ERIC EJ1161684: Comparing Two Groups of Student-Athletes: Implications for Academic and Career Advising

2017·0.15 MB·English

by  ERIC

Comparing Two Groups of Student-Athletes: Implications for
Academic and Career Advising
Mary E. Buzzetta, Florida State University
Janet G. Lenz, Florida State University
Emily Kennelly, Florida State University
In this study, we explored the career variables of pation may exert on a student-athlete’s academic
goal instability, vocational identity, and career and career planning goals (Brown, Glastetter-
decidedness levels in two groups of student- Fender, & Shelton, 2000; Linnemeyer & Brown,
athletes. We compared scholarship student-ath- 2010; Murphy, Petitpas, & Brewer, 1996), advisors
letes who had been selected to participate in a may find it helpful to further explore the
summer academic-support program designed for characteristics of this population.
at-risk students to scholarship athletes who were Student-athletes represent a growing group of
not included in the summer-support program. diverse individuals on college campuses of all
Both groups consisted of student-athletes from sizes. The National Collegiate Athletic Association
various sports with football and basketball the (NCAA) (2015) reported that in academic year
primary sports for the summer program partic- 2014-2015 nearly 482,533 college students
ipants and swimming and cross country the (209,472 females and 273,061 males) participated
primary sports for student-athletes not included in NCAA-sponsored events. In addition, according
in the summer-support program. Results of the to the 2010 NCAA ‘‘Student-Athlete Race and
study indicated that no significant differences Ethnicity Report,’’ which is based on data from
were found between the two groups of college student-athletes across all sports and all divisions,
student-athletes with regard to their goal insta- 70.4% of male athletes identified as Caucasian,
bility, vocational identity, or career decidedness. 18.7% as African American, 4.3% as Hispanic/
Implications for academic and career advising as Latino, 1.5% as Asian, 0.3% as American Indian/
well as future research are discussed. Alaskan Native, and 0.2% as Native Hawaiian/
Pacific Islander (NCAA, 2010). Furthermore,
[doi:10.12930/NACADA-15-041]
77.2% of female athletes identified as Caucasian,
KEY WORDS: academic advising, career advis- 11.6% as African American, 4% as Hispanic/
ing, career decidedness, college student-athletes, Latino, 1.9% as Asian, 0.4% as American Indian/
goal instability, vocational identity Alaskan Native, and 0.2% as Native Hawaiian/
Pacific Islander (NCAA, 2010).
Academic advising has increasingly focused on Research indicates that approximately 1% of
strategies for combining both academic and career student-athletes will have a professional career in
planning because students bring both concerns to sports, which typically lasts 3–4 years (Martinelli,
the advising process (Gordon, 2006; Leslie-Too- 2000; NCAA, 2012). Student-athletes have a
good & Gill, 2008). College student-athletes number of responsibilities to manage, including
represent a unique subpopulation in many higher practice, travel, play, and training. In trying to
education settings (Harding, 2008; Leslie-Toogood balance the dual roles of student and athlete, they
& Gill, 2008). In charting their academic and may experience difficulty in formulating future goals
career paths, these students may interact with and plans (Martens & Cox, 2000; Shurts & Shoffner,
advisors within the athletic department as well as 2004; Sowa & Gressard, 1983). As a result, student-
advisors in academic units and career services athletes need appropriate guidance and assistance
offices. Despite close connections to the athletic– with academic and career planning while progress-
academic advising staff in planning their course ing through their collegiate experience.
