spreadsheet models for program enrollment planning

University of Central Florida

Spreadsheet Models for Program Enrollment

PlanningRobert L. Armacost

Director, University Analysis and Planning SupportSandra Archer

Assistant Director, University Analysis and Planning SupportUniversity of Central Florida

2005 SAIR Conference October 24, 2005

Presentation available at http://uaps.ucf.edu

Spreadsheet Models for Program Enrollment Planning 2October 24, 2005

Goals for the Presentation

Understanding of challenge of program enrollment planning in a growth environment

Understanding of alternative modeling approaches Use of composite spreadsheet model to develop initial

projections Method for estimating degree production New insight into the use of SAS and Excel features to

manage data and create reports


The University of Central Florida

Established in 1963 in Orlando Florida (first classes in 1968), Metropolitan Research University

Grown from 1,948 to 45,000 students in 37 years 38,000 undergraduates and 7,000+

graduates 12 regional campus instructional sites 9th largest public university

Stands for Opportunity

Doctoral intensive 92 Bachelors, 94 Masters, 3 Specialist, and 25 PhD programs

Largest undergraduate enrollment in state Approximately 1,200+ faculty and 3,100 staff Nine colleges

Arts and Sciences, Biomedical Sciences, Business Administration, Education, Engineering and Computer Science, Health and Public Affairs, Honors, Optics and Photonics, and Hospitality Management


Why Do Enrollment Modeling? Predicting income from tuition Planning courses and curriculum Allocating resources to academic departments Long-term master planning Admissions policies

How accurate do these predictions have to be?

See Hopkins, David S. P. and Massy, William F., Planning Models for Colleges and Universities, Stanford University Press, Stanford, CA, 1981 for additional information on enrollment planning


Strategic Planning Florida SUS Board of Governors

10-year strategic plan http://www.flbog.org/StrategicPlan/pdf/StrategicPlan_05-13.pdf

Degree production for the State University System 11 universities Degrees by level Meet workforce needs

Targeted degrees Critical needs in education Critical needs in health care Emerging technologies High wage/high demand

Requires degree projections by 6-digit CIP UCF growth planning

Capacity limits on Orlando campus Determine program mix at campuses


Enrollment Models Objective: find simplest model that predicts future

enrollment based on past enrollment levels and new students enrolling

Methods Regression (REG) Grade progression ratio method (GPR) Markov chain models (MC) Cohort flow models (CF)

Notation Nj(t) = number of students in state j at time t

fj(t) = number of students enrolling in state j at time t j = state index—stands for class level


Regression Models

Student inventory = predicted returning students plus expected new students

Prediction of returning students estimated by multivariate regression

N(t) = F[ Nj(t-1), fj(t-1), Nj(t-2), fj(t-2), … ] + f(t)

Returning students

New students


Grade Progression Ratio Ratio of students in one class level at time t to students in

next-lower class level at time t-1 Assumes

Students follow an orderly progression form one state to another All students in each state move on to next state in one time period

or drop out of the system for good Very simple model good for year-to-year projections Data readily available Not usable in higher education Estimate the GPR from historical data

aj-1,j(t) = Nj(t)/ Nj-1(t-1) Apply GPR to current enrollment level to predict next time

period enrollment


Markov Chain

Stochastic process Fluctuate in time because of random events System can be in various states Markov property—each outcome depends only on the

one immediately preceding it Cross-sectional outlook Transition fraction

pij = fraction of students in class i in one period that can be found

in class j in the subsequent time period


Cohort Flow Models

Adopt a longitudinal outlook Take account of students’ origins Consider students’ accumulated duration of stay at the

university Students are grouped into cohorts at the time they enter

the university Could be viewed as a special case of Markov chain

model where states are expanded to include origin and length of stay


Cohort Flow Models Based on cohort survivor fractions Enrollment in a given level is sum of products of

survivor fraction and cohort size plus new students Estimate of returning students

