Best Practices in Enrollment Modeling: Navigating Methodology and Processes

Best Practices in Enrollment Modeling: Navigating Methodology and Processes Dr. Elayne Reiss University of Central Florida University Analysis and Planning Support 2012 SAIR Conference Lake Buena Vista, FL September 24, 2012

Agenda Enrollment Projections: An Overview Enrollment Literature Review UCF s Enrollment Projection Process Implications for Future Study and Research September 24, 2012 Best Practices in Enrollment Modeling 2

UCF At a Glance Celebrating 50 Years opened in 1968 with 1,500 students now over 60,000 students first in state; second in nation UCF stands for opportunity. rated one of 50 Best Value universities by Kiplinger s Personal Finance a top 100 Best Buy College according to Forbes Top Tier National University, School to Watch, and a top 5 up-andcoming schools by U.S. News & World Report among top 50 nationwide for most FTIC National Merit Scholars September 24, 2012 Best Practices in Enrollment Modeling 3

Enrollment Projections: An Overview The purpose of modeling is to reduce complex institutional problems to simpler proportions, so that the human skills of decision makers can most effectively be brought to bear on the issues to be resolved. (Gaunt & Haight, 1977, p. 305) September 24, 2012 Best Practices in Enrollment Modeling 4

Enrollment Projections: An Overview Enrollment projections (forecasting, modeling) should not be confused with enrollment management. Target Influence Staff Responsible Overall Goal Projections No enrollment target necessary Student demand (controlled by policy) Institutional research and planning Predicting enrollment levels to better determine resource needs as well as tuition and fees collected Management End target generally required Administration (also controlled by policy) Admissions and registrar Crafting institutional demographics and student mix September 24, 2012 Best Practices in Enrollment Modeling 5

Enrollment Projections: An Overview There is no one size fits all approach to projecting postsecondary enrollment. different cultural, financial, and political contexts different challenges with increasing or decreasing enrollment trends Although all institutions are different, general methodologies and inputs can be quite similar. similar types of mathematical (or non-mathematical) models similar explanatory inputs, such as general population forecasts or institutional retention rates September 24, 2012 Best Practices in Enrollment Modeling 6

ENROLLMENT PROJECTION LITERATURE REVIEW September 24, 2012 Best Practices in Enrollment Modeling 7

Purpose and Importance Student enrollment translated into fiscal income is fundamentally important to budget, program, and personnel planning. Accurate enrollment forecasts are crucial for colleges and universities to remain competitive inaccurate enrollment forecasts can lead to poor allocation of funds and resources. (Chen, 2008, p. 2) September 24, 2012 Best Practices in Enrollment Modeling 8

Purpose and Importance Not all institutions have a time-tested general methodology in place for conducting enrollment projections. one-or-two-person IR offices various levels of skill sets among IR staff some institutions provide limited latitude to their IR offices in presenting these types of figures Even institutions with well-established enrollment projection methodologies can allow processes to evolve. changing demographics can lead to potentially different inputs access to different software and knowledge bases can allow for streamlining of processes examining how institutional peers may conduct projections September 24, 2012 Best Practices in Enrollment Modeling 9

Enrollment Projections: Brief History Some of the earliest scholarly literature regarding postsecondary enrollment projections was published in the 1950 s and 1960 s. Many of the methods being discussed throughout the 1960 s and 1970 s literature are still in use at institutions today. Markov chain models cohort flow models regression models A big focus for scholars in projecting enrollment through the 1970 s involved the creation of computerized simulation models intended to apply to multiple institutions September 24, 2012 Best Practices in Enrollment Modeling 10

Types of Models Common methods for projecting enrollment fall into several categories and can be grouped into the more straightforward and traditional to the more mathematical and recent. Surveys Subjective judgment Ratio methods Cohort survival Regression analysis Markov modeling Time series analysis Neural network Simulation Fuzzy time series Traditional, straightforward Recent, mathematical September 24, 2012 Best Practices in Enrollment Modeling 11

