District Leadership Team Webinar #1:Data Based Decision Making

Center for Education and Lifelong Learning

The Equity Project at Indiana University

Culturally Responsive Positive Behavioral Interventions and Supports

www.indiana.edu/~pbisin

Focus of Webinar

Review of the Role of the DLT Assesses district strengths and needs

related to using Data for Decision Making in CR-PBIS

Discuss Next Steps for the DLT in your District

Continued Work with PBIS-IN

The Blueprint

FUNDING VISIBILITY/ 2 WAY

COMMUNICATION

Political SUPPORT

POLICY

District LEADERSHIP TEAM (Coordination)

TRAINING

EQUITY

COACHING

EVALUATION BEHAVIORAL EXPERTISE

LOCAL SCHOOL/DISTRICT IMPLEMENTATION DEMONSTRATIONS

USING DATA FOR DECISION MAKING

5

Data! Data! Data!

How do we collect data? How do we analyze data?

How often? Who? When?

How can the data and information be used to improve our discipline system? Applying to all students equally Use of demographic data Behaviors and locations Classroom managed vs. Office managed

Defining the Problem

Review your data to determine current strengths and areas of need: Compare district enrollment numbers to

current outcomes Disaggregate data by ethnicity, gender, SES,

grade level

District A Composition

Racial/Ethnic Group

District Enrollment Numbers

Composition in District

American Indian 22 0.2%African American 1353 13.4%Asian 786 7.8%Hispanic 495 4.9%White 6993 69.1%Multiracial 464 4.6%Total 10113

Am

eric

an

Ind

ian

Asi

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/Pa

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Afr

ica

n A

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rica

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ite

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10

20

30

40

50

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70

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0.2

7.813.4

4.9

69.1

4.6

% Enrollment

% Enrollment

Data-Based Decision making using ODRs

Examine ODRs( number of office referrals):– Per day per month– Based on location– Based on type of behavior– By student– By time of day– By subgroup (i.e. ethnicity, gender, special

education status) Examine consequences of referrals

– Suspension and expulsion data– Disaggregated suspension and expulsion data

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Analyzing ODR data Do we have a problem?

Avg./per day per month Elem. .22/100 students per day. MS .50/100 per day HS .68/100 per day

Trends and Peaks What kind of problem?

Behaviors of concern Where?

Hot spots, cool spots10

Analyzing ODR data

When? Time of Day

By Whom? Lots of students or few students? What

percentage of students have been to office? Subgroups?

Do you have an disproportionate representation problem?

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Discipline# of

Events# of

Students# of Days

Number of Events/Number of Students

Amer Indian AsianAfrican

American Hisp/Latin White OtherOffice Discipline Ref. (ODR) 2691 1195 2 2 122 87 1215 505 127 59 1088 485 137 57

Out-of School

Suspension 1692 856 6 6 79 49 794 347 79 70 200 333 120 51Total

Suspensions 4383 2051 8 8 201 136 2009 852 206 129 1288 818 257 108Expulsion 25 25 528 0 0 1 1 8 8 1 1 14 14 1 1

Discipline# of

Events# of

Students# of Days

Composition in District/ Composition in Discipline

Amer Indian AsianAfrican

American Hisp/Latin White Other

Office Discipline Ref. (ODR) 2691 1195 .2% .2% 7.8% 7.3% 13.4% 42.3% 4.9% 4.9% 69.1% 40.6% 4.6% 4.8%

Out-of School

Suspension 1692 856 .7% 5.7% 40.5% 8.2% 38.9% 6.0%Total

Suspensions 4383 2051 .4% 6.6% 41.5% 6.3% 39.9% 5.3%

Expulsion 25 25 528 0 4.0% 32% 4.0% 56% 4.0%

District A Data Summary

American Indian Asian/Pacific Islander African American Hispanic White Multiracial

% Enrollment 0.2 7.8 13.4 4.9 69.1 4.6

% ODRs 0.2 7.3 42.3 4.9 40.5 4.8

% OSS 0.4 5.7 40.5 8.2 38.9 6

5

15

25

35

45

55

65

75

District A Discipline Patterns Compared to Enrollment

NEXT STEPS FOR YOUR DLT

Your Next Steps

Task Timeline Who’s Involved

Evaluate Data Management System

Collect Discipline Data Through December 2011

Meet as a DLT to analyze the data

Identify 2 DLT representatives to attend DLT session in February

Contact Us

www.indiana.edu/~pbisin Contact us with questions:

Renae Azziz

razziz@virtuosoed.com

Shana Ritter

rritter@indiana.edu