Cohorts typically defined for fall semester Extensive data analysis required to determine

survivor fractions (retention) Combine with semester transition fractions to

generate annual estimate


Combined Cohort-Markov Model

New student

s

Summer term

Stopouts &

graduates

Current Fall

Previous Falls by Cohort

New student

s

Spring term

New student

s

Stopouts &

graduatesPrevious Summer

Survivors Transition

Transition

Transition

Cohort Markov


UCF Approach Continued growth assumption

Headcount growth at annual 5.7% rate for past four years Overall enrollment by level

Use combined cohort-Markov model for next five years Use combined population and high school graduate growth

rate projections for years 6-10 Enrollment and degrees by program

Conduct at HEGIS code level Develop initial enrollment projections and degree projections Programs conduct review of estimates and modify projections

Comparison with last year’s projections


Overall Enrollment ProjectionFall Headcount Projection

33,45336,013

38,79541,185 42,391

44,32246,540

48,16749,822 51,264 52,498 53,469 54,870 56,070 57,561

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

Historical Projection

3.1% average annual growth


Enrollment and Degrees by Program Initial enrollment projections based on previous five years

enrollment by HEGIS code level Use average of three methods

Linear projection model Logarithmic projection model Overall university annual growth rates applied to previous year

enrollment Adjust for planned increased enrollment in targeted programs

Estimate degree production based on current enrollment Average annual degree production rate =

DO NOT USE AVERAGE OF ANNUAL RATES Adjust for planned increased productivity in targeted programs

Sum of degrees awarded over five yearsSum of fall enrollment over five years


Program Enrollment Projections

This process is repeated for each program and level combination:

Bachelors, Masters, Doctoral x HEGIS level (step 1, 2, and 3)

Certificate, Unclassified Undergraduate, Unclassified Graduate x HEGIS level (step 1 and 3 only)

5 years of historical fall headcount enrollment

Step 1:Step 1:3 Modeling Methods:1) Linear2) Log3) UCF Overall

Input Data Excel Process

2005 - 2014 predicted

enrollment headcount

Output Data

Average of 3 models

5 years of historical fall headcount enrollment

Step 2:Step 2:

Calculated ratio of

degrees to enrollment

headcount for past 5 years

Input Data

Excel Process

2005 - 2014 predicted degrees

Output Data

5 years of historical degrees awarded

Input Data X2005 - 2014

predicted enrollment headcount

Step 3:Step 3:

Excel sheets distributed to colleges for adjustments

2005 - 2014 predicted enrollment headcount

2005 - 2014 predicted degrees

Excel sheets returned from

colleges to UAPS

Consolidated returned data =

2005 - 2014 enrollment

headcount and degree predictions

SAS Process


Program Enrollment Projections

Average of 3 models produces one

program enrollment forecast by level

from 2005 - 20014

Ratio of program enrollment to

degrees by level from 2000 - 2004

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014B: HC-Predicted 2004 24 54 107 139 139 152 164 176 188 199 210 220 231 241 251B: D-Predicted 2004 23 3 15 24 34 37 40 43 46 48 51 53 56 58 61B: HC-linear 24 54 107 138 152 197 231 265 299 333 367 401 435 469 503B: HC-log 24 54 107 138 152 165 178 190 199 208 216 224 230 237 242B: HC-UCF 24 54 107 138 152 160 164 169 174 178 181 182 187 187 193B: HC-Average 24 54 107 138 152 174 191 208 224 240 255 269 284 298 313B: HC-Negotiated 24 54 107 138 152 174 191 208 224 240 255 269 284 298 313B: Degrees 23 3 15 24 16 30 33 35 38 41 43 46 48 51 53B: D-Negotiated 23 3 15 24 16 30 33 35 38 41 43 46 48 51 53

