Financial Aid Publications

Employing Noncognitive Variables to Improve Admissions and Increase Student Retention


Today, a growing number of North American postsecondary institutions are incorporating the use of noncogntive variables into their admissions process. Why is there more and more interest in utilizing these non-academic variables? It’s all about success! The results at those colleges and universities that have added these measures to their admissions requirements are showing strong correlations to student’s academic success, persistence, and graduation. AACRAO Consulting has packaged these variables along with business processes to effectively manage them within the admission process. Our name for these services is FairSelect.

What are Noncognitive Variables?

Noncognitive variables are based on more than 30 years of research by William Sedlacek, Professor Emeritus, University of Maryland College Park. According to Sedlacek, “The term noncognitive is used here to refer to variables relating to adjustment, motivation and perception,” and can be assessed efficiently in a variety of ways, and incorporated into any admissions process (Sedlacek, 2004, 2011). Noncognitive information complements “traditional verbal and quantitative (often called cognitive) areas typically measured by standardized tests. Noncognitive variables are useful for assessing all students, but they are particularly critical for assessing nontraditional students, since standardized tests and prior grades may afford only a limited view of their potential” (Sedlacek 2004; Lauren, 2008). The use of these variables in admission decisions has been tested within the US legal system and ruled to be viable.

Description of Noncognitive Variables

  • Positive Self-Concept: Demonstrates confidence, strength of character, determination, and independence.
  • Realistic Self-Appraisal: Recognizes and accepts any strengths and deficiencies, especially academic, and works hard at self-development. Recognizes need to broaden individuality.
  • Understands and Knows How to Handle the System: Exhibits a realistic view of the system based upon personal experiences and is committed to improving the existing system. Takes an assertive approach to dealing with existing wrongs, but is not hostile to society nor is a “cop-out.” Involves handling any “isms” (e.g., racism, sexism).
  • Prefers Long-Range to Short-Term or Immediate Needs: Able to respond to deferred gratification; plans ahead and sets goals.
  • Availability of Strong Support Person: Seeks and takes advantage of a strong support network or has someone to turn to in a crisis or for encouragement.
  • Successful Leadership Experience: Demonstrates strong leadership in any area: church, sports, non-educational groups, gang leader, etc.
  • Demonstrated Community Service: Identifies with a community, is involved in community work.
  • Nontraditional Knowledge Acquired: Acquires knowledge in a sustained and/or culturally related ways in any area, including social, personal, or interpersonal.

Institutions engaged in measuring these noncognitive variables are showing positive results in better predicting students’ success, regardless of their incoming GPA or test score. While high school curriculum, GPA, and SAT/ACT scores continue to be useful in measuring some aspects of students’ abilities, a more comprehensive assessment of an applicants’ potential can be made by assessing both academic and life skills. This approach is generally referred to as “holistic admissions” or in Canada as “broad-based admissions”.

Using Noncognitive Variables

Adding noncognitive variables to admissions requirements can provide better assessment of student ability and potential, while increasing diversity, and accounting for different learning styles and cultural backgrounds. Those institutions that have employed noncognitive variables find that they have learned more about a student much earlier, and that they can better serve the student once they have matriculated. This affords the institution an opportunity, and more importantly, a responsibility, to serve the student more comprehensively, and much earlier in the educational process. This is an important part of why students are more successful once they matriculate. Campus faculty and staff can learn to help students be better prepared and informed on how to access and use campus services before they have arrived on campus. This greatly aids a new student’s ability to be successful, and a more confident, self-assured student can result. In addition to admissions, noncognitive variables have been used to improve scholarship selections as well.


AACRAO’s FairSelect services help your campus understand and implement noncognitive variables. Michele Sandlin, foremost practitioner and a twelve year implementation veteran of holistic admissions/noncognitive variables, teams with William Sedlacek to work with institutions worldwide. They can assist your institution by providing information and training regarding theory, research, legal backing, question and scoring development, alignment with academics and student affairs, business processes, and staff training.


