1. McKinney-Vento Education for Homeless Children and Youth Program (EHCY) Improving the Quality of LEA-Level Data March 6, 2014 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  The Data Quality Initiative  The purpose and approach of the data quality review  Questions the presentation will address 1. What LEAs have missing Homeless Enrolled counts? 2. How can State Coordinators identify LEAs that may be underreporting their Homeless Enrolled counts? 3. Can the proposed approach help State Coordinators review LEA data?

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 the quality of LEA data

5. Purpose and Approach of the Data Quality Review Data Sources (2011–12 school year)  Analysis based on data that states already report through EDFacts for:  The Consolidated State Performance Report (CSPR), and  The Common Core of Data (CCD).  LEA characteristics examined  Locale  Size  Free and Reduced-Price Lunch (FRPL)  McKinney-Vento subgrant recipient status  District type  Homeless Enrolled Rate 5

6. Purpose and Approach of the Data Quality Review  The goal is to improve the quality of LEA-level data on homeless students.  The approach is to identify data that are potentially problematic.  Focus on counts of Homeless Enrolled students that are  Missing, and  Outliers. 6

7. Questions 7 1. What LEAs have missing Homeless Enrolled counts? 2. How can State Coordinators identify LEAs that may be underreporting their Homeless Enrolled counts? 3. Can the proposed approach help State Coordinators review LEA data?

8. Question 1: Missing Homeless Enrolled  LEAs with missing Homeless Enrolled counts were identified and differences noted by  McKinney-Vento subgrant recipient status and  LEA type (e.g., RESAs).  States are not required to report zero counts at the LEA level, so Homeless Enrolled was not reported for many LEAs.  In addition, the analysis noted LEAs with  Missing Homeless Enrolled counts and a Free-and-Reduced- Price Lunch (FRPL) percentage greater than 50 percent. 8

9. Question 2: Underreported Homeless Enrolled 9

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11. Question 2: Underreported Homeless Enrolled 11  Distribution of Index values, with the cutoff for outlying values highlighted. 77 percent of LEAs are to the left of the cutpoint (i.e., are not outliers). This bar includes all LEAs with an Index score over 500

12. Question 2: Underreported Homeless Enrolled 12  To measure the magnitude of potential underreporting, calculate a difference score for LEAs with outlying Index scores.  The difference score compares LEA’s reported Homeless Enrolled counts with a predicted count.  The predicted count is the number of Homeless Enrolled needed for the LEA to not be considered an outlier (given the FRPL rate).

13. Question 3: Review LEA Data 13  Create a state workbook for every state.  This Excel file contains data from each state, along with maps of LEAs with missing and outlying Homeless Enrolled data.

14. 14  Questions?  Comments?  Suggestions?

15. For additional information, please contact: 15 Brad Keller BradKeller@Westat.com Scott Goldman ScottGoldman@Westat.com Data Quality Initiative Westat