Beyond Average Benchmarking - Use of the Delaware Cost Study data - - PowerPoint PPT Presentation

Beyond Average Benchmarking

• Use of the Delaware Cost Study

data and Data Envelopment Analysis to Target Productivity Improvement and Promote Excellence in Resource Utilization

Tom Eleuterio tommyu@udel.edu Manager, Higher Education Consortia Ti Yan yant@udel.edu Research Analyst, Higher Education Consortia Office of Institutional Research and Effectiveness University of Delaware

Delaware Cost Study Progress Report

• Key decisions are often made at the department

level to allocate faculty and financial resources to instruction, research and service.

• Benchmarking is used to provide

straightforward information for program evaluation and strategic planning. How

do you know if a program is efficient?

Big Picture

Why We Need a Better Benchmarking Approach? Problem Space: Benchmark w/ whom

• n what?

A Method that Makes the Best Use of Available Data Resources

• Data Envelopment Analysis (DEA)
• Basics and Application to Program-level Instructional Productivity and Cost Data
• Data Selection for Use in DEA
• Data Availability and Model Fit
• DEA Results and Discussion: Case Study
• Alternative DEA Models
• Case Studies to Match Specific Program Levels or Data Sparse Sample Spaces

Outline

Adjusted Models for Different Types of Programs

Audience Poll

How does your institution evaluate each program’s instructional costs and productivity?

Problem Space 1: Benchmarking with Whom

• Individual academic programs might not be aligned with their

institutional classification.

• A program that only offers bachelor degrees is operating within a Carnegie class

research high university.

• Aligning benchmarks for the departmental/program-level cost of

instruction requires comparison across program-level peers.

• A program’s peers are not equivalent to institutional peers.

All Institutional Costs Costs Allocated to Departments Direct Instructional Costs Personnel: Faculty and support staff Salary Benefits Non-Personnel Direct Public Service Costs (sep. budgeted) Direct Research Costs (sep. budgeted) Indirect Costs Externally Funded Research & Dept. Match Academic Administration Central Costs

Institutions submit their data and access our national benchmarking norms for key performance indicators (KPI) such as: Refined Means of Cost $ per Student Credit Hour by CIP2 or CIP4 Refined Means of Cost $ per FTE student by CIP2 or CIP4 Three Benchmarking norms are produced: Carnegie Classes, Highest Degrees and percentage of Undergraduate Degrees.

Problem Space 2 : Benchmarking on What

Problem Space 2 : Benchmarking on What

• The averaged Cost per SCH or per Student does not reflect your

relevant position as compared to available norms.

• Benchmarking should inform decision makers with specific,

quantitative and measurable goals for continuous improvement.

To Address the Two Problems

• 1. Data-driven Comparator Group Selection by discipline finds out

which institutions offer programs that are the most comparable in terms of instructional productivity.

• 2. Data Envelopment Analysis (DEA) identifies the best-performing

units among the group members and examines the differences in

• utput metrics.
• Among those comparator groups, who produces the optimal combination of

instructional outputs within the constraints of their resourced inputs?

• Who is doing the best and how is this accomplished?

Recommendations to obtain improved efficiencies “Best" Virtual Producer (LP) Relative Efficiency Extreme Point Method

DEA (Data Envelopment Analysis)

Efficient Frontier Efficiencies: Efficiency Number

DEA - It’s all about evaluating EFFICIENCY

• Ways to be efficient
• Maximizing output with keeping input constant.
• Minimizing input with keeping output constant.
• Or both, at the same time.
• How to measure efficiency
• Efficiency scores – a score produced by the DEA model ranging

from 0 to 1, with 1 being the most efficient.

• Total Weighted Output / Total Weight Input

5 10 15 20 25 5 10 15 20 25 30 35 40 45 Output2 Output 1 A B C E (29.2,7.3)

DMU Input Output1 Output2 A 100 40 B 100 20 5 C 100 10 20

• A,C: Efficient; B: Inefficient
• E: “Best” Virtual DMU of B

Decision Making Units (DMU) represent a group of organizational units that are compared in the process of DEA based on their measurements on a certain set of inputs and outputs.

Data Envelopment Analysis (DEA)

Fig 1 shows a set of DMUs with each consuming the same amount of a single resource (e.g. a fixed number of faculty members) and producing different amounts of two outputs: y1 (e.g. Lower Division Student Credit Hours) and y2 (e.g. Upper Division Student Credit Hours). Solid lines construct the efficiency frontier on which all DMUs are efficient ( efficiency # =1).

