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www.wisconsinpbisnetwork.org/tier1.html Tier 1/Universal Training The Wisconsin RtI Center/Wisconsin PBIS Network (CFDA #84.027) acknowledges the support of the Wisconsin Department of Public Instruction in the development of this product and for the continued support of this federally- funded grant program. There are no copyright restrictions on this document; however, please credit the Wisconsin DPI and support of federal funds when copying all or part of this material. D. Data Entry & Analysis Plan Established 2013-2014
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www.wisconsinpbisnetwork.org/tier1.html Module D: Data Entry & Analysis Plan Established PBIS Implementation Goal 13. Data system to collect and analyze ODR data The database can quickly output data in graph format and allows the team access to ALL of the following information: average referrals per day per month, by location, by problem behavior, by time of day, by student, and compare between years 14. Additional data collected (attendance, grades, faculty attendance, surveys) Team collects and considers data other than discipline data to help determine progress and successes (i.e. attendance, grades, faculty attendance, school surveys, etc.) 15. Data analyzed monthly (minimum) Data is printed, analyzed, and put into graph format or other easy to understand format by a member of the team monthly (minimum) 16. Data shared with team and faculty monthly (minimum) Data is shared with the PBIS team and faculty at least once a month Workbook Examples and Tools
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www.wisconsinpbisnetwork.org/tier1.html Do we Have the Data System Needed for Active Decision-Making? Are we collecting the right information? What, when, where, who (why?) Is data collection efficient? Less than 15 sec to fill out, less than 30 sec to enter Do we get data in the right format? Graphic format Do we get the data at the right time? Before and during meetings, data no more than 24-hours old Are data used for decision-making by all? Data presented to all faculty and other stakeholders > monthly Data available for whole school, small group, and individual student evaluation Data collected on fidelity (what we do) as well as impact (student behavior)
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Collect and Use and UseData Develop Hypothesis Discuss and Select Solutions Develop and Implement Action Plan Evaluate and Revise Action Plan Problem Solving Meeting Foundations Team Initiated Problem Solving (TIPS) Model Identify Problems Newton, J. S., Todd, A. W., Algozzine, K., Horner, R. H., & Algozzine, B. (2009). The Team Initiated Problem Solving (TIPS) Training Manual. Educational and Community Supports, University of Oregon, unpublished training manual. 4
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www.wisconsinpbisnetwork.org/tier1.html Using Data Do we have a problem? Refine the description of the problem? What behavior, who, where, when, why Test hypotheses Build practical solutions Define how to monitor if solution is effective
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www.wisconsinpbisnetwork.org/tier1.html Identifying Problems/Issues What data to monitor ODR per day per month? OSS, ISS, attendance, teacher report? Team implementation checklist (are we doing what we planned to do?) What question to answer Do we have a problem? What questions to ask of level, trend, peaks How do our data compare with last year? How do our data compare with national/regional norms? How do our data compare with our preferred/expected status? If a problem is identified, then ask What are the data we need to make a good decision?
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www.wisconsinpbisnetwork.org/tier1.html Elementary School with 150 Students Compare with National Average 150 / 100 = 1.50 1.50 X.34 =.51
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www.wisconsinpbisnetwork.org/tier1.html
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The Big 5 +2 Big 5: Referrals per day per month Reason for referral Location Time of day Student Big 2: Disaggregated by race/ethnicity Disaggregated by Special ed. status
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www.wisconsinpbisnetwork.org/tier1.html Risk Ratios: System and Student Outcome Risk Ratio is based on disaggregated ODR and suspension data RISK RATIO CALCULATOR is in supplemental files folder of tier I training To calculate it: % of subgroup enrollment with an outcome (ODR, Suspension, etc) divided by % of white enrollment with same outcome i.e. 85% of Latino/Latina students received ODR 42.5% of white students received ODR Risk for white students is 1.0; ratio below 1.0 decreased risk, ratio above is increased risk
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www.wisconsinpbisnetwork.org/tier1.html
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Activity D.1 10 minutes Using risk ratio formula and your existing data: Pick an educational outcome (suspension, discipline contact, etc) Calculate the percent of enrolled subgroup with that outcome Divide by the percent of enrolled dominant group with that same outcome Result is the risk. Difference of +/-.25 you start attending to (roughly) What conversation does that lead to? What do you notice?
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www.wisconsinpbisnetwork.org/tier1.html
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Focusing Your Efforts More than 40% of students receive a referral? Focus on SCHOOL-WIDE system More than 60% of referrals come from classroom? Focus on CLASSROOM systems More than 35% of referral come from non-classroom setting? Focus on NON-CLASSROOM systems
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www.wisconsinpbisnetwork.org/tier1.html Wisconsin Data Collection Options School-Wide Information System www.PBISapps.org Wisconsin Outcome Data Collection Tool For schools not entering ODR data elsewhere Aggregate Outcome Data Collection Tool For schools entering ODR data elsewhere but in need of visual output (graphs) Your district’s current Student Information System Can you get big 5 + 2? Considerations for Tier 2 Data Collection?
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www.wisconsinpbisnetwork.org/tier1.html Efficiency – WHO? Data entry Report creation Data extraction Information Big 5+2 Other data reports Sharing – WHO? Who When Discuss Your Current Data System
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www.wisconsinpbisnetwork.org/tier1.html Complete Module D: Data Entry and Analysis Plan Established Self Assessment and Action Plan Statements 13-16 Insert link to new format
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Module D: Data Entry & Analysis Plan Established 13. Data system to collect and analyze ODR data The database can quickly output data in graph format and allows the team access to ALL of the following information: average referrals per day per month, by location, by problem behavior, by time of day, by student, and compare between years 14. Additional data collected (attendance, grades, faculty attendance, surveys) Team collects and considers data other than discipline data to help determine progress and successes (i.e. attendance, grades, faculty attendance, school surveys, etc.) 15. Data analyzed monthly (minimum) Data is printed, analyzed, and put into graph format or other easy to understand format by a member of the team monthly (minimum) 16. Data shared with team and faculty monthly (minimum) Data is shared with the PBIS team and faculty at least once a month
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