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Journal for the Study of Sports and Athletes in Education
ISSN: 1935-7397 (Print) 1935-7400 (Online) Journal homepage: https://www.tandfonline.com/loi/yssa20
Leveling the Playing Field: Faculty Influence on theAcademic Success of Low-Income, First-GenerationStudent-Athletes
Justin C. Ortagus & Dan Merson
To cite this article: Justin C. Ortagus & Dan Merson (2015) Leveling the Playing Field:Faculty Influence on the Academic Success of Low-Income, First-Generation Student-Athletes, Journal for the Study of Sports and Athletes in Education, 9:1, 29-49, DOI:10.1179/1935739715Z.00000000034
To link to this article: https://doi.org/10.1179/1935739715Z.00000000034
Published online: 28 Feb 2015.
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Leveling the Playing Field: Faculty
Influence on the Academic Success of
Low-Income, First-Generation
Student-Athletes
Justin C. Ortagus1 and Dan Merson
2
1 Pennslyvania State University, PA, USA2 Leonhard Center for Enhancement of Engineering Education,Pennsylvania State University, PA, USA
Minimal scholarly research focuses on low-income, first-generation (LIFG)
intercollegiate athletes. Student-athletes are a unique population on campus,
and LIFG students face additional challenges related to academic achieve-
ment due to increased financial and family obligations. In this study, we
provide a profile of LIFG student-athletes and examine the extent to which
faculty interactions, concerns, and perceptions affect LIFG student-athletes’
academic success in higher education. After controlling for student-athlete
profile characteristics, faculty-student interaction was found to be the most
effective predictor of academic success for LIFG and non-LIFG student-
athletes. Our findings affirm previous research that suggests the importance
of students’ college experiences outweighs the influence of their background
characteristics when predicting academic achievement.
keywords low income, first generation, student-athletes, academic success,
faculty influence
INTRODUCTION
Public debate continually calls into question the linkage between student and
athlete as intercollegiate athletics has evolved from extracurricular activity to high-
stakes competition. Student-athletes throughout higher education are afforded a
special opportunity to compete at a high level and achieve athletic success.
Student-athletes face unique time constraints that affect their ability to succeed in
the classroom with their non-athlete peers (Wolverton, 2008). Low-income, first-
generation (LIFG) student-athletes must overcome additional burdens in order to
journal for the study of sports and athletes in education,
Vol. 9 No. 1, April, 2015, 29–49
� W. S. Maney & Son Ltd 2015 DOI 10.1179/1935739715Z.00000000034
succeed and persist as a college student. In our secondary data analysis of dataobtained by the Student-Athlete Climate Study (SACS) research team at The
Pennsylvania State University (Rankin et al., 2011), we examine the extent to
which faculty interactions, concerns, and perceptions affect LIFG student-athletes’academic success. The purpose of our study is to examine the influence of faculty
members on the academic success of LIFG student-athletes. By doing so, we hope
to provide insight into how to provide LIFG student-athletes with the sameopportunity to be successful as other students on a college campus.
Central Research Questions
N After controlling for student-athletes’ individual profile characteristics, to
what extent do faculty members affect LIFG student-athletes’ …
N grade point average?
N intent to persist?
N academic and intellectual development?
LITERATURE REVIEW
The likelihood of academic success for student-athletes is stratified by their race,
family income, and parents’ educational attainment (Corrigan, 2003). Low-income, first-generation (LIFG) college students have been found to be among the
least likely to persist through graduation (Thayer, 2000). In addition, retention
rates for college students are lowest among low-income racial and ethnic minoritysubgroups (Kahlenberg, 2004). Higher education institutions have attempted to
address empirical findings of inequity in retention rates and other academic success
measures in order to ensure all students are afforded an equal opportunity tosucceed, but inequities related to race, income, and parental educational
attainment continue to permeate higher education.
For LIFG student-athletes, finances are integral to their academic success.
Although Timpane and Hauptman (2004) suggest that low-income students areroughly 50% more likely to enroll in college than they were 30 years ago, their
college experiences are not the same as their peers from more affluent
backgrounds. Walpole (2008) found that low-income, first-generation studentsreported less interaction with faculty members, spent less time studying, reported
less involvement in campus activities, and worked more than their non-LIFG peers.
Donovan (1984) studied 403 low-income African American students from 69colleges and universities, finding that nearly 40% no longer attended their
institution after only three years. In addition, Donovan reported that college
experiences are more important than background characteristics when predictingthe success of low-income Students of Color.
The SACS research team classified a low-income student-athlete as one whose
family earns an annual salary of roughly $30,000 or less (Rankin et al., 2011).
First-generation students are defined as students whose parents have not earned abachelor’s degree (Thayer, 2000). While low-income and first-generation student-
athletes have distinct experiences, both sub-populations of student-athletes are
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30 JUSTIN C. ORTAGUS and DAN MERSON
discussed together in this study due to the significant amount of overlap in the
literature. Previous research has found that roughly two-thirds of low-income
students are also first-generation students and many of the challenges and
collegiate experiences between low-income students and first-generation students
are shared (Corrigan, 2003).
Although the majority of LIFG student-athletes are awarded athletic scholar-
ships, many among the population rely on need-based financial aid (Fitzgerald,
2003). Financial aid is often a complicated process for first-generation students
who may not be aware of the type of aid available to them. First-generation
students and their families struggle to understand the financial aid process and lack
the requisite knowledge of college finances and budget management (Richardson
& Skinner, 1992; Flint, 1992). The United States General Accounting Office
(1995) reported that grants have the largest and most positive impact on the
persistence of low-income students, whereas loan aid resulted in an increased
likelihood of students leaving college before graduating. Unlike their non-athlete
peers, the majority of student-athletes spend nearly 40 hours per week participat-
ing in their respective sports (Wolverton, 2008), leaving little or no time available
for a part-time job.