Common Variables No matter the modeling method, certain variables can typically be found across model types. population projections for the surrounding area high school graduation rates historical institutional enrollment data enrollment data from a competing institution various economic measures, such as unemployment rate, consumer price index, average per capita income Some variables may seem appropriate, but may not be the most effective in predicting incoming enrollment counts. incoming student grades test scores (SAT, ACT) assorted demographics September 24, 2012 Best Practices in Enrollment Modeling 12

Selecting the Best Model One of the most difficult pieces of enrollment modeling can lie in the selection process itself. Here s what researchers have recommended for the selection of a modeling process. sufficiently simple logic for the layman, but complex enough to be meaningful when utilizing historic figures, necessary to have a sufficiently longitudinal basis variables should make sense in the context of enrollment (correlation is not causation!) predictor variables should be available for future years fitting for the time frame any costs of forecasting should not outweigh benefits September 24, 2012 Best Practices in Enrollment Modeling 13

Pros and Cons of Modeling Methods All methods have benefits and drawbacks for use. Here s what researchers have stated on three selected methods. Method Pros Cons Ratio Methods (Alon, 2005) Regression Models (Ahlburg, McPherson, & Schapiro, 1994; Chen, 2008) Does not require deep mathematical knowledge Can provide a reasonable degree of accuracy Accessible to most individuals with reasonable mathematical abilities Can model enrollment and help determine what factors are truly related to enrollment shifts Backed by little quantitative evidence Process can be unintuitive, as past trends may not always serve as the best predictors Can be easily influenced by outliers Fuzzy Time Series (Song & Chissom, 1993) Small average forecasting error Can accompany management judgment Lenient requirements for historical data Method is likely to be most unfamiliar to IR professionals Forecasts depend on subjective output interpretations September 24, 2012 Best Practices in Enrollment Modeling 14

Peer Review of Modeling Methods Our search for determining an effective model led us to perform a high-level search for the methodology used by our comparison and aspirational peer institutions, among others. Very few institutions posted any information on their websites about the methods they used to project enrollment. Recruitment methods are understandably secretive, but why mathematical methodologies? Possibilities that many institutions do not utilize methodologies that are particularly mathematical? September 24, 2012 Best Practices in Enrollment Modeling 15

Peer Review of Modeling Methods Our search for determining an effective model led us to perform a high-level search for the methodology used by our comparison and aspirational peer institutions, among others. Some institutions had significant modeling information posted, but more in the form of an academic exercise rather than actual practice. scholarly articles sometimes addressed mathematics of college/university projections without discussing long-term viability other institutions openly shared a historical enrollment dataset to be analyzed by mathematicians internationally (e.g., University of Alabama) September 24, 2012 Best Practices in Enrollment Modeling 16

Peer Review of Modeling Methods Our search for determining an effective model led us to perform a high-level search for the methodology used by our comparison and aspirational peer institutions, among others. Available models included both models for individual institutions and projections developed for multi-campus systems. September 24, 2012 Best Practices in Enrollment Modeling 17

Sampling of Institutional Models A 1997 report from the Florida Postsecondary Education Planning Commission indicated the use of sector-level and state-level regression analyses to determine statewide postsecondary enrollment. The University of Hawaii Community College System noted the use of basic trends and the ratio method in its 2011 projections. The University of North Carolina Charlotte did not name a specific method for its 2007 projections, but based its structure on high school graduation rates, higher education enrollment patterns, and county-level growth data. September 24, 2012 Best Practices in Enrollment Modeling 18

Sampling of Institutional Models The Maryland Higher Education Commission cited a combination of ratio methods and regression models in its 2011 projections for its postsecondary institutions at all levels, largely examining population and high school graduate factors. The University of Delaware openly presented its enrollment projections, which utilizes prior enrollment and retention rates in a ratio-type model. September 24, 2012 Best Practices in Enrollment Modeling 19

