data-based decision making: basics osep center on positive behavioral interventions & supports...
TRANSCRIPT
Data-based Decision Making: BasicsOSEP Center on Positive Behavioral
Interventions & Supports
February 2006
www.PBIS.org www.SWIS.org
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