Jaschik, S. (January 18, 2013). What is Merit? Inside Higher Ed.

Lauren, B. (2008). The College Admissions Officer’s Guide. AACRAO. P. 99-108.

Sedlacek, W. E. (2004). Beyond the big test: Noncognitive assessment in higher education. San Francisco: Jossey-Bass.

Sedlacek, W. E. (2011). Using noncognitive variables in assessing readiness for higher education. Readings on Equal Education. 25, 187-205.

This article was authored by AACRAO Senior Consultant William Sedlacek and Managing Consultant Michele Sandlin.

Download the paper here: Employing Noncognitive Variables to Improve Admissions and Increase Student Retention

Evaluating Staff Workload: The Need for a Standardized Tool for Institutional Planning


A current limitation of many institutions is the ability to determine what is the proper workload for staff. Because of the standardization of the credit hour, most institutions have a standard calculation for faculty workload. However, no such measure exists for evaluating staff load, which can be a significant hindrance to student service and staff morale.

One of the difficulties for administrators is that credit hours and full-time equivalency (FTE) calculations of student load used for financial projections are almost meaningless when it comes to assessing staff workload. One reason for this is that these units of measure (credit hours and FTE) do not directly correspond to the time required to serve students. For that, student headcount is a much more realistic measurement. Each student requires roughly the same amount of time and service regardless of the number of courses she takes or whether he is part-time or full-time. Each of these students must still be recruited and apply for a program, apply for and be awarded financial aid, be advised and register for classes, and be evaluated for progress toward program completion. All of these activities must occur whether a student in enrolled in one credit hour or 18 credits per term.

The disconnect happens since institutional planning and budgeting is most often based on credit hour enrollment rather than student headcount since few institutions bill students a flat rate per term (even those institutions with block tuition rates still are affected by service to students over multiple semesters depending on their time to completion). Also, planning and budgeting rarely consider student headcount in the assessment of staffing needs. Even those institutions that may take student enrollment into account do so more globally rather than segmenting programs and students based on unique service-level requirements. For example, transfer students require more time for evaluations and advising than do traditional students who follow an institution’s curriculum from start to finish.

Variables Involved in Staff Service Levels

So, what is the answer to this disconnect between student service and the planning needed to deliver that service? A solution must be developed that can account for the variables across institutions while, at the same time, providing a standard format that can be easily completed and understood for institutional planning and budgeting (and for integration with human resources).

sample staff workload 2

To develop such a standardized evaluation and planning tool, each institution must determine a baseline headcount for all programs against which each staff position will be evaluated, a program
complexity factor that compares the complexity of the program served to the baseline, and the actual student headcount for the program served.

Baseline Headcount: Each institution or department must establish a baseline against which to measure all other programs at the institution or within the department. The ideal is to select the most standard academic program at the institution (e.g., the traditional undergraduate program at a four year institution) and determine the number of students that a staff member can serve effectively. This should be determined through a variety of measures including turnaround time for communications and other student service tasks as well as broader student satisfaction regarding staff services. Once a baseline is established, that headcount can be used to assess service levels for each program at the institution or within the department.

Program Complexity Factor: Each academic program within the institution has unique service requirements that influence the tasks and staff time needed to serve students well. This factor should be determined in comparison with the program used to establish the baseline headcount for the institution or department. For example, an adult degree program that requires direct communication and advising, as well as routine re-evaluation of transfer credit, may require two to three times the amount of staff time as compared to another program at the institution. In contrast, a cohort-based program, with a standard program and little variation in advising may require less staff time than the baseline. In the actual tool, this factor will be multiplied by the actual headcount for the program to compare to the baseline.

Administrative Load: Many staff positions require responsibilities that are not focused on student service but on serving the operational needs of the institution or department. This might include management of other staff, committee representation, or technical support of institutional or department systems. Such administrative load should also be represented in a tool designed to assess staff workload and should be represented as a percentage of the overall position requirements.