OVERVIEW OF UCF S ENROLLMENT PROJECTION MODELING METHODS September 24, 2012 Best Practices in Enrollment Modeling 20

UCF Enrollment Projections Overview five year model project incoming FTIC, transfers, graduate enrollees population and high school graduation projections (FL Office of Economic and Demographic Research & FL DOE) feeder community college projections (FL DOE - Div of State Colleges) market share forecasts predict headcount enrollment combined Cohort-Markov model continuation rates and transition fractions course loads to determine SCH 1 ten year (and beyond) model applies population and high school graduates growth rates 2 program projections conducted every 3-4 years Grad studies collects short-term projections annually 3 September 24, 2012 Best Practices in Enrollment Modeling 21

Combined Cohort-Markov Model Cohort-flow model Markov chain model New students New students New students Previous Falls by Cohort Survivors Current Fall Transition Spring term Transition Summer term Previous Summer Transition Stopouts & graduates Stopouts & graduates Students are grouped into cohorts at the time they enter the university (cohort survivor fractions) Transition fraction p ij = fraction of students in class i in one period that can be found in class j in the subsequent time period September 24, 2012 Best Practices in Enrollment Modeling 22

Data Inputs to Determine HC New Students by Class/Term New student input by type (FTIC, CC Transfer, Grad) New student allocation fractions by type and class (Fr., So, Jr., Sr.) Students Returning in the Fall Undergraduate retention fractions (cohort, two-yr avg) Graduate continuation fractions (non-cohort, two-yr avg) Students Returning in the Spring / Summer Semester transition fractions September 24, 2012 Best Practices in Enrollment Modeling 23

5-Year Model Output By Term UNIVERSITY OF CENTRAL FLORIDA ESTIMATED ENROLLMENT BY CLASSIFICATION AND STUDENT TYPE Scenario c 12May2011 2011-2012 By Classification SUMMER 2011 ACTUALS OR MANUAL UPDATES UNDERGRAD UNIVERSITY FTIC's FRESH SOPH JR SR TOTAL UNCLASS GRADUATE TOTAL HEADCOUNT 2,650 3,192 3,749 7,470 15,699 30,110 586 5,123 35,819 LOWER SCH 14,061 17,207 14,458 14,666 16,862 63,193 672 127 63,992 UPPER SCH 212 484 7,637 34,908 91,097 134,126 978 500 135,604 GRADUATE SCH 0 0 0 3 214 217 911 26,800 27,928 TOTAL SCH 14,273 17,691 22,095 49,577 108,173 197,536 2,561 27,427 227,524 FALL 2011 ACTUALS OR MANUAL UPDATES UNIVERSITY FTIC's FRESH SOPH JR SR TOTAL UNCLASS GRADUATE TOTAL HEADCOUNT 3,798 6,483 7,551 13,923 21,546 49,503 686 7,705 57,894 LOWER SCH 47,269 78,671 62,034 48,326 31,910 220,941 540 142 221,623 UPPER SCH 2,325 3,499 32,368 116,199 207,120 359,186 1,004 749 360,939 GRADUATE SCH 0 0 0 16 648 664 1,115 53,540 55,319 TOTAL SCH 49,594 82,170 94,402 164,541 239,678 580,791 2,659 54,431 637,881 Headcount SPRING 2012 PREDICTED UNIVERSITY FTIC's FRESH SOPH JR SR TOTAL UNCLASS GRADUATE TOTAL HEADCOUNT 100 4,458 6,992 14,097 23,309 48,856 785 7,399 57,040 LOWER SCH 1,101 55,235 59,424 47,951 36,355 198,965 577 130 199,673 UPPER SCH 96 2,803 29,313 119,756 222,724 374,596 1,166 693 376,455 GRADUATE SCH 0 0 0 34 986 1,020 1,419 51,610 54,049 TOTAL SCH 1,197 58,038 SCH 88,737by Level 167,741 260,066 574,582 3,162 52,433 630,177 September 24, 2012 Best Practices in Enrollment Modeling 24

