exploring the relationship between admissions, financial aid, and the ...

EXPLORING THE RELATIONSHIP BETWEEN ADMISSIONS, FINANCIAL AID, AND THE FINANCIAL HEALTH OF CCCU INSTITUTIONS: A QUANTITATIVE ANALYSIS

by Janice L. Supplee

A DISSERTATION

Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy

Major: Educational Studies (Educational Leadership and Higher Education)

Under the Supervision of Professor Marilyn L. Grady

Lincoln, Nebraska

March, 2014

EXPLORING THE RELATIONSHIP BETWEEN ADMISSIONS, FINANCIAL AID, AND THE FINANCIAL HEALTH OF CCCU INSTITUTIONS: A QUANTITATIVE ANALYSIS Janice L. Supplee, Ph.D. University of Nebraska, 2014 Adviser: Marilyn L. Grady

Recent headlines about the financial challenges confronting small, tuition– dependent private colleges are bleak. Rising costs, declining enrollments, growing student debt, increasing default rates, rising tuition discounts, insufficient endowments, and falling institutional credit ratings have all grabbed the attention of the national media. Acknowledging this challenging environment, this study explored factors contributing to the financial health of a subset of small private colleges—members of the Council for Christian Colleges and Universities (CCCU). A multilevel statistical analysis provided an understanding of the relationship of five enrollment and financial aid–related predictor variables (admitted student yield rate; annual percent change in undergraduate enrollment; percent of total enrollment from adult, graduate, and professional programs; unfunded discount rate; and institutional aid from gifts or endowment) to the participating institutions’ Composite Financial Index (CFI) during a four–year period. Using data from the North American Coalition for Christian Admissions Professionals (NACCAP) Admissions Benchmarking Survey and the Financial Aid Survey of CCCU Institutions as well as responses to a CFI survey of member institutions,

the relationships of the variables were examined using both descriptive and inferential statistics. Five hypotheses were tested. In considering the interaction of each predictor variable with time, the model showed that the admitted student yield rate and unfunded discount rate constrained growth in the CFI in some time periods. In contrast, the annual percent change in undergraduate enrollment positively influenced the direction of the CFI in one time period. The only predictor variable that emerged as significant in the final model was institutional aid from gifts or endowment. The results suggested that, for CCCU institutions, enrollment strategies alone did not improve institutional financial performance. Also, the influence of graduate and adult programs on financial health remained unclear. To improve financial position, leaders should consider balanced strategies that include stable or moderate undergraduate enrollment growth, conservative use of tuition discounting, and intentional efforts to increase endowment and other gift resources.

iii

Copyright 2014, Janice L. Supplee

iv ACKNOWLEDGEMENTS

It was a sunny April day when I made the decision to start this doctoral program at the University of Nebraska. After weeks of indecision, I spent 30 minutes with my future adviser, Dr. Marilyn Grady, on the phone. At the time, I knew little of her many professional accomplishments and accolades. All I knew was that she inspired me to dream and gave me a roadmap to follow. That began with her request for daily emails sharing dissertation topic ideas and included her encouragement to start the program by coming to Lincoln for the three-week May term. I followed her lead and never looked back. From the start, Dr. Grady saw the potential in this research and guided me in the writing process. Her colleague, Dr. Barbara LaCost, and Houston Lester from the Nebraska Evaluation and Research Center helped me to shape the study design and interpret the results. I am indebted to their investment in this process. One of my goals was to conduct research that would advance the cause of Christian higher education. People like Jesse Rine, Kyle Royer, Tim Fuller, Jeff Olson, Dan Nelson, and Bruce Hoeker shared that vision and contributed their advice, time, resources, direction, data, and support. I am thankful to each one. I am also grateful for the support and encouragement of Cedarville University and the outstanding colleagues with whom I am privileged to serve. Surprise acknowledgements, notes of encouragement, and prayers for my success marked these years of study. My stays in Lincoln were often hosted by the Clines, who made me feel like family when my own was far away. They share a part in this project as well.

v Finally, I am thankful to my husband Ed and son Ben for making the sacrifices that allowed me to pursue this goal. This has been a shared project, even though much of it was completed alone. Thank you for making the difficult days easier and celebrating each accomplishment along the way. I love you both!

vi TABLE OF CONTENTS ABSTRACT ......................................................................................................................... i COPYRIGHT ..................................................................................................................... iii ACKNOWLEDGEMENTS ............................................................................................... iv TABLE OF CONTENTS ................................................................................................... vi LIST OF FIGURES ........................................................................................................... ix LIST OF TABLES ............................................................................................................. xi CHAPTER 1: INTRODUCTION ........................................................................................1 Overview ..................................................................................................................1 The Financial Condition of Private Higher Education ............................................1 Historical Perspective ..............................................................................................3 Council for Christian Colleges and Universities .....................................................6 Professional and Career Development .......................................................10 Student Programs .......................................................................................11 Advocacy and Public Policy ......................................................................11 Research and Resources .............................................................................12 Significant Challenges ...............................................................................13 Summary ....................................................................................................13 Personal Connections, Experiences, and Values ...................................................13 Personal Connections .................................................................................14 Education and Experience..........................................................................14 Researcher Bias ..........................................................................................15 Significance of the Study .......................................................................................16

vii Statement of Purpose .............................................................................................17 Research Question .................................................................................................17 Organization of the Dissertation ............................................................................18 CHAPTER 2: REVIEW OF RELATED LITERATURE ..................................................19 Overview ................................................................................................................19 Economic Theory ...................................................................................................19 Financial Performance ...........................................................................................25 Institutional Case Studies .......................................................................................36 Enrollment and Financial Aid ................................................................................40 Admitted Student Yield Rate .....................................................................41 Change in Undergraduate Enrollment .......................................................46 Non–Traditional Adult, Graduate, and Professional Program Enrollment..................................................................................................48 Tuition Discount Rates and Institutional Aid ............................................53 Previous Correlational Research ............................................................................65 Definition of Terms................................................................................................72 Summary ................................................................................................................74 CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY ......................................76 Overview ................................................................................................................76 Summary of the Data Collection Process ..............................................................76 Secondary Data Sources ........................................................................................77 NACCAP Admissions Benchmarking Survey...........................................78 Financial Aid Survey of CCCU Members .................................................79

viii Primary Data Source: CFI Survey .........................................................................81 Access to Data Sources ..............................................................................82 Population and Time Period...................................................................................82 Missing Data ..........................................................................................................83 Variables ................................................................................................................84 Predictor Variables.....................................................................................84 Criterion Variable ......................................................................................86 Hypotheses .............................................................................................................88 Analytical Strategy.................................................................................................89 Summary ................................................................................................................92 CHAPTER 4: DATA ANALYSIS ....................................................................................93 Overview ................................................................................................................93 Data Collection and Preparation ............................................................................93 Descriptive Statistics ..............................................................................................94 Preliminary Analysis: Confirming the Need for a Multilevel Model ....................99 Analysis of the Data Associated with the Main Research Hypotheses .................99 Step 1: Pattern of Change over Time .......................................................100 Step 2: Interaction of each Predictor Variable with Time .......................100 Step 3: Variable Interactions with Time Removed (Main Effects) .........104 Summary ..............................................................................................................106 CHAPTER 5: DISCUSSION AND CONCLUSIONS ....................................................107 Overview ..............................................................................................................107 Review of Purpose ...............................................................................................107

ix Summary of Findings from Descriptive Statistics ...............................................107 Freshmen Recruitment .............................................................................108 Total Enrollment ......................................................................................108 Tuition Revenues .....................................................................................109 Institutional Aid .......................................................................................109 CFI Score .................................................................................................110 Research Hypotheses and Results........................................................................113 Contribution to the Literature ..............................................................................115 Limitations of the Study.......................................................................................117 Implications for Theory, Policy, and Practice .....................................................119 Implications for Future Research .........................................................................122 Conclusion ...........................................................................................................125 REFERENCES ................................................................................................................126 APPENDIX A: NORTH AMERICAN COALITION FOR CHRISTIAN ADMISSIONS PROFESSIONALS (NACCAP).............................................................140 APPENDIX B: CCCU FINANCIAL AID SURVEY WORKSHEET ............................155 APPENDIX C: CFI SURVEY .........................................................................................171 APPENDIX D: CFI SURVEY RECRUITMENT EMAIL FROM ABACC ..................174 APPENDIX E: IRB RESPONSE ....................................................................................175 APPENDIX F: CFI SURVEY RECRUITMENT E–MAILS ..........................................176

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LIST OF FIGURES Figure 1: Explanation of tuition discounts. ....................................................................... 54 Figure 2: Diagram of the data collection process. ............................................................ 77 Figure 3: Hypothetical scatter plot with lines of best fit for each institution. .................. 91 Figure 4: Pattern of change in the CFI score over time. ................................................. 100

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LIST OF TABLES

Table 1: Performance Measures Tracked by Colleges and Universities .......................... 35 Table 2: Predictor and Criterion Variables ....................................................................... 84 Table 3: CFI Scale ............................................................................................................ 88 Table 4: Deriving the Sample ........................................................................................... 95 Table 5: Descriptive Statistics .......................................................................................... 95 Table 6: CFI-Reporting Institutions as Compared to Non-Reporting Institutions ........... 98 Table 7: Solution for Fixed Effects: Interaction of each Predictor Variable with Time ........................................................................................................................ 101 Table 8: Interaction of Each Predictor Variable with Time and the Research Hypotheses ...................................................................................................... 103 Table 9: Solution for Fixed Effects: Main Effects .......................................................... 105 Table 10: Main Effects and the Research Hypotheses.................................................... 105 Table 11: Results of Five Hypotheses ............................................................................ 114

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CHAPTER 1 INTRODUCTION Overview This chapter provides the context for a research study assessing the relationship of admissions and financial aid to the financial health of member institutions of the Council for Christian Colleges and Universities (CCCU). In order to understand the significance of this study, I provide background information on the financial condition of private higher education, including both a current assessment and a historical perspective. Next, I present an introduction to the CCCU, including its place within higher education and the reason this subset of private colleges was chosen for further study. After a section exploring my own personal connections with the topic, I conclude the chapter with a statement of the research problem, the research question, and the significance of the study. The Financial Condition of Private Higher Education Recent headlines about the financial challenges confronting higher education are bleak. Rising costs, declining yield rates, burgeoning student debt, and falling credit ratings are just a few of the issues being tracked by the popular national media and higher education news sources. Recently, Selingo (2012) addressed the financial viability of American colleges and universities—particularly the private sector. He highlighted two major themes threatening the financial health of private higher education: 

Efforts to increase enrollment are getting more difficult and more expensive.

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Rising college costs are forcing families to seek more affordable options.

Weisbrod and Asch (2010) added (a) endowment losses, (b) falling investments, (c) credit tightening, (d) declining private donations, and (e) declining state funding as additional factors contributing to the serious financial situation facing private higher education. Moody’s Investors Service summarized the dilemma in its 2013 report, stating that all traditional sources of higher education revenues are under pressure (Kiley, 2013). Most private colleges rely on tuition to fund their operations. Tuition, fees, room, and board account for two–thirds or more of revenue at most small private colleges (Chabotar, 2010; Almanac of Higher Education, 2011). These small colleges enroll just 4% to 10% of all students nationwide (Schuman, 2005). Fewer paying students provide less revenue and greater financial stress (Weisbrod & Asch, 2010). In their efforts to generate more revenue, private colleges have turned to the practice of tuition discounting. Tuition discounting involves allocating a percentage of tuition revenues to fund merit or need–based financial aid for students. The goal is to increase both enrollment and net tuition revenue to the institution. Tuition discount rates peaked at 43% in 2012, yet colleges and universities were spending more to enroll students and getting less cash from them (Selingo, 2012). At its worst, tuition discounting is an arms race and death spiral for private institutions (Goral, 2003). For the colleges and universities that chose not to increase their discount rates in 2012, many missed their enrollment targets and were forced to deal with fewer students, less tuition revenue, and possibly a budget deficit (Selingo, 2012). For the majority of private institutions that are dependent on tuition as their primary source of revenue,

3 neither increasing the discount rate nor missing enrollment targets is a financially sustainable outcome in the long–term. In addition, the practice of tuition discounting inevitably drives up costs at a time when the market of students who are willing to pay higher tuition prices is shrinking. From 1999 to 2009, tuition and fees increased between 33% and 56% while median family income was stagnant (U.S. Government Accountability Office, 2012). A 2012 Sallie Mae study affirmed that the percentage of families who eliminated college choices because of cost rose to its highest level (69%) in 2012, and enrollments at lower–cost community colleges increased from 23% to 29% in just a two–year period (2012). Families are focusing more on affordability and less on finding the best fit (Ashburn et al., 2008). Sevier (2012), enrollment management author and speaker, wrote on his blog, “It seems to me that when an industry…and many of its customers…are both in financial peril, there is something significantly wrong.” Historical Perspective The pessimism about the viability of private higher education is not new. Commentators in the 1970s looked at inflation and the financial difficulties plaguing private institutions and predicted that, by the 1990s, private universities—except for the elites—would have largely disappeared, having been absorbed into state systems, or having divested themselves of all but their few profitable operations (Cohen & Kisker, 2010). American higher education began as a private enterprise, but always relied on multiple sources of funding. The original colleges founded in the colonies received

4 support from legislative bodies, tuition, and individual college fundraising efforts. The colleges were never wealthy, but managed to operate in “genteel poverty,” enrolling only wealthier boys whose families could afford the fees (Cohen & Kisker, 2010). Fundraising increased in the Emergent Nation era (1790–1869), but early private colleges remained under–resourced. Tuition was affordable, and the college ranks included students from nearly all economic backgrounds. However, fewer than 2% of all 18–year olds enrolled in higher education during this time period (Cohen & Kisker, 2010). For private colleges, philanthropy became an essential function during the University Transformation Era (1870–1944). While the richest colleges enjoyed sizable donations from the nation’s new industrialists, many of the small private colleges remained poorly funded. The tuition gap between public and private education began to widen during this time period. Cohen and Kisker (2010) cited Geiger’s work that explained that private colleges were charging from $100 to $160 per year, while public universities typically charged just $30 to $40 per year. The Mass Higher Education Era (1945–1975) was a time of prosperity for most colleges and universities. At the same time, the nation was beginning to come to terms with its patterns of discrimination, and a new priority emerged to make higher education accessible to all. Federal and state governments dramatically increased their investment in higher education through the GI Bill and new grant and loan programs. The expenses in college budgets were also growing, at the same rate or even faster than enrollments and revenues. Bowen (1980) questioned the wisdom of the spending, asserting that the more affluent institutions seemed to be applying “their incremental expenditures to successively less important purposes.”