% of Full-time Status: A comprehensive tool also needs to be scalable for all positions within the institution or department. For those positions that are less than full-time, a percentage should be entered into the tool to account for the staff FTE and the effective headcount served by the position should be modified appropriately by this factor as well.


All of the variables noted above must be combined to compare a staff member’s existing service level and workload to the benchmark. Once that comparison is completed, administrators can see at a
glance if each staff member has capacity to serve additional students or if he is over capacity and an additional staff member must be added to meet service expectations.

Long-Term Benefits

There are several benefits of a comprehensive tool for evaluating staff workload. One is that such a tool can be standardized to compare all positions within a department or for the entire institution if needed. The factors that comprise the tool can also be coded into an institution’s ERP system and built into standardized reports for regular evaluation by institutional decision makers. In addition, the tool can be customized for each institution to account for unique needs and variations (i.e., staff serving multiple programs). The benchmarks that are the basis of the tool can be evaluated regularly to insure that the base information is accurate (especially compared against service expectations and student satisfaction measures). Finally, the tool can be used to determine the capacity of each staff and can be charted against anticipated enrollment projections for budgetary planning purposes, thus demystifying the evaluation process of adding staff and promoting financial stability for the department and the institution.

Sample 2 Reid

This article was authored by AACRAO Senior Consultant Dr. Reid Kisling.

Download this paper here: Evaluating Staff Workload: The Need for a Standardized Tool for Institutional Planning

Leveraging Financial Aid: A Powerful Tool for Enrollment Success


As college costs continue to rise against stagnant family incomes, the pressure to provide more financial aid from an institution’s operating budget has also risen. This rise in aid is accompanied by questions of its place in the financial health of the university. Are we spending too much? Is it being spent on the right students? These are common questions in university board rooms, faculty senate meetings and enrollment management committees. A strategic analysis of financial aid can help the enrollment manager and his or her institution to answer these difficult and sometimes elusive questions. It can also lead to an improved financial aid strategy, which is an important component of attracting and retaining students the institution seeks to serve.

Like many enrollment management techniques, leveraging uses data to inform decision-making. In a rare place within the realm of data utilization, leveraging rivals predictive modeling and retention analysis in its complexity and use of data across variables from different data areas of the institution and (in best practices) external data, as well. Leveraging may be defined as an analysis of student enrollment behavior through the lens of financial aid that leads to confirmation of or changes to institutional aid strategy. It is also a Continuous Quality Improvement process that uses analysis to refine and improve the institution’s financial aid strategy within the constraints of its available resources.

Financial aid leveraging seeks to achieve three goals:

  1. Provide aid packages that yield the optimal mix of students, including those who may not otherwise enroll at the institution (recruitment);
  2. Help close gaps between costs and resources that may prevent students from persisting to degree (retention); and
  3. Meet net tuition goals.

Before embarking on a leveraging analysis, there are two preliminary steps that you must take. These are more conceptual and strategic in nature than the process steps that follow them. First, you must determine the lens through which you will examine the gap between resources and costs (“need gap”). Will you look at the cost of attendance through the lens often used by the financial aid office, which includes direct and indirect costs? This is a common calculation of costs and is also known as the student budget. It includes estimates for books, transportation, miscellaneous costs and room and board when the student does not live in campus housing. Or, will you look at costs through the lens used by many students and parents to include just those paid directly to the university — tuition, fees and campus housing for your residents? There are good arguments for both lenses but the way in which you leverage aid will have different results, depending upon your answer to this question.

Second, you need to have clear enrollment goals that go beyond the number of students you seek to enroll. These goals will form the inquiry questions that impact the design of the analysis and variables included in the data set. For example, if a college seeks more out-of-state students, state of residence must be included in the data set. If it seeks more math majors or men, intended major and gender, respectively, must be included.

The process of financial aid leveraging is then broken into four additional steps that comprise the process of leveraging financial aid: data gathering and cleansing, analysis, implementation and review.

