to receive the outcome than the other Risk Ratio =

Step 1: Problem Identification Common Metrics – Risk Ratio SWIS Ethnicity Report Referral Risk Index Table # Students Enrolled # Students with Referrals % Students with Referrals Risk Index Native 6 3 50.00% 0.50 Asian 7 2 28.57% 0.29 Black 65 47 72.31% 0.72 Latino 100 40 40.00% 0.40 Pacific 4 0.00% 0.00 White 300 103 34.33% 0.34 Multi-Racial 22 Total 504 195 1.0 is equal risk > 1.0 is overrepresentation < 1.0 is underrepresentation Risk Index of Specified Group Risk Index of Comparison Group overrepresentation ----- Meeting Notes (3/12/15 09:07) ----- How many times more likely that one group is to receive the outcome than the other Risk for Native Students Risk for White Students = 50.00% 34.33% 1.456 = 1.46 Risk for Asian Students Risk for White Students = 28.57% 34.33% 0.832 = 0.83 underrepresentation

Step 1: Problem Identification Common Metrics – Risk Ratio What does this mean? Tells us how much more or less likely one group is to receive a certain outcome when compared to another group. For Example: Risk for Native Students Risk for White Students = 50.00% 34.33% 1.46 = How do we read this? ----- Meeting Notes (3/12/15 09:07) ----- How many times more likely that one group is to receive the outcome than the other In this school, Native students are 1.46 times more likely to receive an Office Discipline Referral than the White students.

Is 1.46 times more likely….bad?
Well, 1.0 would be an even chance or equitable outcomes. What is bad? 1.01, , , , … ? What is the threshold for bad? What is “significant disproportionality?”

ED, OSEP, IDEA, and Disproportionality
The Feds don’t define a threshold, but they will likely suggest all states report disproportionality using one methodology: Risk Ratio with All Other students as the comparator group States will be required to establish and use a “reasonable threshold” as criteria for identifying significant disproportionality. …So what should be the goal?

Risk Ratio Goals Previous years from same school
Local, state or national norms SWIS Median Risk Ratio African American to White = 1.84 25th percentile = 1.38 Logical Criterion Equal Employment Opportunity Commission (EEOC) Disparate impact criterion – “4/5s Rule” Goal risk ratio range between .80 and 1.25 State Threshold for “Significant Disproportionality”

Step 1: Problem Identification Common Metrics – Risk Ratio How would we calculate Risk Ratio for All Others Risk Index of Specified Group Combined Risk Index of All Other Students Not in the Specified Group ----- Meeting Notes (3/12/15 09:07) ----- How many times more likely that one group is to receive the outcome than the other Risk Index of Black Students Risk Index of All Other Students who are not Black

Risk Ratio – Comparison Group – All Others Risk of one subgroup divided by risk of those not in that subgroup For Example: Risk index for Black or African American students = 47 / 65 = 72.31% Risk Index All Others = 195 – 47 = 148 All Other students with referrals 504 – 65 = 439 All Other students enrolled 148 / 439 = 33.71% Risk Index for All Others Risk Ratio for Blacks compared to All Others = 72.31% / 33.71% = > 2.15 It is 2.15 times as likely that Black students will receive a referral as compared to the group of All Other students in the school who are not Black. # Students Enrolled # Students with Referrals % Students with Referrals Risk Index Native 6 3 50.00% 0.50 Asian 7 2 28.57% 0.29 Black 65 47 72.31% 0.72 Latino 100 40 40.00% 0.40 Pacific 4 0.00% 0.00 White 300 103 34.33% 0.34 Multi-Racial 22 Total 504 195 Calculating All Others not Black

Automatically calculates the risk ratio for each of the various racial/ethnic subgroups enrolled in the school when compared to the group of All Other students. 2.15

Common Metrics - Composition
Step 1: Problem Identification Common Metrics - Composition Percent of Students with Referrals Compares subgroup’s percentage of school population to the subgroup’s percentage of just the students with ODRs Is the percent of students who have ODRs for one subgroup proportionate to that subgroup’s portion of school enrollment? # Students Enrolled # Students with Referrals % Students of Enrolled Students % Students with Referrals Native 6 3 1.19% 1.54% Asian 7 2 1.39% 1.03% Black 65 47 12.90% 24.10% Latino 100 40 19.84% 20.51% Pacific 4 0.79% 0.00 White 300 103 59.52% 52.82% Multi-Racial 22 4.37% Total 504 195 100%

