1. Daniel J. Losen Independent Consultant Breaking Down the Data: Developing a Deeper Understanding at the District Level

2. The Good News Data can be used to locate solutions as well as problems. Trend data can help identify educational leaders and policies that have major impact. Non-special education data are particularly useful. The data analysis can be simplified for use at the district and school level. Data can be used to address external explanations.

3. Problems Most districts do not have an abundance of educators who are knowledgeable about analyzing racial and ethnic data. Training Educators is important to expand capacity. Keeping data simple is requires unpacking more complex indicators used in state and federal monitoring. Over-burdened data specialists. Resistance to looking at and discussing racial data. Privacy issues at the building level.

4. Recommendations: Look at two or more years worth of data. Look at disparities in key disability categories together. Compare building level data. Look at grade-level data (and compare at building level) Disaggregate data by free and reduced lunch status. Disaggregate by gender. Emphasize differences. Look at raw numbers first. Look at risk. Compare differences in risk more directly. Look at several categories together (reading achievement alongside suspensions, next to identification). Look at rates of referral to special education.

5. Data Analysis to Identify Problems and Trigger Funds for Solutions Principle #1: Look at the data in its simplest form first and ask basic questions. Numbers: Are there high numbers of children of a given racial group being identified. Unit of analysis: Percent = Risk: How many (per hundred) of the enrolled Latinos are labeled as having SLD. Risk? If I picked a Latino child from this district at random, what is the likelihood that he or she would be labeled SLD Ask: How does the risk for Latinos in my district compare? Explore different comparison groups.

6. Comparison Groups Compare to whites in the district. Compare to other groups in the district with similar socio- economic status (i.e. Blacks and Latinos). Compare to state AND national averages for all students. Ask if this seems high? Low? Compare percentage point differences in risk value.

7. District’s Risk for “Mental Retardation” by Racial/Ethnic Groups

8. District’s Risk for Emotional Behavioral Disability by Racial/Ethnic Groups 2004-05

9. Before risk ratios: What does risk mean? Native American children are over three times as likely as White children to be labeled ED. Black and Latino children are about twice as likely to be labeled ED as Whites. Asian American children have very low identification rates for ED compared to all other groups. There were large differences between the racial groups but were the rates “high?”

10. District’s Risk for Specific Learning Disability by Racial/Ethnic Groups 2004-05

11. Q: Why is it important to analyze risk as well as risk ratio: Risk ratios alone leave out important information.

12. What Does it Mean? About one in 20 Black, Native American and Latino children identified for SLD… ….at almost twice the rate of Whites. Asian Americans seem to be identified at a very different rate. Concerned? Each district and each group tells a different story.

13. What is Revealed? Far more children are identified as SLD. Some disability categories in the district have far more children in them than others (compare MR with SLD). White and Asian identification rates are consistently lower in these “subjective” categories.

14. Look at Identification For Different Disability Categories Together Then add restrictiveness of educational setting, and discipline. Then put those next to regular education data. Also compare to “medically diagnosed” disability categories.

15. Make Comparisons Broaden groupings Comparisons get easier to understand if made on a regular basis Keep the method of comparison the same to the extent appropriate

16. Principle # 2 Continued: Look At Multiple Categories Look at many data sources before deciding that there is no problem, especially where some data suggests a problem exists. Multiple data sources are better at building confidence in results. Look at patterns with other categories in regular and special education. Discipline? Pay close attention to the categories of disciplinary action. Don’t include data that are not revealing of differences or necessary (i.e. not every category all the time).

17. Why Discipline Data? Racial Disparities in Discipline is a required indicator for IDEA monitoring and enforcement. As numbers go down for identification, or restrictiveness of setting look to see if they go up in regular education suspension and expulsion. Disciplinary exclusion from school can have a long-term negative impact on academic outcomes.

18. Building Level Data: Look for Red Flags

19. Risk Comparisons Use graphs, as problems are often easy to spot visually. Principle # 2 Look at More Than One Category and More Than One Area Analysis for action, not paralysis by analysis…. Seek to understand the data connections.

20. Patterns of Racial Disparity in Indiana 2006-2007 (U.S. Dept of Ed.)

21. New Haven: Middle Schools J. Robinson Middle: 200 of 265 Black males suspended in 02- 03 = 75% of the enrolled Black males. Roberto Clemente Middle: 85 of 205 Latino Males: = 41.5% of the enrolled Latino males 73.6% of Black males at Troup Middle School were suspended at least once.

22. Principle # 3: Look at data over time for trends and anomalies Did a policy change? Did leadership change? Did a dramatic change in one area correspond with a dramatic change in a different area?

