Data-based Decision Making: Basics

1. Data-based Decision Making: Basics OSEP Center on Positive Behavioral Interventions & Supports February 2006 www.PBIS.org www.SWIS.org George.sugai@uconn.edu C/3

2. Supporting Social Competence & Academic Achievement 4 PBS Elements OUTCOMES Supporting Decision Making Supporting Staff Behavior DATA SYSTEMS PRACTICES Supporting Student Behavior

3. 3 Elements of Data-based Decision Making • High quality data from clear definitions, processes, & implementation (e.g., sw behavior support) • Efficient datastorage & manipulation system (e.g., SWIS) • Process for data-based decision making & action planning process (e.g., team)

4. 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

5. Start with Questions & Outcomes! • Use data to verify/justify/prioritize • Describe in measurable terms • Specify realistic & achievable criterion for success

6. School-wide PBS Systems Implementation Logic

7. Kinds of Data • Office discipline reports • Behavioral incidents • Attendance • Suspension/Detention • Observations • Self-assessments • Surveys, focus groups • Etc.

8. Office Discipline Referral Caution • Reflects 3 factors • Student • Staff member • Office • Reflects overt rule violations • Underestimations

9. 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

12. M/m

13. M

14. M/M

15. Is action needed? Is action needed?

16. Is action needed?

17. Is action needed?

18. Is action needed?

19. Is action needed?

20. Is action needed?

21. Is action needed?

22. What?

23. What?

24. What?

25. Where?

26. Where?

27. Who? Students per Number of Referrals

28. Who?

29. When?

30. When?

31. “Real” Data • “A. E. Newman” Elementary School • ~450 K-5 students • ~40% free/reduced lunch • Suburban

32. # Behavior Incidents/Day/Month

33. # BI by Problem Behavior Type

34. # Major BI/Day/Month

35. # BI by Location

36. # BI by Time of Day

37. # BI by Staff Member

38. # Major BI by Staff Member

39. SW v. Individual • Examine impact of individual student behavioral incidents on school-wide behavior incidents

40. # Major BI by Student w/ >1

41. # BI by Student w/ >3

42. SW v. Individual

43. 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 What about CLEO? • 12 BI Dec. 2000 – Jun. 2001 • 19 BI Sep. 2001 – Dec. 2001

44. CLEO: # BI/Day/Month

45. CLEO: # BI by Type

46. CLEO: # BI by Location

47. Guidelines: To greatest extent possible…. • Use available data • Make data collection easy (<1% of staff time) • Develop relevant questions • Display data in efficient ways

48. 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

49. Conclude • Data are good…but only as good as systems in place for • PBS • Collecting & summarizing • Analyzing • Decision making, action planning, & sustained implementation