5 As the Mass Higher education Era came to a close, the revenue generated per student began to decline, and colleges and universities were looking for ways to cut costs and increase efficiency. But, cost cutting within higher education does not come easy! Archibald and Feldman (2008) explained the phenomenon in economic terms, noting that cost control cannot be achieved without productivity growth. The problem in higher education is that productivity growth leads to lower quality, and the trajectory for most institutions includes increased spending to achieve the nebulous goal of institutional prestige. Some cost increases are unavoidable. Socially imposed costs, for example, render institutions powerless to control certain expenditures (Cohen & Kisker, 2010). Costs are driven up by increased regulations, services required to meet the needs of underprepared students, rising salaries, and overall expansion of the enterprise. As costs increased, private colleges began the practice of tuition discounting, since their tuition charges were no longer affordable to any but the richest students (Cohen & Kisker, 2010). Tuition increased at a rate twice that of the consumer price index, but was still affordable for most families benefitting from institutional and governmental aid. The Era of Consolidation (1976–1993) saw the rise in revenues and expenditures come to a halt, and higher education administrators were forced to implement cost reductions. Funding from both federal and state governments declined as a percent of total revenue, and institutions began to rely more on tuition and fees (Cohen & Kisker, 2010). State appropriations began to decline as a percentage of total college budgets. In fact, if the level of state funding had remained proportionate to personal income levels in

6 1977, appropriations should be about 20% higher now (Bruininks, Keeney, & Thorp, 2010). To make up for lost government funding, tuition increases rapidly outpaced inflation and family income growth. From 1984 to 2008, college tuition and fees increased 439% compared to a 147% increase in median family income (Peruso, 2010). To help families deal with rising college costs, federal and state aid also increased, but mostly in the form of loans. In 1976, grants (non–repayable aid) accounted for 80% of federal aid. That figure had dropped to 61% by 1980. By 1994, grants represented just 28% of all federal support (Cohen & Kisker, 2010). Private higher education is facing financial challenges on every level: operational costs, pricing and affordability, tuition dependence, and available funding. These challenges are not all new, but neither are they temporary. Many believe that we are observing a permanent change in the higher education environment (Bruininks, et al., 2010; Kiley, 2013). Colleges and universities need a sustainable financial model for the future. This research contributes to the understanding of admissions and financial aid as influencers of financial health, specifically at CCCU institutions. Council for Christian Colleges and Universities American higher education began as a religious endeavor, training men for church ministry and public service based on a distinctly Christian worldview. The goal statements of the early Colonial colleges reflect this priority: 

Harvard: “To know God and Jesus Christ which is eternal life (John 17:3) and therefore to lay Christ in the bottom as the only foundation of all sound knowledge and learning.”

7 

Yale: “That every student shall consider the main end of his study to wit to know God in Jesus Christ and answerably to lead a Godly, sober life.”



Columbia: “To teach and engage the children to know God in Jesus Christ and to love and serve him …” (as cited in Ringenberg, 2006, p. 38).

Even as American society spread westward and small colleges sprung up across the land, most were founded by religious denominations seeking to influence the next generation with their teachings and faith. But a subtle transition toward secularism was also occurring during this time. Religion and science seemingly worked at odds with each other, and gradually faith was set aside as irrelevant to (and even at–odds with) higher education. Cohen & Kisker (2010) asserted that the early religious influence slowed scientific advancement and intellectual inquiry in the young nation. George M. Marsden, author of the thoughtful book, The Outrageous Idea of Christian Scholarship (1997), described the university builders of our nation as individuals who believed that scientific investigation would advance civilization and promote the kingdom of God. Yet, in their zeal they had indeed limited academic inquiry. By the late 1800s, American higher education had largely thrown off its religious heritage. In place of the Protestant worldview, an establishment of non–belief emerged. University culture was not necessarily hostile to religion, but religious beliefs were largely irrelevant to the academic enterprise (Marsden, 1997). Yet, through this transition, a subset of America’s colleges and universities remained committed to its conservative Protestant roots, intentionally seeking to revitalize Christian scholarship and integrate faith into all aspects of intellectual inquiry.

8 The CCCU was founded in 1976 with a mission “to advance the cause of Christ–centered higher education and to help [its] institutions transform lives by faithfully relating scholarship and service to biblical truth” (Council for Christian Colleges and Universities, 2013). The CCCU is a tax–exempt 501(c)(3) nonprofit organization and is headquartered in Washington, D.C. While representing a relatively small percentage of all four–year colleges and universities, enrollment growth at CCCU institutions has outpaced other higher education sectors. Between 1990 and 2004, enrollment at CCCU institutions grew by 70.6%, compared to 28% for other independent colleges. Public institutions grew by just 12.4% during that time period (Kwon, 2005). Total student enrollment in 2011–12 was 323,492 (Council for Christian Colleges and Universities, 2013), representing additional enrollment growth of 40% since 2004. Enrollment at CCCU institutions represented 2.7% of the total enrollment at all baccalaureate–granting institutions (National Center for Education Statistics, 2010). As of Fall 2013, CCCU membership included 119 institutions from 28 religious denominations in the United States and Canada. An additional 55 affiliate institutions were located in 20 countries (Council for Christian Colleges and Universities, 2013). CCCU–member colleges and universities accounted for 6.4% of all baccalaureate– granting institutions in the U.S. (National Center for Education Statistics, 2010). The following institutions held full membership in the CCCU as of May 2012.

Abilene Christian University (Abilene, TX) Anderson University–IN (Anderson, IN) Anderson University–SC (Anderson, SC) Asbury University (Wilmore, KY) Azusa Pacific University (Azusa, CA) Belhaven University (Jackson, MS) Bethel College––IN (Mishawaka, IN) Bethel University (Saint Paul, MN) Biola University (La Mirada, CA) Bluefield College (Bluefield, VA) Bluffton University (Bluffton, OH) Bryan College (Dayton, TN) California Baptist University (Riverside, CA) Calvin College (Grand Rapids, MI) Campbellsville University (Campbellsville, KY) Carson–Newman College (Jefferson City, TN) Cedarville University (Cedarville, OH) Charleston Southern University (Charleston, SC) College of the Ozarks (Point Lookout, MO) Colorado Christian University (Lakewood, CO) Concordia University Irvine (Irvine, CA) Corban University (Salem, OR) Cornerstone University (Grand Rapids, MI) Covenant College (Lookout Mountain, GA) Crown College (Saint Bonifacius, MN) Dallas Baptist University (Dallas, TX) Dordt College (Sioux Center, IA) East Texas Baptist University (Marshall, TX) Eastern Mennonite University (Harrisonburg, VA) Eastern Nazarene College (Quincy, MA) Eastern University (St. Davids, PA) Emmanuel College (Franklin Springs, GA) Erskine College (Due West, SC) Evangel University (Springfield, MO) Fresno Pacific University (Fresno, CA) Geneva College (Beaver Falls, PA) George Fox University (Newberg, OR) Gordon College (Wenham, MA) Goshen College (Goshen, IN) Grace College & Seminary (Winona Lake, IN) Greenville College (Greenville, IL) Hannibal–LaGrange University (Hannibal, MO) Hardin–Simmons University (Abilene, TX) Hope International University (Fullerton, CA) Houghton College (Houghton, NY) Houston Baptist University (Houston, TX) Howard Payne University (Brownwood, TX) Huntington University (Huntington, IN) Indiana Wesleyan University (Marion, IN) John Brown University (Siloam Springs, AR) Judson College––AL (Marion, AL )

Judson University (Elgin, IL ) Kentucky Christian University (Grayson, KY) King College (Bristol, TN) King's University College, The (Edmonton, AB) Lee University (Cleveland, TN) LeTourneau University (Longview, TX) Lipscomb University (Nashville, TN) Louisiana College (Pineville, LA) Malone University (Canton, OH ) Master's College & Seminary (Santa Clarita, CA) Messiah College (Grantham, PA) MidAmerica Nazarene University (Olathe, KS) Milligan College (Johnson City, TN) Mississippi College (Clinton, MS) Missouri Baptist University (Saint Louis, MO) Montreat College (Montreat, NC) Mount Vernon Nazarene University (Mount Vernon, OH) North Central University (Minneapolis, MN) North Greenville University (Tigerville, SC) North Park University (Chicago, IL) Northwest Christian University (Eugene, OR) Northwest Nazarene University (Nampa, ID) Northwest University (Kirkland, WA) Northwestern College––IA (Orange City, IA) Nyack College (Nyack, NY) Oklahoma Baptist University (Shawnee, OK) Oklahoma Christian University (Edmond, OK ) Oklahoma Wesleyan University (Bartlesville, OK) Olivet Nazarene University (Bourbonnais, IL) Oral Roberts University (Tulsa, OK) Palm Beach Atlantic University (West Palm Beach, FL) Point Loma Nazarene University (San Diego, CA) Redeemer University College (Ancaster, ON) Regent University (Virginia Beach, VA) Roberts Wesleyan College (Rochester, NY) San Diego Christian College (El Cajon, CA) Seattle Pacific University (Seattle, WA) Shorter University (Rome, GA) Simpson University (Redding, CA) Southeastern University (Lakeland, FL) Southern Nazarene University (Bethany, OK) Southern Wesleyan University (Central, SC) Southwest Baptist University (Bolivar, MO) Spring Arbor University (Spring Arbor, MI) Sterling College (Sterling, KS) Tabor College (Hillsboro, KS) Taylor University (Upland, IN) Toccoa Falls College (Toccoa Falls, GA)

10 Trevecca Nazarene University (Nashville, TN) Trinity Christian College (Palos Heights, IL) Trinity International University (Deerfield, IL) Trinity Western University (Langley, BC) Union University (Jackson, TN) University Of Mary Hardin–Baylor (Belton, TX) University of Mobile (Mobile, AL) University of Northwestern (Saint Paul, MN) University of Sioux Falls (Sioux Falls, SD) University of the Southwest (Hobbs, NM)

Vanguard University of Southern California (Costa Mesa, CA) Warner Pacific College (Portland, OR) Warner University (Lake Wales, FL) Waynesburg University (Waynesburg, PA) Westmont College (Santa Barbara, CA) Wheaton College (Wheaton, IL) Whitworth University (Spokane, WA) William Jessup University (Rocklin, CA) Williams Baptist College (Walnut Ridge, AR) York College (York, NE)

To gain membership to the CCCU, institutions must demonstrate a commitment to Christ–centered higher education, including a policy to hire only Christians for all full– time faculty and administrative positions. They must be located in the U.S. or Canada and maintain full regional accreditation. With few exceptions, institutions must be four–year comprehensive colleges and universities offering a broad curriculum based in the arts and sciences. Finally, institutions must provide evidence of a strong financial position. The CCCU offers more than 100 programs and services to its member institutions (Council for Christian Colleges and Universities, 2013). These activities can be grouped into the following general categories: research and resources, professional and career development, student programs, and advocacy and public policy. Professional and Career Development The CCCU sponsors annual events and conferences to encourage collaboration and professional development among its members. Examples include individual peer conferences for new faculty, campus ministers, academic officers, enrollment officers, financial officers, institutional advancement officers, public relations officers, student development teams, technology leaders, financial aid administrators, and presidents. Specific development programs for trustees and presidents are also available.

11 Leadership Development Institutes, including one specifically for women, seek to equip emerging leaders. These programs include mentoring, networking, leadership training, and reading of current literature and research. The CCCU also provides faculty study tours and an online career center for those seeking jobs within Christian higher education. The CCCU has established awards to acknowledge outstanding leadership. Student Programs Seventy–eight percent of the CCCU’s revenues are generated through collaborative student programs (Council for Christian Colleges and Universities, 2013). Examples include study–abroad initiatives available to all students enrolled at CCCU institutions as well as the CCCU–managed American studies program; contemporary music center; Los Angeles film studies center; Washington journalism center; Australia, China, India, Latin American, Russian, Middle East, and Uganda studies centers; and several programs in Oxford, England. Advocacy and Public Policy The CCCU represents its members as an advocate for federal and state laws and policies that “sustain religious rights necessary to offer a holistic Christ-centered education” (Council for Christian Colleges and Universities, 2013). To this end, the CCCU is a member of the following organizations: 

American Council on Education



Council of Independent Colleges



International Council for Higher Education



National Association of Independent Colleges & Universities



Washington Higher Education Secretariat

12 Research and Resources The CCCU produces research that serves the interests of its member institutions. Examples include faculty salary surveys, tuition comparison surveys, comprehensive market research, and various enrollment related reports. In addition, the organization’s resource library covers diverse topics such as board policies, sabbaticals for leaders, institutional research, faith and learning, trustees and governance, and many more. To accomplish its research agenda, the organization engages external consultants to conduct large–scale research studies or partners with research teams and individual scholars for less extensive efforts. Its most recent research agenda (Council for Christian Colleges and Universities, 2013), published April 2012, includes topics related to: 

Philosophical, cultural, sociological, historical, and legal context of Christian higher education;



Affordability, student financial aid, institutional finance, state and federal regulations, and government relations;



Institutional governance, leadership, strategic planning, organizational change, and innovation;



Preparation, employment, and productivity of Christian college faculty, including their professional roles, evaluation, and promotion;



Pedagogy, curricula, and assessment of student learning within Christian higher education;



Student choice, persistence, and post–graduation outcomes;



How students learn and develop cognitively and psycho–socially, especially in the context of the Christian college experience.