DEA: Illustration

• Two possible best-practice virtual producers for P5

are labeled as P’5 and P’’5.

• P’5 can be achieved if y1 and y2 could be increased

proportionally as depicted in last slide.

• If y2 could not be increased for P5, the alternative

is to increase y1 solely to reach the efficiency frontier, shown as P’’5.

• In the case of P6, only one possible best virtual

producer is labeled as P’6.

Figure retrieved 5/25/2017 from http://deazone.com/en/resources/tutorial/graphical-representation

Output 2 Output 1

• Tenure-tracked FTE are quite different from other types of FTE.
• No difference between SCHs taught by different types of faculty members.
• SCHs taught at different student levels are not replaceable.
• The ability to gain separate budget indicates the research and service ability.
• Total SCHs taught at different levels
• Lower/Upper Division, Graduate, Individual Instructions

Output variables

Assumptions when Applying DEA to Delaware Cost Study Results Define the Inputs and Outputs

• Number of FTE Instructional Faculty
• Tenured/Tenure-eligible and All others
• Total direct expenditures for instruction

Input variables

Bef efore D DEA: d data-infor

• rmed p

peer eer s select ection u using Englis lish C CIP 23.0 3.0101 f from f four c con

• nsecutiv

tive y yea ears of

• f

cos

• st s

t stu tudy d data a as case s e stu tudy on

• ne
• Latent Class Analysis (LCA), a subset of Structural Equation Modeling

(SEM), was used to identify four comparator groups using a longitudinal dataset obtained from 2012 – 2015; sample n = 71.

• Naïve Bayesian method was used to classify all one-year (2015)

participants to the settled groups by LCA; sample n = 174.

• DEA results and discussion: English CIP 23.0101 cluster #3 case study
• Discussion of next steps possible for use by all Carnegie universities,

colleges or departments

Latent Class Analysis

• CIP-specific
• Four-year participants 4

consecutive years ( 2012 thru 2015)

• No missing data
• 6 metrics
• Highest degree (categorical)
• # of all degrees
• % of bachelors in all degrees
• % of UG OCS in total OCS
• % of TT OCS in total OCS
• Standardized research and

public service $.

• Initial sample of 71 cases

clustered in 4 groups.

Data Envelopment Analysis Naïve Bayes Clusters

• CIP-specific
• 2015 participants by FICE
• Missing data allowed from

earlier years ( 2012 thru 2014).

• Identifying refined groups of

peers in one-year participation.

• Group peers are comparable

in terms of the 6 metrics and ready for efficiency assessment in the next step.

• CIP-specific
• Each individual group

peers from 2015 participants.

• Inputs: faculty

number and rank, direct instructional $.

• Outputs: SCHs at UG

and GR levels.

• Efficiency numbers

and Best-practice virtual DMU generated.

A Map of 174 participants in a CIP 23.0101 English, for The Year of 2015 Delaware Cost Study, classified by four groups.

31 Cluster #1 Institutions from the 2015 Delaware Cost Study with 174 total participants in CIP 23.0101

univname class hideg alldeg state univname class hideg alldeg state

Boise State University R3 2BM ID University of Delaware R1 1BMD DE DePaul University R3 2BM IL University of Kansas R1 1BMD KS Florida International University R1 2BM FL University of Massachusetts Amherst R1 1BMD MA Georgia State University R1 1BMD GA University of Missouri - Columbia R1 1BMD MO Grand Valley State University M1 2BM MI University of Missouri - St. Louis R2 2BM MO Kansas State University R1 2BM KS University of New Hampshire Main Campus R2 1BMD NH Miami University - Oxford R2 1BMD OH University of North Carolina at Chapel Hill R1 1BMD NC North Carolina State University at Raleigh R1 2BM NC University of Tennessee - Knoxville R1 1BMD TN Northern Arizona University R2 1BMD AZ University of Utah R1 1BMD UT Northern Illinois University R2 1BMD IL University of Vermont R2 2BM VT Ohio State University - Main Campus R1 1BMD OH University of Virginia - Charlottesville R1 1BMD VA Simon Fraser University R2 1BMD BC Virginia Polytechnic Institute & State University R1 1BMD VA SUNY at Albany R1 1BMD NY Wilfrid Laurier University M1 1BMD ONT University of California - Irvine R1 1BMD CA Wright State University - Main Campus R3 2BM OH University of Central Florida R1 1BMD FL Youngstown State University M1 2BM OH University of Connecticut R1 1BMD CT