Automatically calculates the proportionality between subgroup’s percent of population and subgroup’s percent of students with referrals. overrepresentation underrepresentation

% Students of Enrolled Students
Step 1: Problem Identification Common Metrics - Composition Percent of Total Referrals Compares subgroup’s percentage of school population to the subgroup’s percentage of all ODRs written Is the percent of ODRs for one subgroup proportionate to that subgroup’s portion of the school enrollment? # of Students Enrolled # of Referrals Written % Students of Enrolled Students % of Referrals Written Native 6 9 1.19% 1.35% Asian 7 5 1.39% 0.75% Black 65 143 12.90% 21.47% Latino 100 155 19.84% 23.27% Pacific 4 0.79% 0.00 White 300 354 59.52% 53.15% Multi-Racial 22 4.37% Total 504 666 100%

Automatically calculates the proportionality between subgroup’s percent of population and subgroup’s percent of total referrals. overrepresentation underrepresentation

SWIS Ethnicity Reports
Percentage of each subgroup that is at risk of a certain outcome. Subgroup’s percentage of enrollment compared to their percentage of just the students with referrals. Each subgroup’s risk of a certain outcome compared to group of all other students. Subgroup’s percentage of enrollment compared to their percentage of all the referrals written.

Upcoming Revision to the Procedures
Step 1: Problem Identification Upcoming Revision to the Procedures Relative metric – Risk Index Risk Ratio All Others as comparison group Composite metrics Students with Referrals by Ethnicity Total Referrals by Ethnicity Absolute metric ODRs/100 Students/Day vs. National Median

How do we calculate ODRs per 100 Students per Day
Absolute Metric How do we calculate ODRs per 100 Students per Day # of Students Enrolled # of Referrals Written % Students of Enrolled Students % of Referrals Written Native 6 9 1.19% 1.35% Asian 7 5 1.39% 0.75% Black 65 143 12.90% 21.47% Latino 100 155 19.84% 23.27% Pacific 4 0.79% 0.00 White 300 354 59.52% 53.15% Multi-Racial 22 4.37% Total 504 666 100% As easy as 1…2…3…  ((143 / 65) * 100 ) / 170 = 1.29 ((354 / 300) * 100 ) / 170 = 0.69 Divide the number of ODRs for a subgroup by the number enrolled Multiply result by 100 Divide result by the number of days that occurred

National Data Summary

Review Step 1: Problem Identification Select multiple metrics to use
Relative measure – Use Risk Indices to calculate Risk Ratio Absolute measure – ODRs per 100 Students per Day Composite measurements – What is composition of the problem? Calculate metrics and compare to reasonable goals Previous years from same school Local, state or national norms Logical criteria – “4/5ths Rule” State threshold for “significant disproportionality” Monitor metrics throughout the year Monthly or quarterly Be careful of small “Ns” Be careful of risk indices

Step 2: Problem Analysis
1. Problem Identification 2. Problem Analysis Is there a problem? Why is it happening? Once you have identified that there is a problem you can then analyze the problem to get more precise about the context and why it keeps occurring.

Purpose: Identify underlying causes of the problem Focus: Systems & practices that can be changed Evaluate: Tier 1 (universal systems) Check fidelity of implementation Disparities other than discipline Academic Placement Attendance School climate Graduation Discipline data for patterns of bias Explicit bias Implicit bias Research indicates that oftentimes bias is implicit rather than explicit. Implicit bias tends to be localized to specific situations (e.g., behaviors, locations).

Defining Problems with Precision Who is involved? What are the problem behaviors? Where is it happening? When is it happening? Why are these things happening? Perceived function of problem behavior

When we believe we have a problem….
Step 2: Problem Analysis When we believe we have a problem…. Assess PBIS implementation fidelity Performance gap Achievement gap Academic placement Attendance School climate Identified Subgroup Location Time of Day Problem Behavior Motivation Many seventh grade Black students are receiving referrals from the classroom in the afternoon for inappropriate language. Referrals are perceived to be task avoidance and getting adult attention.