13 Although this study was not endorsed by the CCCU, its findings contribute to the organization’s stated research agenda. Significant Challenges Like the broader landscape of higher education, the CCCU and its member institutions face significant challenges. The sustainability of the financial model for independent higher education is most significant (R. Mahurin, personal communication, September 24, 2012). As costs rise, student debt increases, and family incomes stagnate, students and families will not be able to afford Christian higher education, even if they value it highly. The CCCU also faces external legal threats related to hiring requirements and continued access to Title IV financial aid funds for students who attend Christian colleges. Within its own ranks, because the CCCU does not have a specific statement of faith, contentious social and political issues may make it more difficult for CCCU– member institutions to maintain their focus and unity (R. Mahurin, personal communication, September 24, 2012). Summary CCCU–member institutions are seeking to educate students within the context of a delicate balance of deep faith and scholarly learning. These institutions have a critical place within American higher education, but they face similar challenges to other private colleges and universities. This research explored the role of admissions and financial aid in the financial health of CCCU institutions, as a distinct subset of private higher education in the United States. Personal Connections, Experiences, and Values

14 As a researcher, this study was meaningful to me on several levels. First, it was directly related to my educational background. Second, it built on professional experiences, yet challenged engagement with new information and perspectives. Third, it contributed to the body of research that informs and advances the cause of Christian higher education. Personal Connections My connection with Christian higher education and enrollment management began during childhood. When I was in 10th grade, my father began a career as a faculty member at Cedarville University, a CCCU–member institution. Being a “faculty kid,” I worked in the admissions office starting in 11th grade. I entered application data, processed acceptance letters, and led campus tours. Admissions and recruitment were part of my first work experience and launched a career path in marketing or recruitment– oriented positions that have culminated in my current role. Because of my personal experience, I am very interested in Christian colleges and universities and their history and place within American higher education. Both my parents and my husband’s parents attended Christian colleges, and we and all of our siblings (except one) did as well. I believe there is a place for a rigorous academic experience that also acknowledges the spiritual development of the whole person. Education and Experience Academically, my degrees are in organizational communication and business. I have been educated to think in terms of budgets, investments, accountability, performance, and profitability. Those concepts are at the heart of this study. With the economic challenges facing higher education, how can institutions emerge strong and

15 vibrant? This research examined a small piece of the very complex matter of higher education finance. The findings provide perspective and data on the relationship of enrollment and financial aid to institutional health. Further, I now serve as vice president for enrollment management and marketing at Cedarville University. I have been in this role since 2009 and have worked at the university since 1995. Enrollment management can be a pressure–filled career. For tuition–dependent institutions, achieving enrollment goals may mean the difference between achieving the budget or announcing a staff reduction. For this reason, understanding the contribution that enrollment performance makes to financial health is critically important. For many institutions, a successful enrollment management operation is synonymous with annual enrollment growth. The prevailing opinion is that adding a steady stream of new students is the pathway to funding new programs, new faculty, and new facilities. But, is enrollment growth a predictor of financial strength? This study will extend the research conducted by Meyer and Sikkink (2004) and provide perspective on the relationship between enrollment and institutional financial health. Researcher Bias As I conclude this research study, I must acknowledge that I view higher education topics through the lens of an enrollment and marketing professional. Others who invest their lives in the academic enterprise will (and should) view the world of higher education differently from me. Consider that, while I may readily see the value of eliminating a program if too few students are enrolled, my faculty colleagues articulate the intrinsic contribution of their program to a well–rounded education. Specific to this

16 study, my peers in financial leadership view financial aid as an unfunded cost that can quickly consume net tuition revenue, while I see it as an investment that will increase enrollment. Further, this research relied upon data from two annual surveys that report the enrollment and financial performance of CCCU–member institutions: 

North American Coalition for Christian Admissions Professionals (NACCAP) Admissions Benchmarking Survey

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Financial Aid Survey of CCCU Institutions

I am familiar with the surveys because my division is responsible to complete the Admissions Benchmarking Survey and the Financial Aid Survey. Further, my participation on the university’s senior leadership team has given me access to results of my institution’s financial ratios report. My personal familiarity with the data—especially as an enrollment management professional and administrator at one of the member institutions—was considered as I conducted this research. Because I work for a CCCU university, my bias may have been to cast the financial health of Christian universities in as positive a light as possible. As I analyzed and interpreted the data, I intentionally used the literature review and the statistical data to ground my conclusions. Significance of the Study In view of the financial challenges facing higher education, a study exploring the relationship of enrollment and financial aid to the financial health of CCCU institutions was both relevant and timely. First, this research expands the literature on factors contributing to institutional financial health. Specifically, the influences of freshmen

17 admitted student yield rate; change in undergraduate enrollment; enrollment in non– traditional adult, graduate and non–professional programs; unfunded tuition discount rate; and institutional aid from endowed or donor sources were assessed. Second, this research provides data and strategic analysis to inform decisions and priorities of senior enrollment and financial leaders at CCCU institutions. Understanding how enrollment contributes to financial performance can help leaders to determine where to invest energy and resources. Further, understanding the interaction among these areas contributes to greater understanding and, ideally, increases collaboration between enrollment and financial leaders. Third, this research contributes to the advancement of Christian higher education, specifically CCCU institutions. This research accomplished not only an academic purpose, but also generated data that benefits CCCU institutions and their leaders. Statement of Purpose The purpose of this quantitative research study was to assess the relationship of enrollment and financial aid to the financial strength of CCCU institutions. A multilevel statistical analysis provided an in–depth understanding of the relationship of five predictor variables associated with enrollment and financial aid to the criterion variable of financial strength, as measured by the Composite Financial Index (CFI) score. Research Question One research question guided this study: How are variables associated with enrollment and financial aid related to the financial health of CCCU institutions? Using data from the Admissions Benchmarking Survey and the Financial Aid Survey as well as responses to a CFI survey of CCCU–member institutions, the relationships of five

18 predictor variables associated with enrollment and financial aid to a criterion variable measuring financial strength (CFI score) were examined using both descriptive and inferential statistics. Organization of the Dissertation This dissertation has five chapters. This chapter provided an introduction and context for the research. Chapter 2 presents a review of the literature related to economic theories that inform higher education finance, measures of institutional financial performance, higher education financial case studies, enrollment and financial aid measures, and previous correlational research. Chapter 3 contains the research design and methodology, including the hypotheses and analytical strategy. Chapter 4 presents the findings of the data analysis. Chapter 5 concludes with a discussion of the findings and implications for policy, practice, theory, and future research.

19 CHAPTER 2 REVIEW OF RELATED LITERATURE Overview This literature review provides a context for this quantitative study of enrollment and financial aid and their relationship to the financial health of CCCU institutions. This review explores economic theory, methods for assessing institutional financial performance, relevant case studies, enrollment and financial aid variables, and previous studies that analyzed variables related to institutional financial performance. The chapter concludes with a definition of terms and explanation of this research study’s contribution to the existing literature. Economic Theory Economic theory informs the business of higher education at every level; therefore, an understanding of relevant economic concepts and models is foundational to understanding the assumptions and benefits of this study. Paulsen and Toutkoushian (2006) wrote an introduction to basic economic concepts that influence higher education. The following are most relevant to this study. Optimization refers to the goal to maximize resources or production or revenues, while subject to resource constraints. For higher education, these goals may include enrollment growth, additions of new programs, or increased revenue; but those goals are constrained by students’ college choices, faculty and facility resources and availability, access to financial aid, or students’ willingness or ability to pay, just as examples. The second economic principle, opportunity costs, states that when an organization pursues one choice, it must forego another due to scarce resources. Marginal analysis seeks to

20 quantify the additional benefit or cost of adding one more student, program, or project. The economic theory of competitive markets holds that resources are optimized at the intersection of the supply and demand curves. Elasticity refers to sensitivity of demand to changes in price. Tuition discounting models—which are discussed later in this chapter—rely on the notions of marginal analysis and elasticity. Interestingly, the authors noted that higher education operates apart from standard economic principles in that the goal of decision–makers is not profit maximization. Higher education benefits society in both monetary and non–monetary ways and, therefore, government appropriations subsidize its operations. As higher education leaders seek to build healthy financial organizations, decisions about maximizing revenue and resource distribution are built on an understanding of these economic concepts. Doti (2004) focused on the economic concept of price discrimination, which is related to the supply and demand curves for higher education. He argued that colleges and universities are losing their ability to price discriminate and are, therefore, functioning more like economic commodities. Price discrimination alters the standard supply and demand curve by offering grants as a means of increasing the price and generating additional net revenue. This is the economic concept at the foundation of tuition discounting. Doti cited the 2002 National Association of College and University Business Officers (NACUBO) Tuition Discounting Survey which showed a 67.9% increase in tuition fees as compared to a 108.8% increase in grants during the prior 10– year period. He observed that since discounts rose faster than tuition and fees, institutions’ ability to price discriminate had weakened. Doti noted, however, that the trend toward commoditization does not affect all universities. More selective or elite

21 institutions have more inelastic demand curves than their less selective counterparts. CCCU institutions are widely engaged in tuition discounting. If its effectiveness as a tool for increasing net tuition revenue is declining, this has implications for long–term financial performance. Pilbeam’s study of collaboration among higher education institutions (2012) relied upon resource dependence theory. First articulated by Pfeffer and Salancik (1978), resource dependence states that the environment surrounding an organization controls its actions, including the decisions of its leaders. Further, this theory suggests that institutions will seek to reduce dependence in order to increase their autonomy. Higher education is dependent upon a wide range of stakeholders, all of which have a stake in institutional mission and contribute to financial stability. Moves within higher education to pursue more entrepreneurial models and reduce dependence on certain types of revenue—specifically tuition dependence for CCCU institutions—reflect the reality of this economic model. Pilbeam pointed to collaboration among institutions as evidence of efforts to reduce dependence. Browning (2011) referenced tuition discounting as a strategy that also serves to reduce an institution’s dependence on external sources by increasing tuition revenues. Hansmann (2012) explored the changing economic structure of higher education and asserted that market forces will be increasingly influential. He described higher education as an associative good, where the value derives not only from the institution itself, but also from the personal qualities of the institution’s other students or stakeholders. He presented rationale for why public education, in the past 25 years, has captured four times its previous market share. He opined that private colleges’

22 comparative lack of access to capital has limited their ability to respond to increased demand during that period. Hansmann also discussed demand–side government subsidies that altered the higher education landscape when federal subsidies were made available to proprietary schools starting with the 1972 amendment to the Higher Education Act of 1965. Accusations of abuse by this sector have led to increased regulation, accountability, and—arguably—costs for all higher education institutions. Citing competition from proprietary schools, the rise of online education, and the increasing role of the federal government, Hansmann concluded that market forces will continue to affect the economics undergirding higher education finance. Cheslock (2006) focused on the economic theories informing higher education revenue models. He described net revenue activities which either generate profits or require subsidies from other activities. Net revenue is defined as revenue generated less the cost of an activity. Institutions also benefit from fixed revenues, include state or federal appropriations or endowment assets, as examples. Institutions with more activities generating net revenues or access to greater fixed revenue resources will have more flexibility to engage in non–revenue generating activities. This notion is relevant to CCCU institutions that may not have significant fixed revenues, but invest heavily in mission–focused activities. Like Paulsen & Toutkoushian, Cheslock also explored the concept of marginal revenue, providing the definition that “marginal revenue is the change in the total revenue from producing an additional unit of an activity” (p. 28). This is relevant to discussions of tuition discounting, since the additional revenue generated by one additional student must be offset by the grants provided and costs to educate.

23 Cheslock asserted that a large endowment is the best indicator of an institution’s financial strength, since it is a source of consistent and fixed revenue. Archibald and Feldman (2008) have conducted extensive research on the economic theory explaining the rise in higher education costs. Their 2008 article in the Journal of Higher Education contrasted the two prevailing schools of thought related to higher education costs. First, the cost–disease explanation has a long history within economics and builds on principles common to other similar industries. Cost–disease theory is built on the notion that costs are reduced only when productivity is increased. The ability to increase productivity is related to how labor is used in the industry. Personal service industries, including higher education, have limited capability to increase productivity through technology; therefore, costs rise as labor costs rise. To decrease costs in these service industries, quality typically also suffers. The second dominant theory is Howard Bowen’s revenue theory of costs. Bowen focused on the unique economic factors associated with higher education and generally asserted that the source of cost increases is the rising revenue stream available to colleges and universities. In other words, Bowen’s theory stated that institutions will spend all of the funds that they can raise, and only public restraint eliminates wasteful overspending. Archibald and Feldman countered that institutions do not maximize their revenue; rather, they leave resources on the table in favor of other goals like prestige or quality. Archibald and Feldman admitted their bias toward the cost disease theory, but used higher education cost data over several time periods to explore and compare the validity of both economic models. Their conclusion was that the data is insufficient to prove either theory. The authors then focused on cost data over time, analyzing more than 80 product categories

24 and their price changes between 1950 and 1996. Archibald and Feldman hypothesized that if the cost disease theory were an appropriate explanation for higher education cost increases that it would appear at or near the top of the list along with other professional services. This hypothesis was upheld, with the exception of the brokerage and investment industry where technology increased productivity dramatically and lowered costs during the research time frame. Archibald and Feldman concluded, then, that it is most appropriate to evaluate higher education costs using a model that is applicable across industries rather than a model that is specific to the unique features of one industry. Based on this finding, the authors asserted that it is critically important for colleges and universities to reduce costs while preserving the quality of the services they provide. Winston (2003) explored the economic theory behind institutional price setting. They noted that colleges and universities are highly unconventional in their varied sources of revenue, production processes, organizational values, and the hierarchical structure of competition within the industry. With the presence of donations, institutions can be in economic equilibrium even though the price it charges for its product (net tuition) is less than its production costs. Donations make up as much as 75% of total institutional revenues; however, these donations are distributed very unevenly, with the highest endowed institutions subsidizing costs at a level 10 times that of the institutions at the bottom. Further, institutions use their donations in different ways. Students determined to be of higher quality (in terms of what they will contribute to the institution) will pay less for their education than the lower quality student. Competition for these higher quality students, then, is based on the institution’s access to donor resources. Winston summarized that donations break the link between cost and price, allowing for

25 student subsidies based on quality. Since donor resources are so unevenly distributed, this creates a hierarchy of institutions, differentiated by their ability to compete for top academic students. In summary, economic theory informs higher education finance. Optimization and opportunity costs relate to strategic decision–making about allocation of resources. Marginal costs and price discrimination inform tuition discounting strategies. Resource dependence acknowledges that higher education is beholden to multiple stakeholders, and for the many tuition–dependent institutions, enrollment becomes the critical focal point. Net revenue and fixed revenue concepts again underscore the impact of rising tuition discount rates and the importance of alternate, fixed streams of revenue for institutional viability. The theories of rising college costs—regardless of whether one adheres to the cost–disease theory or Bowen’s revenue theory of costs—provide meaning to the practical challenges facing colleges and universities that are struggling to improve their financial position by reducing their cost structures without decreasing academic quality. Finally, the presence of donor resources contributes to a unique economic model for higher education, differentiating institutions by their ability to increase subsidies to attract higher quality students. Financial Performance Research related to financial performance provides insight into the critical financial issues facing private higher education. Further, the relevant literature highlights models and tools that predict or assess institutional financial health. Gilmartin (1984) developed and reported on 61 financial and nonfinancial indicators of the viability of colleges and universities in the United States. His definition