23. Chicago Male Suspension Rates By Race Over Time (Source: Illinois State Board of Education)

24. Use Data to Find Solutions Look at simple data first. If educators at the school and district level understand the data they are more likely to own the problem but also may find a school leader with a solution. You cannot dispel myths and misconceptions with fancy numbers. The opportunity to show the connections to general education is strengthened by looking at the data side by side. The stronger the connection between special education and regular education racial data, the harder it is to locate the problem as a problem only within special education or outside the school. The remedy may need to be a coordinated effort addressing several areas. Discomfort with data could lead to remedies that are more narrow than the context demands. (We only care about the things we can count).

25. Use Data to Evaluate Interventions Process of continuous evaluation over time No clear programmatic solution – research on what works Qualitative factors matter, too

26. Getting and Using Data Simple analysis frequently repeated (Principle #1) will help establish a sound practice. Comfort with looking at data on race, disability, and gender is easier if the analysis remains similar.

27. Looking at More Categories Can Dispel Common Misconceptions and Locate Solutions It’s not about race, just poverty. It’s not us, other districts identified them. Principle #2 Look at more categories and areas: i.e. test out the theories with the district’s data on poverty with race, and on district’s rates compared to those entering already identified. Misconceptions linger when left unspoken or unaddressed. And exceptions are important to acknowledge. Teacher support: Going beyond the numerical disparities to resource distribution.

28. Trends Over Time Principle 3: Look at data over time for positive trends and to continuously evaluate remedies. Finding buildings that are consistently successful (i.e. principals) Providing more teacher support and training and then tracking the results

29. Poverty and Race? (Actual District) White Enroll ment # White With % White With Black Enroll ment # Black With % Black With Hisp. Enroll ment # H With % H With Poor2225826.12909733.5243239.5 Non318832810.31364029.4981212.2

30. Percent with Disabilities by Economic Status by Race/Ethnicity

31. DPI EOCA Presentation January 24, 2006 Presenter – Jack Jorgensen Executive Director, Department of Educational Services Madison Metropolitan School District 31 We have a problem, we are trying to fix it, and it’s hard work!

32. Comparing Schools With Similar Demographics Similar demographics can suffice where differences appear large. Classroom level data should be considered. Qualitative analysis is critically important. Grade level data are important, across schools.

33. Problems To Watch For Greater inclusion and lower risk for identification, but higher rates of suspension. Dramatic changes in identification rates, and lower achievement scores. Lower suspension rates, with lower attendance.

34. Looking at Rates of Referral Centralize referral process Helps improve quality of RTI and Early Intervening Services Initially high rates of referral resulted in high rates of identification/elligibility. Then high rates of referral that resulted in non-identification. Ideally, fewer referrals for evaluation, but higher percentages of referred identified. Madison Wisconsin example:

35. First – Defining The Problem 35 1998-1999 School Year A disproportionate number of students of color, especially black males, were being referred to special education (6% vs. 2% for white males). A disproportionate number of students of color, especially black males were being placed into special education (80% placement rate).

36. A Multi-faceted Response 36 Aug. 1999 – Moved the responsibility for conducting initial evaluations from school- based staff to a centrally coordinated IEP system (CCIS) Oct. 2001 – Superintendent initiated the development of an early intervention system Sept. 2002 – Initiated a study group on disproportionality of minority students in special education Sept. 2003 – District-wide conversations on race and equity were initiated

37. Centrally Coordinated IEP System (CCIS) 37 A centrally coordinated IEP system (CCIS) for processing and completing initial special education referrals of students ages 5-21 Special Education program support teacher is assigned to the IEP Team as: a) special ed. teacher, b) IEP chairperson and c) LEA representative General education teacher and other staff (e.g., psychologist, social worker, nurse, etc.) were appointed as appropriate from the student’s school of attendance

38. Six Years of Data - Have we improved? 38 Rate of referrals Rate of placements Looking at the data by ethnicity, socio-economic status and gender How CCIS is impacting our district’s overall prevalence rate

39. 39 Centrally Coordinated IEP System (CCIS)

40. The End Questions? My Contact Information: harvardlosen@gmail.com losendan@gmail.com Cell: 617-285-4745 Office: 781-861-1222

42. Poverty, Race and K-12 CCIS Referrals for 2004-05

45. Student Subgroup Placement Rate Change Between 1998-99 and 2004-05 White, Female, Low Income-22% White, Female, Not Low Income-13% White, Male, Low Income-2% White, Male, Not Low Income-28% African American, Female, Low Income-22% African American, Female, Not Low Income-30% African American, Male, Low Income-15% African American, Male, Not Low Income-26%