Ensuring Equitable Access to Excellent Educators

1. Ensuring Equitable Access to Excellent Educators

2. Understanding the Data Files December 9, 2014

3. Background Resources shared with States • Guidance: Frequently Asked Questions Webinar: November 17, 2014 • Educator Equity Profiles (2011-12 data) • Compare teacher characteristics in high & low poverty and in high & low minority schools • Statewide analysis • Deeper look at districts and locales that drive statewide gaps Webinar: December 2, 2014 • Data files • Data limited to sources available to the Department • Includes data underlying Educator Equity Profiles, plus more This webinar (December 9, 2014)

4. State Plans Role of data analysis • State Plans to Ensure Equitable Access to Excellent Educators are due June 1, 2015 • SEAs creating State Plans will use data to: • Define key terms • Identify equity gaps • Inform a root cause analysis, explainingwhy equity gaps exist • Monitor and publicly report on progress in eliminating gaps

5. Using Data to Develop State Plans

6. Identify Potential Data Sources Questions to Consider • Which data to use? • SEA should use most recent available data • Department will not require a particular data source; use of the data file sent to SEAs in November is entirely optional • The Department has several potentially useful data sources: • EDFacts - Annual data submitted by States • Civil Rights Data Collection - Biennial data submitted by LEAs • Common Core of Data - Detailed demographic data • Can data sources be merged across systems? What are the challenges to do so? How would this impact results? • Who has access to the data? Who will be assembling the data? Who will be running analyses?

7. Define Key Terms List of terms • Key Terms to be defined in State Plans • Inexperienced teacher • The Department encourages an SEA to define as teachers who are in their first year of practice because research demonstrates that the greatest increase in educator effectiveness occurs after one year on the job. • Unqualified teacher • SEAs may choose to define as teachers who have been rated ineffective by educator evaluation and support systems. • Out-of-field teacher • Minority Student • Poor student • Any other key terms used by the SEA such as “excellent educator,” “effective educator,” or “highly effective educator.”

8. Define Key Terms Students from Low Income Families • Identifying students from low-income families • Community Eligibility Provision may reduce utility of Free and Reduced Price Lunch (FRPL) data as poverty indicator • Consider whether there are better measures, possibly including: • Children ages 5-17 in poverty; • Children in families receiving assistance under the TANF program; or • Children eligible to receive medical assistance under the Medicaid program.

9. Define Key Terms Questions to consider • Which additional metrics or indictors of educator quality – beyond those required key terms – are important? • SEA may also choose to consider, for instance, access to teachers rated as highly effective; teacher turnover rates; or principal quality. • These terms also need to be defined, if used. • Are there other student subgroups that are important to include in your State Plan? Students with disabilities? English Learners? • These terms also need to be defined, if used.

10. Identify Equity Gaps • Equity Gap: • The difference between the rate at which low-income students or students of color are taught by excellent educators and the rate at which their peers are taught by excellent educators. • Note: The Department recognizes that not all SEAs will have access to student-level data; an SEA may choose to use school-level data to identify the relevant equity gaps. • Example: • The proportion of first year teachers in our highest poverty quartile schools is X percentage points higher than in our lowest poverty quartile schools.

11. Identify Equity Gaps Questions to consider • What is the level of observation? • What analyses are possible with student-level data that are not possible with school-level data, and what student-level data are available? • What is the level of analysis? How will the analysis aggregate data? • Within schools? • Across schools/within districts? • Across districts?

12. Conduct a Root Cause Analysis Questions to consider • Root Cause Analysis: • A process of determining and explaining the underlying causes of equity gaps • Consider how quantitative data can be used to develop and refine hypotheses • Example: • Data analysis suggests two possible root causes: high rates of principal turnover in high poverty schools and lower teacher salaries in high poverty schools • Stakeholder engagement provides additional information: teachers report the lack of stable, high quality school leadership is the primary reason teachers leave • Further data analysis suggests that principal turnover is worst in high poverty schools in rural areas

13. Monitor and Report Progress Questions to consider • What are long-term goals for eliminating equity gaps? • What are annual targets toward meeting those goals? • How will you visualize and present data to measure progress over time? • Example: • Monitor and report progress on all indicators for high and low poverty schools • Percent of first year teachers (equity gap in question) • Principal turnover rates (root cause / leading indicator) • Teacher salary (root cause / leading indicator)

14. About the Data Files

15. Data files: Overview Why the department created and shared the files • Convenience • This data files merge a variety of sources of data and maybe useful as you define key terms, identify equity gaps, conduct a root cause analysis, and plan for progress monitoring and reporting. • Use of this data file is optional. • Some SEAs will rely on this data, others will have better data. • Transparency • Gives SEAs access to data underlying Educator Equity Profiles – including CRDC data which many SEAs do not otherwise have. • Opportunity to identify discrepancies in data reported to SEAs and to Department, and to work with LEAs to address. • Allows SEAs to run additional analyses to inform State Plans by using, for example, different definitions of “minority.”