26 of viability simply meant not in distress. He used a comprehensive data set prepared by the Statistical Analysis Group in Education (SAGE), which was created in 1977 by the National Center for Education Statistics. This data set included longitudinal files on financial, faculty, enrollment, and institutional characteristics for 3,125 colleges and universities. From that data, 61 indicators of financial viability were derived. Categories included: reliance on various types of revenues (16 measures), net revenues (3 measures), distribution of expenditures (2 measures), distribution of current fund expenditures (10 measures), expenditures per student or faculty (5 measures), ratios of scholarship expenditures to tuition revenue (2 measures), college fund and endowment balances (7 measures), plant assets and indebtedness (4 measures), enrollment and faculty numbers (6 measures), and student tuition and fees (6 measures). Each indicator was calculated in a static form (single year) and in a change form (year–to–year change). Further, broader categories of distress were defined and established. Enrollment distress included the 10% of colleges with the largest proportional decrease in enrollment during the time period. Salary distress included the 10% of colleges with the largest proportional decline in mean salary for full–time faculty. Calculated for private institutions only, the current–fund balance distress included the 10% of colleges with the largest decline in the ratio of current funds to expenditures. Any colleges that fell within at least two of these categories were considered to be in distress. Of the 101 colleges in distress, 98 were private two–year and four–year institutions or public two–year colleges. Using this model, Gilmartin developed a composite index of viability for each sector and an individual viability score for private institutions. He summarized the sectors that showed the greatest frequency of distressed institutions: liberal arts colleges, teachers colleges,

27 two–year vocational colleges, traditionally black institutions, institutions with predominant black enrollment, women’s colleges, colleges with predominantly female enrollment, Baptist colleges (not Southern Baptist), Presbyterian colleges (not United Presbyterian), Title III institutions, and colleges with higher proportions of students receiving aid. Gilmartin’s research was groundbreaking in attempting to identify colleges in distress using objective criteria and identifying distinctive patterns of weakness as predictors of future viability. Peruso (2010) evaluated the overall financial condition of 390 private colleges, examining their operating results during a period from 1998 through 2007. Introducing his report with the stark reality that increases in college costs outpaced growth in family income by nearly three times (439% vs. 147%) from 1984–2008, Peruso identified tuition dependence as a threat to the viability of many private institutions. He asserted that ratio analysis is the most effective way to assess financial condition. Ratios allow for comparison between institutions and for trend analysis. Peruso highlighted the Strategic Financial Analysis for Higher Education (SFAHE) that provides a comprehensive framework for evaluating higher education financial performance. Ratios of operating results (profitability), resource sufficiency and flexibility (liquidity), financial resources including debt (leverage), and asset performance (asset efficiency) are included. Peruso collected data on the 390–institution sample from the Integrated Postsecondary Education Data System (IPEDS). He calculated and evaluated financial ratio variables, tuition increase, net operating revenues, reserve ratio, capitalization ratio, net assets ratio, tuition discount rate, and tuition dependency. He used the Barron’s Profiles of American Colleges’ measure of selectivity to categorize the sample, and this

28 became his “dummy” variable against which all other variables were compared using ANOVA by regression modeling. Peruso found that tuition increased by 6% per year from 2000–2007, with no difference based on selectivity. Interestingly, those colleges with the largest enrollment increases also had the largest average annual tuition increases. Peruso called for more research in this area. The net operating revenues ratio averaged 9% during the time period, and the reserve ratio—which is influenced by endowment assets—remained steady. There were statistically significant differences in the reserve ratio based on institutional selectivity scores, leading Peruso to conclude that “the endowment and availability of unrestricted assets appears to allow top–ranked colleges considerably more breathing room than their less prestigious competitors” (p. 62). The capitalization ratio declined during the period, indicating an increased use of debt to fund operations. The tuition discount rate increased from 29% to 34%, and selectivity was correlated with tuition discount rate. The author acknowledged that his calculation of tuition discount included both funded and unfunded aid. Colleges that award primarily unfunded aid may have experienced reductions in their net cash from tuition during this time period. Peruso summarized that less prestigious colleges carry greater levels of debt. More than 10% of his sample was defined as tuition dependent. He also acknowledged the pressure on private colleges to limit future cost increases. Peruso’s analysis of these 390 private universities provided an important understanding of the financial challenges facing that sector. Peruso (2010) also conducted a study of annual tuition increases, tuition discount rates, tuition dependency, and financial performance using data for all 390 colleges

29 classified as private, nonprofit baccalaureate colleges that had available IPEDS data from 1998–2007. The financial ratios used in his study were operating results (net operating revenues ratio), liquidity and flexibility (reserve ratio), leverage (capitalization ratio), and asset performance (return on net assets). The tuition increase was calculated as the change in tuition from Year 1 to Year 2, divided by the tuition from Year 1. A tuition discount analysis was also conducted, since the tuition discount reduces net revenue generated by a tuition increase. Finally, tuition dependency was calculated as the ratio of net tuition revenues to total revenues. Each variable was analyzed by institutional selectivity and by region using ANOVA by regression modeling. Peruso found that the average tuition increase during the time frame was 5.4%. Regionally, there were small—but significant—differences in tuition increases; there were no significant differences based on institutional selectivity. The more selective institutions had strong financial ratios in all categories. Further, more selective institutions had higher discount rates; however, the most selective institutions had lower discount rates since demand for their service did not require discounting to achieve enrollment objectives. The less selective institutions also discounted at a lower rate, perhaps because their tuition rate was lower to start. A weakness of this study was that the calculated tuition discount rate included both funded and unfunded aid. For the most selective institutions that also have large endowments, they were able to increase their awarding without impact to their operational budget resources. There were significant differences in tuition dependence both based on region and on selectivity. Peruso called for future research on the relationships among these variables.

30 Fischer, Gordon, Greenlee, and Keating (2004) also focused on private institutions’ financial statements as a way to measure operations. The authors first acknowledged the uniqueness of the U.S. system of higher education where private colleges enjoy a high degree of autonomy from government intervention, alumni contributions and endowments provide a significant financial benefit, long–term funding may be obtained through capital bond markets, and the funding for higher education is sensitive to fluctuations in the economy. Further, the financial reporting requirements for public and private institutions differed at the time of this report. Also lacking consistency, multiple ratios have been developed for assessing financial performance. Moody’s Investors Service, Standard and Poor’s, KPMG, the U.S. Department of Education, and Fitch all calculate performance ratios that differ in some way. The authors affirmed the findings of other research that “the understandability and decision usefulness [of these ratios] have been limited by the diversity of allowable practice” (p. 135). The purpose of this study was to determine whether private institutions chose to present an operating measure in their statement and how that measure was calculated. Of the 1,100 four–year private institutions, 207 provided an annual report and were included in this research. The authors found that 59% of the institutions reported some type of operating measure, including 64% of those that worked with one of the Big Five accounting firms. In general, the authors found that institutions reported tuition, room, board, and similar income in consistent ways, but most other measures of operating performance were subject to institutional interpretation. The authors called for a consensus on reporting standards in order to better understand and assess institutional financial health.

31 Chabotar (1989) reviewed the history and introduction of financial ratio analysis within higher education and nonprofit organizations in general. NACUBO and John Minter Associates pioneered the use of ratios for higher education. A ratio is simply the relationship of two numbers from an organization’s balance sheet or other record. As identified by Peruso, ratios allow for comparisons between institutions and suggest trends that should be addressed. Nonprofit ratios focus on stewardship and accountability, as contrasted with profit measures. Chabotar reminded that ratios in and of themselves are just a part of information that should guide management decision–making. He cautioned that ratios that have few objective standards may contain inconsistent data, complicating the ability to compare across institutions. Chabotar identified five categories of ratios that are appropriate for analysis of nonprofit performance: liquidity, debt capacity, sources of funds, uses of funds, and net operating results. Many of these ratios are included in the definitions section at the conclusion of this literature review. Chabotar closed his study with a case study of how ratio analysis was implemented at one private college and how it informed decision–making. However, like Fischer, Gordon, Greenlee, & Keating (2004), he cautioned that ratio comparisons with other institutions remain problematic since inconsistent accounting practices exist. Nonetheless, Chabotar affirmed the conceptual value of careful benchmarking with other institutions. Harbouk (2011) explored revenue–based budgeting at three private, faith–based institutions. Revenue–based budgeting places responsibility for both revenue generation as well as expense allocation with each operating unit’s leadership, as contrasted with the centralized budgeting structure used at most private colleges and universities. Harbouk’s section on sources of revenue is relevant to this study. He referenced Toutkoushian

32 (2003) who identified six sources of revenues for higher education: students, parents, federal government, state government, private gifts, endowments, and auxiliary enterprises. In evaluating the financial health of an institution, each of these categories directly influences financial performance and becomes a part of financial ratios. Dickmeyer and Nathan (1982) wrote about financial self–assessment tools used by business officers to evaluate their operations and financial flexibility. The authors’ work reviewed best practices in financial self–assessment, tested them statistically, and suggested tools for creating an analytical framework that would guide institutional decision–making. Dickmeyer and Nathan recommended a comprehensive approach, since no single statistic reveals the financial condition of an institution. They also acknowledged that evaluating an institution’s financial status is difficult because of the interrelationship of resources. The authors pointed to accumulated financial wealth as critical to the viability of independent colleges, affirming Cheslock’s assertion (2006) that endowment is the best indicator of financial health. Dickmeyer and Nathan provided three principles to govern financial assessment: 1. Highly volatile income sources require the institution to buffer itself with greater financial resources. Student tuition fits within this category. 2. Greater financial resources are needed if the institution has significant fixed costs, such as debt service or faculty salaries. 3. Non–financial resources affect financial health. For example, enrollment, program offerings, or quality of facilities all inform an institution’s financial picture.

33 They then suggested categories for financial self–assessment, including measures of financial strength, estimated risk, changes affecting financial resources, and changes in non–financial resources. The authors outlined the underlying formulas for each statistic. Dickmeyer and Nathan concluded their report by offering six cautions or limitations on their self–assessment model: (a) the assumptions underlying a statistic must be understood; (b) comparisons with peer institutions may not be as clear as the data would suggest; (c) this self–assessment approach is specifically designed for evaluating a college’s financial condition and should not be used by outside agencies; (d) this approach is limited to financial considerations; (e) statistics provide only a shadow of reality; wisdom and experience inform their meaning; and (f) the statistics may be simple to formulate, but their interpretation requires the expertise of leaders invested in the future of the organization. Hignite (2009), writing on behalf of NACUBO, explained the financial indicators tool (FIT) developed by the Council for Independent Colleges (CIC). The FIT has the CFI score as its centerpiece. The CFI provides an overall picture of the institution’s financial health including the capability for national comparisons with other CIC– member institutions. Chabotar (1989) , who served as a CIC–institution president, contributed to Hignite’s article, providing a caution that too many institutions focus on balancing their budgets, but do not engage in a long–term, broader, systematic review of their financial health. The CFI score provides a vehicle for evaluating institutional performance and presents it in an understandable format that can be shared with stakeholders. Hignite highlighted Ripon College’s implementation of the FIT and the benefits that resulted,

34 including reining in a pattern of overspending and making strategic decisions about the use of debt. Chabotar also contributed the perspective that the FIT allows institutions to understand and address their vulnerabilities. Randall Doerksen, vice president for administration and finance at Friends University in Wichita, Kansas, cautioned, however, that the CFI can be skewed in any year if there are significant changes to investment portfolio size. Although the CFI is a strong measure of institutional financial performance, its embedded ratios are heavily weighted by the value of net assets. This weighting was considered for the purposes of this research, since enrollment and financial aid do not contribute directly to a university’s net assets and, therefore, are undervalued in the formula. Allen, Bacow, and Trombley (2011) made recommendations for how board members should use financial metrics for governance and decision–making. Bacow and Trombley served as university presidents, while Allen was the chair of the Board of Regents for the University of Minnesota System. All acknowledged preparing some type of benchmark report for their boards, with financial indicators receiving the most attention. Boards focused on changes in metrics from previous meetings and added metrics based on goals of their strategic plans. According to the Tufts University office of institutional research, the following performance measures, presented in Table 1, are generally tracked by colleges and universities:

35 Table 1 Performance Measures Tracked by Colleges and Universities Category % of Institutions Reporting Admissions Scores 78.8% Endowment and Expenses 80.3% Enrollment by Special Populations 71.2% Enrollment Figures 77.3% Fees/Tuition Data 47.0% Financial Aid Figures 63.6% General Admissions 71.2% Graduate Admissions 21.2%

Table 1 pERFORMANCE mEASURES tRACKED BY cOLLEGES AND uNIVERSITIES Sevier contributed an addendum to the Trusteeship report and offered five guidelines to Boards for use of metrics: (a) Monitoring over time is most valuable; (b) Determine a peer cohort group for comparison that is appropriate for the institution; (c) Do not use benchmark data as a reason to adapt another institution’s strategy; (d) Be sure to be confident in the accuracy of the number; and (e) Strike the right balance of having too few or too many metrics. Harris and Cullen (2008) offered helpful wisdom for any of the financial assessment models suggested by researchers. Measurement is a process of self–reflection, and what leaders decide to measure is as important as the measurements themselves. Measurement frameworks, like dashboards, will serve a public relations value only if institutions fail to conduct honest assessment for the purpose of continuous improvement. Harris and Cullen concluded that any measures of institutional performance, including financial measures, should be intentionally chosen, then vigorously addressed when “warning lights” appear. CapinCrouse (2011) brought this discussion of measures of financial performance to the doorstep of Christian colleges, which were the focus of this research. Wallace reported that CapinCrouse, a national accounting firm, began compiling CFI data for both

36 CCCU– and Association of Business Administrators of Christian Colleges (ABACC)– member institutions in 2005 and had tracked that data annually since that time. The report highlighted challenges facing this sector of education: rise of competition from community colleges and online education, changing demographics of the student population, and rising tuition costs that are outpacing inflation. Wallace asserted that Christian organizations should be models for effective stewardship and excellence, creating “cultures of evidence that emphasize accountability and integrity” (p. 3). Financial performance indicators should be linked to strategic plans, while recognizing that for faith–based institutions, W. B. Cameron’s quote is relevant: “Not everything that can be counted counts, and not everything that counts can be counted” (1963). In summary, this section provided an overview of tools and approaches to assessing and predicting the financial health of private institutions. The literature identified institutional factors that may predict financial viability, recommended ratios for measuring financial health, included concerns about variability in the underlying formulas of these ratios, and emphasized the importance of leadership and strategic planning to the effective use of financial assessment tools. Institutional Case Studies In this section, I review case–study research related to the financial performance of specific private institutions. This material is relevant in that it reveals factors that either helped the institutions to rebound from a financial tailspin or, ultimately, contributed to their demise. Feerrar (2005) conducted an ethnographic, qualitative research study of six private, liberal arts institutions in Pennsylvania and how each responded to the 2000–

37 2003 economic downturn. He chose the institutions using a purposeful sampling strategy, resulting in two schools each identified as low wealth, medium wealth, and high wealth. Feerrar conducted in–depth interviews with the chief financial officer and the chief academic officer at each institution. His research illuminated how institutional wealth and tuition discounting contributed to each institution’s ability to cope within the recessionary economic environment. The research also documented strategies for enhancing revenues, reducing costs, and managing investments. Feerrar’s findings revealed that the high– wealth schools had lower capacity; the low–wealth institutions had the largest sticker price increases; and only the middle–wealth schools reduced their discount rates during the time period. Annual–fund growth and operating–revenue growth rates also correlated with institutional wealth. Feerrar’s contribution to the literature confirmed the value of institutional wealth in maintaining financial health during times of economic recession. He noted that enrollment growth was common only in the non–high wealth schools, and those schools also had the largest tuition increases during the time period. All of the institutions found ways to reduce budgets. Affirming Dickmeyer and Nathan (1982), effective leadership and decision–making were significant factors in each institution’s ability to respond to the economic challenges. Through enrollment growth, cost containment measures, controlled discounting, and effective fundraising, the subject institutions were able to generate greater revenues than expenses during the downturn. Massa and Parker (2007) recorded the return to profitability of Dickinson College in Carlisle, Pennsylvania. Affirming the motivating leadership of President Bill Durden, the authors identified the steps the institution took to address its situation. First, 75% of Dickinson’s revenues came from tuition. Therefore, enrollment received critical attention.