Historical Data Projection EstimatesBachelor Degrees for Program XYZ

Step 1Step 1:: Predict Future Enrollment

Step 2:Step 2:Predict Future

Degrees

Step 3:Step 3: Distribute Program Enrollment and

Degree Prediction to Colleges for Adjustment

Step 1: Predict Enrollment

Step 2: Predict Degrees

Program XYZ - BACCALAUREATE

0

100

200

300

400

500

600

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Fall

Hea

dcou

nt a

nd A

nnua

l Deg

rees


Demo: Model Projections File #1: Data File

Degrees tab: Contains the number of degrees awarded by major for historical years

Enrollment Data tab: Contains historical enrollment by major Enrollment tab: Contains a pivot table of the enrollment data Contains the macro that will run

File #2: Model template file Contains the Model Sheet

File #3: Receiving file Contains template tabs for the summary sheets

End Result: One Receiving File for each college with one Model Sheet per major and fully populated summary sheets per level


Demo: Model Sheet Template file (.xlt)

Chose to use a template to reduce errors in the development of the process

Can include formatting, styles, text, formulas, VBA macros and custom toolbars.

The default action of an .xlt file is “new”, as opposed to “open” Using a template (.xlt) instead of a normal excel file (.xls)

prevents corruption of this template by not allowing saved changes

LookUp function in Excel to populate the data The VB code will copy and paste in the major code to cell B5 The historical data populates via “lookups”


Demo: Model Sheet (cont.) Enrollment Projections:

Use index for x rather than calendar year Linear Model: F(x) =Ax + b

A = SLOPE(known_y’s,known_x’s) B = INTERCEPT(known_y’s,known_x’s) Future value = MAX(A * (Future Year) + B,0)

Log Model: F(x) =Alog(x) + B A = SLOPE(known_y’s,LN(known_x’s)) B = INTERCEPT(known_y’s,LN(known_x’s)) Future value = MAX(A * LN (Future Year) + B,0)

UCF Overall Growth Year over year university wide growth


Demo: Model Sheet (cont.) Weighted Average of these three enrollment models =

“HC-Average” Opportunity for adjustment = “HC-Negotiated”

Degree Projections = (total number of degrees / sum of yearly enrollments) * projected enrollment

Other Excel functionalities: ISERROR( ) function used if the lookup does not exist IF( ) function creates conditional expressions for error flagging


Demo: Data File Macro Outer Loop on i = run code for each college

Change the pivot table in the Data File to the “ith” college Save the Receiving File as the college name

Inner Loop on j = run code for each major code within each college Puts a copy of the Model Sheet into the Receiving File Copies the major code from the “jth” row of the Data File

Enrollment tab into the Model Sheet All values of the model sheet are now populated via “lookup”

functions Copies and pastes as values the model sheet lookup functions Copies and pastes as links the model sheet data onto the level

summary tabs


Demo: Data File Macro (cont.) Summary tab

Array function allows for a multiple criteria lookup Type formula then control-shift-enter to create an array

function: {=SUM(IF((range1 = criteria1 )*(range2 = critiera2),(multiple

values))}

10 Colleges = 10 Excel files Summary tab Level Summary tabs Model Sheet tabs for each program


Demo: Data Collection 10 Files with 442 major codes x 6 levels = 2,652

separate models! Files then distributed to the college representatives

Make updates on either the detailed model sheets or the level summary sheets

College representatives then sent their files back to us for consolidation

Base SAS used to consolidate the returned Excel files SAS DDE within a macro was applied Files were all in the same layout and format (some needed

slight revising) What is SAS DDE?