Comp mparison

• n o
• f simi

milar D DMUs in cl close proxi ximity to e each ch

• ther a

and t the t total C Cluster # 1 averages ( (Grp A Avg)

Efficiency Number Carnegie Class UG Degrees Total Degrees TT Faculty Total Faculty UG SCH GR SCH Cost per SCH FTE Students Res & Ps per TT Fac

DMU1 0.837 R1 161.7 175.1 30.63 68.65 28481 477 $285 847.2 $5803 DMU2 0.836 R1 193.0 210.3 36.60 108.85 35829 1024 $257 1238.7 $6278 DMU3 1 R1 190.0 206 27.81 86.50 34365 1063 $105 1310.6 $32387 Grp Avg 142.6 177.0 33.8 83.4 27336 1632 $251 955.0 $8033

Applying DEA to Cluster 1: 16 out of 31 or 51.6% of the programs in cluster 1 had efficiency numbers of 1.00. These 16 form the data envelope.

DMU 1 with efficiency # = 0.837 would have moved toward 1.00 by teaching 110 additional graduate student credit hours in the Fall 2015 semester

EFF# REF_FICE_ID Weight TT Faculty All_Other Faculty Direct Costs LD SCH UD SCH UG IND SCH GR SCH GR IND SCH

1 960015 0.580897 27.8 45 $3,710,499 12,953 5,406 408 342 193 1 550015 0.1735265 25 7.7 $4,743,934 5,190 1,992 75 262 51 1 551015 0.3322937 30.5 6.9 $5,233,877 5,484 2,538 23 268 101 FOC_ID

Best-practice Virtual DMU 30.6 29.8 $4,717,801 10,247 4,329 258 333 155

14015 0.837

Focal DMU 30.6 29.8 $8,259,454 8,578 3,624 80 223 33 Best-practice Virtual - Focal ($3,541,653) 1,669 705 178 110 122

Target Increased Number of Grad Students = 12

Data Entry Form (Page 1)

• Avg. Degree produced in the

past three years Faculty by Rank Student Credit Hours (SCH) taught by each faculty rank in fall (Study Year -1) Organized Class Sections (OCS) taught by each faculty rank in fall (Study Year -1) Program name and CIP Institution Information All- Year UG and GR SCH Salaries + Benefits + Other than Personnel Expense = Direct Instructional Expenditures

Click here to review Page 2

Click here to view three-year averages in Table 3 and 4

Institution Results (Page 2)

Institution Ratios 3-yr avg Table 1 Teaching loads by faculty type Table 2 Teaching loads by course level Table 3 SCH and OCS taught by per FTE inst. Faculty Table 4 Instructional Productivity and Cost Ratios

Web portal view of workload & cost benchmarks for DMU #1 showing potential for growth in Graduate SCH versus Carnegie 3-year and average cost/SCH 2012-2014

5.4 times 46 regular faculty FTE yields 249 Graduate SCH; more than twice the required 110 difference found via DEA

Target goal: 12 of 31 tenured faculty members take on

• ne new grad

student

Cost per credit hour is higher than average and research expenditures also higher than average; significance?

The cost per SCH of $285 is about 40% higher than the three- year average of $208 for Research High & Highest Carnegie programs

DMU 2 with efficiency # = 0.836 would have moved toward 1.00 by teaching 56 additional graduate student credit hours in the fall 2015 semester

EFF# REF_FICE_ID Weight TT Faculty All_Other Faculty Direct Costs LD SCH UD SCH UG IND SCH GR SCH GR IND SCH

1 162015 0.00169 27 55 $8,073,872 13,548 2,044 28 1176 36 1 320015 0.98576 34.7 69 $8,769,570 17,651 3,549 91 312 283 1 960015 0.08503 27.8 45 $3,710,499 12,953 5,406 408 342 193 FOC_ID

Best-practice Virtual DMU 36.6 71.9 $8,973,798 18,524 3,962 124 339 295

72015 0.837

Focal DMU 36.6 72.3 $9,485,001 15,481 2,499 104 283 15 Best-practice Virtual - Focal (0.4) ($511,203) 3,043 1,463 20 56 280

