Capturing Culturally Responsive Data Practices in PBIS ... · Capturing Culturally Responsive Data Practices in PBIS ... 3 Title I Staff, 4 Special Education Teachers, 4 Specials

2

522016

Capturing Culturally Responsive Data Practices in PBIS Implementation

Secondary amp Elementary Settings May 5 2016 ndash 130 ndash 230

PBIS Implementerrsquos Forum

Description This session will feature 2 schools working to embed culturally responsive data practices as part of their PBIS implementation Each school one elementary and one secondary will share their journey towards assuring their PBIS data analysis includes data informing the team of disproportionate disciplinary

practices for students by race disability and gender

1

Learning Pathway and Targets

bull Introduce Culturally Responsive PBIS ndash 9 Priorities

bull Share out strengths and barriers in year 1 of CR PBIS implementationaround Data Systems

bull Convey strengths and barriers in year 1 of CR PBIS implementation around Data Teaming

CR PBIS Meet

Westinghouse Universal Data Meet Barrett

Universal Data

1

3

4

522016

What is CR PBIS

CR PBIS Meet


Universal Data

CR PBIS ndash The Grant Opportunity Culturally Responsive Approaches to Managing

Positive Behavior Interventions and Supports

Enhance the MTSS for behavior (PBIS) by building a foundation of prevention that supports the establishment of 1) clear

consistent positive school culture 2) clear discipline definitions and procedures that reduce ambiguity in discipline and

decisions 3) effective instructional approaches to discipline and 4) integration with an academic MTSS to ensure access to the core curriculum and keep students engaged in instruction and

learning

CR PBIS Meet


Universal Data

2

522016

5

Social Marketing Plan to all Stakeholders

Establishing MTSSPBIS

Aligning Authentic Family Documentation Engagement

Community Resource Mapping Evaluation of Data Practices amp

Action Planning Around Data

Climate and Culture Surveys

Professional Development

8 KRAs Key

Responsibility Areas

3

7

8

522016

Westinghouse Academy 6‐12

Barrett Elementary K‐5

Teachers 30 homerooms 49 Teachers

9 Sp Ed teachers 9 Related Arts 5 Career Tech Ed

(Cul Arts Cosmo Health Careers Carpentry amp Bus of Sports Admin)

15 homerooms 3 Title I Staff 4 Special Education Teachers 4 Specials Teachers

Year Implementing 2nd year 8th year

Students 457 ‐ grades 6‐12 250 students grades 1‐5

PBIS Matrix PRIDE Barrett Bears

Students grades

6 48

7 47

8 71

9 91

10 82

11 60

12 58

Meet Westinghouse Academy 6‐12

CR PBIS Meet


Universal Data

4

10

522016

Demographics as of Fall 2014 bull 463 School Performance Profile Rating bull 58 of students chronically absent bull 84 eligible for free and reduced lunch bull 20 of seniors have a GPA of 25 or higher with 90 attendance rate bull 62 of students were suspended atleast once bull Graduation Rate of 83 with 22 attending college

9

CR PBIS Meet


Universal Data

Students

bull 52 turnover bull 55 (269) male bull 45 (222) female bull Ethnicracial data bull 97 ‐ AA bull 2 ‐ MR bull 1 ‐ W

Staff

bull 14 New Teachers bull 30 teachers new at the start of the school year bull 33 (16) Non‐tenured teachers

5

12

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Challenges

Pgh Public Schools ‐ WestinghouseWilkinsburg Merger bull 170 7‐12 grade students from Wilkinsburg School District

20 Teacher turnover during the school year

52 Mobility rate for students

11

Foundations Supporting CR PBIS

bull Courageous Conversations

bull Trauma Informed CarePractices

bull Trauma Informed Care Instructional Practices

bull Monthly Discipline Committee Meeting

bull School Based Behavioral Health Collaboration

bull Creating A Community SchoolAligning community district school and community agencies

