Investing in the future: Comparing funding in academic support services for athletics to academic progress

Hirko: Investing in the future Page 1

Investing in the future: Comparing funding in academic support services for athletics to academic progress

by

Scott Hirko, Ph.D. Assistant Professor, Department of Physical Education & Sport

Central Michigan University Mt. Pleasant, MI

Presented at the annual conference of the American Educational Research Association Philadelphia, PA

April 3, 2014


Abstract

Investigation of the investment by institutions into academic support services for athletic

programs (SASS) helps to understand the impact of decisions by institutional leaders to comply

with National Collegiate Athletic Association (NCAA) policies created to improve academic

performance of college athletes, the Academic Progress Rate (APR). Revenue theory of cost set

the framework to understand that finding the revenues to spend in greater staffing of academic

support services for athletics is worthwhile to comply with the policy as well as to invest in an

institution’s prestige. Freedom of information requests were sent to each public NCAA Division

I institution requesting their SASS budgets between the 2005-06 and 2010-11 academic years.

Data from 118 institutions was used in this study to determine that level of competition was a

significant predictor of investment in academic support staff, tutoring staff, and number of full-

time employees in SASS. Organizing institutions by athletic conference, NCAA subdivision,

and membership in a “Big Six” athletic conference with vast revenues demonstrated significant

differences in the amount of money spent on SASS and the growth rate in SASS. Furthermore,

results found the investment in SASS was a significant predictor of improvement in many sport

teams’ APR scores, especially scores of academically underperforming teams. It was significant

to find those institutions at all levels of competition which spend more money on athletic

academic support services, particularly on tutoring and FTE, realized an improvement in APR

scores on all teams that failed to meet minimum academic benchmarks, especially the high-

profile revenue sports of football and men’s basketball.


Introduction

The impact of competitive sport competition to American higher education is significant

within the educational environment. Astin (1993), Tinto (1993), and subsequent researchers

found an important component to the persistence of a college student and their eventual

academic success includes activities in which students socialize and take an active role – such as

intercollegiate athletics – particularly during a student’s first year in college Braxton, Hirschy, &

McClendon, 2001; Kuh, Cruce, Shoup, & Kinzie, & Gonyea, 2008; Pascarella & Ternzini,

2005). Students receiving athletic scholarships arrive on campus with an expectation to earn

their scholarship by representing their school successfully on the athletic field. This expectation

includes immediate and significant athletic requirements (practice schedules, traveling to

intercollegiate competition, pressures to succeed athletically) added to their academic workload.

High-profile scandals at the most competitive level of American intercollegiate athletics

have raised concerns about the fit and function of intercollegiate athletics within the educational

mission of higher education. Examples of this tension include Ohio State University football

athletes selling awards from their successful intercollegiate athletics competition, or University

of Southern California athletes financially rewarded for their athletics success by individuals

external to the university. Increasing this tension are recent agreements between media

conglomerates and college athletics entities. For example, the Knight Commission on

Intercollegiate Athletics (2011) raised concerns about the impact on academics from the $15

billion lifetime value of media rights to broadcast athletics in the five football Bowl

Championship Series conferences. Moreover, U.S. Secretary of Education Arne Duncan raised

concerns about the NCAA in 2010 awarding more than $54 million in revenue from the NCAA


men’s basketball tournament championship despite teams not being on track to graduate half of

their players (Knight Commission, 2010).

Decisions made to succeed athletically are often perceived to be at odds with an

institution’s academic mission (Duderstadt, 2003; Knight Commission on Intercollegiate

Athletics, 2010; Sperber, 2000; Thelin, 1994). Recent studies have investigated this tension

between academics and athletics. For instance, Koo and Dittmore (2012) investigated the impact

of donations to athletics on giving to academics. Lawrence, Ott, and Hendrick (2009) found

faculty at major research institutions do not believe they can impact financial or academic

oversight of their institution’s athletics department. Yet, many institutions in American higher

education are committed to investing in ways that improve the graduation rates of high-profile

college athletes, particularly men’s basketball and football players. Comeaux and Harrison

(2012) researched the different college environmental impacts on the persistence of athletes.

Notably, Comeaux and Harrison called for the “need for research on the relationship between

institutional commitment and academic success for student-athletes attending Division I

institutions” (p. 241). In answer to that need, this study aimed to shed light on the extent to

which an institution’s investment in academic support centers for athletics (otherwise known as

Student-Athlete Support Services, or SASS) impacts the academic performance of its athletes.

Research Questions

The research question directing this study is: To what extent does an institution’s

investment in athletic academic support staff (SASS) increase projected graduation rates of its

athletes? Additional questions that directed this study included: To what extent does the

NCAA’s APR policy trigger an institution’s investment in more resources to athletic academic


support staff? And, does the sport, athletic conference affiliation, or other level of

competitiveness impact an institution’s investment in athletic academic support services?