Many low-income, first-generation student-athletes cannot obtain employment
to earn wages to cover the differences in expenses not covered by an athletic
scholarship or grant. Family obligations of LIFG students, such as providing
financial assistance to parents, have a negative impact on their academic
achievement (Rendon, 2002). Several studies have found first-generation students
to be less likely to graduate from college when compared to college students whose
parents earned a bachelor’s degree (Thayer, 2000; Terenzini, Springer, Yeager,
Pascarella, & Nora, 1996; Warburton, Bugarin, & Nunez, 2001). In order to
account for low graduation rates, various studies describe first-generation students
as inadequately prepared before entering college (Choy, 2001; Hahs-Vaughn,
2004). While succeeding in the classroom as an LIFG student has been found to be
an arduous task (Phinney & Hass, 2003), any potential difficulties faced as an
LIFG student would be amplified by the decision to participate in intercollegiate
sport because college experiences have been found to outweigh student-athlete
profile characteristics when predicting academic success (Donovan, 1984).
Carodine, Almond, and Gratto (2001) referenced student-athletes’ difficulty
maintaining academic success in the wake of public scrutiny and arduous time
demands not faced by their non-athlete peers. The dismissive notion of student-
athletes as ‘‘dumb jocks’’ has been reported as a detriment to the academic
performance of student-athletes (Harrison, 2007). In addition, national data
pertaining to student-athletes’ experiences on college campuses have been missing
from the literature (Gayles, 2009). This study serves to fill a gap through an
examination of faculty members’ influence on student-athletes’ academic success
based on a litany of profile characteristics, includinggender, race, income, sport,
and NCAA Division.
College or university students experience campus climates differently based
upon social and demographic group membership (Rankin & Reason, 2008;
Chang, 2002). We propose this to be the case for LIFG student-athletes as well.
LEVELING THE PLAYING FIELD 31
Educational scholarship has yet to examine the extent to which various student-
athlete profile characteristics relate to faculty influence and LIFG student-athletes’
attainment of academic success. Although a variety of support services are made
available to student-athletes (Jolly, 2008), faculty members remain the embodi-
ment of the classroom experience. Student-athletes’ social identity groups (e.g.,
gender and race) and perceptions of their educational environment represent
integral factors in their academic outcomes.
Miller, Anderson, Cannon, Perez, and Moore (1998) observed that White
students described their campus racial climate as positive while Students of Color
rated the same campus racial climate as negative. Although White students
recorded high ratings for instructors’ efforts to include multiple viewpoints in the
curriculum and institutional policies related to recruitment and retention of all
races, Students of Color labeled interracial interactions on campus as unfriendly
and reported being the targets of racism. Rankin et al. (2011) aptly noted that
student-athletes’ ‘‘experiences and perceptions are mutually reinforcing, with
students’ perceptions of their campus climate influencing their experiences and
their experiences influencing their perceptions’’ (p. 11).
When examining the academic success of student-athletes at colleges and
universities, various hurdles surface along the journey to graduation. Extreme time
demands and the blurred line between academics and athletics in higher education
contribute to the difficulties facing student-athletes in their quest for academic
success. Various studies across numerous institutions have found negative
relationships between athletic participation and performance in the classroom
(Richards & Aries, 1999; Gayles, 2004; Miller & Kerr, 2002). Given the limited
amount of time available for academic work (Wolverton, 2008), student-athletes’
engagement, specifically their interaction with faculty members, becomes
increasingly important.
Since LIFG students lack sufficient support networks (i.e., family, mentors,
peers) that understand the challenges facing college students (Phinney & Hass,
2003), their interactions with faculty members are critical to their academic
success. Faculty involvement on a frequent basis could serve to compensate for
potential shortcomings in student-athletes’ academic preparation prior to arriving
on campus and instill a culture of student involvement and academic success
(Allen, 1999). Pascarella and Terenzini (2005) found that increases in students’
time and energy devoted to the learning and engagement process yielded improved
potential for academic achievement and persistence at a college or university. The
academic success of student-athletes also hinges on the level of commitment and
support made available by faculty members outside of the athletic department
(Harrison, Harrison, & Moore, 2002). In addition, the quality of formal and
informal faculty interactions with student-athletes has been deemed as critical to
the overall college experience and students’ academic achievement (Comeaux,
2005; Pascarella & Chapman, 1983).
Although the literature on low-income student-athletes’ college experiences is
limited, sociocultural factors that affect students’ retention have been examined.
Tinto (1993) theorized that student attributes influence individual goals and
commitments, which interact with students’ informal and formal institutional
32 JUSTIN C. ORTAGUS and DAN MERSON
experiences. Tinto advanced two dimensions of commitment: institutional
commitment and goal commitment. Institutional commitment references the
degree to which a given student is motivated to persist and graduate. A student’s
goal commitment relates to his or her personal commitment to earn a college
degree. Since institutional and goal commitments are influenced by external
influences and the student’s level of interaction with faculty members and peers,
the level of involvement in the academic and social systems of a higher education
institution by LIFG student-athletes—particularly those of color—could account
for their decision to persist or leave the institution. As students’ institutional and
goal commitments increase, the likelihood of persistence at the selected institution
increases.
