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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

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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

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Issues in comparison

Data source – EAP Numerator

Full-time, all, or FTE? Which subgroups?

Denominator – Per what? Student FTE, research dollars, ??

Which peers – AAU US public

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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

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EAP matrix – With Colorado row numbers and column letters

28 valid cells

With faculty status D: Without faculty status

A: Tenured B: Ten track C: Not TTT

1 Instruction 1A 1B 1C 1D

2 IRPS 2A 2B 2C 2D

3 Research 3A 3B 3C 3D

4 Public service 4A 4B 4C 4D

5 Exec/admin/mgt 5A 5B 5C 5D

6 Other profession’l 6A 6B 6C 6D

7 Tech/paraprof

Not valid

Grad assistants: Col E, part-time only, excluded

7D

8 Clerical/sec 8D

9 Skilled crafts 9D

10 Srv/maint 10D

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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

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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

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Cells where every school reported 10+

Total FTE

With faculty status D: Without faculty status

A: Tenured B: Ten track C: Not TTT

1 Instruction 1A 1B 1C 1D

2 IRPS 2A 2B 2C 2D

3 Research 3A 3B 3C 3D

4 Public service 4A 4B 4C 4D

5 Exec/admin/mgt 5A 5B 5C 5D almost

6 Other profession’l 6A 6B 6C 6D

7 Tech/paraprof

Not valid

7D

8 Clerical/sec 8D

9 Skilled crafts 9D

10 Srv/maint 10D

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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

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

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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)

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DFR Fig. 11 - % of FTE professional staff by assigned position

Exec/admin->

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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

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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

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Keep combining to fix – Get 3 ultimate subgroups

With faculty status D: Without faculty status

A: Tenured B: Ten track C: Not TTT

1 Instruction 1A 1B 1C 1D

2 IRPS 2A 2B 2C 2D

3 Research 3A 3B 3C 3D

4 Public service 4A 4B 4C 4D

5 Exec/admin/mgt 5A 5B 5C 5D

6 Other profession’l 6A 6B 6C 6D

7 Tech/paraprof Pink: TTT (col A, B)

Blue: All other professionals (1-6 C/D)

Yellow: Tech, clerical, skilled, service/maintenance – non-professional (rows 7-10)

7D

8 Clerical/sec 8D

9 Skilled crafts 9D

10 Srv/maint 10D

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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

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Other professional (vertical) vs. TTT (horizontal)

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

600 700 800 900 1,000 1,100 1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200 2,300 2,400 2,500 2,600

Related but different. Far right: Florida. Top: Ohio State

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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

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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

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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

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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

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Actual and predicted totals by student FTE

CU

Actual

Predicted

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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 $$

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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