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Journal for the Study of Sports and Athletes in Education

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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

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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


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


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


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.


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


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.


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.


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


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


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


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


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)


b b b

Gender 0.322 *** 0.246 *** 0.244 ***


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 Concern 0.147 *** 0.147 ***


Low-Income First-Generation 20.231 **

R2 0.050 *** 0.194 *** 0.194 ***

Change in R2 0.144 0.001


*p,.05

**p,.01

***p,.001


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)


b b b

Gender 0.203 *** 0.141 *** 0.143 ***


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 Concern 0.007 0.007


Low-Income First-Generation 0.146 *

R2 0.022 *** 0.219 *** 0.220 *

Change in R2 0.197 0.001


*p,.05

**p,.01

***p,.001


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


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: [email protected]


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