CR PBIS Meet


Universal Data

6

13

14

522016

CR PBIS ‐ Implementing Universal

CR PBIS Meet


Universal Data

The Westinghouse 6‐12 School Model Positive Behavior Interventions amp Supports

Universal School-Wide Assessment

School-Wide Prevention Systems

Tier 2

Tier 3 Simple Individual Interventions (Brief FBABIP Schedule Curriculum Changes etc)

Small Group Interventions (CICO Social and Academic support groups etc)

ODRs Attendance Tardies Grades Credits Progress

Reports etc

Coming Soon to a Pyramid near you of students requiring each level of support

CR PBIS Meet


Universal Data

7

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Universal Supports as of Fall 2014

bull 6‐12 SAP Team ndash Academic and Behavioral

bull Student Leveling System

bull Effective Classroom Management in every classroom

bull Therapeutic Emotional Support Programs

bull School‐Based Behavioral Health

bull Read 180

bull Behavioral Lesson Plans

bull SET

15

Staying Focused on Universal Supports

Matrix ndash Bulldogs

bull Insert Matrix here

16

8

17

18

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Data Systems and Teaming

CR PBIS Meet


Universal Data

Data Systems

bull Action From Excel spreadsheets SWIS (School Wide Information System University of Oregon)

bull Timeline bull SWIS Training Winter 2016 bull Data Entry of all 2015 ndash 2016 data to date bull PBIS Data Team meetings using TIPS

bull Progress

CR PBIS Meet


Universal Data

9

522016

GOAL To determine any disproportionality of disciplinary practices for gender disabilities and

ethnicity

19

Progress from Year 1 and Year 2

Data Teaming Data Systems Year 1 bull Establishing Members

bull Scheduling bull Format

bull Identifying sources bull Determining Authenticity bull Building Universal Systems

Year 2 bull Using TIPS Process bull Use of Team Implementation Checklist (TIC)

bull Holding on to 80 buy in

bull SWIS bull Revising ODR bull Determining Authenticity

20

10

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Hello Baseline

21

National Averages

22

11

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2015 ndash 2016 ldquoHas IEPrdquo

23

ldquoDoes Not Have IEPrdquo

24

12

26

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Disproportionate Disciplinary Practices

25

Subgroup lower than 10 ndash does not calculate

1) Calculate Risk Index 2) Calculate Risk Ratio

Major‐Minority School

37

10

Risk Ratio

13

In year 2 of implementation one goal is to be sure that the data is authentic Are teachers completing ODRs correctly Explicit instruction for staff on how and when to complete an ODR

522016

CR PBIS and Data Systems

bull Have the right data points

bull Understand Risk Ratios and Risk Indexes

bull Race bull Gender bull Disability


27

Next Steps Action Implement TIPS (Team Initiated Problem Solving) Training

Schedule SET for Spring 2017

Progress Strengths

Leadership Securing and Committing Time for Meetings

Barriers Teacher Buy‐In in light of high teacher turn over ‐ sustainability Establishing Roles Transitioning from ldquoadmiring the problemrdquo to ldquoaction based on datardquo

28

14

29

522016

Meet Barrett Elementary

CR PBIS Meet


Universal Data

Steel Valley School District

According to 2005 census data bull The district had a 18340 resident population bull Per capita income was 16902$ bull Just over 1800 students

bull Most recent data indicates that the resident population is still around 18000 bull Median income is approximately $37000 bull The district population has decreased to just over 1400 (as of April 19 2016)

30

15

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Rate of Turnover 37 Grade 1 2 3 4 5

NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)

Barrett Special Education and Ethnicity Data

bull 254 Total Students (as of April 19 2016)

2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12

bull 90 students or 35 of the students have an IEP bull 22 students or 9 are Speech and Language only bull 68 students or 27 make up the other categories

32

16

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522016

Year 8 of PBIS Implementation Goals

bull Attendance (specifically tardies) bull Transition from a school‐wide token economy to the Principalrsquos 200 Club format with moreemphasis on classroom rewards bull Emphasis on fine tuning the Tier 2 SAP procedures and supports such as using data as opposed to teacher recommendation for CICO behavior plans small group lessons and academic interventions bull Committing to evidenced based curriculumresources for Tier 2 academic and behavioral interventions bull Increasing teacher buy in and involvement bull Make the school schedule around the needs of the Special Education students bull Review data through a culturally responsive lens with regard to ethnicity and students with IEPs bull Increasing parent involvement through a targeted group of parents that would meet 6 times during the year to focus on school data and cultural responsive decision making and through offering parent awards and raffle drawings at each event