Projection Review Format Bachelor's Summary

Ty/LyEnrollment / Degrees 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

ThisYear 000819 EXCEPTIONAL EDUCATIONEnrollment 0 0 0 0 51 147 152 158 163 168 173 179 179 179 179

LastYear 000819 EXCEPTIONAL EDUCATIONEnrollment 0 0 0 0 130 140 145 150 155 160 165 170 170 170 170

ThisYear 000819 EXCEPTIONAL EDUCATIONDegrees 0 0 0 37 54 64 66 69 72 76 78 80 80 80 80

LastYear 000819 EXCEPTIONAL EDUCATIONDegrees 0 0 0 37 56 60 62 64 66 69 71 73 73 73 73ThisYear 000802 ELEMENTARY EDUCATION (GENERAL)Enrollment 838 889 908 948 963 1046 1071 1096 1120 1143 1163 1178 1202 1216 1241LastYear 000802 ELEMENTARY EDUCATION (GENERAL)Enrollment 838 889 908 948 967 988 1010 1033 1055 1075 1095 1107 1127 1137 1146ThisYear 000802 ELEMENTARY EDUCATION (GENERAL)Degrees 378 360 377 386 381 437 452 467 482 497 505 512 522 529 539LastYear 000802 ELEMENTARY EDUCATION (GENERAL)Degrees 378 360 377 383 402 411 420 430 439 447 455 461 469 473 477ThisYear 000801 EARLY CHILDHOOD EDUCATIONEnrollment 138 128 145 179 224 234 246 259 271 283 294 304 315 325 336LastYear 000801 EARLY CHILDHOOD EDUCATIONEnrollment 138 128 145 179 174 179 205 210 220 225 230 235 240 245 250ThisYear 000801 EARLY CHILDHOOD EDUCATIONDegrees 72 61 79 52 82 100 107 113 120 126 131 135 141 145 150LastYear 000801 EARLY CHILDHOOD EDUCATIONDegrees 72 61 79 52 79 82 94 96 101 103 105 108 110 112 114ThisYear 000848 ENGLISH LANGUAGE ARTS EDUEnrollment 62 64 68 67 94 96 100 104 108 111 115 118 122 125 128LastYear 000848 ENGLISH LANGUAGE ARTS EDUEnrollment 62 64 68 67 65 65 65 65 65 65 65 65 65 64 63ThisYear 000848 ENGLISH LANGUAGE ARTS EDUDegrees 18 17 21 17 24 26 28 29 31 32 33 34 35 36 37LastYear 000848 ENGLISH LANGUAGE ARTS EDUDegrees 18 17 21 17 20 20 20 20 20 20 20 19 19 19 19ThisYear 000833 MATHEMATICS EDUCATIONEnrollment 55 51 48 61 67 70 72 74 76 78 80 82 84 85 87LastYear 000833 MATHEMATICS EDUCATIONEnrollment 55 51 48 61 63 65 67 69 71 73 75 77 80 82 84ThisYear 000833 MATHEMATICS EDUCATIONDegrees 18 15 18 10 11 18 19 19 20 21 21 22 22 23 23LastYear 000833 MATHEMATICS EDUCATIONDegrees 18 15 18 10 19 20 20 21 21 22 23 23 24 25 26

Hegis

ENROLLMENT PROJECTIONSHistorical Data Projection Estimates


Consolidated Projections by CIPCIP Code CIP Title

Targeted (Institutional Priority Programs are indicated as such) 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13

