Using the Data to Inform Responses and Remedies

Daniel J. LosenIndependent ConsultantBreaking Down the Data: Developing a Deeper Understanding at the District LevelThe Good NewsData 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.ProblemsMost 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.

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.Data Analysis to Identify Problems and Trigger Funds for SolutionsPrinciple #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 SLDAsk: How does the risk for Latinos in my district compare?Explore different comparison groups.5Comparison GroupsCompare 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.

Districts Risk for Mental Retardation by Racial/Ethnic Groups7Districts Risk for Emotional Behavioral Disability by Racial/Ethnic Groups 2004-058Before 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?

9Districts Risk for Specific Learning Disability by Racial/Ethnic Groups 2004-0510Q: Why is it important to analyze risk as well as risk ratio: Risk ratios alone leave out important information.

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.12What 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.

13Look at Identification For Different Disability Categories TogetherThen add restrictiveness of educational setting, and discipline.Then put those next to regular education data.Also compare to medically diagnosed disability categories.Make ComparisonsBroaden groupings Comparisons get easier to understand if made on a regular basisKeep the method of comparison the same to the extent appropriatePrinciple # 2 Continued: Look At Multiple CategoriesLook 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.Dont include data that are not revealing of differences or necessary (i.e. not every category all the time).16Why 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.17Building Level Data: Look for Red Flags18Risk ComparisonsUse graphs, as problems are often easy to spot visually.

Principle # 2 Look at More Than One Category and More Than One AreaAnalysis for action, not paralysis by analysis.Seek to understand the data connections.

19Patterns of Racial Disparity in Indiana 2006-2007 (U.S. Dept of Ed.)

New Haven: Middle SchoolsJ. 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 males73.6% of Black males at Troup Middle School were suspended at least once.21Principle # 3: Look at data over time for trends and anomaliesDid a policy change?Did leadership change?Did a dramatic change in one area correspond with a dramatic change in a different area?22Chicago Male Suspension Rates By Race Over Time (Source: Illinois State Board of Education)23Use Data to Find SolutionsLook 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).

24Use Data to Evaluate InterventionsProcess of continuous evaluation over timeNo clear programmatic solution research on what works Qualitative factors matter, too25Getting and Using DataSimple 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.26Looking at More Categories Can Dispel Common Misconceptions and Locate SolutionsIts not about race, just poverty.Its not us, other districts identified them.

Principle #2 Look at more categories and areas: i.e. test out the theories with the districts data on poverty with race, and on districts 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.

27Trends Over TimePrinciple 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

28Poverty and Race? (Actual District)WhiteEnrollment#WhiteWith%WhiteWithBlackEnrollment# BlackWith%Black WithHisp.Enrollment# HWith% HWithPoor2225826.12909733.5243239.5Non318832810.31364029.4981212.2Percent with Disabilities by Economic Status by Race/Ethnicity30DPI EOCA PresentationJanuary 24, 2006

Presenter Jack Jorgensen

Executive Director, Department of Educational ServicesMadison Metropolitan School District31

We have a problem,we are trying to fix it, andits hard work!DPI EOCA Presentation - Jan 200631Comparing Schools With Similar DemographicsSimilar 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.Problems To Watch ForGreater 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.

Looking at Rates of ReferralCentralize referral processHelps improve quality of RTI and Early Intervening ServicesInitially 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:First Defining The Problem 351998-1999 School YearA 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).A Multi-faceted Response 36Aug. 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 systemSept. 2002 Initiated a study group on disproportionality of minority students in special educationSept. 2003 District-wide conversations on race and equity were initiatedCentrally Coordinated IEP System (CCIS)37A centrally coordinated IEP system (CCIS) for processing and completing initial special education referrals of students ages 5-21Special Education program support teacher is assigned to the IEP Team as: a) special ed. teacher, b) IEP chairperson and c) LEA representativeGeneral education teacher and other staff (e.g., psychologist, social worker, nurse, etc.) were appointed as appropriate from the students school of attendanceSix Years of Data - Have we improved?38Rate of referrals

Rate of placements

Looking at the data by ethnicity, socio-economic status and gender

How CCIS is impacting our districts overall prevalence rate39Centrally Coordinated IEP System(CCIS)The EndQuestions?

My Contact Information:harvardlosen@gmail.comlosendan@gmail.comCell: 617-285-4745Office: 781-861-1222Poverty, Race and K-12 CCIS Referrals for 2004-05

Student SubgroupPlacement Rate ChangeBetween1998-99 and 2004-05White, 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%