Forecasting FTES Using a Yield Projection Model

1. Forecasting FTES Using a Yield Projection Model Sam Ballard, Research Analyst Daniel Miramontez, Research Analyst San Diego Community College District Presented at the 2009 RP/CISOA Conference Tahoe City, CA: April 27, 2009

2. San Diego Community College District

3. Enrollment • FTES for 2007-08 = 41,925 • College Total = 31,938 • Continuing Education Total = 9,987 • Number of sections offered at colleges in 2007-08 = 11,132 • Duplicated Headcount = 397,615 • 3.6% increase in Fall 2008 growth

4. Office of Institutional Research & Planning Organizational Chart

5. Office of Institutional Research & Planning Scope of Work Research and Information for: • Program and services • Program review reports (i.e., EOPS, TRIO, etc.) • External accrediting agencies • Accreditation self-study reports for WASC/ACCJC • Accountability • ARCC report • Planning and decision-making process • Productivity and projection reports (i.e. FTES)

6. FTES Yield Projection Model • Yield Model • Adjusts to the number of sections being offered in the current term • Takes the previous yields multiplied by the current sections being offered

7. Purpose • Primary function of the FTES Yield Projection Model • Manage growth and enrollment • establish growth targets • Budget development • budget guidelines • Who uses the information • Chancellor • College Presidents • Vice Chancellors • Instruction, Student Services and Business Services • FTES Yield Projection Model Pilot Testing • Last 3 years (06 07, 07 08, 08 09)

8. Method • Start with FTES file from comparable term from previous year • Make exclusions • i.e. cancelled sections, non-residents • Total by different variables • i.e. accounting method, subject, course number • Calculate number of sections per course • i.e. 27 sections of PSYC 101

9. Method Cont. • Calculate FTES for the total number of sections • 100.35 FTES for 27 sections • Calculate yield by dividing total FTES by the number of sections • i.e. 100.35/27 = 3.72 FTES per section

10. Method Cont. • Now get file with current sections offered • Aggregate the number of sections offered • Current term is offering 20 sections of PSYC 101 • Match prior year’s yields to current term • Unique ID (PSYC101) • Multiply number of current sections by previous year’s yield • i.e. 20 sections * 3.72 yield = 74.4 FTES

11. Method Cont. • Adjustments • Change in number of sections • CT – ((CT-PT)/PT) • Multiply adjusted sections by .99 • Increase Yields • Yield can be adjusted according to current trends • yield + 0.10

12. Results • In the past three years we projected spring during fall • Spring 2006 to 2007 • The projection was off by 243 FTES • -1.81% error • Spring 2007 to 2008 • The projection was off by 654 FTES • -4.78% error

13. Results Cont. • Spring 2008 to 2009 • The projection was off by 605 FTES • -4.44% error • Data as of 4/8/09

14. Discussion • Limitations • Can only be calculated when the schedule is ready • New courses are given a marginal mean • Only used for three years • Possible Improvements • Add factors • unemployment rate • fill rates • physical improvements • % increase from term

15. Worksheet Exercise!

16. Discussion Questions • What do you see as other limitations of this model? • What are other ways to improve this model? • How does this model compare to other projection models?