Step One: Getting Your Ducks (Data) in a Row
Possibly the most difficult of all the steps is gathering and cleaning data in preparation for the leveraging analysis. It is critically important to include the correct variables. You will need to know who enrolled and who did not and the financial aid packages offered to students who were admitted. Some financial aid offices delete aid packages for those who do not enroll, requiring these to be recreated in simulated packaging, a time-consuming task. It is also common to find incomplete and inconsistent records with the assembled data set. Old codes that were supposed to be inactive have a strange way of appearing, once the data is examined for consistency.

Many enrollment managers want to know if their aid packages are competitive with peer institutions. It is possible to complete a competition analysis using National Student Clearinghouse data. Data requests can be made to the Clearinghouse by member institutions for an annual research fee, which is usually a bargain for what can be learned from it when wisely studied and incorporated into a data set with other variables.

Assembling a data set for analysis requires expertise that may be beyond even experienced enrollment managers. It is important to partner with the institutional researchers at your institution and you will need someone on the project with experience and skill in working with large data sets, statistical analysis and the accompanying software required to assemble and assess your data. For this reason, it is common for institutions to enlist the help of a consultant.

Step Two: Making Sense of the Data
Analysis of the data can answer several questions. The most common analysis performed is the assembly of a “grid” that places the student’s ability to pay against the student’s willingness to pay. Ability is usually measured as the Expected Family Contribution (EFC) from the Free Application for Federal Student Aid (FAFSA). Students who do not send FAFSA results to your institution can also be assess but they are placed into their own category on the grid. It is also important to separate out those students who come to your institution through special financial incentives. These could be athletic aid offers, institutional employee benefits or special talents, such as art or music. The behavior of these students within the context of financial offers is so different from the majority of students that leaving them in the analysis with other students may skew the results.

Willingness to pay may be expressed through academic preparation data. Those students with higher academic preparation levels will have more options for enrollment at various institutions and will likely receive merit aid offers from your competitors. Students with lower preparation levels may be thankful to be admitted to your institution and jump at the chance to enroll with less financial incentive to do so.

The grid of need and ability will contain many cells where these two variables intersect. There are multiple ways to assemble these data into the grid but standard for any assessment is the inclusion of institutional gift aid and total gift aid for each cell, along with the number and ratio of students enrolled to those admitted. Smaller institutions will need multiple years of data (also a good idea for larger ones, as one year’s data may be anomalous to overall performance) to avoid problems resulting from small sample sizes. Separation of student cohorts (freshmen, transfers, graduate, adult learners, in-state or out-of-state, etc.) is an important step, as different cohorts may react quite differently to various aid offers.

Each cell in the grid is carefully analyzed to determine to what extent financial aid may play a role in student enrollment decisions. For example, in the highest ability, lowest need cell, students may be receiving full-ride offers and yielding at very low rates. There may be no financial issue that improves this and these results may be a function of institutional position and/or recruitment techniques. Other students with very high need may not be yielding at high rates because they lack the basic financial resources to cover costs. At this stage, it is a good idea to have someone with extensive experience in financial aid analysis to take a look at these data to help the enrollment manager and institution walk through the questions and possible reasons for low or high yield rates. Using an analysis tool such as an Excel pivot table to construct the grid will allow analysts to “drill down” into each cell and examine the cases that comprise its summary averages.

There are several additional analyses that can add to the understanding of student enrollment behavior through the lens of financial aid. With large enough sample sizes, it is possible to assess behavior between academic programs, seeking to understand if the same merit or need awards elicit similar or different behaviors for engineering or history majors, for example. Behavior may also be different between students from varying regions of your recruitment territory, such as in-district or faraway states. A leveraging analysis can also be used to view the retention of students at your institution, substituting return to studies for initial enrollment as the dependent variable in the analysis.

Step Three: Adjusting Aid Strategies to Meet Institutional Enrollment Goals
From the analysis of institutional aid performance, changes to institutional aid may be made to help improve the yield of students desired to achieve institutional enrollment goals. One example of this is that institutions may decide to meet a higher percentage of the need gap for more desirable students than those less desirable to meet the qualitative enrollment goals of the institution (academic profile, geographic diversity, etc.).