Target Increased Number of Grad Students = 6

Existing depa departments O s Oper perating o g on n the he Efficien ency Fr Frontier in C n Clus uster 1 1 Programs w with I h Instruc uctiona nal E Expens penses es f from $ 8 to 9 Million n in b blue

cip4 cat ref_fice effici ency_ num weight _sol TT Faculty FTE All Other Faculty Direct Instructional Cost Lower Division SCH Upper Division SCH UG IndInst SCH Graduate SCH GR IndInst SCH 230100 1 xxxxx 1 1 13.7 44.2 2,622,606 $ 5,865 4,086 11 392 6 230100 1 xxxxx 1 1 25.1 7.5 3,434,653 $ 3,592 2,896 10 132 19 230100 1 xxxxx 1 1 27.8 45.0 3,710,499 $ 12,953 5,406 408 342 193 230100 1 xxxxx 1 1 24.0 33.8 4,649,810 $ 9,624 2,641 67 579 289 230100 1 xxxxx 1 1 25.0 7.7 4,743,934 $ 5,190 1,992 75 262 51 230100 1 xxxxx 1 1 25.3 41.6 5,144,884 $ 9,154 2,259 395 1,353 296 230100 1 xxxxx 1 1 30.5 6.9 5,233,877 $ 5,484 2,538 23 268 101 230100 1 xxxxx 1 1 44.0 52.5 5,684,469 $ 13,161 3,624 48 735 1,816 230100 1 xxxxx 1 1 34.0 42.6 7,647,757 $ 13,877 2,211 53 548 83 230100 1 xxxxx 1 1 46.4 9.7 7,988,685 $ 4,035 2,203 138 744 568 230100 1 xxxxx 1 1 27.0 55.0 8,073,872 $ 13,548 2,044 28 1,176 36 230100 1 xxxxx 1 1 41.8 52.5 8,319,686 $ 9,729 10,572 53 645 179 230100 1 xxxxx 1 1 34.7 69.0 8,769,570 $ 17,651 3,549 91 312 283 230100 1 xxxxx 1 1 35.5 57.8 8,972,268 $ 11,172 3,395 1,583 534 316 230100 1 xxxxx 1 1 38.8 87.1 12,943,846 $ 16,756 2,924 41 391 917 230100 1 Focal DMU # 1 0.837 30.6 29.8 8,259,454 $ 8,578 3,624 80 223 33 230100 1 Focal DMU # 2 0.836 36.6 72.3 9,485,001 $ 15,481 2,499 104 283 15 Cost Range Group Average 37.1 48.8 8,424,816 $ 11,227 4,353 379 682 276

DEA in Master’s and Baccalaureate Programs

• In programs that award Master’s degrees as the highest offering,

externally sponsored research and public service expenditures are not significant variables in DEA models

• In programs that award Bachelor’s degrees as the highest offering,

graduate instruction is largely absent so graduate student credit hours are not significant variables in DEA models

• Some academic programs are reported by relatively few institutions
• Alternate DEA models permit focusing on differentiating faculty types,

tenured or non-tenured and course levels: lower division, upper division and individualized instruction for optimization.

Data Envelopment Analysis Models Model 1 Model 2 Model 3 Model 4 Input Variables (what is invested by each program) # of Tenured/TT Faculty     # of All Other Faculty     Direct Instructional $    Total Input # 3 3 3 2 Output Variables (what is produced by each program) Lower Division SCH  Taught by TT & Other faculty Taught by TT & Other faculty UG SCH (three subcategories added up) Upper Division SCH  Taught by TT & Other faculty Taught by TT & Other faculty UG Individual SCH  Taught by TT & Other faculty Taught by TT & Other faculty GR Class SCH  Taught by TT & Other faculty GR SCH (two subcategories added up) GR Individual SCH  Taught by TT & Other faculty Total Output # 5 10 6 2 Dataset (What programs are in each model) Highest Degree    % of UG Degree   Program-Level Peers  Minimum Sample Size (How many programs are in each model) 3 times the total number of inputs and outputs 3*(3+5) = 24 3*(3+10) = 39 3*(3+6) = 27 3*(2+2) = 12

Decision Making Exercise: Analyze case studies using DEA models to find the path towards reaching a more efficient use of instructional faculty:

Case Studies: 1)Accounting and DEA Model 2

2) History and DEA Model 3

Key Questions:

• Does the data provide guidance?
• Are the paths to efficiency realistic

given the data being provided?