CR PBIS Meet


Universal Data

Progress bull We placed a staff member at the entrance to welcome students and used a sticker chart to track days on time for any student that accumulated 10+ days tardy and offered additional incentives for improvement In the first 30 days only 2 students showed improvement The 2nd 30 days there were 9 students that earned There are 31 students using this intervention bull Teachers began tracking tardies on major and minor referral forms in SWIS This was found to be inconsistent and a lot of work The team will review and make decisions at our June Action Planning meeting bull Teachers have given positive verbal feedback regarding Special Education students having their LA and Math classes at the same time as the peers in their homerooms Special EdTeachers reported that lesson planning and interventions were able to be more direct bull Teacher Buy‐in improved with one teacher focusing on dress down days and incentives for staff Gift cards were welcomed as well as reimbursement for class incentives bull The PATHS curriculum Second Step Curriculum Life Skills Curriculum NED Show resources DARE LLI Reading Series KID writing are among the utilized curriculums

34CR PBIS Meet


Universal Data

17

522016

Principalrsquos PAW ticket

35

Principalrsquos PAW club

36

18

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Tardy Incentive Chart

37

Parent Awards

38

19

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Beyond Universal

39

Advanced Tiers Team

Challenges Keeping Focus Using the Right Data



Tier 2




Reports etc Universal Core Team

40CR PBIS Meet


Universal Data

SWIS Data Screenshot

20

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41

Risk Indices ‐ Ethnicity

Risk Index= of students with 1+ ODR from your target group

of students in the target group

160 black students have referrals = 89 risk index 178 black students enrolled

17 white students have referrals = 52 risk index 33 white students enrolled

42

21

44

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From Risk Index Risk Ratio

Risk index by itself does not provide a comparison

A comparison of risk indices between two groups is called a risk ratio

43

Risk Ratio for Black and White Subgroups Risk Ratio= Risk Index of the target group

Risk index for comparison group

90 (risk index for Black students) = 173

52 (risk index for White students)

52 (risk index for White students) = 57

90 (risk index for Black students) SohelliphellipBlack students are 173 times more likely to receive an ODR than their

White peers

22

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Risk Indices‐ Special Education

88 students with 1+ ODR that do not have an IEP= 59 150 total students that do not have an IEP

41 students with 1+ ODR that have an IEP = 46 90 total students that have an IEP

45

Risk Ratio for Special and General Ed Subgroups

46 risk index for students with an IEP = 78

59 risk index for students without an IEP

59 risk index for students without an IEP= 128

46 risk index for students with an IEP Sohelliphellipwith students with IEPs are less likely to receive an ODR than

their peers in general education

46

23

What is our plan for the future

1 Action Plan for 20162017 in June

2 Review Code of Conduct and policies to detect anyambiguity in policy or classroom management systems ‐strengthen current systems in order to decrease referrals

3 Continue parent involvement committee for 20162017

47

Policies ‐ Effective Practice Options

Ineffective Effective

Enacting policies that nobody knows Set clear priorities about

Enacting policies that donrsquot change Reduce the effects of explicit bias practice

Policies without accountability for Enable implementation of specific implementation interventions

Reduce discriminatory practices Green A Nese R McIntosh K Nishioka V Eliason B amp Delara AC (2015)

48

522016

24

522016

Equity Policy Recommendations (Green et al (2015)

1 Include a Specific Commitment to Equity bull Create mission statements that include equity bull Enact hiring preferences for equitable discipline

2 Install Effective Practices bull Require clear objective school discipline procedures bull Support implementation of proactive positive approaches to discipline bull Replace exclusionary practices with instructional ones