Given that Tinto’s persistence model is predicated upon a general framework of
assimilation and acculturation, several researchers have questioned the validity of
Tinto’s model to capture the experiences of diverse students (Braxton, 2000;
Kraemer, 1997; Tierney, 1992). Braxton, Sullivan, and Johnson (1997) suggested
that future researchers revise or replace Tinto’s theoretical perspectives when
studying the persistence (or intent to persist) of minority group members. With this
in mind, Rendon’s learner validation model (1994) would appear to be an
appropriate response to the shortfalls of Tinto’s model when examining
nontraditional postsecondary students.
Rendon’s (1994) validation model shows that learner validation, and not merely
involvement, improved the learning experience for nontraditional students.
Although the majority of White and traditional postsecondary students can
become involved on their own when given interaction opportunities at a given
institution, nontraditional students, such as LIFG student-athletes, expect active
intervention and outreach in order to become involved on campus and in the
classroom. Rendon found that ‘‘nontraditional students do not perceive involve-
ment as them taking the initiative. They perceive it when someone takes an active
role in assisting them’’ (p. 44).
Whether nontraditional students are inside or outside of the classroom, Rendon
(1994) demonstrates that these students experienced impactful gains in learning
and persistence when some individual validated the student in some capacity.
Through interpersonal and academic validation, validating agents showed an
active interest in students. As nontraditional students were encouraged and
affirmed as being capable of strong academic work, the critical role of the
institution shifts from offering involvement opportunities to taking an active role
in cultivating the validation of students. In an effort to encourage active learning
and interpersonal growth, faculty and administrators should take the initiative to
ensure nontraditional students feel validated, empowered, and supported in their
academic endeavors and social adjustment (Braxton, 2000).
Previous research suggests that the frequency and quality of formal and informal
faculty-student interaction affect students’ academic success—in particular a
student’s likelihood to persist (Pascarella & Terenzini, 1977, 1980). While
research has linked interactions with faculty members as integral to academic
success (Comeaux, 2005; Pascarella & Chapman, 1983), student-athletes’
perceptions and experiences related to faculty influence diverge according to their
LEVELING THE PLAYING FIELD 33
gender and race. Comeaux (2005) submits that male student-athletes’ interaction
with faculty members significantly influences their academic success in college
compared to female student-athletes. Simons, Bosworth, Fujita, and Jenson (2007)
indicated that Student-Athletes of Color perceived a significantly higher degree of
negative perceptions from faculty members than student-athletes who did not
identify as a minority racial or ethnic group. Regardless of demographic
characteristics, increases in engagement and interactions with faculty members
have been shown to benefit student-athletes in ways similar to the general student
population (Pascarella & Terenzini, 2005).
Discussion of student-athlete profile characteristics would be incomplete
without including the type of sport played (featured or non-featured) and the
NCAA Division (Division I, Division II, or Division III). Gayles and Hu (2009)
found the effect of student engagement on cognitive outcomes varied according to
the type of sport in which student-athletes participate. In addition, Wolniak,
Pierson, and Pascarella (2001) offered evidence that athletes should not be
considered as a homogenous population because experiences in college vary from
institution to institution. For instance, Sellers (1992) noted that Student-Athletes
of Color who participate in revenue-producing sports often enter college
underprepared, causing them to be less likely to achieve academic success when
compared to their peers (Ervin, Saunders, Gillis, Hogrebe, 1985). Regardless of
division level, Umbach, Palmer, Kuh, and Hannah (2006) found that male student-
athletes reported earning lower grades than their non-athlete peers while female
student-athletes had similar grades to non-athlete female students.
Institutional characteristics differ significantly throughout higher education. In
order to examine student-athletes, the NCAA Division (Division I, Division II, or
Division III) in which they compete should be accounted for as an influential
factor. After a review of the literature, the vast majority of the research on student-
athletes has focused on Division I institutions (Baucom & Lantz, 2001). Student-
athletes in Division III avoid similar media, financial, and professional expecta-
tions of student-athletes in Divisions I and II (Grites & James, 1986; Stansbury,
2004), but student-athletes (and non-athlete students) at Division I, Division II,
and NAIA institutions had significantly higher self-reported grades than students
at Division III institutions (Umbach et al., 2006).
Pascarella, Bohr, Nora, and Terenzini (1995), Pascarella et al. (1999) and
Wolniak et al. (2001) have called for studies related to student athletes’
experiences to account for background characteristics (i.e., race and gender) and
institutional contexts (i.e., NCAA division and whether the student-athlete
participates in a featured sport). In addition, Astin (1993) and Kuh (2001) labeled
student engagement as a function of both a student’s individual effort and the
institutional practices and policies. Several studies have referenced the varied
experiences of high-profile or featured sport student-athletes (e.g., football and
basketball) versus those who compete in non-featured sports (e.g., swimming and
golf) while in college (Terenzini, Pascarella, & Blimling, 1999). While featured-
sports student-athletes have been found to score lower on examinations of their
cognitive and critical thinking abilities when compared to their non-athlete peers,
student-athletes who compete in non-featured sports have displayed a diminished
34 JUSTIN C. ORTAGUS and DAN MERSON
buy-in toward academic experiences that promote diversity and self-learning
(Pascarella & Terenzini, 2005).
From our review of the literature, we did not find outcomes related to faculty
influence on LIFG student-athletes versus the overall student-athlete population.
Our goal in this secondary data analysis is to bridge the gap between previous
literature and future initiatives to ensure all student-athletes, regardless of their
financial or social circumstances, are afforded the opportunity to experience
academic success at a higher education institution. The conceptual framework for
our study is displayed in Figure 1.