05.0000 Area, Ethnic, Cultural and Gender Studies - - - - - 2 4 6 8 09.0102 Mass Communication/Media Studies. 125 118 118 119 119 120 120 121 121 09.0401 Journalism. 15 29 29 30 30 30 30 31 31 09.0701 Radio and Television. 89 126 134 136 137 140 140 143 143 09.0903 Advertising. 90 122 122 123 124 125 126 127 128 11.0101 Computer and Information Sciences, General.Emerging Technologies: Computer Science and Information Technology103 100 95 90 85 85 90 95 100 11.0103 Information Technology. Emerging Technologies: Computer Science and Information Technology95 93 102 112 119 126 134 141 154 13.1001 Special Education and Teaching, General.Critical Needs: Education 48 64 66 69 72 76 78 80 80 13.1202 Elementary Education and Teaching. Economic Development: High-Wage/High Demand Jobs342 437 452 467 482 497 505 512 522 13.1210 Early Childhood Education and Teaching.Economic Development: High-Wage/High Demand Jobs83 100 107 113 120 126 131 135 141 13.1302 Art Teacher Education. 18 14 14 14 15 15 16 16 17 13.1305 English/Language Arts Teacher Education.Economic Development: High-Wage/High Demand Jobs26 26 28 29 31 32 33 34 35 13.1306 Foreign Language Teacher Education. Critical Needs: Education 3 3 3 2 3 3 3 3 3 13.1311 Mathematics Teacher Education. Critical Needs: Education 13 18 19 19 20 21 21 22 22 13.1312 Music Teacher Education. Economic Development: High-Wage/High Demand Jobs9 5 6 6 7 8 8 9 10 13.1314 Physical Education Teaching and Coaching.Economic Development: High-Wage/High Demand Jobs48 41 43 48 50 52 54 55 55 13.1316 Science Teacher Education/General Science Teacher Education.Critical Needs: Education 13 16 16 16 16 16 16 16 16 13.1317 Social Science Teacher Education. Economic Development: High-Wage/High Demand Jobs39 34 34 35 36 36 37 37 37 13.1320 Trade and Industrial Teacher Education.Critical Needs: Education 15 20 21 24 26 29 31 33 35 14.0201 Aerospace, Aeronautical and Astronautical Engineering.Emerging Technologies: Mechanical Science and Manufacturing24 38 40 43 45 47 49 51 53 14.0801 Civil Engineering, General. Emerging Technologies: Design and Construction90 85 90 96 101 106 108 110 112 14.0901 Computer Engineering, General. Emerging Technologies: Computer Science and Information Technology85 97 102 107 112 118 120 122 125 14.1001 Electrical, Electronics and Communications Engineering.Emerging Technologies: Mechanical Science and Manufacturing81 102 106 111 115 119 122 125 128 14.1401 Environmental/Environmental Health Engineering.Emerging Technologies: Natural Science and Technology20 19 19 20 21 21 21 22 22 14.1901 Mechanical Engineering. Emerging Technologies: Mechanical Science and Manufacturing77 109 116 123 130 138 143 148 154 14.3501 Industrial Engineering. Emerging Technologies: Mechanical Science and Manufacturing32 33 34 35 37 38 40 41 43 15.0303 Electrical, Electronic and Communications Engineering Technology/Technician.Emerging Technologies: Mechanical Science and Manufacturing29 22 17 17 18 18 18 18 18 15.0899 Mechanical Engineering Related Technologies/Technicians, Other.Emerging Technologies: Mechanical Science and Manufacturing23 25 27 30 32 34 35 36 36 15.1202 Computer Technology/Computer Systems Technology.Emerging Technologies: Computer Science and Information Technology38 40 45 50 56 61 66 70 75 16.0101 Foreign Languages and Literatures, General. 7 4 5 5 5 5 5 5 5 16.0901 French Language and Literature. 3 7 8 8 9 10 10 11 11 16.0905 Spanish Language and Literature. 14 13 14 14 15 15 15 16 16 22.0302 Legal Assistant/Paralegal. 207 221 234 247 260 272 284 294 306 23.0101 English Language and Literature, General. 147 168 169 171 173 175 176 179 180 23.1001 Speech and Rhetorical Studies. 97 104 105 106 107 108 109 110 111 24.0101 Liberal Arts and Sciences/Liberal Studies. 500 497 521 546 570 591 613 632 654


SAS DDE DDE (Dynamic Data Exchange) allows the dynamic

exchange of data between Windows applications such as spreadsheets or databases

Establish a client/server relationship: SAS System acts a client by requesting data, sending data, or

sending commands to the server application Excel 2002 is the server application (any application that

supports DDE as a server can communicate with the SAS System)


SAS DDE (cont.)