schedules, student-athletes may find that the time
Review of Literature
demands of their sport and the other commitments
associated with their athletic role interfere with Positive and Negative Effects of Athletic
their ability to explore academic and career options Participation
and appropriately attend to broader life-planning Previous literature provides insight into the
tasks. Because of the influence that sport partici- ways sport participation affects college student-
26 NACADA Journal Volume 37(1) 2017
Comparing Student–Athlete Groups
athletes, both positively and negatively, and the Patton, 1985). Bertoch (2010) examined the
potential impact of their athlete role on develop- relationship between goal instability and negative
mental tasks, including academic and career thinking in 258 undergraduates enrolled in a
decision making. The benefits of sport participa- career course and found that higher goal insta-
tion include physical, personal, and psychological bility was significantly related to higher levels of
development (Richards & Aries, 1999; Shurts & negative career thinking. In other words, individ-
Shoffner, 2004). Buzzetta, Cisneros, and Zucker uals with high levels of goal instability may
(2011) reported that athletes acquire an ability to experience difficulty engaging in the academic
accept constructive criticism and possess a set of and career decision-making process as a result of
transferable skills relevant to their future success, negative career thoughts related to this process;
including time management, goal orientation, and these may be expressed in statements such as ‘‘I’ll
dedication. In addition, athletic participation can never find a field of study or occupation I really
enhance individuals’ social identity, as partici- like.’’ Blustein (1989) examined the relationship
pants become members of a valued social group between goal instability and career exploration in
on campus (Richards & Aries, 1999). Previous a sample of 106 college students and found that
studies (Richards & Aries, 1999; Shurts & goal directedness was positively associated with
Shoffner, 2004) documented the various benefits self-exploration in the career-development pro-
of sport participation for college student-athletes cess. Blustein (1989) also found a strong
as well as articulated the way sport involvement relationship between goal directedness and career
can assist athletes in coping with key develop- decision-making self-efficacy. Santos (2003) not-
mental tasks, including forming one’s identity and ed that high levels of goal instability were
setting appropriate goals. associated with lower vocational identity levels;
Despite the positive aspects associated with that is, students with little clarity about their
college athletics, researchers have also document- future plans may struggle with goal setting. High
ed some drawbacks associated with athletic goal instability has also been associated with the
participation. Specifically, they have noted that inability to make a career decision following
an athlete’s academic and career planning pro- participation in a career course (Robbins &
gress may be hindered as a result of athletic Patton, 1985). Martin and James (2012) stressed
participation (Kennedy & Dimick, 1987; Murphy the importance of helping student-athletes formu-
et al., 1996). Some studies have suggested that late goals and plans for their lives beyond
athletes experience more difficulty in formulating athletics.
future goals and plans compared to their nonath- Overall, research indicates that goal instability
lete peers (Martens & Cox, 2000; Shurts & is related to a variety of career development
Shoffner, 2004; Sowa & Gressard, 1983). Stu- constructs, including dysfunctional career
dent-athletes’ role commitments may interfere thoughts, career decision-making self-efficacy,
with their ability to explore academic and career and vocational identity. On the basis of previous
options, and they may struggle in appropriately research, which showed that individuals with high
attending to life-planning tasks such as setting goal instability experience dysfunctional think-
goals (Brown et al., 2000; Linnemeyer & Brown, ing, lower levels of career decision-making self-
2010). Although they may have mastered setting efficacy, and lower levels of vocational identity,
goals related to athletic competition, student- we surmised that goal instability may be a useful
athletes may not have translated this focus to their factor to consider in academic and career advising
academic and career goals. interventions designed to help the student-athlete
population. Little is known about student-ath-
Goal Instability letes’ goal orientation, clarity, and motivation as
Research has shown that readiness to engage factors in academic and career decision making.
in future planning behavior is related to an
individual’s level of goal directedness (Robbins Vocational Identity
& Tucker, 1986). Robbins (1987) described goal According to Holland, Daiger, and Power
instability as an individual’s inability to formulate (1980), vocational identity refers to an individu-
a plan of action for one’s career. The inability to al’s self-perceptions of one’s own goals, interests,
formulate and implement realistic life plans stems personality, and talents. Vocational identity de-
from a lack of goal directedness, motivation, and velopment shows a relationship with a number of
ability to initiate self-direction (Robbins & factors that may influence college students.
NACADA Journal Volume 37(1) 2017 27
Buzzetta et al.
Sampson, Peterson, Lenz, Reardon, and Saunders particular field of study or occupational area
(1996) found that individuals with a clear sense of (Finch, 2007; Murphy et al., 1996). Previous
vocational identity have fewer negative thoughts research indicates that individuals with strong
related to career decision making than those with athletic identities are less likely to engage in
lower vocational identity levels. Furthermore, career exploration and related decision-making
Solberg, Good, Fischer, Brown, and Nord processes (Brown et al., 2000; Grove et al., 1997;
(1995) surveyed 426 college students and found Houle, 2010; Lally & Kerr, 2005; Tyrance,
that higher vocational identity levels were Harris, & Post, 2013). Brown et al. (2000)
positively correlated with career decision-making surveyed 189 NCAA Division I student-athletes
self-efficacy and negatively correlated with ca- and found a relationship between identity fore-
reer-decision needs. Ackerman (2012) inter- closure (strong identification with the athlete role)
viewed 14 NCAA Division I student-athletes to and low decision-making self-efficacy.
determine factors that contribute to the develop- In summary, many studies have reported the
ment of a student-athlete’s vocational identity. relevance of goal instability, vocational identity,
Eight factors emerged as contributors toward and career decidedness to students’ academic and
vocational identity development: occupational career planning. Because of the unique challenges
engagement prior to college, parental support, faced by student-athletes in navigating the college
personality characteristics such as determination environment (Leslie-Toogood & Gill, 2008;
and independence, involvement with other social Lyons, Jackson, & Livingston, 2015), the re-
groups, support from professors, support from search on student–athlete populations needs to
coaches, tailored career resources, and under- extend to examination of variables across sub-
standing NCAA and university regulations. populations of college student-athletes.