METHOD
As indicated in our conceptual framework, we controlled for student-athlete
profile variables (gender, race, featured versus non-featured sports, NCAA
Division) that our extensive review of the literature has shown to influence
academic outcomes. Faculty influence variables (faculty-student interaction,
faculty concern for student development and teaching, faculty perception of
student-athletes) served as predictor variables for student-athletes’ academic
success. Specifically, the outcome of student-athletes’ academic success was
comprehensively measured using three variables—current college GPA, intent to
persist, and Academic and Intellectual Development.
Description of SampleThe Student-Athletes Climate Study (SACS) research team from The Pennsylvania
State University collected the data used throughout this analysis. Their survey
included 68 questions that assessed a range of student-athlete characteristics,
experiences, perceptions, and outcomes (both athletic and academic). All 1,281
member institutions of the NCAA were invited to participate in the SACS study.
figure 1 Study conceptual framework.
LEVELING THE PLAYING FIELD 35
A total of 8,481 surveys were submitted from student-athletes at 164 NCAA
member institutions. After weighting the dataset so it accurately represents these
institutions, the final dataset consists of 8,018 respondents, representing 56,965
student-athletes from every NCAA Division, all 23 NCAA Championship Sports,
and every region of the country (Rankin et al., 2011).
The statistical analysis conducted by the SACS research team did not include
LIFG student-athletes as a variable of interest (Rankin et al., 2011). Our study
specifically investigates faculty influence pertaining to the academic success of
LIFG student-athletes. Further, LIFG student-athletes’ academic outcomes and
demographic characteristics are examined across a variety of sports (featured
versus non-featured) and institutions (Divisions I, II, and III).
InstrumentationIn order to measure student-athletes’ current college GPA, we used a question from
the SACS survey that asked student-athletes to designate their GPA within a range
that corresponded to letter grades. Student-athletes could indicate their grade from
a range of 1 (D or below (,1.50)) to 9 (A (3.84–4.00)).
The SACS project was cross-sectional and anonymous, so no actual persistence
data were collected. A student’s intent to persist has been shown to be strongly
associated with actual persistence (Bean, 1980; Pascarella& Chapman, 1983). The
SACS team measured student-athletes’ intent to persist based on the Persistence at
the Institution subscale of The Undergraduate Persistence Intentions Measure
(UPI; Robinson, 1996, 2003). We incorporated the following three questions from
the SACS Persistence scale:
1. I intend to graduate from my current institution.
2. I am considering transferring to another college or university due to
academic reasons.
3. I am considering transferring to another college or university due to athletic
reasons.
The SACS research team amended the UPI subscale to use a Likert metric and
differentiate between academic and athletic reasons for transferring from their
current institution (Rankin et al., 2011). The persistence scale exhibited good
internal consistency reliability (a5.792, n53). All of the outcome and faculty
influence scales we use in this study were created by the SACS team using
Thurstone’s regression refined method of scale score calculation (Rankin et al.,
2011) because its accuracy maximizes the validity of the scale and produces
standardized variables with means of zero and standard deviations close to one
(DiStefano, Zhu, & Mındrila, 2009; Grice, 2002).
For Academic and Intellectual Development (AID), we utilized the questions
related to student-athletes’ self-reported levels of satisfaction with their intellectual
growth and stimulation—which contained components from Pascarella and
Terenzini’s (1980) Academic and Intellectual Development subscale of their
Institutional Integration Scale. The SACS questionnaire design added one question
to the original AID subscale in order to simulate a previous question from the
athletic success scale. The following six questions were answered on a Likert
36 JUSTIN C. ORTAGUS and DAN MERSON
metric of 1 (Strongly Disagree), 2 (Disagree) 3 (Neither Agree nor Disagree), 4
(Agree), and 5 (Strongly Agree):
1. My academic experience has had a positive influence on my intellectual
growth and interest in ideas.
2. I am satisfied with the extent of my intellectual development since enrolling
in this college/university.
3. I am satisfied with my academic experience at this college/university.
4. I have performed academically as well as I anticipated I would.
5. My interest in ideas and intellectual matters has increased since coming tothis college/university.
6. I am performing up to my full academic potential.
AID questions addressed whether the student-athletes were performing up to their
academic potential, student-athletes’ satisfaction pertaining to their academicexperience and intellectual development, and whether the student-athletes have an
increased interest in intellectual matters since arriving at a given college or
university (Rankin et al., 2011). The AID subscale displayed high reliability(a5.882, n56).
The two questions related to whether a student-athlete was low-income and
first-generation served as the basis for classifying a respondent as an LIFG student-athlete (n5173). Consistent with the SACS research team, we defined an LIFG
student-athlete as one whose family earns less than $30,000 and whose parents
had not previously earned a bachelor’s degree (Rankin et al., 2011; Thayer, 2000).All respondents who indicated their annual family income was greater than
$30,000 or whose parents obtained a bachelor’s degree or higher were classified asnon-LIFG.
The SACS team derived the faculty influence variables from a variety of
questions asking student-athletes about their experiences with faculty membersoutside of athletics. Using factor analysis, three distinct factors were identified by
the SACS team (Rankin et al., 2011): Faculty-Student Interaction (a5.84); FacultyConcern for Student Development and Teaching (a5.89); and Faculty Perceptionof Student-Athletes (a5.77). Faculty-Student Interaction measures the amount and
quality of interactions with faculty members. Faculty Concern for StudentDevelopment and Teaching pertains to the perception that faculty members careabout students. With Faculty Perception of Student-Athletes, the SACS team
wanted to measure the extent to which student-athletes believe that faculty
members perceive them as being different than their non-athlete peers. As a result,a higher score is representative of a negative perception. Following are the faculty
influence questions.