Case study for Model 2: Accounting with Master’s Degree as Highest Awarded

• Sixty-five programs in 2015 reporting Accounting CIP 52.0301
• Thirty-two of sixty-five ( 49%) have efficiency numbers equal to one
• Average number of Tenure/Tenure Track Faculty = 10.8
• Average number of Other Faculty = 7.5
• Average Cost per SCH = $266
• Average SCH \ FTE Instructional Faculty = 311

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

• Sixty-six programs in 2015 reporting History CIP 54.0101
• Twenty-one of sixty-six ( 32%) have efficiency numbers equal to one
• Average number of Tenure/Tenure Track Faculty = 6.6
• Average number of Other Faculty = 2.9
• Average Cost per SCH = $191
• Average SCH \ FTE Instructional Faculty = 228

Case study for Model 2: Accounting with Master’s Degree as Highest Awarded

• Compare the highlighted programs, Chart ID # 4 and # 14
• Program #14 has the lowest efficiency number of 0.605345
• Program # 4 has an efficiency number of 1.000
• Programs # 4 and #14 have approximately equal faculty size and cost;

What changes does the data suggest that would improve efficiency for program #4 ?

• What efficiency information does the chart reveal about this group of

programs?

• What direction for improvement is suggested by the best-practice,

virtual DMU?

Case study for Model 2: Accounting with Master’s Degree as Highest Awarded

Cip4 Model Chart_I D Efficienc y_Num TT Faculty FTE All Other Faculty FTE Direct Instructional Cost SCH per FTE Faculty Cost per SCH Total FTE Faculty SCH per TT Faculty Lower Division SCH per Other Faculty Lower Division SCH per TT Faculty Upper Division SCH per Other Faculty Upper Division Graduate per TT Faculty Graduate Graduate SCH per Other Faculty 520300 2 1 1 9.9 8.0 2,738,172 $ 345 215 $ 18 14 439 134 117 53

• 520300

2 2 1 9.0 2.3 1,673,521 $ 282 242 $ 11 69 147 191 69 27 52 520300 2 3 1 9.4 2.3 2,170,572 $ 399 223 $ 12 82 219 262 225 42 59 520300 2 4 1 9.4 5.8 2,114,718 $ 360 176 $ 15 75 290 162 132 71 23 520300 2 5 1 9.5 9.2 3,216,640 $ 583 156 $ 19 3 632 133 201 62 150 520300 2 6 1 10.0 3.1 1,897,331 $ 426 181 $ 13 167 256 125 397 60 19 520300 2 7 1 10.0 3.7 1,994,115 $ 317 238 $ 14 2 568 123 148 50 3 520300 2 8 1 10.0 2.7 1,958,501 $ 314 239 $ 13 65 484 192 24 5

• 520300

2 9 1 10.0 9.0 3,599,973 $ 537 164 $ 19 128 477 119 121 74 185 520300 2 10 0.881856 9.0 3.8 2,048,801 $ 318 254 $ 13 58 285 175 119 50

• 520300

2 11 0.750051 9.0 4.0 2,224,103 $ 278 311 $ 13 31 338 160 57 20 40 520300 2 12 0.725886 10.0 4.8 2,050,863 $ 257 293 $ 15 44 222 127 154 24 13 520300 2 13 0.722004 9.0 6.0 2,292,338 $ 262 297 $ 15 50 291 60 59 57 59 520300 2 14 0.605345 9.0 5.5 2,366,131 $ 233 321 $ 15 71 205 109 49 23 28

Case study for Model 2: Accounting with Master’s Degree as Highest Awarded

Case study for Model 2: Accounting with Master’s Degree as Highest Awarded

Cip4 Model Ref_ID Efficiency _Num TT Faculty FTE All Other Faculty FTE Direct Instructional Cost SCH per TT Faculty FTE Lower Division SCH per Other Faculty FTE Lower Division SCH per TT Faculty FTE Upper Division SCH per Other Faculty FTE Upper Division SCH per TT Faculty FTE UG IndInst Ind Inst per Other Faculty Under Grad Graduate SCH per TT Faculty Graduate Graduate SCH per Other Faculty TT Faculty GR IndInst SCH Ind Inst per Other Faculty Graduate SCH per FTE Faculty Cost per AllYr SCH

520300 2Virtual DMU 1 9 5.5 $ 2,366,131 117 339 180 170 2 47 55 1 427 $ 199 520300 2Focal DMU 0.605 9 5.5 $ 2,366,131 71 205 109 49

• 23

28

• 233 $ 321

difference 46 134 71 121 2 24 28 1 195 $ (122)