Conceptual Framework

Suggs (2010) provided several economic and political concepts for considering the

spending of athletics departments. He related Bowen’s revenue theory of cost to spending “on

capital and organizational strategies to win” (p. 29), and that “removing dependency on resource

providers ensures stability” (p.29). Bowen’s theory is used in this study as a framework to

consider the rationale behind why institutions invest in improving the graduation rates of their

athletes. The same concept helped to consider how these decisions may be useful in other ways

for institutions to meet the academic needs for students outside of athletics. Leonard (1983)

applied the revenue theory of cost to understanding investment in university libraries as a useful

context about the “justification for resources diverted either to or from library programs must be

based on an assessment of the value of the benefits to be derived.” (p. 289). Thus, considering

the investment into athletics academic support services helps to recognize the perceived value of

academic support services.

The revenue theory of cost model further helps to understand decision-making by less-

resourced institutions, such as historically black colleges or universities (HBCUs), if their invest

at a level somewhat proportionate to their own resources leads to similar results in improved

academic performance of their students. Thus, looking at the growth of investment in athletic

academic support centers and comparing them to NCAA’s performance measures of academic

progress may provide an understanding the extent that resource allocation to academic support

services for athletics is a worthwhile investment. And, if so, is this worthwhile for all institutions

and for all teams or only those teams earning revenue?


This framework provides an understanding of the transferability of the value of an

investment into academics supporting the athletic enterprise. The extent to which decision-

makers believe it is wise to invest in athletic academic support services can be framed by the

amount of attention and revenue made by a particular sport at an institution of reputable size.

This lens creates a similar way to understand the decisions by which less-resourced institutions

invest in academic support for their athletic programs, or by which well-resourced institutions

invest in academic support for non-revenue sports (sports other than football and men’s

basketball).

Additionally, research considering the resources invested in student services also served

to frame this study. Webber & Ehrenberg (2009) found that enhancing student service

expenditures, even at the expense of reducing instructional expenditures, may enhance

graduation rates at some institutions. However, the authors noted that student service

expenditures cover a wide range of categories and their study did not analyze which of these

subcategories of expenditures are the ones that matter, not to which groups of students, such as

athletes. Similar consideration is made here with respect to academic support services, of which

support staff are on the front lines of academic engagement with athletes at NCAA Division I

institutions.

Background

The NCAA’s measurement used to project graduation rates, Academic Progress Rates

(APR), was instituted in 2004 to make institutions accountable for the academic performance of

scholarship athletes on every team of an NCAA-sponsored sport in the NCAA’s most

competitive division, Division I. APR assesses each Division I athletic team’s progress toward

graduating its players by measuring retention and academic eligibility of each scholarship


athlete. The intent of APR is to provide a term-by-term progress report of all members of an

athletic team toward graduation (NCAA 2006). When considering penalties for poor academic

performance, the NCAA primarily averages APR scores over four years to allow for some

flexibility in the variability of scores in any particular year (NCAA, 2012).

Since APR was implemented in 2004, many athletic programs have been penalized for

not meeting minimum APR scores -- most significant of these sanctions includes loss of

scholarships during the period of investigation for this study. From 2004-2011, the NCAA

predicted an APR score of 925 equated to a projected 50 percent graduation rate; and, an APR

score of 900 equated to a projected 45 percent graduation rate (NCAA, 2010). Penalties were

imposed on any athletic team failed to meet a minimum APR score of 925 averaged over four

years, or 900 in any one year. Because reprimands, including penalties, are enforced based on an

APR 925 it is considered a benchmark measurement by those who work in athletic academic

support services.

When considering athletic teams impacted by penalties for poor APR scores, institutions

with fewer resources have historically been a significant portion of those institutions. The data

from this study should help to better understand the extent of this situation. The NCAA

recognized the difficulty of less-resourced institutions, in particular, HBCUs, to be able to invest

in academic support services, including personnel. It has initiated an effort to provide greater

resources to help them meet graduation rates; and to provide them more time to meet the APR

minimums before being sanctioned (Hosick, 2012).

In 2011, the NCAA adopted a new rule which banned any team from postseason

competition for failing to reach a minimum APR score of 930 averaged over two years despite

their athletic success. The important affect of this policy is the impact on an institution’s


publicity, revenues, or expenses associated with postseason competition. Notably, the men’s

basketball teams at the University of Connecticut (CNN, 2012), the University of Toledo

(Autullo, 2012), and the University of North Carolina at Wilmington (Johnson, 2012) were

ineligible to compete in the 2013 NCAA tournament championship because of poor APR scores.

The University of Connecticut responded that it “invested in resources to improve academic

performance of its men’s basketball players” (CNN, 2012) in an effort to improve its men’s

basketball APR score. While this rule is not considered during the years of investigation of this

study, it demonstrates the significant impact of APR on decision-making to invest in academic

support services. Indeed, in 2013, the NCAA reported the increase of APR scores for all teams

increased from 954 in 2005 to 970 in 2011, with significant increases in the highest-profile sports

of baseball, men’s basketball, football, and women’s basketball (NCAA, 2013).