Faculty-Student Interaction
In general since coming to this university…
1. My interactions with faculty have had a positive influence on my
intellectual growth and interest in ideas.
2. My non-classroom interactions with faculty have had a positive influence
on my personal growth, values, and attitudes.
LEVELING THE PLAYING FIELD 37
3. My non-classroom interactions with faculty have had a positive influence
on my career goals and aspirations.
4. I feel that the majority of my relationships with faculty are positive.
5. I have developed close personal relationships with at least one faculty
member not associated with athletics.
How often do you…
1. Meet with a faculty member who is not associated with athletics?
2. Actively participate in class?
Please indicate your level of agreement with the following statement.
1. Most of the faculty I have had contact with are interested in helping studentsgrow in more than just academic areas.
Faculty Concern for Student Development and Teaching
1. Few of the faculty members I have had contact with are generally interestedin students.
2. Few of the faculty members I have had contact with are willing to spend time
outside of class to discuss issues of interest and importance to students.
Faculty Perceptions of Student-Athletes
1. I feel that some of my professors discriminate against me because I am a
student-athlete.
2. I feel that some of my professors favor me because I am a student-athlete.
3. I feel that some of my professors view me as more of an athlete than a
student.
Regarding our control variables, each of the following was included based upon
findings related to demographic and societal influences in the literature: gender,race, featured versus non-featured sport, and institution type (NCAA Division).
The SACS research team created dichotomous variables for gender (man or
woman) and race (White or Student-Athlete of Color). Respondents who identifiedas transgender student-athletes were coded as ‘‘system missing’’ due to a small
sample size (n57). Although student-athletes differentiated the specific race with
which they identified, the SACS research team coded the race variable asdichotomous due to low representation from a variety of races. Representatives
from participating institutions identified sports as ‘‘featured’’ or ‘‘non-featured’’ at
their particular institution. Division II and Division III were included among ourcontrol variables, making Division I the reference group because these institutions
were of particular interest due to enhanced support services for student-athletes
(Rankin et al., 2011).
Data Collection ProceduresThe data utilized for our study were cleaned, weighted, and imputed by the SACSresearch team. They imputed missing data in order to keep the sample size beyond
an acceptable threshold for the planned Structural Equation Modeling analysis.
38 JUSTIN C. ORTAGUS and DAN MERSON
Further, the SACS research team weighted the data on gender, race, and Division
to increase or decrease the representation of student-athletes within the study in
order to simulate the overall population (Rankin et al., 2011).
Data AnalysisWe used descriptive statistical techniques to distinguish the characteristics between
the weighted overall sample (n58,018) and the sub-sample of LIFG student-
athletes (n5173). In our subsequent analyses of these data, we used the original
weighted dataset (n58,018) created by the SACS research team. We conducted t-
tests to compare the means of LIFG student-athletes and non-LIFG student-
athletes for the following continuous variables: Faculty-Student Interaction,
Faculty Concern for Student Development and Teaching, Faculty Perception of
Student-Athletes, GPA, AID, and intent to persist. We used a Chi-Square Test of
Independence to examine whether there were statistically significant differences
between LIFG and non-LIFG student-athletes based on gender, race, featured
versus non-featured sport participation, NCAA Division, and how student-athletes
pay for college.
We utilized a hierarchical (or blocked) linear regression model to examine the
extent to which faculty influence variables affect the academic success outcomes
(GPA, AID, intent to persist) of the overall sample of student-athletes while
controlling for student-athlete profile characteristics. Because we sought to analyze
the relationship between various independent variables and each of the academic
success variables (Hinkle, Wiersma, & Jurs, 2003), we produced three blocked
hierarchical regression models—one for each of our outcome variables (current
college GPA, intent to persist, and Academic and Intellectual Development).
Hierarchical regression allows the researcher to dictate the order of predictors to
be used in the regression model by entering the predictors or groups of predictors
into blocks of variables. The researcher is able to choose the sequence of predictor
variables based on theory in lieu of relying on the computer to choose the order of
the predictors in the model based on statistical criteria. As a result, researchers are
able to account for how much variance in the dependent variable can be attributed
to each block of independent variables (Howitt & Cramer, 2008).
We entered student-athlete profile variables, faculty influence variables, and a
single LIFG variable into a regression model in a series of blocks for each of our
three outcome models (GPA, AID, or intent to persist). In the first block, we
entered the following student-athlete profile variables to statistically control for
measures of demographic and institutional characteristics: gender, race, NCAA
Division, and non-featured sport versus featured sport. In order to examine the
effects of faculty influence and demographic characteristics on distinct measures of
student-athletes’ academic success, we included the following faculty influence
variables in the second block of the regression: Faculty-Student Interaction,
Faculty Concern for Student Development and Teaching, and Faculty Perception
of Student-Athletes.
LIFG student-athletes represent our population of interest in the study. The third
block of each regression model contained only the LIFG variable in order to
LEVELING THE PLAYING FIELD 39
account for any variance attributed to being LIFG and compare the sub-sample of
LIFG student-athletes to non-LIFG student-athletes from the sample.
RESULTS
After providing descriptions of the data, we will report how the academic
outcomes for the overall sample and sub-sample of LIFG student-athletes varied
based on demographic, institutional, and faculty influence variables. All described
findings are based on the weighted sample of 8,018 student-athletes from 164
higher education institutions and have been found to be statistically significant.