This may be as simple as “tweaking” awards in certain cells to achieve marginal improvement in several areas or as radical as an overhaul of the institution’s aid scheme. Expected results are placed into a model matrix as targets for the entering class for whom these aid awards will be available. Documentation of the rationale for each change in awards should be made and kept, so that there is a record of why these new expectations were set. From this model, the expected net tuition revenue for each entering cohort can be modeled and incorporated into the overall tuition revenue projections for the institution. Needless to say, the timing of the entering classes should be far enough in advance that the awards have an opportunity to play a part in the recruitment of and yield of the entering students.

It is important to consider how these changes will be communicated to key audiences, including students, parents, guidance personnel, faculty, recruitment staff, financial aid staff, alumni and others who may have a vested stake in the recruitment and enrollment of your students. Making changes to scholarship or grant programs but failing to promote them may minimize the overall enrollment results you hoped to achieve.

Step Four: Rinse and Repeat – Continuous Quality Improvement
Once the entering class has matriculated, it is important to assemble the leveraging matrix for that cohort against the numbers you expected to achieve in each cell. Where expectations were different in any significant way, it is important to review the documentation for the assumptions made to see why the results over or under-performed the expected levels. From this review, additional changes can be made for the next entering classes.

As the institution gains experience with leveraging, it will be better able to forecast the impact of changes in aid strategy on net revenue for new and continuing cohorts of students. It may take several years of purposeful changes to acquire the desired results, especially if they require the infusion of significant new financial aid resources. It may not be prudent to add these new resources in a single year and enrollment managers must be mindful that increased discounting has a long-term rollout as students persist toward their degrees.

It was tempting to title this brief “Financial Aid Leveraging: Six Simple Steps to Enrollment Health” in hopes that readers would quickly see its irony – financial aid leveraging is anything but “simple” and it is not a magic elixir to improve the institution overnight. It is, however, a potentially powerful tool that can be employed by enrollment managers in combination with other proven enrollment techniques. As pressures mount to provide greater amounts of aid to students, having a firm grasp of aid at your institution and a purposeful plan for awarding it will be important assets for enrollment managers.

If you are interested in learning more about financial aid leveraging at your college or university, please contact

Written by Tom Green, Director of Technology and Managing Consultant for AACRAO Consulting.

Download this paper here: Leveraging Financial Aid: A Powerful Tool for Enrollment Success

SEM and Institutional Success Reviewed in AACRAO’s College and University Journal


SEM and Institutional Success: Integrating Enrollment, Finance, and Student Access

Edited by Bob Bontrager
AACRAO, 2008, 93 PP.

Reviewed by Brian A. Vander Schee

Discussions regarding financial pressures, the increasing population of low-income students graduating from high school, and the need to make education affordable are timely and necessary. How institutions will respond to the changing economic, political, and demographic landscape is not well defined. SEM and Institutional Success: Integrating Enrollment, Finance, and Student Access, edited by Bob Bontrager, provides insight into this situation by fostering collegial discussion and institutional action.

The stated purposes of the book are to describe current financial and enrollment challenges; to provide a definition and context for current SEM (Strategic Enrollment Management) practice; to offer new perspectives on the interplay of SEM and institutional finance; to provide a SEM planning model to improve mission, enrollment, and financial outcomes; and to promote the use of SEM to improve student access and success. The intended audience is primarily administrators at four-year public institutions and non-elite private four-year institutions.

The succinct opening chapter provides a solid backdrop for the issues addressed throughout the remainder of the book. Don Hossler discusses the reasons that colleges and universities will experience challenging times in the near future. The reasons include a difficult financial market, demographic shifts, and increasing competition. More and more, community colleges, for-profit private institutions, and universities outside of the United States will compete against nonprofit higher education institutions. Many public institutions will confront flat or decreasing budgets, and private institutions with limited endowments will continue to rely heavily on tuition revenue. Problems related to limited resources and rising costs will be compounded by pressure to serve the needs of a growing number of lower-income high school graduates.