Precise Problem Statement
SWIS Drill Down When we have a problem with disproportionate discipline… Precise Problem Statement

Subgroup: African American Students
Step 2: Problem Analysis SWIS Drill Down Subgroup: African American Students Who? When? What? Where? Why? 3rd grade 11:30 - Noon Physical Aggression Playground Peer Attention 4th grade 8:00 AM– 9:30 AM M-Defiance Classroom Avoid task 7th grade After 12:00 PM Inappropriate Language Classroom Hallway Obtain Peer Attention

Precise Problem Statements
Step 2: Problem Analysis Precise Problem Statements African American students in the 3rd grade are receiving referrals for physical aggression during noon recess. Referrals seem to be related to gaining peer attention. African American students in the 4th grade are more likely to receive referrals for minor defiance in the classroom during the morning instructional block. Referrals seem to be related to task avoidance. African American students in the 7th grade are receiving afternoon referrals in the classroom and hallways for inappropriate language. Referrals are related to avoiding tasks and gaining peer attention.

Step 3: Plan Implementation
1. Problem Identification 2. Problem Analysis Is there a problem? 3. Plan Implementation Why is it happening? What should be done?

Information from Step 2 is used to select tasks and strategies to address the problem. An action plan is created to ensure adequate implementation of the tasks and strategies. Action plans show everyone – WHO will do WHAT by WHEN. Action plans that are published – Help create accountability

Options All issues Calculate and share disproportionality data regularly Inadequate PBIS implementation Implement core features of PBIS to establish a foundation of support Misunderstandings regarding school-wide expectations Enhance culturally-responsive PBIS with input from the students/families Academic achievement gap Implement effective academic instruction

Step 4: Plan Evaluation 1. Problem Identification 2. Problem Analysis Is there a problem? 4. Plan Evaluation 3. Plan Implementation Why is it happening? Is the plan working? What should be done?

Step 4: Plan Evaluation Evaluation Regularly assess Progress
Fidelity of plan implementation Calculate metrics from Step 1 Compare to the goal determined in Step 1 Share results with relevant stakeholders Plan for what is next

Step 4: Plan Evaluation Evaluation Time Frame:
Identify time periods for regularly evaluating and analyzing disproportionality data. Caution: Disproportionality metrics may not be sensitive to rapid change. Consider monthly assessment of implementation & quarterly assessment of disproportionality metrics. Avoid relying on risk indices as they will increase throughout the year. Use multiple measures to ensure that you are tracking the correct thing.

Problem Solving Model 2. Problem Analysis 3. Plan Implementation 4. Plan Evaluation 1. Problem Identification Is there a problem? Why is it happening? Is the plan working? What should be done?

Using Data to Enhance Equity
A Michigan Example Jennifer Rollenhagen October 27, 2016

MIBLSI Equity Team Ruthie Riddle, Ph.D. Arezell Brown, Ed.S.
Steve Goodman, Ph.D. Director Ruthie Riddle, Ph.D. Equity Specialist Beth Hill, LMSW Equity Specialist Beth Brief overview of the team Arezell Brown, Ed.S. Urban Liaison Melissa Nantais, Ph.D. Professional Learning Coordinator Jennifer Rollenhagen, Ed.S. Measurement and Evaluation Specialist

MIBLSI Overall Goals for Equity Work
To demonstrate a meaningful reduction in discipline disproportionality with regards to race and ethnicity To implement and evaluate effective practices utilizing School-wide Positive Behavior Intervention and Supports Implement a model for reducing disproportionality that is durable, sustainable and scalable over time

MIBLSI Specific Equity Goal
To demonstrate outcomes aligning the equity and Promoting Positive School Climate (PPSC) work that can later be infused into the Integrated Model

MIBLSI Performance Measures
7.a. By the end of the pilot years, 80% of the supported schools will implement an integrated Multi-Tiered System of Supports with fidelity that includes additional practices to reduce disproportionality. 7.b. By the end of pilot years, 80% of supported pilot schools implementing with fidelity will show reduced overall levels of exclusionary disciplinary practices (office discipline referrals, suspensions, and expulsions) and reduced risk ratios for students of minority race/ethnicity in comparison to baseline data.

Resource Data Guide:

Data Guide Case Study A Michigan School Example
K-7 building with approximately 700 students Hispanic/Latino 50% White % Black/African Am % Asian % Pacific Islander/ Native Hawaiian <1% Multi-racial % 82% eligible for free and reduced lunch 76% considered economically disadvantaged District cited by Michigan for significant disproportionality The pilot school located in the Midwest had an enrollment of approximately 700 students in grades kindergarten to seven. Approximately 50% of the student population were Hispanic/Latino, 35% White, 7% Multi-Racial, 6% Black/African American, 1% Asian, less than 1% Pacific Islander/Native Hawaiian, and less than 1% American Indian/Alaskan Native. Furthermore, about 82% of the students were eligible for free and reduced lunch and about 76% were considered economically disadvantaged. During the previous school year, the district’s high school had been cited for significant disproportionality by the state department of education. Because of this citation, the district had been working with the state education department to address their significant disproportionality status with all schools across the district.