Although this earlier research demonstrated the
relevance of vocational identity among student- Purpose and Research Question
athletes, further research on vocational identity is Previous studies of student-athletes were fo-
needed to explore differences that may exist cused primarily on comparing athletes to their
within subgroups of student-athletes. With this nonathlete peers (Martens & Cox, 2000; Shurts &
information, interventions better tailored to stu- Shoffner, 2004; Sowa & Gressard, 1983). However,
dents’ unique needs can be developed. Finally, the possible differences between student–athlete
closely related to vocational identity, the extent to groups may help explain the processes student-
which student-athletes report having made a athletes use for decision making and inform ways
career decision that involves consideration of that advising and career planning interventions can
both fields of study and future occupational be tailored to their unique concerns.
alternatives may prove important in helping them In our study, we compared goal instability,
choose appropriate career paths. vocational identity, and career decidedness be-
tween two samples of college student-athletes. One
Career Decision Making and College Student- group was comprised of athletes participating in a
Athletes six-week educational program called Summer
All college students need assistance with Bridge, which was designed to allow student-
academic and career decision making, and some athletes, newly arrived on campus, the opportunity
students need more concentrated help with this to engage in academic preparation courses, attend
process because of their unique circumstances various types of workshops (related to academic
(Gordon, 2006). Student athletes experience and student services), and participate in mandated
complexities related to their various role commit- study halls, academic check-ins, and tutorial
ments as competitors. Both identity foreclosure supports prior to athletic conditioning and weight
and athletic identity have been shown to inhibit training. The mission of the Summer Bridge
career decision making in student-athletes program was described as follows: ‘‘To equip at-
(Brown et al., 2000; Grove, Lavallee, & Gordon, risk freshman student-athletes with the skills
1997; Houle, 2010; Lally & Kerr, 2005). Athletic necessary to graduate from college’’ (Florida State
identity involves the extent to which an individual University, Athletic Academic Support, 2013, p.
identifies with the athlete role (Brewer, Van 1). Student-athletes in the Summer Bridge program
Raalte, & Linder, 1993). Identification with the included at-risk college students who were admit-
student–athlete role may prevent athletes from ted to the university during the summer term.
thoroughly exploring options associated with a These students received hands-on orientation
28 NACADA Journal Volume 37(1) 2017
Comparing Student–Athlete Groups
experience and academic support to assist with the Table 1. Demographic information on two
transition from high school to college. Participants student–athlete groups
were selected for this program on the basis of
Program Status
recommendations from coaches or academic ad-
Summer
vising support staff as well as a review of their high
Bridge Fall Only
school academic record and SAT scores.