Description of SampleWithin the SACS dataset, 173 respondents identified as LIFG student-athletes—
which represented 2.2% of the student-athletes in the sample. We used the
Chi-Square Test of Independence to identify the number and proportion of
student-athletes from the overall sample who represented key characteristics
related to our study. Race, gender, and the type of sport in which a given student-
athlete participated were critical independent variables. Approximately half of the
student-athletes from the overall sample represented Division I institutions
(50.9%). Division II (20.8%) and Division III (28.3%) student-athletes comprised
the remainder of the respondents.
We examined how LIFG student-athletes and their non-LIFG peers paid for
college. There were statistically significant differences in terms of LIFG student-
athletes’ reliance on Pell grants (x2(1, N58,018)5284.516, p,0.001) and need-based
institutional grants (x2(1, N58,018)563.106, p,0.001) to pay for college. More than
four times the proportion of LIFG student-athletes used Pell grants (62.2%) when
compared to the remainder of the sample (14.8%). Regarding need-based
institutional grants, more than twice the proportion of LIFG students (35.5%)
received institutional aid when compared to non-LIFG student-athletes (14%). In
addition, a high percentage (approximately 80%) of both LIFG student-athletes
and non-LIFG student-athletes did not obtain a job to assist with financial
burdens, which affirms literature related to the extreme time constraints of
student-athletes (Wolverton, 2008). The percentage of student-athletes who
received family contributions decreased significantly for LIFG student-athletes
(17.3%) when compared to the rest of the sample (55.5%). The various types of
reported financial aid for LIFG student-athletes and non-LIFG student-athletes are
outlined and contrasted in Table 1.
A Chi-Square Test of Independence provided evidence of a statistically
significant relationship between student-athletes’ race and whether they are
LIFG (x2(1, N58,018)5195.795, p,0.001). More than twice the proportion of
Student-Athletes of Color identified as LIFG (69.4%) than White student-athletes
(30.6%). The racial split among student-athletes not identified as LIFG paralleled
the proportion in the overall sample (76.7% White student-athletes and 23.3%
Student-Athletes of Color).
We conducted t-tests to compare the means of faculty influence and academic
outcome variables for LIFG student-athletes and non-LIFG student-athletes. For
40 JUSTIN C. ORTAGUS and DAN MERSON
GPA, LIFG student-athletes scored significantly lower (M55.95) than non-LIFG
student-athletes (M56.48, t54.244, p,001). LIFG student-athletes also scored
significantly lower (M52.46) than non-LIFG student-athletes on the SACS report
measure of persistence (M52.08, t54.129, p,001). Regarding Faculty Concern
for Student Development and Teaching, LIFG student-athletes scored lower
(M52.21) than non-LIFG student-athletes (M52.05, t52.218, p,.05). No
TABLE 1
TYPES OF FINANCIAL AID REPORTED BY STUDENT-ATHLETES
Total DI DII DIII
n % n % n % n %
Family Contribution
Low-Income, First-Generation 29 16.9 *** 12 14.5 *** 6 12.5 *** 11 26.8 ***
Non-LIFG Sample 4357 54.3 1962 49.1 858 52.9 1537 69.0
Athletic Scholarship
Low-Income, First-Generation 103 59.5 ** 60 72.3 37 75.5 6 14.6 ***
Non-LIFG Sample 3645 45.5 2560 64.0 1032 63.6 53 2.4
Loans
Low-Income, First-Generation 87 50.3 31 37.3 23 46.9 33 80.5
Non-LIFG Sample 3645 45.5 1320 33.0 828 51.0 1473 66.2
Academic Scholarship
Low-Income, First-Generation 72 41.9 20 24.1 * 27 56.2 25 61
Non-LIFG Sample 3410 42.5 1432 35.8 843 51.9 1135 51.0
Personal Contribution/Job
Low-Income, First-Generation 35 20.2 15 18.1 9 18.4 11 26.8
Non-LIFG Sample 1701 21.2 680 17.0 341 21.0 680 30.5
Pell Grant
Low-Income, First-Generation 107 62.2 *** 43 51.8 *** 36 75 *** 28 68.3 ***
Non-LIFG Sample 1160 14.5 485 12.1 287 17.7 388 17.4
Need-Based Institutional Grant ***
Low-Income, First-Generation 62 35.8 24 28.6 *** 15 31.2 *** 23 56.1 ***
Non-LIFG Sample 1095 13.7 329 8.2 207 12.7 559 25.1
Note. n total larger than 8,018 and % total greater than 100% due to multi-selection.
*p,.05
**p,.01
LEVELING THE PLAYING FIELD 41
significant differences were found among AID, Faculty-Student Interaction, and
Faculty Perception of Student-Athletes.
Faculty Influence on Academic OutcomesWe entered student-athlete profile variables, faculty influence variables, and a
single LIFG variable into a three-block hierarchical regression model to examine
whether the selected independent variables served as predictors of a designated
academic outcome (GPA, AID, or intent to persist). The first three-block
hierarchical regression model was significant (F(9, 7,592)5154.718, p,.001) and
the total model explained 15.4% of the variance in student-athletes’ current
college GPA. The results from each block of the first regression model are given in
Table 2. The second block containing the faculty influence variables explained
6.6% of the variance—over one-third of the variance from the entire model. The
third block containing only the LIFG variable was not found to be statistically
significant. Graphs of the residuals from this and each of the following regression
models indicate that each model is appropriately specified and that residuals are
not related to the other variables within the given model.