Data Collection & Analysis

Budget data were collected from a six-year period, from the 2005-2006 academic year to

the 2010-2011 academic years; however, some institutions were only able to provide four or five

years’ worth of data. Single-year Academic Progress Rates (APR) are available from the NCAA

website (Paskus, 2013), as are subsequent penalties and reprimands for each team at every

NCAA Division I member institution. This information was collected over a seven-year period,

from the 2005-2006 academic year to the 2011-2012 academic year. APR data were compiled by

institution, by sport, by athletics conference, and by level of competition to determine the

academic performance of each athletic team over time and penalties incurred. Grouping the data

by conference and subdivision (Football Bowl Subdivision [FBS], Football Championship

Subdivision [FCS], and those without football [NF]) helped to make comparisons by which

different levels of athletic competition impact academic performance over time. Furthermore,


splitting Division I – FBS into two distinct groups based on size of institutional athletics

revenues also proved useful: the “Haves” and “Have Nots.” The “Haves” were members of

athletics conference whose regular season football champion qualified for the prestigious Bowl

Championship Series (BCS), being Atlantic Coast, Big East, Big Ten, Big Twelve, Pacific Ten,

or Southeastern. “Have-Not” schools were institutions that did not belong to one of these six

conferences. As pointed out by the Knight Commission on Intercollegiate Athletics (2013) and

other scholars (Hirko & Sweitzer, 2014), being a member of a BCS automatic qualifying

conference is prestigious and provides access to massive amounts of revenue from conference

media contracts, ticket sales, and donations. Results helped to elucidate what affects leaders at

institutions to decide on investing resources to improve the academic performance toward

graduation of athletes on specific athletic teams.

Investment in academic support services was determined by athletic academic support

services (SASS) budget, tutoring budget, and full-time equivalent (FTE) support staff at NCAA

Division I member institutions. Ridpath (2008) noted how athletics coaches in football and

men’s basketball, compared to all other sports, have less value of academic support staff to assist

the academic performance of players. Yet, the aforementioned evidence of APR scores since

2004 demonstrate more football and men’s basketball have failed to meet the minimum APR

score of 925. Learning about the number of FTE academic support staff to improve the

academic performance of athletes my help to shed a light on whether or not adding support staff,

and how many (or how few), help to improve graduation rates of athletes.

I worked with the Director of Athletic Academic Support Services (SASS) at a Big Ten

member institution to create the questions and the instrument to collect the data. A survey was

created to request annual data between 2005-6 and 2010-11 for each of the following items.


1) total operating budget for academic advising services for intercollegiate athletics

(SASS) (operating budget is defined as: Total expenses including for graduate

assistants, interns, equipment, and other monies necessary to enhance or complete the

mission of athletic academic support);

2) total tutoring budget for academic advising services for intercollegiate athletics

(SASS) (tutoring budget is defined as: total expenses specifically for all personnel

and expenses related to tutoring student-athletes); and,

3) total Full-Time Equivalent (FTE) employees for academic advising services for

athletics.

Initially, the survey was created using Survey Monkey, and included a request of

identifying information of an institution in addition to the aforementioned budgetary questions.

The National Association of Athletic Academic Advisors (N4A) agreed to disseminate an email

to each NCAA Division I Athletic Academic Support Director member. The email included an

online link to the survey with a request to complete the survey within two weeks. However, after

four weeks, only three institutions responded, with only one complete response to the survey. A

new process was created to request the data for the study, in which Freedom of Information Act

requests were sent to every NCAA Division I public institution. The initial survey and follow up

FOIA process of data collection were approved by the researcher’s Institutional Review Board

(IRB) upon understanding the data being publicly available.

In the subsequent process, requests were sent over a 16-month period, beginning late

October 2012 through March 2014. Initial requests were sent via email to directors of athletic

academic support services at four Division I-Football Bowl Subdivision (FBS) conferences in an

effort to determine the process and efficiency of data collection. A total of 39 emails were sent


to public institutions in the Big East Conference, Big Ten Conference, Conference USA, and

Mid-American Conference. Follow up of initial emails were necessary to each public institution

in these four athletics conferences for a variety of reasons:

a) failure to respond,

b) refusal to respond citing state statute,

c) incorrect contact information,

d) request for more time to respond to the request,

e) clarification of the request,

f) incorrect data,

g) lack of available data, and/or

h) required payment for time and cost of data collection process

Of the initial 39 emails, 20 institutions provided data useable for the study within a two month

period.

Subsequently, sending the request to 194 remaining Division I public institutions took

approximately six months to complete; follow up emails to most institutions were required for

the same aforementioned reasons, extending the time necessary for data collection. By March

15, 2014, data collected from 118 institutions were included in the study. Data were organized

for the study demographically based on: a) Athletic Conference, b) Division (I-FBS, I-Football

Championship Subdivision [FCS], and No Football [NF]), and c) Have/Have-not within Division

I-FBS. For this study, institutions with football teams in an athletics conference different than

their other athletics teams were grouped with the football conference, primarily because APR

scores for football teams are those considered among the most concerning within the NCAA.


