Understanding Office Discipline Referral Data
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Transcript of Understanding Office Discipline Referral Data
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Understanding Office Discipline Referral Data
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Steps of Data Collection, Analysis, and Use1. Identify sources of information and data2. Summarize/Organize Data3. Continual Data Analysis4. Build action plan based on data trends
Sources Organize Analysis Action Plan
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The Big Five• Average Referral/Day/Month• # of Referrals by Behavior• # of Referrals by Location• # of Referrals by Time of Day• # of Referrals by Student
Sources
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The Big Five• Average Referral/Day/Month• # of Referrals by Behavior• # of Referrals by Location• # of Referrals by Time of Day• # of Referrals by Student
Sources
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Average Referral Rate per Month
Organize
Harlan Elementary SchoolBehavior Referrals/Day/Month
0123456789
Months
Beh
avio
r Ref
erra
ls/D
ay/M
onth
2000-20012001-20022002-20032003-20042004-20052005-2006
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Organize
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Organize
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Comparison by Location
0
50
100
150
200
250
Gym Other Location Library Hall/ Breeze way Cafeteria P layground Classroom
2002-2003
2003-2004
Organize
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Organize
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Incident Count for Time Code Incident Code Time
1 23 2 16 3 14 4 11 5 16 6 17 7 21 8 49
Total: 167
Time Intervals: 0 = Time missing 1 = 07:00 - 07:59 2 = 08:00 - 08:59 3 = 09:00 - 09:59 4 = 10:00 - 10:59 5 = 11:00 - 11:59 6 = 12:00 - 12:59 7 = 13:00 - 13:59 8 = 14:00 - 14:59 9 = 15:00 - 15:59
10 = 16:00 - 16:59 11 = 17:00 - 17:59 12 = 18:00 - 18:59 13 = 19:00 - 19:59 14 = 20:00 - 20:59 15 = 21:00 - 21:59 16 = 22:00 - 22:59 17 = 23:00 - 23:59
Organize
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Organize
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Referrals by Student
76%
21%
4%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Behavior Reporting Triangle
6+ Referrals
2 - 5 Referrals
0 - 1 Referrals
Organize
Total Enrollment 327
# of Students with 0-1 247 76%
# of Students with 2-4 68 21%
# of Students with 6+ 12 4%
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Other Referral Information• # of referrals per Day of the Week• # of Suspensions (In-School and Out-of-School)• # of Referrals by Classroom• # of Referrals by Staff • # of Referrals compared to # of
acknowledgments• Others?
Sources
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250
300
350
400
450
500
550
600
Mon Tue Wed Thu FriDay of Week
Office Referrals by Day of Week1994-1995
Organize
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Organize
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Suspensions Data
0
10
20
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60
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90
Events Students Contributing
In School SuspensionsOut of School Suspensions
Organize
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Acknowledgements & Minor Incidencesper Day per Month
05
10152025303540
Sep Oct Nov Dec Jan Feb Mar Apr May Jun
Months
# pe
r Day Acknowledgements
Minors
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Why Collect Data• Decision making• Professional accountability• Decisions made with data (information) are more
likely to be (a) implemented, and (b) be effective• You won’t just be relying on a “hunch” to know if
you are effective
Adapted from “Using Data for Decision-making” Rob H. Horner, George Sugai, Teri Lewis-Palmer, & Anne W. Todd
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Key Features of Data Systems That Work
• The data are accurate• The data are easy to collect• Data are used for decision-making• The data must be available when decisions need
to be made• The people who collect the data must see the
information used for decision-making
Adapted from “Using Data for Decision-making” Rob H. Horner, George Sugai, Teri Lewis-Palmer, & Anne W. Todd
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What Data to Collect for Decision-Making
• USE WHAT YOU HAVE• Office Discipline Referrals/Detentions - Measure
of overall environment• Referrals are affected by:
– (a) student behavior – (b) staff behavior – (c) administrative context
• Suspensions/Expulsions• Other Sources
Adapted from “Using Data for Decision-making” Rob H. Horner, George Sugai, Teri Lewis-Palmer, & Anne W. Todd