By comparing student-athletes identified as at Characteristics (n ¼ 31) (n ¼ 69) Total
risk and selected to be part of a summer support Gender
program with student-athletes not identified as at Male 25 24 49
risk, we sought to find any differences in goal Female 6 45 51
directedness, vocational identity, and career decid- Ethnicity/Race
edness. To address gaps in the literature, we Caucasian 10 55 65
proposed the following exploratory research ques- African American 16 6 22
tion: Are there significant differences between two Hispanic 3 3 6
groups of student-athletes, those selected for a Biracial 2 3 5
summer educational program and those admitted Asian 0 1 1
during the regular fall semester, with regard to goal Highest Education
instability, vocational identity, and career decided- Completed
ness levels? high school 22 50 72
Undergraduate
Method
(1st year) 9 15 24
Sample Undergraduate
Descriptive statistics for each group of stu- (3rd year) 0 2 2
dent–athlete participants are presented in Table 1. Undergraduate
The Summer Bridge group (n ¼31) consisted of (5th year) 0 1 1
student-athletes on scholarship chosen to partic- Primary Sport
ipate in the academic-support Summer Bridge Baseball 2 5 7
program. The group consisted of 25 males and 6 Basketball 5 0 5
females who self-identified as Caucasian (n ¼10), Beach volleyball 0 6 6
African American (n ¼16), Hispanic (n ¼3), and Cross country 1 10 11
biracial (n ¼ 2). The majority of participants in Diving 0 1 1
the Summer Bridge group (n ¼22) indicated high Football 14 1 15
school as their highest year of formal education Golf 0 3 3
completed, but 9 reported completion of one year Soccer 2 6 8
of undergraduate education and entered the Softball 1 8 9
university as transfer students. Participants in Swimming 2 18 20
the Summer Bridge group represented nine Tennis 1 0 1
different sports with the highest numbers in Track/field 3 10 13
football (n ¼ 14), basketball (n ¼ 5), track/field Age (years)
(n ¼ 3), baseball (n ¼ 2), soccer (n ¼ 2), and 17 0 4 4
swimming (n ¼2) (Table 1). 18 26 49 75
The Fall Only group (n ¼ 69) consisted of 19 4 11 15
student-athletes on scholarship who began col- 20 1 4 5
lege at the start of the fall semester and did not 22 0 1 1
participate in the Summer Bridge Program. It
consisted of 24 males and 45 females. The Fall
Only group self-identified as Caucasian (n ¼55),
African American (n ¼6), Hispanic (n ¼3), bi- participants in the Fall Only group (n ¼ 50)
racial (n ¼3), and Asian (n ¼1); one individual indicated high school as their highest year of
did not report ethnicity. They were involved in 10 formal education completed, 15 completed one
different sports, including swimming (n ¼ 18), year of undergraduate education, two had com-
cross country (n ¼ 10), track/field (n ¼ 10), pleted three years, one completed five years, and
softball (n ¼8), soccer (n ¼6), baseball (n ¼5), one individual did not report highest year of
and golf (n ¼3) (Table 1). The majority of education completed. Overall, participants’ ages
NACADA Journal Volume 37(1) 2017 29
Buzzetta et al.
in both groups ranged from 17 to 22 years, with indicating higher levels of goal directedness or
the mean age being 18 years. low goal instability. Items are rated on a 6-point
Likert scale: 1 ¼strongly agree, 2 ¼moderately
Measures agree, 3 ¼slightly agree, 4 ¼slightly disagree, 5
A demographic form and two measures were ¼moderately disagree, and 6 ¼strongly disagree.
utilized to collect data from participants in this Sample items include ‘‘I don’t seem to have the
study. The two measures were the Goal Instability drive to get my work done’’ and ‘‘After a while, I
Scale (GIS; Robbins & Patton, 1985) and the lose sight of my goals.’’ Test-retest reliability for
vocational identity (VI) scale of My Vocational GIS data collected over a 2-week interval was .76,
Situation (MVS-VI; Holland et al., 1980). Career and internal item consistency, calculated with
decidedness was assessed using the Range of Cronbach’s alpha, was .81 (Robbins & Patton,
Considered Alternatives (RCA; Gati, Kleiman, 1985). Concurrent validity studies indicated that
Saka, & Zakai, 2003), which was included on the the GIS correlates significantly with a number of
demographic form. variables including self-esteem (r ¼� .64),
The participants listed their age, gender, race personal competencies (r ¼� .48), and career
or ethnicity, highest year of formal education decidedness (r ¼�. 22). Predictive validity studies
indicated that the GIS is a significant predictor of
completed, current or proposed field of study, and
career decidedness following participation in a
primary sport. A brief measure of career decid-
career course (Robbins & Patton, 1985). Confir-
edness, the RCA (Gati & Levin, 2015) was also
matory factor analyses have shown that GIS items
included in the demographic form. The RCA, a
measure a unitary construct of goal instability
self-report measure, is used to assess the degree
(Robbins, Payne, & Chartrand, 1990). Bertoch,
to which individuals have narrowed down the
Lenz, Reardon, and Peterson (2014) demonstrat-
range of occupational alternatives under consid-
ed further evidence of the concurrent validity of
eration, reflecting their decision status and the
the GIS.