3 Create Accountability for Efforts bull Create teams and procedures to enhance equity bull Share disproportionality data regularly bull Build equity outcomes into evaluations

References

bull Green A Nese R McIntosh K Nishioka V Eliason B amp Delara AC (2015) Key elements of policies to address discipline disproportionality A guide for district and school teams OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports wwwpbisorg

bull SWIS Version 56 | August 2015 University of Oregon wwwpbisappsorg

50

25

3

4

522016

What is CR PBIS

CR PBIS Meet


Universal Data

CR PBIS ndash The Grant Opportunity Culturally Responsive Approaches to Managing

Positive Behavior Interventions and Supports

Enhance the MTSS for behavior (PBIS) by building a foundation of prevention that supports the establishment of 1) clear

consistent positive school culture 2) clear discipline definitions and procedures that reduce ambiguity in discipline and

decisions 3) effective instructional approaches to discipline and 4) integration with an academic MTSS to ensure access to the core curriculum and keep students engaged in instruction and

learning

CR PBIS Meet


Universal Data

2

522016

5








8 KRAs Key


3

7

8

522016










Students grades

6 48

7 47

8 71

9 91

10 82

11 60

12 58


CR PBIS Meet


Universal Data

4

10

522016


9

CR PBIS Meet


Universal Data

Students


Staff


5

12

522016

Challenges




11








CR PBIS Meet


Universal Data

6

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

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Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

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25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

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45







46

23





47







48

522016

24

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References



50

25

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5








8 KRAs Key


3

7

8

522016










Students grades

6 48

7 47

8 71

9 91

10 82

11 60

12 58


CR PBIS Meet


Universal Data

4

10

522016


9

CR PBIS Meet


Universal Data

Students


Staff


5

12

522016

Challenges




11








CR PBIS Meet


Universal Data

6

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

7

8

522016










Students grades

6 48

7 47

8 71

9 91

10 82

11 60

12 58


CR PBIS Meet


Universal Data

4

10

522016


9

CR PBIS Meet


Universal Data

Students


Staff


5

12

522016

Challenges




11








CR PBIS Meet


Universal Data

6

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

10

522016


9

CR PBIS Meet


Universal Data

Students


Staff


5

12

522016

Challenges




11








CR PBIS Meet


Universal Data

6

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

12

522016

Challenges




11








CR PBIS Meet


Universal Data

6

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

13

14

522016


CR PBIS Meet


Universal Data




Tier 2




Reports etc


CR PBIS Meet


Universal Data

7

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016







bull Read 180


bull SET

15




16

8

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

17

18

522016


CR PBIS Meet


Universal Data

Data Systems



bull Progress

CR PBIS Meet


Universal Data

9

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016


ethnicity

19








20

10

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016

Hello Baseline

21

National Averages

22

11

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016


23


24

12

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

26

522016


25




37

10

Risk Ratio

13


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25


522016






27



Progress Strengths



28

14

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

29

522016


CR PBIS Meet


Universal Data




30

15

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016


NEW 28 19 19 15 20

DEPARTED ‐15 ‐18 ‐21 ‐25 ‐13

BEGINNING 42 62 47 55 44

END 54 60 45 44 51

of Turnover

31 30 46 51 27

R = D((B+E)2)



2 Asian 1

190 Black 75

4 Hispanic 2

25 Multi racial 10

33 White 12


32

16

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

33

522016



CR PBIS Meet


Universal Data


34CR PBIS Meet


Universal Data

17

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016


35


36

18

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016


37

Parent Awards

38

19

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016

Beyond Universal

39

Advanced Tiers Team




Tier 2





40CR PBIS Meet


Universal Data


20

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016

41






42

21

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

44

522016




43







White peers

22

522016




45







46

23





47







48

522016

24

522016





References



50

25

522016




45







46

23





47







48

522016

24

522016





References



50

25





47







48

522016

24

522016





References



50

25

522016





References



50

25

Documents

Capturing Culturally Responsive Data Practices in PBIS ... · Capturing Culturally Responsive Data Practices in PBIS ... 3 Title I Staff, 4 Special Education Teachers, 4 Specials