Making Data-Based Decisions
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Making Data-Based Decisions
Tim Lewis, Ph.D.University of Missouri
OSEP Center on Positive Behavioral Interventions and Supports
pbis.org
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Data-Based Decision Making1. Determine what questions you want to
answer2. Determine what data will help to answer
questions3. Determine the simplest way to get data4. Put system in place to collect data 5. Analyze data to answer questions
Focus on both Academic and Social Outcomes
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1. Determine what questions you want to answer
Examples• Can we predict problems/success?
– When/where/who? • Possible “function” of problem behavior?• Who needs targeted or intensive academic
supports?• What environmental changes/supports are
needed?
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2. Determine what data will help to answer questions
• Existing data set(s)• Current data collection• Additional / new data
• Confidence in accuracy?• Complete picture?
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3. Determine the simplest way to get data
• Agreement on definitions• Standard forms / process• Frequency of collection• Target “Multi-purpose” data/use
Train ALL staff on use & provide on-going TA
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4. Put system in place to collect data
• Build on existing systems• Add components over time• Central entry point
– Electronic
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5. Analyze data to answer questions
• Trends• Instruction & supports in place/not in-place• Pre/post “big outcomes”• Comparisons (norm / local)
– Relative growth– Absolute growth
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By Location1998-99/#ODR by Location
301
76
2
7955
436 36 38
1 8 10
50
100
150
200
250
300
350
ClassroomLunchroomLobby
Playground/Recess
BusBus LineHallwayBathroom
Gym
Computer LabLibrary Activity
Location
#ODR
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By Behavior1998-99 #ODR/Behavior
0
20
40
60
80
100
120
140
160
180
200
Fighting Class Disruption Non-compliance InappropriateLanguage
Cut Class/Out ofArea
Property Smoking/Drugs
Behavior
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By Student1998-99 By Student
0123456789
10111213141516171819202122232425
1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161 169 177Student #
# of ODR
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By # of Referrals1988-99 #ODR per Student
80
32
16 15
84 4 3 2
42 3 3
0 1 0 1 1 0 0 1 0 10
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23#Referrals
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0
5
10
15
20
Ave Referrals 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 Referrals per Day
Sept Oct Nov Dec Jan Feb Mar Apr May Jun
School Months
Office Referrals per Day per MonthLast year
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0
5
10
15
20
Ave Referrals per Day
Sept Oct Nov Dec Jan Feb Mar Apr May Jun
School Months
Office Referrals per Day per MonthLast Year and This Year
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IF...
FOCUS ON...
£ More than 40% of students receive one or more office
referrals £ More than 2.5 office referrals per student
School Wide System
£ More than 35% of office referrals come from non-classroom settings
£ More than 15% of students referred from non-classroom settings
Non-Classroom System
£ More than 60% of office referrals come from the classroom
£ 50% or more of office referrals come from less than 10% of classrooms
Classroom Systems
£ More than 10-15 students receive 5 or more office referrals
Targeted Group Interventions / Classroom Systems
£ Less than 10 students with 10 or more office referrals £ Less than 10 students continue rate of referrals after
receiving targeted group settings £ Small number of students destabilizing overall
functioning of school
Individual Student Systems
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Final Thoughts
• Don’t collect data for collection sake – make sure informs the process
• Don’t “drown” in data – keep focused on the question
• Data without context are simply numbers