38 The focus was on collecting and analyzing data using a logistical regression enrollment projection model developed by a Dickinson professor and its institutional research director. Dickinson’s journey back to profitability relied heavily on data to illuminate enrollment trends, factors affecting students’ college choice, alumni and parent perceptions, external demographic data, and operational reports. The outcomes were an intentional focus on marketing and branding efforts, expanded and data–driven recruitment efforts, and a reduction in the discount rate. As of the report date, Dickinson’s enrollment goals had been met, its discount rate had been reduced and was sustainable, and it was generating surpluses and strong endowment returns. Sanoff (2006) also highlighted Dickinson’s turnaround in a 2006 Chronicle of Higher Education report. He affirmed that enrollment growth (30% growth between 2004 and 2005 alone) along with reduction in the discount rate from 52% to 34% were critical to the turnaround. A renewed focus on fundraising—especially alumni support—and a more diversified approach to investing also contributed. Dickinson’s case supported the focus of this study that enrollment and financial aid influence the financial health of the institution. Wilgenbusch, who holds a doctorate in educational administration from the University of Nebraska–Lincoln, is the focus of a University Business case study by Feemster (2000). Wilgenbusch served as president of Oregon’s Marylhurst University from 1984 through 2008. When she took the helm, the former all–woman’s college had just 300 students and was threatened with closure. Affirming Marylhurst’s unique position as a liberal–arts college serving non–traditional students, Wilgenbusch instilled a fiscal discipline and focus that transformed the school’s financial situation. First,

39 Marylhurst committed itself to being the lowest–priced private school in Oregon. Second, like Dickinson, it launched an extensive branding and marketing campaign; and third, it restructured its budget model to make academic departments into profit centers. Wilgenbusch’s six strategies for financial strength were (i) mission determines finances, (ii) knowledge is power, (iii) no tuition discounting, (iv) inflation caps tuition, (v) gifts are dangerous, and (vi) budgets are promises. Biemiller and Brainard’s case study of three Iowa private institutions “scrapping for students and survival” (2011) found that financial challenges were associated with the Midwest’s declining population of high school graduates, discount rates that were too high (50% as compared to a 42% national average), over–dependence on tuition revenues, and too much institutional debt. The subject institutions tried attractive offers (like iPads for every freshmen), evening and weekend programs, and new online programs to increase enrollment and generate new revenues. The authors quoted Sylvia Manning, president of the Higher Learning Commission of the North Central Association of Schools and Colleges: “The problem with online is, that’s the solution everyone has come up with, and at some point the market’s going to get saturated” (para. 34). The authors affirmed that online and non–traditional programs may not be the financial solution that some colleges imagine. Province (2009) conducted a quantitative studying of factors leading to the closure of 40 private colleges between 1965 and 2005. The entire population of closed institutions during that time period was 139; however, a data set could be gathered due to time and limited resources on a sample of only 40. Based on his review of the literature, Province developed 31 possible indicators of institutional closure. He used an ANOVA to

40 identify significance levels for each of the 31 factors and determined that 17 of the original 31 were statistically significant. Of the 17, seven were at the .001 level. The seven most significant indicators of institutional viability were debt service more than 10% of the operating budget, deferred maintenance at least 40% unfunded, conversion yield 20% behind that of competitors, ratio of net assets to total expenses decreasing, ratio of primary assets to total debt decreasing, ratio of debt service to total expenses greater than 10% and increasing, and endowment decreasing. In summary, I reviewed case study research involving 11 private universities facing financial stress. Themes emerged related to enrollment growth as a strategy for financial stabilization, the need for data–driven decision–making, branding and marketing as tools for increasing enrollment and raising funds, and the importance of reducing—or perhaps eliminating—tuition discounting. The case study of three Iowa institutions raised questions about the strategy of adding online and non–traditional programs as a path to financial stability. Enrollment and Financial Aid This section highlights the literature of enrollment and financial aid and their contribution to institutional financial performance. Specific focus will be given to the predictive variables to be studied in this research: admitted student yield rates; undergraduate, graduate, and adult enrollment; institutional aid funded by endowment or annual gifts; and the practice of tuition discounting. Volumes have been written on these topics. A Google Scholar search for articles related to “enrollment” yielded 898,000 results. Therefore, the literature in this section—with a few exceptions—has been limited

41 to peer–reviewed research published within the past 10 years. Further, I researched the variables of interest within the context of private higher education. Admitted Student Yield Rate The admitted student yield rate is defined as the percentage of students who enroll at an institution out of those accepted for admission. Similarly, the yield rate can be viewed as the probability that an accepted student will enroll at the institution. A higher yield rate may signal that more admitted students consider the university to be a top choice, while a lower yield rate may indicate admitted students have preferred options elsewhere. This section includes research in which the admitted student yield rate is considered as an enrollment variable of interest. Vander Schee (2010) implemented relationship–marketing strategies at two small private, liberal arts colleges and measured the impact of those strategies on enrollment and admissions yield. In both institutions, enrollment officers were tasked with building a connection with prospective students on behalf of the institution and providing personal support and encouragement from the point of initial inquiry until a student’s matriculation on campus. Specifically, enrollment officers were trained on communication skills, problem–solving skills, organizational transactions, and financial aid counseling. The outcomes of the study included greater job satisfaction for the enrollment officers, an increase in productivity in the financial aid office, increased student satisfaction with the admissions process, and most importantly, significant increases in the admissions yield. Institution A saw its yield increase from a three–year average of 57.9% prior to implementation to 70.2% the year after. Institution B’s three– year yield average of 41.8% increased to 54.3% after relationship marketing was

42 implemented. VanderSchee’s study suggested that strong relationships between an enrollment officer and the prospective student will increase an institution’s admitted student yield rate. Focusing on recent declines in admission office budgets, Greene and Greene (2011) recommended cost–effective strategies to increase yield and enrollment. They cited personalized communication, responsiveness to student inquiries, involvement of parents and families, regional admissions officers, multicultural bus tours, special– interest events on campus, and outreaches to guidance counselors as effective options. Chang (2006) conducted a quantitative study using data mining technologies to identify factors that predict enrollment. One of the major challenges facing enrollment professionals is to understand why all admitted students do not eventually enroll. Chang’s study—conducted using admissions data from one institution—was designed to determine if students enroll randomly or whether there are groups or characteristics that predict enrollment. Chang followed a six–step data mining procedure: 

Business understanding—conducted a review to understand the institution’s populations and their behavior patterns;



Data understanding—assessed the availability of relevant data; during this step the decision was made to focus exclusively on the admitted undergraduate degree–seeking freshmen pool;



Data preparation—prepared the appropriate data set with predictor variables for high school GPA, high school rank, high school size, SAT or ACT score, admissions index score, gender, ethnicity, age, region, domicile of origin, admission type, traditional or nontraditional status,

43 major program, frequency of various types of communication, and sources of the initial contact. 

Modeling—developed three predictive models using a classification and regression tree (C&RT), neural networks, and logistic regression.



Evaluation—divided target population into two data sets, one to develop the models and a second to test them. The three models were also examined as to their level of agreement, which was 66%.



Deployment—used the model to predict the next year’s enrollment, but predictive accuracy declined.

Chang’s research suggested that admitted students did not enroll randomly at the institution, and to some extent, enrollment could be predicted. Significant predictive variables included local students, certain test score ranges, region, and original contact source. Glover’s research (1986) chronicled the development of a decision–support system for enrollment management at the University of Hartford. Although the article was published in 1986, it was cited in several other works and contains helpful background information. Glover defined enrollment management as “any institutional attempt to influence the number, mix, and quality of students through recruitment and retention strategies” (p. 16). He identified nine goals of enrollment management, one of which was to increase the admissions yield from the number of accepted applicants. The University of Hartford’s decision–support system predicted admissions yield based on geographic proximity to home, SAT scores, sex, financial–aid category, freshman/transfer, resident/commuter, GPA, and levels of scholarship awarded. Each of

44 these variables contributed to a forecasting analysis that calculated the probability that an individual student would enroll if admitted. Lay, Maguire, and Litten (1982) also wrote in the early 1980s, but were cited in recent studies related to enrollment strategies. They conducted a segmentation analysis of Boston College’s applicant pool and each subgroup’s relative probability of enrolling using automatic interaction detector (AID), an analytic technique developed by Moran and Sonquist in 1963. A form of multivariate analysis, AID uses an algorithm to find the variable that produced the maximum separation between two subgroups on a selected dependent variable. It is robust enough to evaluate 40 predictors or more. In the Boston College model, an SAT score above or below 1100 was the best predictor of admissions yield at the institution. Alumni connections and the program of choice—in conjunction with the SAT score—were also relevant predictors. Lay, Maguire, and Litten defined a highly responsive subgroup as one with a yield rate of greater than 60% or more. A second model revealed the influence of parents in increasing admissions yield. Students whose parents rated Boston College as “excellent” yielded at an 80% rate. Interestingly, sex influenced yield rate, with males yielding at lower rates. Xavier University’s implementation of social networking was the subject of Hayes, Ruschman, and Walker’s case study research (2009). The researchers asserted that with the growth of social networking, it was logical that colleges and universities should also engage it as a tool to recruit students. They reported Allocca’s findings that 72% of college–bound students preferred to interact with admissions departments and student counselors online and via instant messaging (as cited in Hayes, et al., 2009). Xavier implemented a customized, university–specific social networking site called “Road to

45 Xavier.” The project had four stated goals, one of which was to enhance yield statistics. The enrollment team found that there was a significant relationship between those who logged into Xavier’s proprietary social networking Web site and the likelihood of attending the university. That was also true for the frequency of the site visits. Engagement with the social media site has now been factored into Xavier’s predictive modeling process, along with campus visits, conversations with counselors, and completion of the FAFSA. Meredith (2004) conducted an empirical analysis of the impact of U.S.News & World Report rankings on enrollment performance. Meredith cited Monks and Ehrenberg’s previous research (as cited in Meredith, 2004, p. 445) that found that changes in a school’s U.S.News ranking had a significant impact on the yield of the next incoming class. Interestingly, U.S.News uses an institution’s average SAT/ACT scores, acceptance rate, percentage of students from the top 10% of their high school class, and admitted student yield as four measures of student quality. Meredith expanded Monks and Ehrenberg’s earlier research to a broader data set of institutions and found that ranking changes between quartiles (i.e. moving from the second quartile up to a Top 25 school) had a significant—and positive—impact on admissions outcomes. Interestingly, a move from the third quartile to the second quartile was not as significant. Also, admissions outcomes at public universities were more responsive to ranking changes than those of private universities. The results were inconclusive with regard to the impact of a U.S.News ranking change on the admissions outcomes of smaller schools. Hernon and Dugan’s brief article, “Assessment and Evaluation,” (2009) included the admissions yield as a student outcomes metric that is appropriate for measuring the

46 effectiveness of an institution. They also noted that such metrics might be used for drawing comparisons with peer institutions. Lockwood and Hadd (2007) explored the concept of brand in higher education. They identified market perceptions and engagement with individuals associated with the institution as two factors related to student recruitment and admission. Mahoney (2010) reflected on the tumultuous 2008–2009 recruitment cycle for admissions offices around the country. Although much of the article was unrelated to this study, his comment that college admission had changed from being counseling–focused to business–focused underscored the attention that is being given to yield rates by enrollment management professionals. In summary, the literature affirmed the importance of admitted student yield rates as an important enrollment management performance measure. All institutions are seeking to increase their yield rates. Predicting yield rates is done through data mining and complex predictive modeling applications. Although variables that affect yield rates vary by institution, the literature identified the following factors as contributing to increased yield rates: relationship–marketing strategies, certain local or geographic regions, test score ranges, original contact source, improvement in U.S.News ranking, parental support for the institution, and engagement with social networking. Change in Undergraduate Enrollment Several of the research articles reviewed in this literature included findings related to change in enrollment and its impact on the overall financial health of colleges and universities. Significant observations are repeated here.

47 Martin (2002), who explained the economic theory underlying tuition discounting, asserted that an increase in enrollment will always cause financial problems for the institution unless the marginal revenue generated by the student exceeds the marginal costs in both the short and long–run. Corey (2005) agreed, finding that colleges and universities are increasingly dependent upon enrollment revenues, so will resort to increasing aid even though the long–term result may be an even greater negative. Citing Russo and Coomes, Erickson (2004) affirmed that liberal arts schools turn to enrollment growth as a solution to economic survival, since they cannot raise tuition enough to cover rising costs. Lassila (2010) concluded that there is a positive relationship between institutional discount rate and enrollment and that the practice of discounting may be a successful strategy for enrolling students of color. Browning (2011) observed that while less stable financial institutions used discounting to increase their enrollment, there was some indication that they compromised their long–term financial stability in the process. Peruso, Jr., (2010) found that colleges with the largest enrollment increases also had the largest average annual tuition increases. Feerrar’s qualitative research study (2005) of six private, liberal arts institutions in Pennsylvania revealed that enrollment growth was common only in the non–high wealth schools. Meyer and Sikkink (2004) studied the relationship of enrollment growth to financial performance at CCCU institutions and found that institutions that gained more than 7% in enrollment ended up the weakest financially. Those that lost 2% to 7% of their enrollment ended with overall financial strength greater than those that grew in enrollment.