In chapter two, Bob Bontrager summarizes other authors’ as well as hi sown perspectives on SEM. In addition to a comprehensive yet concise overview of the rise of SEM, Bontrager provides a table – a clear visual – regarding the changing demographic and economic status of high school graduates over the next fifteen years. The chapter closes with a defense of SEM’s use of financial aid leveraging. Although Bontrager acknowledges the views of critics, his text reads somewhat defensively. This may reflect the understandable frustration of SEM professionals who are expected to appease faculty, the president, and the board of trustees as well as advocates for increased access for financially needy students. Balancing conflicting mandates is even more difficult when net tuition revenue may be increased by shifting aid dollars to less needy students who are more likely to attend, pay, and, eventually, graduate.

In chapter three, Gil Brown draws attention to the model in which cost = price + subsidy. His equation suggests that what students pay for their education never covers its true cost because some portion of the actual cost inevitably is covered by subsidies such as investment income, gits, or public funding. His chapter provides valuable details, as when Brown explains why a 6.5 percent increase in tuition (for example) would be required to fund a 4 percent salary increase at an institution that receives a 1 percent increase in state support which accounts for 60 percent of institutional revenue. Brown also explains fund accounting and its appropriate use at colleges and universities. Nevertheless, the chapter’s detailed description of how sponsored research complicates the fund accounting process seems out of place. The challenges are clearly articulated, but they are not clearly connected to the content of the remainder of the chapter.

The fourth chapter, also by Gil Brown, describes how SEM can be used to leverage under-utilized capacity to increase revenues. Brown suggests that “under-enrolled programs and courses provide opportunities for institutions to realize additional revenue without increasing marginal costs” (p. 56). However, being aware of such opportunities is not the same as realizing them; little practical guidance is given regarding how to do so. nevertheless, Brown does provide sound advice about preparing multi-year budgets with contingencies. The key is to keep enrollment goals flat and to treat increases as windfalls rather than to increase enrollment expectations each year just to keep pace with rising instructional costs. Unallocated revenue then could be used to increase access for students with significant financial need.

Bob Bontrager and Gil Brown outline the SEM planning model in chapter five. The chapter opens with a concise and accurate description of how institutions set their projected enrollment goals based on established budgets. The authors then outline the four phases of the SEM planning model: (1) developing comprehensive enrollment goals, (2) identifying strategic enrollment investments and measurable outcomes, (3) tracking enrollment, net revenue, and institutional budget outcomes, and (4) creating reinvestment strategies. Their methodology, using fictitious data for a large public university, is sound: such an institution might be best equipped to increase access to higher education. However, it is hard not to consider that those institutions least able to manage economic challenges and issues of access are those with much smaller budgets and no state funding. Thus, highlighting the nuances of the SEM planning model in a different institutional context — for example, a small private college with a small endowment — likely would be more useful to those readers who could use the most help.

In chapter six, Bontrager discusses enrollment leadership. He concludes that there is a need for “a new level of leadership, to identify and deploy innovative strategies that create avenues for all members of society to achieve their educational goals” (p. 89). his is an appropriate challenge for those in higher education who are slow to change and/or who continue to rely on unsuccessful strategies from the past.

SEM and Institutional Success highlights the issues of finance and access faced by most institutions. The book is easy to read, provides practical strategies, and should encourage SEM professionals to further explore application of the SEM planning model to other institutional contexts.

About the Author
Brian A. Vander Schee, PH.d., is Assistant Professor of Marketing at Aurora University in Aurora, Illinois. Previously, he served as vice president for enrollment management at two different colleges. His doctorate, in higher education administration, is from the University of Connecticut.

To purchase a copy of SEM and Institutional Success: Integrating Enrollment, Finance, and Student Access or other SEM related publications, please visit or call 301-490-7651.

This article originally appeared in College & University (Volume 84, No. 3 [2009]), and is being reproduced/distributed with the permission of the American Association of Collegiate Registrars and Admissions Officers.