Table 1. Responsive institutions to FOIA requests, organized by demographics

Athletic Conference Division Have/Have-Not # of institutions Atlantic Coast FBS Have 4 America East No Football - 3 Atlantic 10 No Football - 1 Atlantic Sun No Football - 3 Big South FCS - 1 Big 12 FBS Have 8 Big East FBS Have 5 Big Sky FCS - 7 Big Ten FBS Have 6 Big West FCS - 4 Colonial FCS - 5 Great West NF - 1 Horizon NF - 4 Mid-American FBS Have-Not 8 Mid-Eastern NF - 1 Missouri Valley FCS - 9 Mountain West FBS Have-Not 7 Northeast FCS - 1 Ohio Valley FCS - 4 Pacific-10 FBS Have 7 Pioneer FCS - 1 Southeastern FBS Have 3 Southern FCS - 5 Southland FCS - 8 Summit NF - 2 Sun Belt FBS Have-Not 3 USA FBS Have-Not 2 Western Athletic FBS Have-Not 5

A graduate assistant assisted with entering the publicly available data into a spreadsheet

and organizing by the categories in Table 1. APR data were organized by institution and team,

as well as whether or not each team scored below a 925 in any given year. The aforementioned

grouping process was used to enhance understanding of the relationship between athletic

academic support spending and the NCAA’s academic performance policies.


Data Analysis

Quantitative analysis was used in this study. Specific attention was paid to those

institutions who received APR penalties or reprimands. Basic descriptive statistics and means

were used to look at trends by year and over the entire period of the study. Growth rates in

athletic academic spending patterns by conference, division, and Have/Have-not status were also

considered. In addition, fever graphs of athletic academic support budgets and APR scores by

team over time were used to consider differences or similarities. Those athletics teams among

the 118 institutions that had fewer than 25 instances across the six year period of the study below

a 925 APR were excluded from data analysis by team because of the few number of instances of

failing to meet the policy benchmark. Predicting how athletic competitiveness impacts athletic

academic support budgets was also considered using ANOVAs of divisions and athletic

conferences with academic support budgets, tutoring budgets, number of FTE full time

employees, and year-to-year changes of these three areas.

Pearson correlations by team (38 different teams sanctioned by the NCAA) of each

school in the entire data set (all 118 institutions) were used to determine if there was any

significant relationship between each team’s APR score and: a) athletic academic support

budgets, b) tutoring budgets, c) year-to-year changes in athletic academic support budgets, and d)

year-to-year changes in tutoring budgets. In a way to be most efficient, these correlations were

then used to run ANOVAs between spending budgets and those teams that scored below a 925

APR.

Results

Averages and growth rates were computed for athletic academic support services (SASS)

budgets, tutoring budgets, and full-time employees (FTE) for the entire dataset and for each


competitive level: FBS Haves, FBS Have-Nots, FCS, and NF. Between 2005-6 and 2010-11,

SASS budgets rose, on average, by 19.7%: full time employees (FTE) rose 15.5%, and, tutoring

budgets rose, on average, by 37.0%. Grouping by level of competitiveness demonstrated

significant differences in the amount and growth of SASS spending (Table 2; Figures 1 and 2).

Notably, the average of FBS Have-Nots SASS budgets fell 4.0%, FTEs fell 16.5%, while

tutoring budgets increased 14.5%. The average SASS budget of FBS Haves grew to nearly $1

million more on average than that for the FBS Have-Nots. FBS Haves had nearly 8.5 more FTE

than the Have-Nots, nine more FTE than FCS schools, and nearly eight more FTE than NF

school. By 2011, the average NF athletic academic support budgets grew to be nearly $5,300

greater than that of the average FBS Have-Nots budgets. Tutoring budgets increased at every

level of competitiveness as high as an 87.4% increase for NF schools.

Table 2: Average athletic academic support (SASS) budgets, tutoring budgets, and number of

academic support full-time employees (FTE) in NCAA Division I

Academic support budget Tutoring budget Full-time employees

2005-06 2010-11 % change

2005-06 2010-11 % change

2005-06

2010-11

% change

All teams $310,986 $372,318 19.7% $57,706 $79,058 37.0% 4.32 5.0 15.5% FBS Haves $768,657 $1,136,261 47.8% $145,076 $248,717 71.4% 8.71 11.64 33.6% FBS Have-Nots

$166,782 $160,068 -4.0% $35,111 $40,215 14.5% 3.56 2.98 -16.5%

FCS $67,703 $103,216 52.5% $17,540 $27,547 57.1% 1.48 2.40 61.4% NF $127,622 $165,331 29.5% $6,307 $11,821 87.4% 2.49 3.73 50.0%


Figure 1: Average Athletic Support Services (SASS) expenses for NCAA Division 1 athletics,

2006-2011

Figure 2: Average Athletic Support Services (SASS) tutoring expenses for NCAA Division 1

athletics, 2006-2011


Pearson correlations across the dataset revealed week to moderate-to-weak relationships

between APR scores for many sport teams and their institutions’ athletic academic support

budgets and tutoring budgets (Table 3). For total SASS operating budgets, a moderate

significant correlation was found of .262 for football, .251 for baseball, .234 for wrestling, and

weak correlations were found of .162 for men’s basketball, and .126 for women’s basketball.

Weak significant correlations of tutoring budgets were found of .243 for football, .235 for

wrestling, as were weak correlations of .173 for men’s basketball and .138 for women’s

basketball. In general, women’s teams were found to have weaker significant correlations

between operating and tutoring budgets and APR scores than the men’s teams. The correlations

across the database provided some direction into further investigation of the relationship between

overall athletic academic support budgets, and tutoring budgets, and specific athletic teams’

Academic Progress Rates (APR).