To use DDE in SAS, issue the file name statement:

FILENAME filref DDE ‘DDE-triplet’ <DDE-options>

The DDE-triplet argument refers to the DDE external file in the following form: ‘application-name|topic!item’

(SAS Institute, 1999)


SAS DDE (cont.) Filename statement used to establish a DDE link to the

Excel application This will allow us to later issue commands to Excel, using the

fileref “commands” In the DDE triplet, the application-name is Excel, the

topic is SYSTEM and the item is not specifiedFILENAME commands DDE 'EXCEL|SYSTEM'; %macro importfile(dir,file,abr,College); data _null_; file commands; put "[open(""C:\&dir.\&file..xls"")]"; run;


SAS DDE (cont.) Once the Excel file is opened Establish a DDE link to the specified range in Excel In the DDE triplet, the application-name is Excel, the

topic is the file “&file” with the tab name “BACC-SUMMARY” and the item is the range of data from Row4, Column 1 to Row 1000, Column 25.

filename BACC1 dde "Excel|[&file..xls]BACC-SUMMARY!R4C1:R1000C25";


SAS DDE (cont.) Infile statement reads the data in the specified range

into a SAS data set Infile statement options:

missover specifies that SAS should continue to read in a record, even if some value are missing

notab prevents SAS from converting tabs in Excel to blanks dlm = ’09’x specifies that the file is tab delimited dsd specifies that two delimiters represent a missing value LRCL = option specifies the record length (in bytes)

data BACC2; infile BACC1 MISSOVER NOTAB LRECL=5000 dlm='09'x dsd; informat VarList $char50. YR2000-YR2014 10.0; input VarList $ YR2000-YR2014 ; run;


SAS DDE (cont.)

SAS can then close the file Macro calls are written for each college file that was

sent and returneddata _null_; file commands; put "[close(""C:\&dir.\&file..xls"")]"; run; %mend;

%importfile(Sent,Arts & Sciences 18May2005,CAS_sent,CAS); %importfile(Returned,Arts & Sciences 1June2005,CAS_returned,CAS);


SAS DDE (cont.) SAS code combines the data:

Modeled projection data that was sent to the colleges Revised projections collected from the colleges Updated data (for example, recent degrees conferred)

Comparisons are now easy to make between last year’s, this year’s, modeled, and revised projections

Exported to excel for reports Conditional formatting, Pivot tables, Charts

Further development Remove “zero” data lines Rounding issues


References

SAS version 8.02 is used, along with MS Excel 2002 SP3 in a Windows XP Professional V5.1 SP2 operating system.

SAS Institute Inc., SAS OnlineDoc®, Version 8, Cary, NC: SAS Institute Inc., 1999. http://v8doc.sas.com/sashtml/

Walkenbach, John, Microsoft Excel 2000 Power Programming with VBA, IDG Books Worldwide, Inc 1999.

Microsoft Office Online Assistance: Assistance > Excel 2003 > Startup and Settings > Managing Files http://office.microsoft.com/en-us/assistance/CH062527921033.aspx


Summary Significant variability in projections in growth mode Need to use multiple projection methods Detailed review by programs is essential Importance of comparison with historical and previous

projections Evolution from program projections to program PLAN Excel is powerful tool

Create projections Display

SAS provides excellent capability for managing data and creating reports

All plans and models available at http://uaps/ucf/edu/enrollment


Questions

???

Ms. Sandra ArcherAssistant Director, University

Analysis and Planning SupportUniversity of Central Florida12424 Research Parkway, Suite

215Orlando, FL [email protected] http://uaps.ucf.edu

Dr. Robert L. ArmacostDirector, University Analysis and

Planning SupportUniversity of Central Florida12424 Research Parkway, Suite

215Orlando, FL [email protected] http://uaps.ucf.edu

mailto:[email protected]

http://uaps.ucf.edu/

mailto:[email protected]

http://uaps.ucf.edu/

spreadsheet models for program enrollment planning

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