16. Data and documentation Data Resources sent to States • Data (state-specific) • Includes all elements used to create the Educator Equity Profiles, plus all 2011-12 CRDC data. • Data Overview • Review the data overview document to learn more about the data sources and documentation for those sources • Data Codebook • Review the codebook to identify the data elements of interest • Use the “variable category” column to filter based on the different categories of variables • Use the CRDC question code field to link back to the CRDC table layout document (referenced in the data overview document)

17. Data sources and elements Educator equity profiles data • School Identifiers • CCD and CRDC school IDs • LEA ID • State IDs • Source • Common Core of Data • (CCD) • Enrollment overall • Enrollment by race/ethnicity • Free/Reduced price lunch eligibility • School urbanicity • School type • School operating status • EDFacts • Elementary classes taught by Highly Qualified Teachers • Secondary classes taught by Highly Qualified Teachers • Comparable Wage Index • (CWI) • LEA-level CWI • State-level CWI • Civil Rights Data Collection • (CRDC) • Teacher Experience • Teacher Absenteeism • Teacher Certification • Teacher Salary

18. Data sources and elements 2011–12 civil rights data collection • Pathways to College & Career • Early Childhood Education • Gifted & Talented • Algebra I Enrollment and Passing by grade 7 or 8, 9 or 10, and 11 or 12 • Student Retention by grade • Enrollment & School Characteristics • Overall Enrollment • Prekindergarten • IDEA and Section 504 • Interscholastic Athletics • Single Sex Classes • Staffing & Resources • Teacher Experience • Teacher Absenteeism • Teacher Certification • School Counselors • School-level Expenditures • College & Career Readiness • Geometry: courses & enrollment • Algebra II: courses & enrollment • Other Advanced Mathematics: courses & enrollment • Calculus: courses & enrollment • Biology: courses & enrollment • Chemistry: courses & enrollment • Physics: courses & enrollment • AP: Courses, Test-taking, and Test-Passing • IB Enrollment • SAT/ACT* • Discipline, Bullying & Harassment, Restraint & Seclusion • Corporal punishment • Suspensions: in-school and out-of-school • Expulsions: with & without ed. services, zero tolerance • Referrals to law enforcement & school-related arrests • Students subjected to and instances of mechanical restraint, physical restraint, and seclusion • Bullying and harassment on the basis of sex, national origin, and disability

19. File layout Data File Data Codebook

20. Universe of Schools School Identification numbers • The data file includes all schools that were either in the CCD or in the CRDC. The universe of schools required to report is different across these data collections. All schools in the file have either a value in the combokey (CRDC ID) field or the ncessch (NCES ID) field. • Most schools have a value in both fields, and they are the same • Some charter schools have unique CRDC LEA IDs, even though they match to an NCES ID • Some schools in CRDC match to more than one NCES school • For these CRDC schools, the value in the ncessch_crdc field specifies the school’s matching NCES ID

21. Data Details EDFACTS • The elements that were reported directly to the Department are: • Elementary classes: hq381, nhq381, total381 • Secondary classes: hq383, nhq383, total383 • The file also contains three “derived” values (hq, nhq, and hqtotal), which are simple sums of the respective elementary and secondary class counts

22. Data details Civil Rights Data Collection – Privacy Protection • Following FERPA guidelines, CRDC data are rounded to protect the privacy of individual student data • Student counts are rounded to the mid point in groups of three. For example: • 1, 2, 3  Rounded to 2 • 4, 5, 6  Rounded to 5 • 7, 8, 9 Rounded to 8 • This privacy protection method may inflate total counts for CRDC data elements in which there are prevalent cases of schools reporting only one student (e.g., one student retained is rounded to two students retained).

23. Data details Comparable Wage Index

24. Additional Resources

25. Help with state plans AVAILABLE RESOURCES for conducting data-based analyses • Equitable Access Support Network • Convenings, webinars, and State-specific supports, including voluntary review of draft plans • Communities of Practice (coming soon) • A Communities360⁰ website which will include a clearinghouse of relevant resources, tools, and research • EASN@ed.gov • Center on Great Teachers and Leaders • Equitable Access Toolkit (Stakeholder engagement guide, data analysis tool, root cause workbook, sample plan) • gtlcenter@air.org

26. Help us help you Identify the supports that are needed • What support do you need to understand the data Department data files? • What support do you need accessing the data? Do you know what systems the data are in? What questions do you have? What challenges does your state have? • What additional resources or tools do you need? • What types of technical assistance will be most useful for you? Please type into the chatbox and send a private message to the host.

27. Questions? Email us at: OESE.EquitableAccess@ed.gov