1. McKinney-Vento Education for Homeless Children and Youth Program (EHCY) Improving the Quality of LEA Level Data February 28, 2013 Prepared for: Office of Elementary and Secondary Education, U.S. Department of Education Prepared under the Data Quality Initiative contract (ED-PEP-11-C-0062) for Policy and Program Studies Service, U.S. Department of Education

2. Presentation Overview 2 1. The Data Quality Initiative 2. Purpose and Approach of the Data Quality Review 3. Data Quality Review Topics 4. Sample Data Review 5. Next steps

3. The Data Quality Initiative (DQI)…. 3  Provides assistance to the U.S. Department of Education PK-12 programs and grantees to improve the quality and use of program performance data for GPRA reporting and performance management,  Is overseen by the U.S. Department of Education’s Policy and Program Studies Service (PPSS) in the Office of Planning, Evaluation and Policy Development (OPEPD),  Is conducted by Westat and Compass Evaluation and Research.

4. Purpose and Approach of the Data Quality Review 4 Focus on LEA-level data Identify potential data problems (e.g., outliers) Develop guidance and TA to allow states to conduct data analyses Inform target monitoring and assistance to LEAs Improve quality of LEA data

5. Purpose and Approach of the Data Quality Review Data Sources  Analysis based on data that states already report:  The Consolidated State Performance Report (CSPR), and  The Common Core of Data (CCD) submitted through EDFacts. 5

6. Purpose and Approach of the Data Quality Review Data Checks  Inconsistent data  Data for primary nighttime residence vs. enrolled (do totals match, are the same grades reported?)  Incomplete data  Do all Local Education Agencies (LEAs) report data?  Inaccurate data  Typos: numbers transposed or wrong numbers entered  Number of students instead of number of subgrants 6

7. Data Quality Review Topics 7 1. Do LEAs with similar characteristics report similar numbers and percentages of homeless students enrolled in their schools? 2. Do LEAs with similar characteristics report similar numbers and percentages of homeless students served by McKinney-Vento? 3. Do LEAs with similar characteristics report similar rates of academic proficiency among their homeless students? How do these rates compare to the student body as a whole? 4. Do LEAs with similar levels of certain characteristics report similar rates of academic proficiency among their homeless students? How do these rates compare to the student body as a whole?

8. Sample Data Review Do LEAs with similar characteristics report similar numbers and percentages of homeless students enrolled in their schools?  LEA Characteristics  LEA size (student population),  Locale (city, suburban, town, or rural),  Poverty (percent Free and Reduced-Price Lunch (FRPL) eligible), and  Geography (state and region).  Topics for Analysis  The number of homeless students enrolled in each LEA, and  The percentage of total enrollment that is homeless. 8

9. Sample Data Review 9  The next five slides contain  Sample results of a data review,  Showing the composition of homelessness at the national, state, and LEA level.  These slides will examine data in different characteristics, to provide context to the outlier analysis.

10. Sample Data Review: Geography (across nation, by Census regions) 10 These are sample data, and are for illustration only. Please do not cite them.

11. Sample Data Review: Geography (across states, within a region) 11 These are sample data, and are for illustration only. Please do not cite them.

12. Sample Data Review: Poverty 12 These are sample data, and are for illustration only. Please do not cite them. State F average

13. Sample Data Review: Locale 13 These are sample data, and are for illustration only. Please do not cite them. State F average

14. Sample Data Review: LEA size 14 These are sample data, and are for illustration only. Please do not cite them.

15. Next steps 15  Continue the data quality review,  Identify common issues, and  Develop technical assistance to help states, including:  Report templates for SEA staff to use with LEA data (Sept. 2013)  Training to help states review and analyze LEA data (future)  Additional tools for reviewing data across years (future)  Work with you to identify and address existing and future data issues.

16. 16  Questions?  Comments?  Suggestions?

17. For additional information, please contact: 17 Brad Keller Data Quality Initiative Westat bradkeller@westat.com