Using EAP to Look at Relative Staffing Levels -- Potential and Pitfalls

1. Using EAP to Look at Relative Staffing Levels --Potential and Pitfalls Lou McClelland and Robert Stubbs University of Colorado at Boulder February 6, 2006, AAUDE

2. Who wants the comparisons? • Staff – Are we over or under-staffed relative to peers? • Regents, administration – Can we • Plead poverty, need for more? • Reduce staff and still be in line? • Legislators, public

3. Issues in comparison • Data source – EAP (EAP = IPEDS employees by assigned position) • Numerator • Full-time, all, or FTE? • Which subgroups? • Denominator – Per what? • Student FTE, research dollars, ?? • Which peers – AAU US public

4. Data source – EAPEmployees by assigned position • IPEDS winter submission • Now driver of all HR surveys • Employees as of 11/1, by • Full-time vs. part-time • Medical vs. not – We excluded all medical • 10 “primary function/occupational activity” • Tenured, tenure-track, “faculty status not on tenure track,” w/o faculty status – not fully crossed with functions

5. EAP matrix – With Colorado row numbers and column letters

6. Numerator • Full-time, all, or FTE? • Used FTE = Full-time plus 1/3 part-time • Retains all data, easy, sensible to audience, used in Data Feedback Report • Which subgroups? • Comparisons using the 28 individual cells depend on comparable classification methods across institutions • Check this

7. Checking cells in the matrix • Used EAP 2005, with fall 2004 data • Results very similar for EAP 2004 • Check raw distribution of counts over 34 institutions for 28 cells • Only 5 of 28 cells have 10+ FTE for every institution

8. Cells where every school reported 10+

9. Check for paired columns or rows • Every school has TTT – tenured and tenure-track faculty, columns A+B, minimum 600 • Look at distribution of counts over rows 1-6 • Institutions still reporting most TTT as • Row 1: Instruction or • Row 2: IRPS, Instruction, research, public service

10. # TTT row 2 (IRPS) x # TTT row 1 (instr)Clearly must combine rows 1 and 2

11. Also not comparable for TTT in row 5:Exec, admin, management • CO, NC, NE, IA, FL reported > 10% • 13 schools reported none • AZ, all UC, MI, Buffalo, OR, Pitt, Penn St, TX A&M • Suspect reporting practice or local terminology, not reality, is the difference • Does it matter? • It does in the IPEDS Data Feedback Report (DFR)

12. DFR Fig. 11 - % of FTE professional staff by assigned position Exec/admin->

13. Categorizations matter in the DFR • DFR lists pct of FTE in each of rows 1-6 • Not number per SFTE • Easy to misread – follows per-student-FTE figures • Row 5: Exec-admin-mgt • Peer median 6% • Colorado 14% • We said: At other schools, tenured deans etc. are not in Row 5, so cannot compare this percentage

14. Do public AAU’s have research staff? • Row 3 is research: Columns A B C D • Sum of the columns, row 3 • Zero: 10 schools • Over 1,000: 3 schools (Berkeley, CO, MD) • And, those reported in row 3 may be • TTT, Columns A/B • Faculty status not TTT, Column C • Without faculty status, Column D

15. Keep combining to fix – Get 3 ultimate subgroups

16. Examine the 3 subgroups • All schools have counts in all groups • Average count about • TTT: 1500 • Other professional: 4000 • Non-professional: 3000 • Schools with more in one subgroup generally have more in all subgroups • Correlations across 34 schools 70-80 • Plots show few obvious outliers

17. Other professional (vertical) vs. TTT (horizontal) Related but different. Far right: Florida. Top: Ohio State

18. The numerator at last • Staff FTE • Excluding grad assistants • For total plus three subgroups • TTT Tenured and tenure track • All professional staff not TTT • Tech, clerical, skilled crafts, service, maintenance -- Non-professional

19. The denominator! • Staff per what? • Must normalize for size somehow • What sensibly relates? • Student FTE • Research dollars • Student or degree mix • Student FTE alone seems insufficient • So try multiple predictors

20. Predicting staff total and subgroup FTE • AAU publics • Without Pitt, Rutgers, Penn State (FASB so no $) • Without schools with medical • N = 13, model without Colorado • Predictors • Student FTE • Research expenditures • Pct of degrees that are doctorates • Correlates .80 with research $$ so proxies • Land grant

21. Predictor combinations that work • TTT = SFTE + land grant • Other professional = SFTE + %doc – land grant • Non-professional = SFTE • Total = SFTE + %doc • All R-squared .80-.91

22. Actual and predicted totals by student FTE

23. Punch line for Colorado • CU staff FTE, pct different from predicted • -11% for TTT • +2% for other professional • -29% for non-professional • -7 to -12% overall – 440 to 780 < predicted • These may make sense • Cut the TTT last • Many other professional paid with research $$

24. EAP and relative staffing levels • Pitfalls • Fine categorizations definitely not comparable • Three subgroups may not be either • Potential • Available for all institutions • Can readily see some of the incomparabilities • Analyses like this show others • But will there be any schools left if eliminate all? • Probably related to reality • Better than nothing