48 Non–Traditional Adult, Graduate, and Professional Program Enrollment Comparatively little research has been conducted to assess the contribution that graduate and adult programs make to the financial health of private colleges and universities. In fact, Plageman (2011) reviewed the Journal of Higher Education from 1966 to 2001 and found that less than one percent of the articles published during that time period were related to adult education in any way. Although many authors claim that adult learners are shaping the future of higher education in America, that future—not to mention the past—remains largely unknown and undocumented. In this section, I review the limited literature that has been published related to adult and graduate programs and institutional financial performance. Bash (2003) examined the special issues facing adult learning programs. He cited both low status on their own campuses and lack of institutional support, even while their home institutions viewed them as “cash cows.” Bash stated, “The unvarnished truth of the matter is that, unfortunately, the academy has too often treated adult students primarily as a lucrative source of income” (p. 19). Adult programs are capable of generating significant short–term revenues because they tend to have fewer faculty, less infrastructure needs, and lower support costs than traditional programs. However, Bash warned that this short–sighted approach may be damaging to adult programs in the longer term. From a financial perspective, Bash noted that the entrepreneurial nature of adult programs may be what the academy needs in the future to survive and thrive. Corey’s broader research on the relationship between tuition price, institutional aid, enrollment, and tuition revenue (2007) found that private institutions that were able to increase their proportion of graduate students had a positive effect on their net revenue

49 generation rate (NRGR). The NRGR can be calculated as the net tuition revenue divided by the gross tuition revenue, or as one minus the discount rate. The NRGR provides a measure of the percentage of the student tuition dollars that can be used for operations. Corey concluded that private institutions may be able to use master’s programs to subsidize their increasingly expensive undergraduate programs. Exploring the notion of entrepreneurialism further in his “What Serving Adult Learners Can Teach Us,” Bash (2003) asserted that faculty at traditional colleges and universities have historically eschewed, or even publicly denounced, a market–focused approach to educational delivery. Yet, a practical shift has occurred, and adult learners now represent 47% of all students enrolled at colleges and universities. He asserted that successful colleges will need to be responsive to the needs of these new students who view themselves as customers. Erickson (2004) conducted research on the impact of adaptive strategies on the mission of Christian colleges. Adaptive strategies look externally to the market for direction, place a high value on growth and change, and would include initiatives to add programs that serve audiences outside of the traditional–aged student. Erickson conducted research on a data set of 40 CCCU institutions between 1991 and 2003, assessing whether they had engaged in 16 identified adaptive activities. Related to this section were change in graduate enrollment as a percent of total enrollment, percent of CCCU institutions that had an accelerated degree completion program, percent of CCCU institutions offering complete degrees via distance learning, percent offering distance learning courses, percent offering adult continuing education courses, percent offering certificate programs, and percent change in the number of graduate degrees offered.

50 Erickson found that during the time period, graduate enrollment at CCCU institutions grew by 94.8% while undergraduate enrollment grew by 50.5%. Average undergraduate enrollment declined slightly as a percent of the total. Further, 93% of the CCCU institutions had accelerated adult degree completion programs; 20% offered online degrees; 48% offered some online courses; 78% offered adult continuing education courses; 40% offered certificate programs; and graduate degree offerings increased by 91% during the time period. The average number of off–campus sites was 8.3 for this sample of CCCU institutions. Erickson concluded that CCCU institutions were engaging in a variety of adaptive strategies, but were seeking to maintain their missional focus. He acknowledged the tension in that many of these tactics were not serving students who were part of the original mission and vision of the institutions. Similarly, Flory (2002) conducted a qualitative study of two CCCU institutions located in southern California. Both had non–traditional, adult degree–completion programs. Flory’s goal was to assess their ability to maintain their mission while implementing these programs. Flory cited a 1999 study conducted by the Mission, Formation, and Diversity Project that found two–thirds of the surveyed mainline Protestant, evangelical, and Catholic institutions offered bachelor’s degree programs for adults. About half of all conservative Protestant colleges had established at least one adult program within the previous 10 years. Although Flory’s work was designed to understand if and how the two institutions maintained their mission while offering non–traditional programs, he acknowledged that the reason both colleges ventured into adult education was for the financial benefit. Neither institution had a significant endowment, and both were entirely dependent on student tuition and fees. He found that both programs were

51 financially beneficial, primarily because of the low overhead associated with their operations. Both staffed their programs with adjunct faculty or encouraged full–time faculty to teach overloads. Both programs generated between $1.5 and $2 million in gross revenues annually. For one of the participating institutions, the net revenue from its adult program matched that received from its 950 traditional students. For the other institution, which was larger, the adult revenues represented just 3% to 4% of the total budget. Flory’s analysis showed that the institution with the greater reliance on adult program revenue had been less aggressive (or perhaps just less successful) about integrating its original mission with the new degree–completion program. The other institution—which was not dependent on the adult–program revenue—had retained its mission more intentionally. Giles (2012), a graduate faculty member at a CCCU institution that has led the way in expanding adult programs, researched how adult–degree programs influence their host institutions. She found that more private institutions are offering distance degree programs than their public counterparts. Also, most had a separate organizational structure for their adult programs. Citing Sisel, Hansman, and Kasworm, Giles reported that adult students taking classes for credit grew 171% between 1970 and 1991. Citing Huber, Lowry, Foster, and Carnevale, Giles reported that online enrollment grew from 350,000 adults and $1.75 billion in revenue in 2001 to 3.2 million adults and $7.1 billion just four years later. Inspired by this growth, many private college administrators view adult programs as an easy solution to their financial challenges. On the other hand, traditional faculty—who may appreciate the revenue—still question the credibility of adult education. Giles affirmed that adult programs can be quite profitable, generating

52 millions of dollars of revenue that are often used to subsidize the traditional residential program. She insisted, however, that administrators of adult programs need to ensure that adequate revenues are being reinvested in adult operations. Also, as these programs grow, their support costs necessarily increase. Institutions should not grow accustomed to the revenues without a plan to cover the increased workload and expenses. Hagedorn (2005) addressed four friction points for adult programs in the academy: access, success, retention, and institutional accommodation. With regard to success, Hagedorn noted that just 30 years ago, researchers reported adult failure rates as high as 75% and recommended the cancellation of such programs because of the high costs and the dismal outcomes. Husson and Kennedy (2003) offered perspectives on how to start and maintain successful adult programs. Their work noted that the launch of these programs can be a serious strain on institutional resources. They cautioned that administrators must consider the physical and human resources needed to be successful, not to mention the required investment in advertising and marketing. Matkin (2004) affirmed that the business model associated with adult programs has moved to a central place in higher education. Online delivery has lowered costs, increased access, and improved efficiencies. Further, the cost to educate adults is significantly less than educating traditional students, although Matkin noted that the new emphasis on instructional design will require increased capital investments. Unfortunately, the ability to understand the financial contributions of adult programs may be blurred, since their revenues are often used to subsidize residential degree programs. He recommended a full–costing approach where all direct and indirect program costs are

53 calculated to assure pricing levels are appropriate. Finally, Matkin cautioned administrators not to overestimate the demand for adult, online programs, nor to underestimate the marketing costs required to be successful. Stewart (2010) focused on graduate education, specifically as a driver of national prosperity and innovation. She noted that between 1998 and 2008, graduate enrollments of domestic students grew on average nearly 3.5% annually. Martin’s empirical model of tuition discounting which explored the economic theory of marginal student costs and marginal student revenues affirmed that graduate programs lowered costs–per–student and, therefore, generated more marginal revenue (Martin, 2002). In summary, the literature affirms the potential for adult and graduate programs to contribute significant revenues to private college financial statements. This potential— and in some cases actuality—has led some administrators to view these programs as “cash cows.” However, the researchers cautioned higher education leaders not to underestimate the costs required for long–term success in the adult market. Unfortunately, no quantitative research has directly analyzed the relationship between adult and graduate program performance and the overall financial health of colleges and universities. Tuition Discount Rates and Institutional Aid Tuition discounting and institutional aid represent two windows looking in on the same general concept. The tuition discount rate is the percentage of revenue from tuition and fees that is returned to students in the form of institutional aid. Some of this aid is funded by endowment or other gift streams, while other tuition discounts are unfunded, representing negative charges to revenue. With these varied perspectives and funding

54 sources, calculations and definitions differ, which is a source of confusion within the literature. In this section, I review research articles that provide theories and definitions of tuition discounting, offer rationale for or against discounting, include analysis of institutional aid strategies, or present findings where discount rate or institutional aid were a variable of interest. The purpose of Allan’s “Taxonomy of Tuition Discounting” (1999) was to provide definitions, historical perspective, and analysis of practical and political considerations related to tuition discounting. He suggested that confusion exists because discounting means one thing to college administrators (i.e. foregone tuition revenue) and something different to financial aid and admissions staff, students, and parents (i.e. scholarships and grants) and because different colleges use discounting for different purposes—to achieve enrollment goals or to shape the class. Allan (1999) offered a visual explanation—presented in Figure 1—to clarify the meanings of terms associated with tuition discounting:

Figure 1. Explanation of tuition discounts. The “simple tuition discount” represents a pure waiver of tuition and is also known as unfunded aid. This is the discount rate that was studied in this dissertation because it is potentially most problematic to the institution since it represents a decline in revenues that could be used for other operational priorities. Recent changes to accounting

55 standards now require that this discount be clearly presented as a reduction in revenue and not a budgeted expense. The “scholarship allowance” adds aid that is funded from gifts or endowment. The discount rate reported by NACUBO uses this broader calculation rate. Finally, the “student tuition discount” includes all non–repayable aid that a student receives from any source. This is of obvious interest to families and enrollment managers, but is rarely used in discussions of tuition discounting. Allan’s work concluded with affirmation that tuition discounting generates more net tuition revenue for institutions than simply reducing the price a commensurate amount would achieve, since lowering the price would reduce the tuition paid by higher income students (often with lower academic credentials). Allan also made a strong appeal for the value of funding financial aid through the endowment since this reduces the pressure to maximize revenue and permits the institution to pursue other goals. Ehrenberg (2010) explained the economics underlying tuition and fees at colleges and universities. He asserted that no undergraduate students pay the full cost of their education, but rather are subsidized by endowments, annual giving streams, and the value of the physical plant. At the richest private institutions, institutional grants come from endowment income, while less resourced institutions (which represent the majority) resort to unfunded discounting to attract students. Butcher, Kearns, and McEwan (2013) added insight that alumni account for one– third of total donations to private universities and that endowment returns help to offset the difference between a university’s tuition rate and its actual cost of delivery. On average, the price–to–cost ratio for private universities is 45.9% and much lower for the highest–ranked institutions.

56 Cunningham and Cochi–Ficano (2002) explored the reasons associated with alumni giving and affirmed that charitable donations can be a significant determinant of an institution’s long–term success. Donations increase a college’s ability to subsidize, allowing the institution to increase student quality, offset aid policies, and enhance its competitive position. The researchers found that higher endowment per student and larger tuition discounts increased future alumni generosity, which continues a cycle of long– term financial benefit to the institution. Alumni giving also increased based on original SAT score and the institution’s faculty–student ratio. The authors summarized that it is in an institution’s financial self–interest to attract strong academic students. Holmes (2009) conducted a quantitative analysis of the alumni support provided to Middlebury College, Middlebury, Vermont, with a goal to understand motivations for giving. Although the study is largely outside the bounds of my research, Holmes’ statistics on private college voluntary contributions were helpful. Holmes reported that— among all U.S. colleges and universities—alumni are the largest source of voluntary support, accounting for nearly 28% of total contributions and financing 7.1% of total institutional expenditures. Martin (2002) explained the economic theory underlying tuition discounting, specifically what is required for it to be a successful strategy. He stated that the substantial subsidy that is provided to students leaves institutions (on average) operating in a deficit with respect to student–generated revenues and total expenditures. Therefore, an increase in enrollment will always cause financial problems for the institution unless the marginal revenue generated by the student exceeds the marginal cost in both the short and long–run. Costs are a function of institutional capacity: physical plant, instruction,

57 and subsidies. The subsidy capacity reflects the institution’s ability to fund the discount using its endowment. If the endowment is insufficient, the institution must acknowledge the opportunity cost of foregoing revenue even when enrollment increases. Martin continued by demonstrating that efforts to recruit higher quality students require larger scholarships and worsen the institution’s financial condition. His article concluded with an empirical model to assess the break–even point where student costs begin to exceed student revenue. Using the model, he analyzed discounting at Carnegie I and II liberal arts colleges and found that Carnegie I institutions discounted more intensively, likely due to their stronger financial condition and the higher–quality student they recruit. Endowment, endowment income, and tuition discount were all positively correlated with revenue for both Carnegie I and II institutions. Martin’s research suggested that discounting strategies that seek to grow enrollment and increase or maintain the quality profile of the student body simultaneously may be damaging to the financial health of the institution. Martin recommended that administrators who desire to increase enrollment or student quality should first raise the endowment (or subsidy) required to fund the strategy. Martin (2004) also identified the most common discounting mistakes and offered alternatives to avoid those problems. He focused his work on two primary areas: (i) the simple discount rate as a measure of the opportunity cost to the institution (that is, tuition and fee revenue lost to the institution) and (ii) marginal cost pricing. Martin stated that universities should focus on maximizing their funded discount rate (by increasing the size of their endowment) and minimizing their unfunded rate as a short–term strategy for increasing enrollment while the institution has capacity for growth. He suggested that an

