1. Mark Fermanich, CU-Denver January 3, 2011
State Membership and Transfer Data Analysis Colorado 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 – to 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 In
Out
Net
Elementary
183,142
64,546
118,596
Middle
82,506
20,848
61,658
High
131,988
107,715
24,273
Total
397,636
193,109
204,527

5. Annual Transfer Patterns
Three year average net transfers by month:
July
August
Sept.
Oct.
Nov.
Dec.
8,904
65,012
5,252
(1,231)
(1,719)
(5,019)
Jan.
Feb.
March
April
May
June
1,749
(2,031)
(1,709)
(991)
(38,566)
(11,958)

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 – to 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
Fall
ADM
% Chg
ADA
2008
801,698
786,151
-1.9%
735,453
-8.3%
2009
817,459
797,088
-2.5%
747,729
-8.5%
2010
831,633
815,590
762,014
-8.4%
3-Yr. Ave.
816,930
799,610
-2.1%
748,398

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 Analysis Key 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 Analysis Key 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