Tenured Faculty teaching SCH / FTE closest to Virtual, Best- Practice DMU

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

• Sixty-six programs reporting History CIP 54.0101
• Twenty-one of sixty-six ( 32%) have efficiency numbers equal to one
• Average number of Tenure/Tenure Track Faculty = 6.6
• Average number of Other Faculty = 2.9
• Average Cost per SCH = $191
• Average SCH \ FTE Instructional Faculty = 228

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

• Compare the highlighted programs, Chart ID # 5 and # 11
• Program #11 has the lowest efficiency number of 0.4958962
• Program # 5 has an efficiency number of 1.000
• Programs # 5 and #11 have approximately equal faculty size and cost;

What changes does the data suggest that would improve efficiency for program #5 ?

• What efficiency information does the chart reveal about this group of

programs?

• What direction for improvement is suggested by the best-practice,

virtual DMU?

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

Cip4 Model Chart ID Efficiency _Num TT Faculty FTE All Other Faculty FTE Direct Instructional Cost SCH per FTE Faculty Cost per SCH Total FTE Faculty SCH per TT Faculty Lower Division SCH per Other Faculty Lower Division SCH per TT Faculty Upper Division SCH per Other Faculty Upper Division SCH per TT Faculty UG IndInst SCH per Other Faculty UG: IndInst 540100 3 1 1 9.0 6.3 1,032,634 $ 328 104 $ 15 193 388 71 31

• 540100

3 2 1 10.0 4.3 1,089,962 $ 385 101 $ 14 237 602 56

• 3
• 540100

3 3 1 10.0 3.0 1,185,532 $ 431 129 $ 13 329 517 68 30

• 3

540100 3 4 1 10.3 1.3 940,522 $ 339 130 $ 12 292 365 44

• 540100

3 5 1 11.5 2.5 1,093,295 $ 272 146 $ 14 110 290 140 83 3 540100 3 6 0.8885175 11.0 2.5 1,287,759 $ 190 237 $ 14 97 193 73 84 8

• 540100

3 7 0.8272436 9.0 2.3 1,593,558 $ 265 265 $ 11 168 345 68 32 4

• 540100

3 8 0.8244653 11.0 1.8 949,002 $ 214 185 $ 13 156 187 62

• 2
• 540100

3 9 0.7482362 10.5 3.0 1,551,057 $ 252 270 $ 14 186 204 69 38 1

• 540100

3 10 0.6617317 9.1 2.5 879,289 $ 227 180 $ 12 135 343 56 13 1

• 540100

3 11 0.4958962 11.0 3.7 1,145,817 $ 182 224 $ 15 117 297 26

• 1

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

• 100

200 300 400 500 600 700 800 900 1,000 1 2 3 4 5 6 7 8 9 10 11

SCH per FTE by faculty type and course level

SCH per TT Faculty Lower Division SCH per Other Faculty Lower Division SCH per TT Faculty Upper Division SCH per Other Faculty Upper Division

Case study for Model 3: History with Bachelor’s Degree as Highest Awarded

Cip4 Model Focal_ID Ref_ID Efficiency _Num Weight _Sol TT Faculty FTE All Other Faculty FTE Direct Instructional Cost SCH /TT Faculty FTE Lower Division SCH /Other Faculty FTE Lower Division SCH /TT Faculty FTE Upper Division SCH /Other Faculty FTE Upper Division SCH / TT Faculty FTE UG IndInst SCH / Other Faculty FTE UG IndInst SCH per FTE Faculty Cost per SCH 540100 3 11 Best-practice Virtual DMU 10.9 3.7 1,145,817 $ 238 598 54 1 3

• 369

108 $ 540100 3 11 Focal DMU 0.495896 11.0 3.7 1,145,817 $ 117 297 26

• 1
• 182

224 $ difference (0.05)

• 120

302 28 1 1

• 187

(116) $

Insights from the Delaware Study and the Education Policy Initiative

Instructional Cost Drivers 1992-2017

Two decade study, focused at the academic discipline level,

• f faculty instructional workload and costs, sponsored

research and public service from over 700 four-year, public and private non-profit higher education institutions “The National Study of Instructional Cost & Productivity”, conducted annually by the Higher Education Consortia at University of Delaware. Results reported here from a grant for public policy research by the Smith Richardson Foundation Research collaboration with the Education Policy Initiative

• Dr. Kevin Stange, University of Michigan and Dr. Steven

Hemelt University of North Carolina- Chapel Hill