58 institution can increase its discounting capacity by increasing its endowment, lowering its costs, or requiring its auxiliary enterprises to contribute to operational revenues. With regard to marginal cost pricing, Martin explained that this rule implies that, when an institution has excess capacity, it should continue to discount tuition and admit students as long as the marginal revenue exceeds the marginal cost of the student. However, Martin demonstrated that the rule—taken to its extreme—will drive an institution to operate at a deficit. Rather, colleges and universities should build their discounting strategies around the average cost to enroll a student. Directly applicable to this research, Martin noted that institutions with religious affiliations were less likely to choose discount rates that were higher than optimal and were also less likely to experience deficits. He concluded his analysis by asserting that there is abundant anecdotal and objective evidence to indicate that current tuition discounting practices have weakened the financial condition and lowered the quality of many institutions. Massa and Parker (2007) affirmed that “discounting gone wild can handcuff a college” (p. 96), but done appropriately, discounting can be a strategy that manages the composition of the student body, increases revenue, and upholds institutional objectives. Kraatz, Ventresca, and Deng (2010) followed a very different line of criticism of enrollment management—and specifically tuition discounting—as a value–threatening administrative innovation. They studied 515 private liberal arts colleges from 1987 through 2006 and tested an integrative theoretical model in an event–history analysis that tracked the initial adoption of enrollment management at the institutions. The authors’ criticism of enrollment management and tuition discounting practices was more philosophical in nature and emphasized the unforeseen and undesired consequences that

59 work in opposition to organizational values. Further, they asserted that tuition discounting exchanged the charitable function of financial aid for an investment perspective. Citing Lapovsky and Hubbell, they claimed that enrollment management has reduced access for the economically disadvantaged while providing subsidies to those who have the ability to pay. The authors expressed concern about the proportion of budget being allocated to the enrollment management function and its increasing organizational influence, viewing enrollment management as the evidence of capitalism and market ideologies infiltrating the academic environment. Although the authors’ assessment of organizational factors that make institutions vulnerable to the threat of enrollment management is beyond the boundaries of this study, their concerns contributed to the literature on tuition discounting and whether it is an appropriate and effective financial aid strategy. Davis (2003) affirmed many of the concerns about tuition discounting already presented in this section. Davis’ research used data from the National Postsecondary Student Aid Study (NPSAS) surveys conducted by the National Center for Education Statistics between 1995–1996 and 1999–2000. Davis suggested that discounting may reduce student accessibility and affordability, may reduce funding for instruction and student services, and may create fiscal risks for some institutions. Specifically, the data showed that the average per–student grant awards to lower–income students as compared to middle– and upper–income students narrowed significantly during the time period of the study. In 1995, awards for highest–income families were just 39% of the lowest– income students. By 1999, awards to highest–income families were 82% of their lowest– income counterparts. Further, at private colleges, growth in aid to higher–income students

60 far outpaced growth in aid for lower income students. During the four–year period, families with incomes under $40,000 saw an increase of 22% in their grant aid. Families with incomes between $80,000 and $99,999 saw their aid grow by 85% during the same four–year period. The data also suggested (but did not prove) that lower enrollments of needy students at private institutions may have been the result of the transition from need–based to merit–based aid strategies. The NPSAS survey data also demonstrated that tuition discounting did not significantly increase student quality, and SAT–median scores actually decreased at 45% of private institutions between 1995 and 1999. Finally, smaller private colleges (which would describe nearly all of the CCCU institutions in this study) saw their net tuition increase just 5.3% while gross revenues increased 6.3% during the four–year period. This suggests that their discounting policies were actually resulting in reduced tuition revenues. With these concerns about tuition discounting, Corey (2005) hypothesized why institutions continue to pursue it as a strategy even when net tuition revenue is reduced. He claimed that 25% of institutions are decreasing net tuition revenue as a result of their discounting strategies. Corey offered three explanations for this institutional behavior: (i) the drive for increased prestige and quality, (ii) increased competition for decreasing resources, and (iii) simple error. The U.S. higher education system is highly stratified, and every layer is seeking to improve its relative position, operating with the belief that the greater the perception of quality, the greater the access to resources. Therefore, institutions may choose to forego net tuition revenue as an “investment” in quality. Further, pricing levels are set by the “elites” (which have almost unlimited capacity to fund aid) but filter down to each lower level. Institutions at the lower levels have less

61 capacity to raise their prices and simultaneously invest more in institutional aid, so they are forced to increase their unfunded discount rates, effectively reducing their net tuition revenues. Colleges and universities are increasingly dependent upon enrollment revenues, so will resort to increasing aid even though the long–term result may be an even greater negative. Finally, Corey proposed that, with the complexity of higher education organizations, admissions and financial aid may pursue aid strategies that suit their specific goals, but have unintended negative consequences for the organization as a whole. Corey (2007) explored trends related to tuition price, institutional aid, changes in enrollment, and revenues generated by institutions. He introduced a new metric entitled the net revenue generation rate (NVGR), which is the rate at which net tuition revenue is generated through tuition charged to students and then reduced by the investment in financial aid provided to those students. He noted that although tuition prices have seen dramatic increases, institutions have mitigated these increases by increasing their discount rates (i.e. offering more funded or unfunded institutional aid); however, for some institutions this has resulted in less net revenue. Specifically commenting on size of endowment as a predictor of discount rate, Corey affirmed that institutions with the largest endowments do not have significantly higher discount rates. Rather, they are investing those resources in higher levels of quality, not in lower net costs for students. Desrochers, Lenihan, and Wellman (2009) affirmed this assertion, demonstrating that, despite significant increases in private gifts, investment and endowment income between 1998 and 2008, private colleges and universities increased tuition each year. They suggested that the additional funds were being used to increase institutional spending in

62 other areas. While heavily endowed institutions may be able to justify tuition increases and offset costs through their endowment, most institutions must resort to unfunded tuition discounting to maintain affordability for families. Corey explored this dynamic using a descriptive and regression analysis of a sample of 316 private four–year, 235 public four–year, and 172 public two–year institutions between 1988 and 2000. Corey’s variables of interest were tuition price, enrollment, gross tuition revenue, institutional aid, net tuition revenue, discount rate, NRGR, and marginal discount rate. For private institutions, his research affirmed that higher tuition prices resulted in higher discount rates, and larger enrollments were related to higher NRGR’s. High–yield graduate programs appeared to generate higher NRGR’s for private institutions and were recommended as a strategy for subsidizing the increasing costs of undergraduate education. Duggan and Mathews (2005) noted two significant changes to tuition discounting since it was first introduced in the 1970s. First, discounts are no longer based solely on financial need and are increasingly used as an enrollment management tool to shape the profile of the class. As a consequence, college–funded aid to wealthy students grew faster than aid to low–income students in the last 10 years. Second, discounts were originally funded by endowments and gifts, but are increasingly being funded from tuition revenue. Lassila’s quantitative research (2010) on tuition discount rates explored the relationship between tuition discounting and enrollment growth. Lassila defined institutional tuition discounting as the art and science of establishing a net price of attendance of postsecondary students at amounts that will maximize tuition revenue while achieving enrollment goals. After reviewing literature that presented conflicting views

63 on whether tuition discounting is positively related to enrollment, Lassila performed empirical ordinary least squares regression to assess the impact of tuition discounting on enrollment. His sample was U.S. four–year private, not–for–profit, degree–granting colleges and universities, and the calculation of tuition discount included both funded and unfunded institutional gift aid. In his single–year analysis, Lassila found that a positive relationship existed between institutional discount rate and enrollment, including the enrollment of Black and Hispanic students. However, in assessing the relationship over time, there was no significant relationship between enrollment or enrollment by race/ethnicity. Despite the second finding, Lassila concluded that there is a positive relationship between institutional discount rate and enrollment and that the practice of discounting may be a successful strategy for enrolling students of color. Hillman (2012) studied 174 public four–year colleges and found that tuition discounting enhanced net tuition revenue, but only to a limited extent. His research showed that unfunded discounts above 13% eroded gains in net tuition revenue. Hillman incorporated a simple economic model where funded and unfunded tuition discount rates were the predictor variables. A second model was then developed that expanded the institutional predictors to include minority representation, academic profile (based on median SAT scores), and the amount of state subsidy. Hillman implemented the Arellano–Bond generalized method of moments for his analysis. He concluded that tuition discounting from unfunded sources has significant opportunity costs that must be considered; however, the strategic use of aid may maximize or enhance net tuition revenue, but does not assure it. Finally, he asserted that unfunded tuition discounting can

64 have a negative impact on an institution’s financial health. His findings may not be generalizable to private institutions. Doyle (2008) studied private college awarding strategies and found that the amount of institutional grant awarded to a student correlated positively with family income. He asserted that the neediest students appeared to be receiving the least institutional aid. Conversely, public institutions awarded gift aid to only 18% of their students, and it is negatively correlated to family income. Doyle expressed doubt about the long–term success of an enrollment strategy that is designed to fill seats and increase institutional prestige, but does not address financial needs. Epple, Romano, and Sieg (2006) developed a model that estimated price based on endowment size and technology for 768 private universities. Their model assumed that colleges will always seek to maximize quality and affirmed that the market is competitive. Colleges with low– or medium–quality levels had limited market power. Colleges offered less financial aid to higher income students, but in pursuit of the nebulous goal of quality, offered more financial aid for higher–ability students. The researchers summarized that the market power of a college is reflected in its ability to extract higher revenues from students by charging prices that exceed marginal costs. Supiano (2011) highlighted Cedarville University’s change in discount rate strategy, which was implemented in 2009–2010. The University had maintained a low– discount rate strategy throughout its history, but was losing its competitive position and watching its yield and enrollments erode. The board of trustees approved a tuition discounting strategy that increased the university’s freshmen discount rate from 23% to

65 32% in one year. The freshmen class grew from 724 to 859, and net tuition revenue also increased from $12.4 million to $13.75 million. Day (2007) focused on the future of financial aid leveraging, noting that today’s practice of using sophisticated analyses of yield patterns and price–demand response – which have been resulting in increased unfunded aid—is not sustainable. He recommended that institutions adapt strategies that optimize net revenue over the lifecycle of the student (not just the entry year), provide creative alternatives to help families finance college through flexible payment options, continue to increase efficiencies and reduce costs, and enhance the effectiveness of their admissions operations. In summary, the literature and opinions about the value of tuition discounting to an institution were mixed. Research suggested that tuition discounting increases enrollment and net tuition revenue, at least for a time. However, the longer–term benefits are uncertain. Institutions already in stronger financial positions have greater capacity to increase discount rates. Some researchers questioned whether discounting is appropriate from a philosophical basis and expressed concern that it helps those who need assistance least and is not proven to increase access. Previous Correlational Research This section reviews previous research that used correlational statistical analysis to understand the relationships between and among variables of interest and financial performance measures. Three quantitative studies were particularly informative for this dissertation research: (i) Browning’s correlational study (2011) of financial performance to tuition discount rates at private universities, (ii) Meyer and Sikkink’s study (2004) of

66 the relationship of enrollment growth to financial performance at CCCU institutions, and (iii) Hunter’s study (2012) that explored variables that were positive and negative indicators of financial stability or instability. These are presented first in this section, followed by related research conducted by Lee (2009) and Huang (2007). Browning (2011) performed a statistical analysis of tuition discount rates and their correlation to the financial position of U.S. private colleges and universities. She calculated discount rates using publicly available Integrated Postsecondary Education Data System (IPEDS) data and used the five ratios of the financial vulnerability index (FVI) as the measure of financial position. Browning controlled for selectivity based on each college’s ranking in Barron’s Profiles of American Colleges. Browning’s sample included all private, not–for–profit, baccalaureate–level and above institutions in the U.S. (N = 1,244). Once institutions were removed due to incomplete data sets, the sample ranged from 1,111 to 1,126 institutions for the years 2003 through 2007. The FVI has five embedded financial measures: debt ratio (ratio of debt to total assets), revenue concentration index (ratio of number of revenue sources available and the diversification of the revenue streams), surplus margin ratio (measure of profitability), administrative costs ratio (ratio of administrative costs to total revenues), and the size ratio (ratio of total financial size as a function of its total assets). Browning harvested the relevant data from IPEDS and The Institute for College Access and Success (TICAS) database, and calculated the ratios for her own analysis. Interestingly, Browning considered using the CFI as her financial strength variable of interest, but the data was not publicly available, so chose to use the FVI as its proxy.

67 Browning’s general assumption was that institutions adjust their tuition discounting based on the availability and need for resources. She tested three hypotheses: 1. Institutions with stable financial positions can use tuition discounts to increase access for low–income students. 2. If institutions are financially unstable and enrollment numbers decrease over time, then average tuition discount rates will increase over time. 3. If institutions are financially unstable, then as their financial position decreases, their tuition discount rate will increase. Browning used both descriptive and analytical methods. An ordinary least–squares regression equation was used to test Hypotheses 1 and 3. For Hypothesis 2, an ANOVA model was used to compare mean tuition discount rates from two time periods: 2003–04 and 2007–08. Browning’s findings offered support for Hypotheses 1 and 3. However, Hypothesis 2 could not be supported. Instead, financially unstable institutions with shrinking enrollments did not increase tuition discount rates. Other findings included higher tuition discount rates at the larger institutions (1,000 to 4,999 students); higher tuition discount rates as the proportion of white students increased; and lower tuition discount rates at institutions with higher proportions of Pell–eligible students. These findings may lend some credence to Lapovsky and Hubbell’s critique of tuition discounting as a strategy that reduces access for the economically disadvantaged while providing subsidies to those who have the ability to pay (2003). In summary, Browning found that the more financially stable institutions used their resources to attract students. While less–stable financial institutions also used

68 discounting to increase their enrollment, there was some indication that they compromised their long–term financial stability in the process. In other words, tuition discounting must be used strategically and take into consideration the long–term financial health of the institution. Meyer and Sikkink (2004) conducted an analysis of CCCU institutions, correlating annual enrollment growth with three ratios of financial performance for a period extending from 1975 through 1995. Data were derived from the Computer–Aided Science Policy Analysis and Research (CASPAR) system. Defying what many may perceive to be conventional wisdom, Meyer and Sikkink hypothesized that—above a low minimum enrollment threshold—increasing enrollments is not a long–term strategy for financial stability. Meyer and Sikkink’s sample included CCCU institutions with total enrollment growth greater than 2.5%. Enrollment growth was correlated with three financial ratios: assets to liabilities, liquidity ratio (current fund balances to expenditures and mandatory transfers), and current operations ratio (operating surplus or deficit). CASPAR data was available for 46 CCCU institutions for Ratio 1; 80, for Ratio 2; and 93, for Ratio 3. Looking at Ratio 1—which the authors claimed was the best indicator of overall financial health—on average, during the time period of the study, institutions that gained more than seven percent in enrollment ended up the weakest financially. Those that lost two to seven percent of their enrollment ended with overall financial strength greater than those that grew in enrollment. The researchers did a detailed examination of the Pearson correlation coefficients for each year. The result was that the greater the base–year increase, the more negative the change in the institutions’ assets–to–liabilities ratio.

