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State Membership and Transfer Data Analysis Colorado ADM Study Advisory Committee

State Membership and Transfer Data Analysis Colorado ADM Study Advisory Committee. Mark Fermanich, CU-Denver January 3, 2011. Goals of Analysis. Understand patterns of district membership counts over the course of the school year – peaks and valleys

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State Membership and Transfer Data Analysis Colorado ADM Study Advisory Committee

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  1. State Membership and TransferData AnalysisColorado ADM Study Advisory Committee Mark Fermanich, CU-Denver January 3, 2011

  2. Goals of Analysis • Understand patterns of district membership counts over the course of the school year – peaks and valleys • Estimate district ADM and how it varies from current October count numbers • Is there any relationship between certain district characteristics and patterns of variation? • Caveat – transfer and ADM/ADA data are not edited, audited or high stakes for funding

  3. Student Transfers • Three years of data – 2007-08 to 2009-10 from CDE End of Year data collection (collected July/Aug) • Analysis done at the school level to examine differences among elementary, MS & HS, then aggregated to district totals • Based on 45 student entry and exit codes • Entry codes for new or returning enrollees • Exit codes for students leaving/graduating • Counted monthly in- and out-transfers from July 1 through June 30 each year

  4. Student Transfer Summary Data • Three year average number of transfers: • Total membership breakout: • Elementary 55% of total • MS 19% of total • HS 27% of total

  5. Annual Transfer Patterns • Three year average net transfers by month:

  6. Monthly Transfer Patterns

  7. Total Estimated Membership by Month

  8. Net Transfers by School Level by Month (2009-10)

  9. Student Transfer Analysis Key Findings • Student transfers into and out of districts vary significantly over the course of the year • Greatest influx of students occurs at the beginning of the school year in July and August and continues at a much lower rate into September. • January also has a small net positive number of transfers into districts • The remaining months experience net negative transfers out of districts, with May and June experiencing the greatest numbers of students exiting districts due to high school graduation. • Similarly, districts experience their highest enrollment levels in the fall, especially in September and October, with enrollment numbers steadily decreasing monthly as the school year moves into spring.

  10. Estimating District Average Membership • Three years of data – 2007-08 to 2009-10 from CDE Safety & Discipline data collection (collected May/June) • Data used for estimating ADM • Total student days possible (total days of school year student could be enrolled) • Length of school year in days • Also reported ADA • Analysis done at the school level to examine differences among elementary, MS & HS, then aggregated to district totals

  11. Student Count Summary/Comparisons • These numbers exclude charter schools. • Little difference in aggregate district totals with/without charters included. • Will do some analysis of charters yet this week

  12. Comparison of School Counts

  13. District Setting and School Count Changes

  14. Poverty and School Count Changes

  15. District Size and School Count Changes

  16. Attendance and School Count Changes

  17. Estimated ADM AnalysisKey Findings • On average, estimated ADM is about 2 percent less than the October count for the same year. This suggests that on average enrollments decrease somewhat between fall and spring. • The range of the differences between districts’ October count and ADM is significant. • Maximum net gain in ADM over the October count was more than 27 percent • Maximum net loss was nearly 16 percent. • However, these extremes were found in a relatively few districts • Only 12 districts had percentage differences in double digits • Occurred primarily in smaller districts with enrollments under 500 students

  18. Estimated ADM AnalysisKey Findings • The states’ largest districts experienced net changes similar to the state average. • District characteristics such as geographic setting, poverty level, and attendance and graduation rates do not appear to have a consistent, statistically significant affect. • There is some indication that attendance may have some unsystematic influence • Rural districts may have somewhat higher negative differences on average between their October counts and ADM than districts in other settings

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