10-Year Projection Extension Model short-term detailed model projects t 1 t 5 extension model projects t 6 t 10 Where prediction year = t 0 applies growth factor to t 5 estimates to obtain t 6 and repeats the process on an annual basis until t 10 estimates are obtained Lower, Upper, or Graduate growth factor average population growth and high school graduation projections September 24, 2012 Best Practices in Enrollment Modeling 25

10-Year Model Inputs population growth for Florida from Office of Economic and Demographic Research (http://edr.state.fl.us/) projections by county for persons in the 18-24 and 25-44 age groups, focusing on UCF s main feeder counties graduation projections from Florida Department of Education high school grad projections involve different numbers of years away for different student groups (e.g., FTIC, CC Transfer) to account for divergent behavior September 24, 2012 Best Practices in Enrollment Modeling 26

10-Year Model: Results growth factors applied to 5-year model output FTE and HC 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 2023-24 2024-25 2025-26 Total FTE Actual Detailed Prediction Model Population and HS Proj. Model Population Projections (2021-2025) Lower FTEs 11,977 12,320 12,524 12,775 13,109 13,508 13,950 14,030 14,053 14,139 14,105 14,222 14,413 14,602 14,791 14,979 Upper FTEs 20,669 22,453 23,305 23,893 24,697 25,829 27,164 27,392 27,444 27,700 27,742 27,924 27,991 28,043 28,466 28,886 UG FTEs 32,646 34,773 35,829 36,668 37,805 39,337 41,114 41,422 41,497 41,839 41,847 42,147 42,403 42,646 43,257 43,865 YOY Growth 5.04% 6.52% 3.03% 2.34% 3.10% 4.05% 4.52% 0.75% 0.18% 0.82% 0.02% 0.72% 0.61% 0.57% 1.43% 1.40% Grad I FTEs 3,663 3,707 3,714 3,680 3,624 3,589 3,571 3,590 3,601 3,632 3,640 3,689 3,715 3,740 3,763 3,784 Grad II FTEs 802 813 814 807 795 787 783 787 790 796 798 809 815 820 825 830 Grad FTEs 4,466 4,520 4,529 4,487 4,419 4,376 4,354 4,378 4,390 4,428 4,438 4,499 4,530 4,560 4,588 4,614 YOY Growth 5.56% 1.21% 0.19% -0.93% -1.51% -0.97% -0.50% 0.54% 0.29% 0.87% 0.22% 1.36% 0.71% 0.66% 0.61% 0.57% Med Prof FTE 100 180 280 360 420 460 480 480 480 480 480 480 480 480 480 480 Med Prof FTE 0 0 0 0 60 159 257 355 355 355 355 355 355 355 355 355 Total FTE 37,211 39,473 40,637 41,514 42,704 44,332 46,205 46,635 46,722 47,102 47,120 47,480 47,769 48,041 48,680 49,314 YOY Growth 5.27% 6.08% 2.95% 2.16% 2.87% 3.81% 4.23% 0.93% 0.19% 0.81% 0.04% 0.76% 0.61% 0.57% 1.33% 1.30% Fall Total Headcount Unclass HC 926 926 924 921 923 924 924 925 926 927 928 929 930 931 932 933 Lower HC 14,111 14,089 14,262 14,520 14,773 15,030 15,291 15,378 15,403 15,498 15,461 15,589 15,798 16,006 16,213 16,418 Upper HC 33,236 36,298 37,284 37,933 39,149 40,929 42,964 43,326 43,408 43,812 43,879 44,167 44,272 44,356 45,024 45,688 UG HC 47,347 50,387 51,546 52,453 53,922 55,959 58,255 58,704 58,811 59,310 59,340 59,757 60,070 60,361 61,236 62,107 YOY Growth 5.03% 6.42% 2.30% 1.76% 2.80% 3.78% 4.10% 0.77% 0.18% 0.85% 0.05% 0.70% 0.52% 0.49% 1.45% 1.42% Beg Grad HC 6,339 6,359 6,372 6,306 6,187 6,117 6,078 6,111 6,129 6,182 6,195 6,280 6,324 6,366 6,405 6,441 Adv Grad HC 1,625 1,630 1,633 1,616 1,586 1,568 1,558 1,567 1,571 1,585 1,588 1,610 1,621 1,632 1,642 1,651 Grad HC 7,964 7,989 8,005 7,922 7,773 7,686 7,637 7,678 7,700 7,766 7,784 7,890 7,945 7,997 8,046 8,092 YOY Growth 5.95% 0.32% 0.20% -1.04% -1.88% -1.12% -0.64% 0.54% 0.29% 0.87% 0.22% 1.36% 0.71% 0.66% 0.61% 0.57% Med Prof HC 100 180 280 360 420 460 480 480 480 480 480 480 480 480 480 480 Dental HC 0 0 0 0 60 159 257 355 355 355 355 355 355 355 355 355 TOTAL HC 56,337 59,481 60,755 61,656 63,098 65,187 67,553 68,142 68,272 68,839 68,887 69,411 69,781 70,125 71,050 71,967 YOY Growth 5.02% 5.58% 2.14% 1.48% 2.34% 3.31% 3.63% 0.87% 0.19% 0.83% 0.07% 0.76% 0.53% 0.49% 1.32% 1.29% September 24, 2012 Best Practices in Enrollment Modeling 27