69 For Ratio 2, those with larger enrollment increases had greater liquidity increases. The Pearson correlation coefficient seemed to show a small increase in current–fund balances immediate following an enrollment increase, but then the balances dropped. For Ratio 3, in the two years following enrollment decline, the current–operations ratio suffered. The examination of the Pearson correlation data for the 92 institutions that had stable or increasing enrollments over the 18 years found no positive correlation with Ratio 3. Meyer and Sikkink summarized that Ratios 2 and 3 do not show improvement with enrollment gains. Hunter (2012) studied the health of small, private colleges and explored the variables that were positive and negative indicators of financial stability or instability. He used chi–square statistics, independent and paired samples t–tests, and multiple regression model analyses to explore how four families of independent variables (institutional characteristics, strategic choices, financial indicators, and the external environment) were related to the Department of Education’s Test of Financial Strength for 673 four–year, private institutions with fewer than 2,000 students during the time periods of 1998–99 and 2008–09. In all, 38 independent variables, grouped into four families, and one dependent variable were analyzed. Hunter’s full–model regression analysis of the 1998–99 data set explained 37% of the variance in the dependent variable, and no single independent variable was statistically significant, likely due to the correlations between the independent variables themselves. He then included only the 18 variables in the model that had been correlated with the dependent variable and statistically significant in a bivariate Pearson product moment correlation. In this analysis, Hunter found that the annual operating reserve and

70 tuition dependence variables had the strongest association with the financial strength score. Hunter also selected key variables for analysis based on his literature review. These variables included cash on hand, tuition dependency, total undergraduate enrollment, length of presidential tenure, unrestricted giving, athletic affiliation, and student retention. His resulting model explained just 15% of the variance in the financial strength score. Length of presidential tenure emerged as most significant. Finally, Hunter analyzed his independent variables in their groups and found that stronger operating reserves, higher cost to attend, longer presidential tenures, and more unrestricted gifts positively impacted institutions’ test of financial strength scores. Hunter then repeated these analyses for the 2008–09 time period. Larger undergraduate enrollments, more unrestricted gifts, stronger cash and operating reserves, and lower debt service positively impacted financial strength in the full–model regression analysis. In his select–variable analysis, again based on his literature review, his results indicated that larger undergraduate enrollments, stronger cash reserves and more unrestricted gifts were positively related with the financial strength score. These results were similar for the analysis by family groups. Interestingly, two consistent variables negatively impacted financial health in both time periods: athletic affiliation (specifically NAIA membership) and discount rate. Hunter’s research was the most comprehensive study available exploring both internal and external factors that contributed to the financial health of small, private colleges. Lee (2009) analyzed a sample of 766 private colleges and universities listed in Moody’s Municipal Financial Ratio Database, Guidestar.org, and IPEDS. He accessed each institution’s CFI score, as developed by KPMG and Prager, McCarty, and Sealy,

71 LLC, and Bearing Point, Inc. and classified each institution’s financial position as strong or weak. Lee then used logistic regression to analyze the relationship between 10 independent variables and an institution’s CFI score classification as strong or weak (CFI >3 Strong; CFI 3 are strong and scores < 3 are weak, the reported mean for these CCCU institutions is at the lower end of the strong continuum, although 19 of the 32 schools reported an average CFI score of less than 3.01 for the research period. To evaluate whether the 32 institutions that reported CFI scores were significantly different from those that did not report their data, I conducted an independent sample t test on each of the predictor variables. Table 6 summarizes the results.

98 Table 6 CFI–Reporting Institutions as Compared to Non–Reporting Institutions Predictor Variables Mean Mean (CFI–Reporting (Non–Reporting Institutions) Institutions) Admitted Student Yield Rate 40.64% 41.76 Annual Percent Change in Undergraduate Enrollment Percent of Total Enrollment from adult, graduate, and professional Unfunded Discount Rate

0.25%

1.58%

26.89%

32.85%

31.19%

34.16%

Percent of Institutional Aid from Gifts or Endowment

6.28%

6.92%

Independent Sample t Test t = –0.78 df = 76, P =.4378 t = –1.8 df = 71, P =.0761 t = –2.18 df = 74, P =.0324 t = –2.19 df = 75, P =.0316 t = –0.44 df = 76, P =.6612

Table 6 CFI-Reporting Institutions as Compared to Non-Reporting Institutions There was no statistical difference between the CFI-reporting and non-reporting institutions for the Admitted Student Yield Rate, Annual Percent Change in Undergraduate Enrollment, and Percent of Institutional Aid from Gifts or Endowment variables (p > .05). However, it is important to acknowledge that a statistical difference existed between the two groups on two variables. The non-CFI reporting institutions showed a higher percentage of their total enrollment coming from adult, graduate, and professional programs (p < .05). This may have been a function of institution size, as the average undergraduate enrollment for the CFI–reporting institutions was 1,752, compared to 1,504 for the non–reporting institutions. Further, on average, the non–reporting institutions had somewhat higher unfunded discount rates as compared to the CFI– reporting institutions (p < .05). In this section, I presented descriptive statistics that were relevant to the institutional data. The following information was presented: how the sample was derived, summary observations related to the data, and a comparison of the sample (CFI–reporting

99 institutions) as compared to the non–reporting information. The next section explains the preliminary step that was taken to build the multilevel model for hypothesis testing and data analysis. Preliminary Analysis: Confirming the Need for a Multilevel Model As presented in the previous chapter, the analytical strategy for this study involved building a multilevel model that accounts for the dependency of multiple years of data coming from the same institution. This type of model can handle the variability that may occur both between and within institutions over time, and it appropriately addresses the likelihood that data from the same institution will be more similar to each other than to data from other institutions. Thus, the initial step in the process was to determine the between–institution variability, or intraclass correlation coefficient (ICC). The ICC is a descriptive statistic that can be used when quantitative measurements are made on groups of data. In this study, the four years of observations for each institution formed the groups of interest. The ICC describes how strongly units in the same group resemble each other. While it is viewed as a type of correlation, unlike most other correlation measures, it is uniquely designed for comparing observations within and between groups. The ICC yielded a result of 72.03% and was statistically significant (P < .0001), meaning that 72.03% of the variability was between institutions. Thus, we were justified in using a multilevel model to analyze these data. Analysis of the Data Associated with the Main Research Hypotheses This section provides the analysis of the data associated with the main research hypotheses, which was conducted in three steps: 1. pattern of change over time, 2.

100 interaction of each predictor variable with time, and 3. variable interactions with time removed. Note that all of the predictors were mean–centered and scaled to be percentages. Step 1: Pattern of Change over Time Step 1 of the multilevel model evaluated the pattern of change in the criterion variable—the CFI score—over time. The results of the least squares means analysis are depicted in graph form in Figure 4.

Figure 4. Pattern of change in the CFI score over time. Note that the pattern of change in the averages of the CFI scores was not linear. The average CFI scores increased between 2008 and 2010, but dropped back in 2011. There was a significant main effect for time. Step 2: Interaction of each Predictor Variable with Time In the second phase, the multilevel model analyzed each of the predictor’s interactions with time and the resulting effect on the CFI score. The goal was to determine how the CFI score was affected, if at all, as the predictor variable changed over the four–year time frame. For the purposes of the model–building process, an alpha level of .1 was used to determine if the fixed effects (predictors) were significant. We also used a step–down

101 approach, meaning that we started with the most complicated and got simpler. We chose to use a .1 significance level in Step 2 because the step-down approach causes variation in the p values as variables are removed, several of the p values were grouped just above or below the .05 significance level, and there was little consequence if a Type I error occurred. We, first, removed the two variables that did not produce significant interactions—the Percent of Institutional Aid from Gifts or Endowment and the Percent of Total Enrollment from Adult, Graduate, and Professional. Table 7 depicts the results yielded by the model. Table 7 Solution for Fixed Effects: Interaction of each Predictor Variable with Time Solution for Fixed Effects Effect Year b Standard df Error Intercept 1.3979 0.8417 73.6 Time 1 2008 0 Time 2 2009 2.1455 0.7153 58 Time 3 2010 2.4104 0.7407 65.9 Time 4 2011 1.3158 0.8036 72.5 Admitted Student Yield 0.04822 0.06054 74.3 Rate Percent of Total Enrollment –0.00876 0.02147 37.4 from Adult, Graduate, and Professional Annual Percent Change in –0.1700 0.1238 68.2 Undergraduate Enrollment Unfunded Discount Rate 0.09604 0.07212 77.1 Percent of Institutional Aid 0.2584 0.08193 63.2 from Gifts or Endowment Admitted Student Yield 2008 0 Rate Admitted Student Yield 2009 –0.07156 0.06228 57.3 Rate Admitted Student Yield 2010 –0.1213 0.06039 55.8 Rate Admitted Student Yield 2011 –0.1225 0.06233 54.2 Rate

t Value

P

1.66

0.1010

3.00 3.25 1.64 0.80

0.0040 0.0018 0.1059 0.4283

–0.41

0.6855

–1.37

0.1743

1.33 3.15

0.1869 0.0025

–1.15

0.2553

–2.01

0.0494

–1.96

0.0546

Table 7 Solution for Fixed Effects: Interaction of each Predictor Variable with Time

102 Table 7 (continued) Solution for Fixed Effects: Interaction of each Predictor Variable with Time Solution for Fixed Effects Effect Year b Standard df Error Annual Percent Change in 2008 0 Undergraduate Enrollment Annual Percent Change in 2009 0.2227 0.1402 68 Undergraduate Enrollment Annual Percent Change in 2010 0.1099 0.1329 65 Undergraduate Enrollment Annual Percent Change in 2011 0.2780 0.1659 69 Undergraduate Enrollment Unfunded Discount Rate 2008 0 Unfunded Discount Rate 2009 –0.07112 0.08170 56 Unfunded Discount Rate 2010 –0.1533 0.07500 59.5 Unfunded Discount Rate 2011 –0.07160 0.07152 60.1

t Value

P

1.59

0.1168

0.83

0.4112

1.68

0.0983

–0.87 –2.04 –1.00

0.3878 0.0454 0.3207

The primary data in this table can be interpreted as follows. First, three variables showed significance for their interactions adjusting for the effect of time on the CFI rate—the Admitted Student Yield Rate, the Unfunded Discount Rate, and the Annual Percent Change in Undergraduate Enrollment. For every 1% increase in the Admitted Student Yield Rate, the effect in 2010 as compared to 2008 is predicted to be .1213 smaller (p = .0494). For every 1% increase in the Admitted Student Yield Rate, the effect in 2011 as compared to 2008 is predicted to be .1225 smaller (p = .0546). Stated simply, as the Admitted Student Yield Rate increased, it slowed the growth of the CFI score in those years. For every 1% increase in the Unfunded Discount Rate, the effect of 2010 as compared to 2008 is predicted to be .1533 smaller (p = .0454). In other words, as the Unfunded Discount Rate increased, it slowed the growth of the CFI score in that year. In contrast, for every 1% increase in the Annual Percent Change in Undergraduate Enrollment, the effect is predicted to be more pronounced, with the effect of 2011 as

103 compared to 2008 predicted to be .2780 larger (p = .0983). Restated for clarity, as the Annual Percent Change in Undergraduate Enrollment increased, it likewise increased the amount of growth in the CFI score in that year. The model also revealed a significant difference between 2010 and 2008 for institutions that were at the average of Admitted Student Yield Rate, Annual Percent Change in Undergraduate Enrollment, and Unfunded Discount Rate. When looking at the effect of each of the five variables over time on the CFI score, only the Percent of Institutional Aid from Gifts or Endowment had a significant main effect of .2584 (p = .0025). Institutions that had higher levels of institutional gift aid from endowed and annually funded sources could expect to have larger CFI scores over the entire time period. For every 1% increase in institutional gift aid, the CFI was predicted to increase by .2584. Table 8 summarizes the effect of each of the five variables over time on the CFI score as they relate to the five hypotheses of this study: Table 8 Interaction of Each Predictor Variable with Time and the Research Hypotheses Hypothesis Interaction with Time Main Effect CCCU institutions with higher Positive trends in yield rate Not significant Admitted Student Yield Rates will tend constrained CFI growth in .04822 (p = .4283) to have stronger financial performance two time periods. measures. 2010 = -.1213 (p = .0494) 2011 = -.1225 (p = .0546) Annual increases in enrollment will Positive trends in Not significant have a negative relationship with lower undergraduate enrollment -.1700 (p = .1743) financial performance measures. contributed to CFI growth in 1 time period. 2011 = .2780 (p = .0983) There is no significant relationship Not significant Not significant between percent of total enrollment (p > .1) -.00876 (p = .6855) from graduate or non–traditional programs and the financial performance of an institution.

Table 8 Interaction of Each Predictor Variable with Time and the Research Hypotheses

104 Table 8 (continued) Interaction of Each Predictor Variable with Time and the Research Hypotheses Hypothesis Interaction with Time Main Effect Higher unfunded discount rates will Increases in unfunded Not significant have a positive relationship with discount rates constrained .09604 (p = .1869) stronger financial performance CFI growth in 1 time measures. period. 2010 = -.1533 (p = .0454) Higher percentage of institutional gift Not significant Significant main effect aid from endowment or restricted funds (p > .1) .2584 (p = .0025) will have a positive relationship with stronger financial performance measures.

At Step 2, the model explained 90.1% of the variability in the CFI scores (i.e. R2 = .9010). Step 3: Variable Interactions with Time Removed (Main Effects) The final step of the multilevel model involved removing the interactions between time and the variables. In essence, we created a multiple regression model that accounted for the dependence among the observations that came from the same institution. The focus was on each predictor’s relationship with the CFI score. The only variable that emerged as significant was the Percent of Institutional Aid from Gifts or Endowment. (.1699, p = .0416). Table 9 depicts the results yielded by the model.

105 Table 9 Solution for Fixed Effects: Main Effects Solution for Fixed Effects Effect Year b Standard Error Intercept 1.6826 0.7244 Time 1 2008 0 Time 2 2009 1.7895 0.5278 Time 3 2010 2.5536 0.5657 Time 4 2011 1.0291 0.6090 Admitted Student Yield Rate –0.03419 0.03659 Percent of Total Enrollment –0.00164 0.02245 from Adult, Graduate, and Professional Annual Percent Change in –0.01291 0.03128 Undergraduate Enrollment Unfunded Discount Rate 0.01892 0.05160 Percent of Institutional Aid 0.1699 0.08177 from Gifts or Endowment

df

t Value

P

54.7

2.32

0.0239

53.1 60.4 66.4 78.9 34.7

3.39 4.51 1.69 –0.93 –0.07

0.0013