Subsequently, visual representation through fever graphs provided another way to

consider the relationship between athletic academic support budgets and APR over time. Fever

graphs of those teams with the greatest aforementioned SASS budget-APR correlations were

produced, with individual sport APRs on one y-axis, SASS budgets on another y-axis, and

academic year on the x-axis (Figures 3 through 7).


Table 3: Correlations of team APR scores with athletic academic support budgets and tutoring

budgets, 2006-2011

Sport Team N N below APR 925

Total operating budget across N

Tutoring budget across N

M_Baseball 552 130 .251** .235** M_Basketball 769 260 .162** .173** M_XC 666 86 .156** .136** M_Football 609 143 .262** .243** M_Golf 665 94 .180** .113* M_Soccer 350 67 .136* .099 M_Swimming 277 28 .089 .101 M_Tennis 502 77 .161** .126* M_Indoor_Track 480 85 .123** .028 M_Outdoor_Track 613 91 .093* .019 M_Wrestling 152 49 .234** .117 W_Basketball 769 110 .126** .138** W_XC 769 60 .139** .109* W_Golf 605 41 .092* .029 W_Softball 622 36 .155** .117* W_Soccer 705 27 .124** .057 W_Swimming 453 9 .107* .146* W_Tennis 700 63 .083* .069 W_Indoor_Track 672 54 .182** .089 W_Outdoor_Track 687 49 .177** .090 W_Volleyball 682 56 .075 .020 Note. ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Correlations were considered for only those teams with a total of at least 100 annual APR scores across the six years of the database. “N” represents the total number of team annual APR scores for each team across the six years of the database. “N below APR 925” represents the total number of team APR scores below a 925 score for each team in each year across the six years of the database.


Figure 3. Academic athletic support services (SASS) budgets and Men’s Baseball APR


Figure 4. Academic athletic support services (SASS) budgets and Football APR


Figure 5. Academic athletic support services (SASS) budgets and Men’s Basketball APR


Figure 6. Academic athletic support services (SASS) budgets and Men’s Wrestling APR


Figure 7. Academic athletic support services (SASS) budgets and Women’s Basketball APR

Analysis of variance (ANOVA) was used to consider the significance of level of

competitiveness on SASS budgets, tutoring budgets, and FTE (Table 4). Significant findings

demonstrated that division and athletic conference affiliation significantly predicted size of

athletic academic support services budget, tutoring budget, year-to-year differences in budget,

and the number of FTE athletic academic support staff.


Table 4. Significant ANOVA results of variables presented by categorical grouping

Category and variable SS df F Division and SASS budget 1.411E+14 2, 619 171.857*** Division and Year-‐to-‐Year difference in SASS Budget

5.743E+12 2, 518 12.769***

Division and Tutoring Budget 1.330E+13 2, 426 49.577*** Division and Year-‐to-‐Year difference in Tutoring Budget

7.158E+11 2, 354 9.070***

Division and FTE 15105.194 2, 629 174.052*** Athletic Conference and SASS budget 1.411E+14 27, 594 77.934*** Athletic Conference and Year-‐to-‐Year difference in SASS Budget

5.743E+12 27, 493 2.666***

Athletic Conference and Tutoring Budget 1.330E+13 25, 403 9.082*** Athletic Conference and FTE 15105.19 27, 604 40.477***

***p<.001, ** p<.01, * p<.05

Athletic teams which had a score of less than 925 APR were identified in the database

(Table 5) and flagged as a separate category in an effort to understand the relationship between

athletic academic support budgets (SASS) and academic performance. Analysis of variance

(ANOVA) was then used to predict whether or not changes in athletic academic support services

budgets, tutoring budgets, or FTE predicted the academic performance score of sub-925 APR

teams by sport (Table 6).


Table 5. Total number of athletic teams, and athletic teams with sub-925 APR scores listed by level of competition, 2006-2011 All Division I

Haves Division I Have-Nots

Division I FCS

Division I NF

Sport N N below

APR 925

N N below

APR 925

N N below

APR 925

N N below

APR 925

N N below

APR 925

M_Baseball 552 130 214 26 140 23 202 51 126 30 M_Basketball 769 260 231 60 175 65 216 82 147 53 M_XC 666 86 217 19 119 15 190 28 140 24 M_Football 609 143 231 33 175 54 203 56 0 0 M_Golf 665 94 224 17 154 31 188 33 99 13 M_Soccer 350 67 91 9 70 17 83 13 106 28 M_Swimming 277 28 140 10 42 7 46 5 49 6 M_Tennis 502 77 168 16 98 11 123 30 113 18 M_Indoor_Track 480 85 214 20 98 21 159 25 102 19 M_Outdoor_Track 613 91 217 21 98 20 181 26 117 24 M_Wrestling 152 49 105 17 49 10 28 11 20 11 W_Basketball 769 110 231 23 175 19 216 43 147 25 W_XC 769 60 231 7 175 19 216 18 147 16 W_Golf 605 41 203 11 144 7 172 14 86 9 W_Softball 622 36 203 4 156 9 201 9 119 14 W_Soccer 705 27 224 2 164 6 200 8 117 11 W_Swimming 453 9 196 0 109 2 89 4 59 3 W_Tennis 700 63 224 12 161 15 181 25 134 11 W_Indoor_Track 672 54 228 1 168 21 208 15 130 17 W_Outdoor_Track 687 49 231 1 168 18 216 15 131 15 W_Volleyball 682 56 224 8 175 16 208 20 140 12 TOTALS 12299 1615 4247 317 2813 406 3526 531 2229 359 Note. Totals were provided only those teams with a total of at least 100 annual APR scores across the six years of the entire database. “N” represents the total number of team annual APR scores for each team across the six years of the database. “N below APR 925” represents the total number of team APR scores below a 925 score for each team in each year across the six years of the database.