PBIS Implementation Prior to initial equity pilot work starting in , this school had implemented PBIS for 5 years. Fidelity of implementation varied across the years. School Leadership Team (SLT) guided the implementation process. Membership of team included principal, social worker, intervention specialist, and several teachers.

Timeline of Equity Pilot Work
 PPSC Partner with Equity Specialist Data Guide Equity Specialist Trainer Notes: 5 SLT meetings 4 All Staff PD PPSC Scope & Sequence, SLT & Staff PD 2 SLT Meetings

General Problem-Solving Model
2. Problem Analysis 3. Plan Implementation 4. Plan Evaluation 1. Problem Identification Is there a problem? Why is it happening? Is the plan working? What should be done? Ruth

Step 1: Problem Identification
Absolute Metric Definition: Rates or percentages that are shown separately for each group, either raw number or discrepancy between the group and an overall goal Office Discipline Referrals per 100 Students per Day (Monthly) Relative Metric Absolute metrics that are compared to each other Risk Ratio

2014-2015 SWIS Risk Ratio Report

Risk Ratio Report

Trainer Notes: Monthly Office Referral Rate per 100 students per subgroup

Fidelity of PBIS implementation Benchmark of Quality May % May % May % April % SWPBIS Tiered Fidelity Inventory Tier 1 November % February % Trainer Notes: Remember, here we are focusing on the purpose with identifying the underlying causes of the problem and the focus is on systems and practices that can be changed. Here we want to start the problem analysis with assess PBIS Fidelity.

Precise Problem Statement African American students are more likely to receive an office discipline referral for defiance and disruption in the classroom in the afternoon to obtain peer attention. Vulnerable Decision Point Ambiguity of problem behavior definitions and possible afternoon fatigue and hunger Trainer Notes; Here we are defining disproportionate discipline with precision. Who is involved, what are the problem behaviors, where is it happening, when is it happening, and why are these things happening?

No specific Precise Problem Statement was generated this year The scope of the equity work was to work with the School Leadership Team and staff to introduce and begin building knowledge of: Vulnerable Decision Points Neutralizing Routines Implicit Bias Trainer Notes; Here we are defining disproportionate discipline with precision. Who is involved, what are the problem behaviors, where is it happening, when is it happening, and why are these things happening?

Data Guide Pilot Address ambiguity of problem behavior definitions Accuracy of student demographic information entered in SWIS Firm up SWPBIS Tier 1 implementation

Full Partnership with MIBLSI Equity Specialist Used PBIS Data Guide to conduct data review that focuses on disproportionality with School Leadership Team and staff Review data (TFI, SWIS, Sub-group referral rate, Risk Ratio, Climate Survey, Achievement Gap data) Firm up PBIS implementation Introduce Important Concepts of Disproportionality Reduce ambiguity in determining major discipline incidents Culturally responsive PBIS Implicit bias and vulnerable decision points

Example SLT Action Plan for PBIS Implementation

Plan 4: Plan Evaluation Implement plans developed after analyzing data and after leadership team meetings Review action plans at monthly meetings to determine what has been implemented and if there are any barriers to implementation Determine which data will need to be collected, by when, and by whom Plan evaluation is a continuous process with adjustments to make it better

Lessons Learned Use of the Data Guide is an ongoing, continuous data solving process that will not change with a single event. Completing steps in the Data Guide requires time and involvement from district / school leadership to align the work with other initiatives. The School Leadership Team and staff benefited from regularly seeing the discipline data with a focus on race and discipline. Identifying the problem and possible solutions requires an understanding of the school organization, school culture, and available resources.

Next Steps for Promoting Positive School Climate with Equity Specialist Looking at the data Student Outcome Data (SWIS) Fidelity Data (SWPBIS TFI) School Climate Data Setting up the System District-wide Leadership Implementation Team trainings & meetings School Leadership Team trainings – SWPBIS Develop coaching and training capacity Continue implementation of SWPBIS Effective instruction – Training SLT on Classroom PBIS Spring Data Review

Working to do it better example

Using Data to Enhance Equity:
Data-driven Conversations on Equity & Disproportionality Use your available tools and resources to make sure you are offering all students their best chance for success. Did you get what you need? Bert Eliason & Jennifer Rollenhagen PBIS Leadership Forum, 2016

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