We examined the complete model and observed that (after controlling for
student-athlete profile variables) identifying as a Student-Athlete of Color has a
strong negative association with GPA (b52.614, p,.001). Faculty-Student
Interaction (b5.216, p,.001) and Faculty Concern for Student Development
and Teaching (b5.224, p,.001) both had a positive influence on GPA, while
TABLE 2
HIERARCHICAL REGRESSION MODEL OF GPA (N 5 7602)
Block 1: Individual Characteristics Block 2: Faculty Influence Block 3: LIFG
b b b
Woman 0.660 *** 0.574 *** 0.573 ***
Student-Athlete of Color 20.710 *** 20.620 *** 20.614 ***
Division II 20.069 20.085 20.084
Division III 20.082 20.214 *** 20.214 ***
Featured Sport Status 20.175 *** 20.101 * 20.101 *
Faculty-Student Interaction 0.215 *** 0.216 ***
Faculty Concern 0.224 *** 0.224 ***
Faculty Perceptions 20.205 *** 20.205 ***
Low-Income First-Generation 20.111
R2 0.088 *** 0.154 *** 0.154
Change in R2 0.066 0.000
Note. b5beta, the unstandardized regression coefficient
*p,.05
**p,.01
***p,.001
42 JUSTIN C. ORTAGUS and DAN MERSON
Faculty Perceptions/Beliefs of Student-Athletes (b52.205, p,.001) had a negative
influence of similar magnitude. As previously noted, identifying as an LIFG
student-athlete was not found to be a statistically significant influence on GPA.
For the second statistically significant hierarchical regression model (F(9,
7,615)5206.242, p,.001), the first block of control variables accounted for 5%
of the variance in student-athletes’ intent to persist (Table 3). The second block,
which contains the faculty influence variables, explained 14.4% of the variance—
approximately three-quarters of the total from the overall model (19.5%). The
third block, which includes only the LIFG variable, was statistically significant but
only accounted for 0.1% of the variance within the model.
Being a Student-Athlete of Color (b52.138, p,.001), a Division II student-
athlete (b52.236, p,.001), or a Division III student-athlete (b52.235, p,.001)
were negatively associated with one’s intent to persist. Faculty-Student Interaction
had a statistically significant influence on intent to persist (b5.117, p,.001), while
Faculty Perceptions/Beliefs of Student-Athletes (b52.282, p,.001) had a negative
influence. The regression model confirms previous literature by showing that self-
reporting as an LIFG student-athlete had a negative influence on one’s intent to
persist (b52.231, p,.01).
The third three-block hierarchical regression model examining student-athletes’
Academic and Intellectual Development was significant (F(9, 7,615)5239.395,
p,.001; Table 4). The control variables explained 2.2% of the variance in
TABLE 3
HIERARCHICAL REGRESSION MODEL OF PERSISTENCE (N 5 7625)
Block 1: Individual Characteristics Block 2: Faculty Influence Block 3: LIFG
b b b
Gender 0.322 *** 0.246 *** 0.244 ***
Student-Athlete of Color 20.223 *** 20.150 *** 20.138 ***
Division II 20.222 *** 20.237 *** 20.236 ***
Division III 20.102 *** 20.235 *** 20.235 ***
Featured Sport Status 20.086 ** 20.003 20.003
Faculty-Student Interaction 0.116 *** 0.117 ***
Faculty Concern 0.147 *** 0.147 ***
Faculty Perceptions 20.282 *** 20.282 ***
Low-Income First-Generation 20.231 **
R2 0.050 *** 0.194 *** 0.194 ***
Change in R2 0.144 0.001
Note. b5beta, the unstandardized regression coefficient
*p,.05
**p,.01
***p,.001
LEVELING THE PLAYING FIELD 43
student-athletes’ AID. The second block containing the faculty influence variables
explained 19.7% of the variance—which accounted for nearly all of the 22% of
variance in student-athletes’ self-reported AID. The third block containing only the
LIFG variable was statistically significant but did not account for variance within
the model.
In the overall model, we found that identifying as a Student-Athlete of Color
(b52.150, p,.001) is negatively associated with AID. Faculty-Student Interaction
had a strong influence on AID (b5.431, p,.001), and Faculty Perceptions/Beliefs
of Student-Athletes (b52.077, p,.001) had a slightly negative influence on AID.
However, identifying as an LIFG student-athlete had a positive influence on AID
(b5.146, p,.05).
DISCUSSION
Consistent with the literature, Student-Athletes of Color and men were found to be
overrepresented among LIFG student-athletes. Compared to their peers, a smaller
proportion of LIFG student-athletes used family contributions to pay for college
and a larger proportion used Pell grants and need-based institutional aid.
Both LIFG and non-LIFG student-athletes continually balance their academic
workload with time constraints connected to participation in athletics (Wolverton,
2008). Athletic coaches typically shape student-athletes’ experiences related to
TABLE 4
HIERARCHICAL REGRESSION MODEL OF AID (N 5 7625)
Block 1: Individual Characteristics Block 2: Faculty Influence Block 3: LIFG
b b b
Gender 0.203 *** 0.141 *** 0.143 ***
Student-Athlete of Color 20.201 *** 20.150 *** 20.157 ***
Division II 0.077 ** 0.001 0.001
Division III 0.113 *** 0.002 0.001
Featured Sport Status 20.031 20.007 20.007
Faculty-Student Interaction 0.431 *** 0.431 ***
Faculty Concern 0.007 0.007
Faculty Perceptions 20.077 *** 20.077 ***
Low-Income First-Generation 0.146 *
R2 0.022 *** 0.219 *** 0.220 *
Change in R2 0.197 0.001
Note. b5beta, the unstandardized regression coefficient
*p,.05
**p,.01
***p,.001
44 JUSTIN C. ORTAGUS and DAN MERSON
intercollegiate athletics, but student-athletes’ academic success is significantly
associated with the interactions, concerns, and perceptions of faculty members at
their institution. While being an LIFG student-athlete is a statistically significant
predictor of Academic and Intellectual Development and persistence, the amount
of variance attributed to it is minimal. Faculty-Student Interaction was found to be
the strongest indicator of academic success for LIFG student-athletes. Although
LIFG students typically report less interaction with faculty members than their
non-LIFG peers (Walpole, 2008), the effectiveness of faculty-student interactions
for LIFG student-athletes was found to be more influential than student-athlete
profile characteristics. This is consistent with the previously described theoretical
models based on the work of Tinto, Kuh, and Rendon, showing the importance of
students having positive interactions with faculty members.