SASS budgets were found to be a significant predictor of sub-925 APR scores for:

Baseball, Men’s Basketball, Men’s Cross Country, Football, Men’s Soccer, Men’s Tennis,

Men’s Indoor Track, Men’s Outdoor Track, Wrestling, Women’s Basketball, Women’s Cross

Country, Women’s Golf, Women’s Softball, Women’s Soccer, Women’s Swimming, Women’s

Tennis, Women’s Indoor Track, and Women’s Outdoor Track. Year-to-year difference in SASS

budgets was found to be a significant predictor of sub-925 APR scores for: Baseball, Football,


Men’s Golf, Men’s Indoor Track, Women’s Basketball, Women’s Softball, Women’s Indoor

Track, and Women’s Outdoor Track. Tutoring budgets were found to be a significant predictor

of sub-925 APR scores for: Baseball, Men’s Basketball, Men’s Cross Country, Football, Men’s

Golf, Women’s Basketball, Women’s Indoor Track, and Women’s Outdoor Track. Year-to-year

difference in tutoring budgets was found to be a significant predictor of sub-925 APR scores for:

Baseball, Men’s Golf, and Women’s Softball. Full-time employees (FTE) were found to be a

significant predictor of sub-925 APR scores for: Baseball, Men’s Basketball, Men’s Cross

Country, Men’s Football, Men’s Soccer, Men’s Tennis, Men’s Indoor Track, Men’s Outdoor

Track, Men’s Wrestling, Women’s Basketball, Women’s Cross Country, Women’s Golf,

Women’s Softball, Women’s Soccer, Women’s Swimming, Women’s Tennis, Women’s Indoor

Track, and Women’s Outdoor Track.

Table 6. Significant ANOVA results of SASS budgets, Tutoring budgets, and FTE compared to

sport teams that scored below an APR 925 in any given year

Variable and category SS df F BASEBALL SASS Budget and Baseball sub-‐925 APR scores 1.372E+14 2, 560 7.783*** Year-‐to-‐Year difference in SASS Budget and Baseball sub-‐925 APR scores

5.623E+12 2, 469 3.093**

Tutoring Budget and Baseball sub 925 APR scores 1.300E+13 2, 384 6.437** FTE Fulltime and Baseball sub 925 APR scores 12004.396 2, 570 9.348*** Year-‐to-‐Year difference in Tutoring Budget and Baseball sub-‐925 APR scores

6.995E+11 2, 319 16.485***

MEN’S BASKETBALL SASS Budget and Men’s Basketball sub-‐925 APR scores

1.411E+14 2, 619 3.344**

Tutoring Budget and Men’s Basketball sub 925 APR scores

1.330E+13 2, 426 2.761*

FTE Fulltime and Men’s Basketball sub 925 APR scores

15105.19 2, 629 6.764**

MEN’S CROSS COUNTRY SASS Budget and Men’s Cross Country sub-‐925 APR scores

1.349E+14 2, 542 6.461**

Tutoring Budget and Men’s Cross Country sub 925 APR scores

1.305E+13 2, 382 2.774*

FTE Fulltime and Men’s Cross Country sub 925 APR scores

14369.35 2, 546 11.239***


Variable and category SS df F FOOTBALL SASS Budget and Football sub-‐925 APR score 1.274E+14 1, 486 19.872*** Year-‐to-‐Year difference in SASS Budget and Football sub-‐925 APR score

5.557E+12 1, 407 2.855*

Tutoring budget and Football sub-‐925 APR score 1.234E+13 1, 350 8.351** FTE full time and Football sub-‐925 APR score 13200.82 1, 485 13.673***

MEN’S GOLF SASS Budget and Men’s Golf sub-‐925 APR score 1.310E+14 1, 530 12.772*** Tutoring budget and Men’s Golf sub-‐925 APR score

1.245e+13 1, 374 4.438*

MEN’S SOCCER SASS Budget and Men’s Soccer sub-‐925 APR score 6.621E+13 2, 277 2.687* FTE full time and Men’s Soccer sub-‐925 APR score

5077.1 2, 283 6.605**

MEN’S TENNIS SASS Budget and Men’s Tennis sub-‐925 APR score 1.213E+14 2, 405 4.627** FTE full time and Men’s Tennis sub-‐925 APR score

10243.42 2, 415 9.022***

MEN’S INDOOR TRACK SASS Budget and Men’s Indoor Track sub-‐925 APR score

1.254E+14 2, 470 5.164**

Year-‐to-‐Year difference in SASS Budget and Men’s Indoor Track sub-‐925 APR score

