1. Data Driven Decision Making Missouri PBS Summer Institute June 28 & 29, 2006

2. Purpose Provide guidelines for using data for team planning Provide guidelines for using data for on- going problem solving Apply guidelines to examples

3. Improving Decision Making From ProblemSolution To Problem Problem Solving Solution

4. Key features of data systems that work The data are accurate and valid The data are very easy to collect (1 % of staff time) Data are presented in picture (graph) form Data are used for decision-making – The data must be available when decisions need to be made (weekly?) – Difference between data needs at a school building and data needs for a district – The people who collect the data must see the information used for decision-making.

5. Why collect discipline data? Decision making Professional accountability Decisions made with data (information) are more likely to be 1) implemented and 2) effective.

6. What data to collect for decision making? Use what you have: Attendance Suspensions/Expulsions Vandalism Office discipline referrals/detentions – Measure of overall environment. Referrals are affected by 1) student behavior 2) staff behavior and 3) administrative context – An under-estimate of what is really happening – Office referrals per day per month

7. When should data be collected? Continuously Data collection should be an embedded part of the school cycle, not something “extra” Data should be summarized prior to meetings of decision-makers Data will be inaccurate and irrelevant unless the people who collect and summarize it see the data used for decision making.

8. Organizing Data for “active decision making” Counts are good, but not always useful To compare across months use “average office discipline referrals per day per month”

9. Using Data for On-going Problem Solving Start with the decision, not the data Use data in “decision layers” (Gilbert, 1978) – Is there a problem? (overall rate of ODR) – Localize the problem (location, problem behavior, students, time of day) Don’t drown in the data It’s “OK” to be doing well Be efficient

10. Interpreting Office Referral Data: Is there a problem? Absolute level (depending on size of school) – Middle, High Schools (1> per day per 100) – Elementary Schools (1> per day per 250) Trends – Peaks before breaks? – Gradual increasing trend across year? Compare levels to last year – Improvement?

11. What systems are problematic? Referrals by problem behavior? – What problem behavior is most common? Referrals by location? – Are there specific problem locations? Referrals by student? – Are there many students receiving referrals or only a small number of students with many referrals? Referrals by time of day? – Are there specific times when problems occur?

12. Designing Solutions If many students are making the same mistake it typically is the system that needs to change, not the students. Teach, monitor and reward before relying on punishment.

13. Application Exercise What is going well? Do you have a problem? Where? With whom? What other information might you want? Given what you know, what considerations would you have for possible action?

14. SWIS: School-Wide Information System http://www.swis.org SWIS Readiness Checklist SWIS Compatibility Checklist

15. Summary Transform data into “information” that is used for decision making Present data within a process of problem solving – Use the trouble-shooting tree logic – Big Five first (how much, who, what, where, why) Ensure the accuracy and timeliness of data