Student-athletes’ racial and social identity groups represent important influences
on their academic outcomes. Previous literature found that students experience
campus environments differently based upon demographic and social group
membership (Chang, 2002). LIFG Student-Athletes of Color typically have lower
levels of academic success and more negative perceptions of collegiate environ-
ments than their peers (Miller et al., 1998), but Faculty-Student Interaction proved
to be a stronger predictor of academic outcomes than any student-athlete profile
characteristic. Our findings build upon Donovan’s (1984) assertion that college
experiences are more important than background characteristics when predicting
the success of low-income Students of Color and apply it to LIFG student-athletes
in general. Several studies referenced the usefulness of interactions with faculty
members when attempting to optimize academic success for underprepared
students in a university setting (Pascarella & Terenzini, 2005; Choy, 2001; Hahs-
Vaughn, 2004; Sellers, 1992).
Previous literature established the importance of faculty-student interaction in
higher education. In our study, faculty influence variables were found to account
for one-half to nine times as much of the variance among academic outcomes
compared to demographic or social characteristics. Demographic and social
characteristics of students and employees are an important consideration when
developing policy for higher education institutions. However, faculty-student
interactions are the strongest and most effective predictors of academic success for
LIFG and non-LIFG student-athletes. Previous research found that LIFG students
reported less interaction with faculty members, spent less time studying, reported
less involvement in campus activities, and worked more than their peers (Walpole,
2008). Our findings support the importance of institutional practice and policy
that encourage LIFG student-athletes’ positive interactions with faculty members.
Given that our study examines nontraditional students (LIFG student-athletes),
Rendon’s validation model (1994) is highly relevant to the implications of our
findings. Learner validation, and not merely involvement, can be a way for faculty
and staff to take an active role in improving the confidence, learning, and
persistence of nontraditional students. Specifically, if LIFG student-athletes receive
validation from faculty members in and/or out of the classroom, they would
appear to have greater confidence in their ability to succeed in the classroom and a
higher likelihood of academic success. Although the SACS research team did not
LEVELING THE PLAYING FIELD 45
directly measure instances of out-of-class validation, the questions included in theFaculty-Student Interaction scale refer to various positive aspects of such
interactions. We believe Rendon’s model could be applicable in relation to
learning gains experienced by LIFG student-athletes as a result of validation byacademic and athletic personnel, and we encourage future research that focuses in
this area.Student-athletes face public scrutiny and strenuous time demands associated
with intercollegiate athletics participation. Although the stated goals of higher
education institutions continually reference the importance of diversity andstudent learning, student-athletes often face barriers to academic success due to
various demographic and institutional characteristics. They may also feel the
‘‘stereotype threat’’ associated with the ‘‘dumb jock’’ stereotype prevalent inhigher education (Harrison, 2007). For example, in one study, only 15% of
student-athletes said they were perceived positively by faculty members compared
to a third who believed they were perceived negatively (Simons et al., 2007). Ouranalysis indicated the importance of faculty members’ concern for student-athletes
and their perceptions of them as being different from other students. Athletics
departments can take steps to improve faculty members’ beliefs by, among otherthings, (a) coordinating information sessions or workshops about the demands of
athletic participation, the difficulties LIFG students face, and the importance of
faculty-student interaction, (b) facilitate meet-and-greets between faculty membersand student-athletes to both improve perceptions and initiate positive interactions,
and (c) ask Faculty Athletics Representatives, team advisors, and other faculty
members who are already involved with athletics to nominate and encouragefellow faculty members who they believe would provide a positive interactive
experience with student-athletes.
Student-athletes often face barriers to academic success due to variousdemographic and institutional characteristics, including those related to their
low-income, first-generation status. Our results highlight areas where athletics and
academic administrators can influence LIFG student-athletes’ academic success.While the support system afforded to student-athletes is typically helpful, greater
faculty involvement would serve to increase faculty-student interaction, showcase
a direct indication of faculty concern for student development and teaching, andcurb negative perceptions of student-athletes by faculty members throughout
higher education.
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Notes on contributor
Justin C. Ortagus studies online education, organization issues, and student-
athletes in higher education. He is a Ph.D. candidate in the Higher Educationprogram at The Pennsylvania State University. He also works as a graduate
assistant at Penn State’s Center for the Study of Higher Education.
Dan Merson studies college student outcomes, college environments, and STEMeducation. He is a research associate with the Leonhard Center for Enhancement
of Engineering Education at Penn State. He also consults on campus climate
assessment with Rankin & Associates Consulting and provides professionalmethodological consulting to researchers and graduate students.
Correspondence to: jco136@psu.edu
LEVELING THE PLAYING FIELD 49
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