4.85E+12 2, 391 2.59*

FTE full time and Men’s Indoor Track sub-‐925 APR score

12953.63 2, 463 5.862**

MEN’S OUTDOOR TRACK SASS Budget and Men’s Outdoor Track sub-‐925 APR score

1.303E+14 1, 496 8.670**

FTE full time and Men’s Outdoor Track sub-‐925 APR score

13396.81 1, 490 5.329*

MEN’S WRESTLING SASS Budget and Men’s Wrestling sub-‐925 APR score

4.091E+13 2, 153 3.588*

FTE full time and Men’s Wrestling sub-‐925 APR score

3508.98 2, 153 4.211*

WOMEN’S BASKETBALL SASS Budget and Women’s Basketball sub-‐925 APR score

1.408E+14 1, 617 9.953**

Year-‐to-‐Year difference in SASS Budget and Women’s Basketball sub-‐925 APR score

5.742E+12 1, 518 3.676*

Tutoring budget and Women’s Basketball sub-‐925 APR score

1.328E+13 1, 425 3.900*

FTE full time and Women’s Basketball sub-‐925 APR score

14927.96 1, 622 8.134**

WOMEN’S CROSS COUNTRY SASS Budget and Women’s Cross Country sub-‐925 APR score

1.411E+14 2, 619 4.245**

FTE full time and Women’s Cross Country sub-‐925 APR score

2, 629 2, 629 7.131***

WOMEN’S GOLF SASS Budget and Women’s Golf sub-‐925 APR score 1.201E+14 2, 510 10.630*** FTE full time and Women’s Golf sub-‐925 APR score

2, 523 2, 523 9.877***

WOMEN’S SOFTBALL SASS Budget and Women’s Softball sub-‐925 APR score

1.287E+14 2, 553 4.635*

Year-‐to-‐Year difference in SASS Budget and Women’s Softball sub-‐925 APR score

5.506E+12 2, 463 6.175**

Year-‐to-‐Year difference in Tutoring budget and Women’s Softball sub-‐925 APR score

11610.23 2, 563 11.334***

FTE full time and Women’s Softball sub-‐925 APR score

6.901E+11 2, 314 7.474***


Variable and category SS df F WOMEN’S SOCCER SASS Budget and Women’s Soccer sub-‐925 APR score

1.367E+14 2, 571 4.811**

FTE full time and Women’s Soccer sub-‐925 APR score

14565.53 2, 581 10.832***

WOMEN’S SWIMMING SASS Budget and Women’s Swimming sub-‐925 APR score

1.108E+14 2, 374 5.002**

FTE full time and Women’s Swimming sub-‐925 APR score

8915.58 2, 374 12.055***

WOMEN’S TENNIS SASS Budget and Women’s Tennis sub-‐925 APR score

1.351E+14 2, 563 4.045*

FTE full time and Women’s Tennis sub-‐925 APR score

14558.652 2, 572 9.405***

WOMEN’S INDOOR TRACK SASS Budget and Women’s Indoor Track sub-‐925 APR score

1.390E+14 2, 593 7.661***

Year-‐to-‐Year difference in SASS Budget and Women’s Indoor Track sub-‐925 APR score

5.477E+12 2, 496 4.254*

Tutoring budget and Women’s Indoor Track sub-‐925 APR score

1.323E+13 2, 418 3.508*

FTE full time and Women’s Indoor Track sub-‐925 APR score

14623.02 2, 599 7.420***

WOMEN’S OUTDOOR TRACK

SASS Budget and Women’s Outdoor Track sub-‐925 APR score

2.252E+11 2, 605 8.798***

Tutoring budget and Women’s Outdoor Track sub-‐925 APR score

1.328E+13 2, 424 3.829*

FTE full time and Women’s Outdoor Track sub-‐925 APR score

14831.47 2, 611 8.319***

***p<.001, ** p<.01, * p<.05

Conclusions

The intent of this study was to learn the relationship between academic performance of

athletic teams as measured by the NCAA’s Academic Progress Rate (APR) and athletic

academic support services (SASS), including total operating budgets, tutoring budgets, and full-

time employees (FTE). The amount of money spent on SASS was significantly different based

on level of competitiveness: it was in a different stratosphere for the minority of schools with the

most competitive football teams (Haves) compared to the majority of schools who are trying to

be competitive (Have-Nots) and the schools in other competitive divisions. After grouping the

data by differing levels of competitiveness, it was not a surprise to find the level of an athletics


program impacted the amount of money spent on SASS, as well as having a significant effect on

teams which were below the academic benchmark of an APR 925.

In other words, institutions whose athletic programs have greater access to massive

amount of revenues via their football affiliation (the Haves) spent far more money on their

athletic academic support services than all other levels of competition. Further, the growth in

SASS spending by the Haves is an “arms race” at a level that the others cannot keep pace. SASS

tutoring budgets and FTE further demonstrated how the money in SASS budgets is being spent,

and that investing more money in each at all levels of competition can predict improvement in

APR scores for many sports, particularly football and men’s basketball.

