Making Data-Based Decisions

Making Data-Based Decisions

Tim Lewis, Ph.D.University of Missouri

OSEP Center on Positive Behavioral Interventions and Supports

pbis.org

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

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?

2. Determine what data will help to answer questions

• Existing data set(s)• Current data collection• Additional / new data

• Confidence in accuracy?• Complete picture?

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

4. Put system in place to collect data

• Build on existing systems• Add components over time• Central entry point

– Electronic

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

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

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

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

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

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

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

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

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