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Essentials of Data-Based Decision Making for Positive Behavioral Interventions in Schools

This document outlines fundamental concepts of data-based decision making within the framework of Positive Behavioral Interventions and Supports (PBIS). It emphasizes the importance of high-quality data for effective decision-making processes, detailing types of data collected, including office discipline referrals and behavioral incident reports. The guide advocates for team-based leadership and pre-defined success criteria to enhance social competence and academic achievement. It also provides practical tips for schools on utilizing data effectively for continuous improvement in behavior support systems.

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Essentials of Data-Based Decision Making for Positive Behavioral Interventions in Schools

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  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

  10. M/m

  11. M

  12. M/M

  13. Is action needed? Is action needed?

  14. Is action needed?

  15. Is action needed?

  16. Is action needed?

  17. Is action needed?

  18. Is action needed?

  19. Is action needed?

  20. What?

  21. What?

  22. What?

  23. Where?

  24. Where?

  25. Who? Students per Number of Referrals

  26. Who?

  27. When?

  28. When?

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

  30. # Behavior Incidents/Day/Month

  31. # BI by Problem Behavior Type

  32. # Major BI/Day/Month

  33. # BI by Location

  34. # BI by Time of Day

  35. # BI by Staff Member

  36. # Major BI by Staff Member

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

  38. # Major BI by Student w/ >1

  39. # BI by Student w/ >3

  40. SW v. Individual

  41. 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

  42. CLEO: # BI/Day/Month

  43. CLEO: # BI by Type

  44. CLEO: # BI by Location

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

  46. 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

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

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