data-based decision making: basics osep center on positive behavioral interventions & supports...

Data-based Decision Making: BasicsOSEP Center on Positive Behavioral

Interventions & Supports

February 2006

www.PBIS.org www.SWIS.org

George.sugai@uconn.edu

C/3

SYST

EMS

PRACTICES

DATASupportingStaff Behavior

SupportingStudent Behavior

OUTCOMES

Supporting Social Competence &Academic Achievement

SupportingDecisionMaking

4 PBS Elements

3 Elements of Data-based Decision Making

1. High quality data from clear definitions, processes, & implementation (e.g., sw behavior support)

2. Efficient data storage & manipulation system (e.g., SWIS)

3. Process for data-based decision making & action planning process (e.g., team)

Assumptions• Continuum of school-wide system of

positive behavior support in place

• “Good” data available

• Team-based leadership

• In-building expertise

• School-level decision making needed

Start with Questions & Outcomes!

• Use data to verify/justify/prioritize

• Describe in measurable terms

• Specify realistic & achievable criterion for success

LEADERSHIP TEAM

SCHOOL-WIDE

Build DataSystem

Establishmeasurable

outcome

Collect, analyze, &prioritize data

Ensure efficient,accurate, & durable

implementation

Implement

Monitorimplementation &

progress

Selectevidence-based

practice

School-wide PBS Systems Implementation Logic

Kinds of Data• Office discipline reports

• Behavioral incidents

• Attendance

• Suspension/Detention

• Observations

• Self-assessments

• Surveys, focus groups

• Etc.

Office Discipline Referral Caution

• Reflects 3 factors

– Student

– Staff member

– Office

• Reflects overt rule violations

• Underestimations

General Approach: “Big 5”

• # referrals per day per month

• # referrals by student

• # referrals by location

• #/kinds of problem behaviors

• # problem behaviors by time of day

0

0.5

1

1.5

2

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast year

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast year

Days: 175 Referrals: 471 Avg: 2.69

M/m

Days: 175 Referrals: 86 Avg: 0.49

M

M/M

Is action needed?

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthThis YearIs action needed?

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May

School Months

Office Referrals per Day per MonthThis Year

Is action needed?

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthThis year (Middle)

Is action needed?

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast Year and This Year

Is action needed?

0

5

10

15

20 A

ve R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast Year and This Year

Is action needed?

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast Year and This Year

Is action needed?

0

5

10

15

20 A

ve R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthLast Year and This Year

Is action needed?

What?

0

10

20

30

40

50

Num

ber

of R

efe

rrals

Lang Achol ArsonBombCombsDefianDisruptDressAgg/fgtTheftHarassProp D Skip Tardy Tobac Vand Weap

Types of Problem Behavior

Referrals per Prob Behavior

What?

0

10

20

30

40

50

Num

ber

of R

efe

rrals

Lang Achol ArsonBombCombsDefianDisruptDressAgg/fgtTheftHarassProp D Skip Tardy Tobac Vand Weap

Types of Problem Behavior

Referrals per Prob Behavior

What?

0

5

10

15

Num

ber

of R

efe

rrals

Lang Achol ArsonBombCombsDefianDisruptDressAgg/fgtTheftHarassProp D Skip Tardy Tobac Vand Weap

Types of Problem Behavior

Referrals per Prob Behavior

Where?

0

10

20

30

40

50

Num

ber

of O

ffic

e R

efe

rrals

Bath RBus A Bus Caf ClassComm Gym Hall Libr Play G Spec Other

School Locations

Referrals by Location

0

10

20

30

40

50

Num

ber

of O

ffic

e R

efe

rrals

Bath RBus A Bus Caf ClassComm Gym Hall Libr Play G Spec Other

School Locations

Referrals by LocationWhere?

Who?

0

10

20

Num

ber

of R

efe

rrals

per

Stu

dent

Students

Students per Number of Referrals

Who?

0

10

20

Num

ber

of R

efe

rrals

per

Stu

dent

Students

Students per Number of Referrals

When?

0

5

10

15

20

25

30 N

um

ber

of R

efe

rrals

7:00 7:30 8:00 8:30 9:00 9:30 10:0010:3011:00 11:3012:0012:30 1:00 1:30 2:00 2:30 3:00 3:30

Time of Day

Referrals by Time of Day

When?

0

5

10

15

20

25

30

Num

ber

of R

efe

rrals

7:00 7:30 8:00 8:30 9:00 9:30 10:0010:3011:00 11:3012:0012:30 1:00 1:30 2:00 2:30 3:00 3:30

Time of Day

Referrals by Time of Day

“Real” Data• “A. E. Newman” Elementary School

– ~450 K-5 students

– ~40% free/reduced lunch

– Suburban

# Behavior Incidents/Day/Month

# BI by Problem Behavior Type

# Major BI/Day/Month

# BI by Location

# BI by Time of Day

# BI by Staff Member

# Major BI by Staff Member

SW v. Individual

• Examine impact of individual student behavioral incidents on school-wide behavior incidents

# Major BI by Student w/ >1

# BI by Student w/ >3

SW v. IndividualMajors + Minors Majors Only

# % # %

1-2 89 20% 44 10%

3-5 27 6% 10 2%

>5 30 7% 4 1%

What about CLEO?• 12 BI Dec. 2000 – Jun. 2001

• 19 BI Sep. 2001 – Dec. 2001

Suspensions/Expulsions Per Year

2000-01 2001-02

Events Days Events Days

In School Suspensions 0 0 2 2

Out of School Suspensions 1 1 3 2.5

Expulsions 0 0 0 0

CLEO: # BI/Day/Month

CLEO: # BI by Type

CLEO: # BI by Location

Guidelines: To greatest extent possible….

• Use available data

• Make data collection easy (<1% of staff time)

• Develop relevant questions

• Display data in efficient ways

• Develop regular & frequent schedule/routine for data review & decision making

• Utilize multiple data types & sources

• Establish clarity about office v. staff managed behavior

• Invest in local expertise

Conclude• Data are good…but only as good

as systems in place for– PBS

– Collecting & summarizing

– Analyzing

– Decision making, action planning, & sustained implementation