Using data for decisions:

1. Using data for decisions: What you can do to positively impact the disproportionate use of discipline. Fall conference 2006 David Guardino

2. Goals for Session • Describe disproportionality in discipline • Review IDEA 2004 requirements – SPR&I Indicator B4 • Define the role of data-based decision-making • Propose features of referral data that are most useful for decision-making • Provide guidelines for using data to impact problem behavior and disproportionate use of discipline • Provide guidelines for using data for on-going problem solving

3. Disproportionate use of discipline • CDF (1975): Black students suspended 2-3x as frequently • Studies since find disproportionality in: • Office referrals • Suspension & Expulsion • Corporal Punishment • Evidence of disproportionality by economic status, gender, and disability designation

4. Disproportionality Starts in the Classroom Skiba (2000)

5. The Interaction of Race and Gender • Middle School Out-of-School Suspension (Raffaele Mendez, 2003) • Black Males: 50% • Black Females: 33% • White Males: 25% • White Females: 9.3% • Black males 16x as likely as white females to be suspended (Gregory, 1996)

6. Race, Gender, and Special Education • Black + Male + Special Ed + Poor • = 67% suspension rate • These students are 5% of population, but: • 24% of 3-5 suspensions • 34% of 6-8 suspensions • 48% of 9-11 suspensions • 56% of 12-14 suspensions • 100% of 14+ suspensions Raffaele Mendez (2003)

7. Alternative Explanations of Disciplinary Disproportionality • Disproportionality is related to SES • SES and disproportionality correlate, but… • Effects of race remain after control • Do black students misbehave more? • No supporting evidence • May in fact be treated more severely for same offenses

8. Students with Disabilities are Suspended Disproportionately • Leone et al (2000) • Kentucky: 14% of enrolled, 20% of susps. • Maryland: 13.1% of enrolled, 24% of susps. • Similar in Minn., Del. Kansas (2.7 x as likely) • But 21st Annual Report to Congress (2000) finds no evidence of disciplinary disproportionality, based on 1994 OCR Report

10. Students with ED are Really Over-Represented • % of Students Reporting Susp/Exp (Wagner et al., 2005) • Elem/Mid: 47.7% of ED HS: 72.9% ED • El/Mid: 11.7% of other HS: 27.6% Other • Kansas: ED 7.5X as likely to be suspended as others with disability; 12x as likely as all students (Cooley, 1995)

11. Are There Disparities in Behavior? • More severe: • GAO (2001): Serious misbehavior for SpEd at 50 incidents/1000 students vs. 15/1000 for gen.ed. • Fiore & Reynolds (1996): Discrepancies in Weapons, Other Dangerous Behaviors, Violence Against Staff • Less severe: • McFadden et al. (1992): Students with disabs less likely to be truant; more likely for bothering others, defiance • Cooley (1995): No differences in reasons for referral • Leone et al. (2000): Do students with disabilities just get caught more often?

12. IDEA Section 618. • New Data (618)(a) • Racial and ethnic disparities in the incidence and duration of discipline including suspensions of one day or more. • Gender and English Language Learners added for collection and reporting. • Annual public reporting of data at the state level.

13. Revised 618 (d) • New Requirements (618)(d): analyze district level data for significant disparities by race and ethnicity in identification, placement, and incidence and duration of discipline. • Public reporting of interventions at the district level. • Finding of significant disproportionality in identification or placement triggers mandatory reservation of 15% of IDEA part B funds (the maximum) for early intervening services under 613(f).

14. 15% Use of Funds for Early Intervening Services • The comments to the final regulations clarify that the 15% requirement is triggered when the state determines a district has “significant racial disproportionality” in either identification, or placement, or discipline, of students with disabilities.

15. 15% Solutions • The 15% requirement means there must be a focus on preventing racial disproportionality and highlights the responsibility to address the needs of students in the over-identified racial groups. • The regulations clarify that other students may benefit from these services as well.

16. How does Oregon identify disproportionality in discipline? • SPR&I Indicator B4: Rates of suspension and expulsion: A. Percent of districts identified by the State as having a significant discrepancy in the rates of suspensions and expulsions of children with disabilities for greater than 10 days in a school year; and B. Percent of districts identified by the State as having a significant discrepancy in the rates of suspensions and expulsions of greater than 10 days in a school year of children with disabilities by race and ethnicity. • Oregon uses a two-tier process to define significant discrepancy. Significant discrepancy is defined as a rate of long-term suspension and expulsion greater than expected based on chi-square analysis and not justified by unique district characteristics.

17. Why Collect Discipline Information? • Decision making • What decisions do you make? • What data do you need to make these decisions? • Professional Accountability • Decisions made with data (information) are more likely to be (a) implemented, and (b) effective

18. Tertiary Prevention: Specialized Individualized Systems for Students with High-Risk Behavior SCHOOL-WIDE POSITIVE BEHAVIOR SUPPORT ~5% Secondary Prevention: Specialized Group Systems for Students with At-Risk Behavior ~15% Primary Prevention: School-/Classroom- Wide Systems for All Students, Staff, & Settings ~80% of Students

19. 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

20. Supporting Social Competence & Academic Achievement OUTCOMES Supporting Decision Making Supporting Staff Behavior DATA SYSTEMS PRACTICES Supporting Student Behavior

21. 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) format • Data are current (no more than 48 hours old) • 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 versus data needs for a district • The people who collect the data must see the information used for decision-making.

22. 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 • An under-estimate of what is really happening • Office Referrals per Day per Month • Attendance • Suspensions/Expulsions • Vandalism

23. Office Discipline Referral Processes/Form • Coherent system in place to collect office discipline referral data • Faculty and staff agree on categories/definitions • Faculty and staff agree on process • Office Discipline Referral Form includes crucial information • Name, date, time • Staff • Problem Behavior, maintaining function • Location

24. What data are needed for effective decision making (The Big Five) • How Much: Office discipline referrals (ODR) • ODR per school day • ODR per school day per 100 students • What: ODR by type of problem behavior • Where: ODR by location • When: ODR by time of day • Who: ODR by student • Why: ODR by perceived motivation

25. 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 (e.g. weekly) • Data will be inaccurate and irrelevant unless the people who collect and summarize it see the data used for decision-making.

26. Using Data for On-Going Problem Solving • Start with the decisions not the data • Use data problem solve • Is there a problem? (overall rate of ODR) • Localize the problem • (location, problem behavior, students, time of day) • Get specific • Use data to guide asking of “the right questions” • Don’t drown in the data • It’s “OK” to be doing well • Be efficient

28. Is there a problem? • Office Referrals per Day per Month • Attendance • Faculty Reports

29. 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?

30. Middle School with 500 students

31. Middle School with 500 students

32. What systems are problematic? • Referrals by problem behavior? • What problem behaviors are 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?

35. Middle School

36. Elementary School

37. Referrals per Student

38. Referrals per Student

40. 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.

41. Ethnicity and disability reports • Rationale • The power of information • The risks of disproportionality • Key Questions • What proportion of enrolled students in school are from each ethnicity/disability? • What proportion of referrals are contributed from students in each ethnicity/disability? • What proportion of students with at least one referral are from each ethnicity/disability? • What proportion of students within each ethnicity/disability have received at least one office discipline referral?

42. Data-based Action Planning Process • Use Team • Identify data sources • Collect • Summarize data • Analyze data • Build & implement action plan based on data

43. 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, when) • Ensure the accuracy and timeliness of data.