Program-Level Projections We gather 10 years of historical enrollment (where possible) for each program at the university and estimate the next 10 years via mathematical models. average of three methods adjusted linear projection model logarithmic projection model overall university annual growth rates applied to previous yr. enrollment Degree production also projected and is based on several years of current enrollment. September 24, 2012 Best Practices in Enrollment Modeling 28

Program-Level Projections Projections are sent to each college for review. each college receives only their programs to review organized Excel file is sent with detailed instructions each college is likely to know more than we will about program limits, new programs, soon-to-be inactivated or suspended programs We receive the adjusted projections and create a summary report for each college, including university-wide and college-specific trends. September 24, 2012 Best Practices in Enrollment Modeling 29

Model Conclusions 5-Year Model fairly accurate in the short term; increasing error in future years data-driven process based on historical student behavior detailed down to student level and term adaptable for future developments 10-Year Model expanded from 5-year model applies high school graduation and population projections, weighted by the areas that supply our students September 24, 2012 Best Practices in Enrollment Modeling 30

IMPLICATIONS AND CONCLUSIONS September 24, 2012 Best Practices in Enrollment Modeling 31

Future Study and Research Despite the appearance of more complex enrollment modeling methods, institutions are sticking with simplistic, easy-to-grasp models. A lack of publicly posted enrollment models suggests that further research needs to be conducted to truly determine the overall picture of postsecondary enrollment modeling. what models are being used? what factors led to their selection? how accurate have the models been in predicting enrollment? September 24, 2012 Best Practices in Enrollment Modeling 32

Summary Enrollment Projections: An Overview Enrollment Literature Review UCF s Enrollment Projection Process Implications for Future Study and Research September 24, 2012 Best Practices in Enrollment Modeling 33

Contact Information Dr. Elayne Reiss Assistant Director (elayne@ucf.edu) University Analysis and Planning Support University of Central Florida 12424 Research Pkwy, Ste 215 Orlando FL 32826-3207 Phone: (407) 882-0285 This presentation will be available at http://uaps.ucf.edu The mission of the office of University Analysis and Planning Support (UAPS) is to enhance the management capability within the University of Central Florida (UCF) by providing models and information to support and empower academic units, administrative units, and external stakeholders to utilize analysis and research results as the cornerstone for informed decision-making. September 24, 2012 Best Practices in Enrollment Modeling 34