Except for the Have-Nots in I-FBS, there was an increase across in SASS budgets,

tutoring budgets, and FTE between 2006 and 2011. This is not a surprise, as the NCAA’s APR

policy has become well engrained in intercollegiate athletics and in higher education. This study

shows that penalties associated with poor academic performance as measured by the APR has

likely impacted the decision by schools to invest more money in SASS. The fact there was a

decrease in spending by the Have-Nots is concerning, particularly since more Have-Not teams

scored below an APR 925 than for the Haves, including the high-profile revenue sports of

football and men’s basketball. However, the data indicate greater spending in earlier years, and

less spending in more recent years – this correlates with more APR reprimands in earlier years

than in later years. Fewer penalties in more recent years may have led to more institutions

believing they had already made the investment necessary to improve APR scores, and thus feel

less of a need to spend more on SASS. Or, the findings for Have-Nots may differ if they

included institutions missing from the study because of their lack of data or lack of

responsiveness.


It is important to note that there may be many factors other than investment in SASS that

lead to lower APR scores in a given year, or over a period of time. Maybe institutions responded

to lower APR scores by changing admissions standards such as raising them to admit better

academically prepared students. Some schools may have altered their curriculum or directed

athletes to less challenging courses in order to improve APR scores. Maybe newly hired coaches

with academic incentives in their hiring contracts led to coaches recruiting more academically

prepared athletes. A myriad of other decisions may help athletes improve progress toward

graduation (and meet APR minimums). These areas are worth further investigation and to

compare with SASS budgets. However, the data in this study are particularly alarming and

further demonstrate another way to view the differences in the level of competitiveness between

the Haves and the rest of intercollegiate athletics.

Over the period of this study, the lower levels of competition in Division I-FCS and

Division I-NF had more teams that were below the APR 925 academic benchmark standard that

could lead to NCAA reprimand. While this may also be alarming, it seems these institutions get

the message: on average, the growth rate of spending on SASS and tutoring budgets seems to

indicate their understanding of the need to invest in more resources to help comply with the APR

policy.

Spending more money on tutoring expenses is an increasing trend in athletic academic

support, and it is worthy to note it is particularly useful in improving APR scores in the most

high profile sports of baseball, men’s basketball, football, and women’s basketball. These sports

are those that receive the greatest reprimands or penalties for not meeting the minimum APR

benchmark of 925; so, spending on more tutoring can help move these teams back into

compliance.


In other words, this study demonstrates that institutions at all levels of competition which

spend more money on athletic academic support services, particularly on tutoring and FTE, will

realize an improvement in APR scores on all teams that fail to meet minimum benchmarks,

especially the revenue sports of football and men’s basketball.

Implications

Results from this study should help institutional leaders when considering the type of

improvements they want to make in improving graduation rates on their campus. Answers to the

research questions shed some light on the costs of investing in improving academic performance

for particular athletes. Applying the concept of revenue theory of cost to investment in academic

support services for athletic teams with poor academic performance provides a framework to

understand the rationale behind such investments. Understanding the cost for increasing APR

scores by team is useful for an institution’s budgeting strategy. In addition, the NCAA’s recent

policy that prohibits postseason competition (and may lead to a potential loss of revenue) for

poor APR scores should place greater rationale for institutions to invest in resources to improve

the graduation rates of football and men’s basketball players.

Higher education can learn about the costs of APR policy and the costs by which

academic performance measures directed toward certain sectors of students are feasible or

successful. Thus, athletics can help decision-makers learn about the extent that investing in

specific academic improvement programs for particular student needs will lead to successful

results if and only if there is a binding policy in place with penalties for lack of performance.

Much of this study is understanding a return on investment – decision-makers will better

comprehend that putting resources into academic support is a useful the solution for improving


athletic teams’ Academic Progress Rates. The investment need not be significant to make a

difference. Thus, leaders should consider a variety of answers to improve academic

performance: a change in curricular standards, offering new types of courses for athletes, or

finding new ways to ensure that newly admitted athletes are academically prepared for a rigorous

academic work load.

While this study does not measure actual learning, it does study how policies can align

the high profile needs of one area of campus, namely athletics, to the mission of helping students

graduate. Revenue cost theory set the framework by which we can understand the direction of

these policies and that a greater investment to staffing of academic support services for athletics

is worthwhile.

Limitations and Future Studies

This study was limited to data collected from public institutions and relating to academic

support services (SASS) budgets, tutoring budgets, and FTE for academic support services. A

look at academic support services for private institutions may reveal different, or similar, results

to the findings from public institutions.

This study includes data that can be extended to not only compare APR scores to SASS

budgets in any given year, but also compare APR scores in subsequent years and prior years to

any one year’s budget. Such an investigation may shed light onto whether changes in SASS

budget from one year to the next impact APR in several subsequent years; or, can help determine

how long it may take for investment in SASS to improve a team’s APR. Similarly, looking at

the impact of one year (or multi-year) APR scores on SASS budgets over a several year period

can provide a glimpse as to the extent that APR impacts the decision to invest in SASS.


Furthermore, there are many institutional factors that likely impact an APR score, with

the amount of financial investment in SASS being just one factor. Other factors worthy of

investigation are institutional admission standards for athletes, decisions of academic major by

sport, time demands on athletes, academic performance-based incentives in coaching contracts,

and other institutional cultural characteristics that may impact academic performance.


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