crystallization of their career plans. Scores on the
The MVS-VI (Holland et al., 1980) was used
RCA range from 1 to 6, with 6 suggesting the
to measure vocational identity in this study. The
highest level of career decidedness. Participants
VI subscale is composed of 18 true–false items
choose from one of six statements to indicate
used to measure individuals’ perceptions of their
their career decision status (Gati & Levin, 2015):
own goals, interests, personality, and talents. The
� I do not even have a general direction. total score is obtained by summing the number of
� I have only a general direction. false responses, with higher scores indicating a
� I am deliberating among a small number clearer sense of vocational identity. Sample items
of specific occupations. include ‘‘I am not sure that my present occupa-
tional choice or job is right for me’’ and ‘‘No
� I am considering a specific occupation,
single occupation appeals strongly to me.’’ A high
but would like to explore other options
degree of internal consistency (Kuder–Richard-
before I make my decision.
son Formula 20) was found for the VI subscale
� I know which occupation I am interested
and ranged from .86 to .89 (Holland et al., 1980).
in, but I would like to feel sure of my
Test–retest reliability scores for intervals of 1 to 3
choice.
months was .75 (Holland, Johnston, & Asama,
� I am already sure of the occupation I will
1993). Holland et al. (1980) reported evidence of
choose. (p. 195)
the construct validity for the VI.
The RCA proves useful in investigating the
adaptability of the way individuals make career Procedure
decisions (Gati & Levin, 2014), assessing the Student-athletes selected for the Summer
effect of an Internet-based career intervention Bridge program were invited to participate in
(Gati et al., 2003), and comparing methods for the research during their visit to the career center.
choosing among career alternatives (Amit & Gati, Those who chose to participate completed the
2013). research forms prior to the start of program
The GIS (Robbins & Patton, 1985), a 10-item activities, classes, or interventions associated with
self-report instrument, is used to measure an the Summer Bridge program, including the
individual’s ability to initiate self-direction. Total career-center overview. The second group was
scores range from 10 to 60, with higher scores recruited during the initial fall orientation meeting
30 NACADA Journal Volume 37(1) 2017
Comparing Student–Athlete Groups
for student-athletes, and each participant com- Table 2. Means and standard deviations for
pleted the research forms at the initial welcome variables
meeting. None of the student-athletes in the Fall
Variable Minimum Maximum M SD
Only sample had attended the Summer Bridge
VI 2.00 18.00 11.88 3.88
program. Prior to collecting any data from
GI 30.00 60.00 50.00 7.25
participants, we reviewed consent information,
CD 1.00 6.00 3.82 1.47
explained the purpose of the study as well as the
risks and benefits of participating, and addressed Note. VI ¼Vocational identity (Holland et al.,
possible questions or concerns raised by partic- 2008); GI ¼Goal instability (Robbins &
ipants. The students were informed that their Patton, 1985); CD ¼Career decidedness
participation was strictly voluntary, and no (Gati et al., 2003)
incentives were provided for participation. Indi-
viduals who expressed an interest in participating
ness scores. Although career decidedness was
in the research study completed the informed-
significantly related to vocational identity, it was
consent paperwork, a demographic form, and two
not significantly related to goal instability (r ¼.10).
brief measures—the MVS–VI and the GIS.
This significant positive relationship suggested that
A one-way multivariate analysis of variance
the multicollinearity between the independent
(MANCOVA) was utilized to examine differences
variables was not a threat for the model used
in goal instability, vocational identity, and career
(Table 3).
decidedness levels in a sample of 100 college
The results of the one-way MANCOVA re-
student-athletes. Participant gender status was
vealed a nonsignificant multivariate effect between
controlled. Gender was selected as a covariate
groups (Wilks’ k ¼ .954; F [3, 86] ¼ 1.39, p ¼
because the percentage of males and females
.253). In addition, the model accounted for 4.6% of
significantly differed by group (v 2 ¼18.00, df ¼
the variation between groups. Despite a nonsignif-
1, p , .001) (Table 1). Therefore, gender was
icant multivariate effect, the results of the univar-
added to the model as a covariate to partition any
iate tests are presented in Table 4.
variation among the dependent variables attribut-
ed to gender. The MANCOVA statistic was
Discussion
selected as the omnibus test to ascertain whether
In this study, we compared two groups of
a multivariate effect existed between groups.
scholarship student-athletes on goal instability,
Results vocational identity, and career decidedness. One
group of student-athletes, who were identified as
We sought to answer the exploratory research
at risk, participated in the Summer Bridge
question: Are there significant differences between
educational program designed to orient them to
two groups of college student athletes, those
campus and help them prepare academically in
selected for a summer educational program and
advance of fall semester. The second group of
those admitted during the regular fall semester, in
regard to goal instability, vocational identity, and student-athletes were not identified as at risk, and
career decidedness levels? This question was they enrolled at the start of fall semester without
examined using the GIS (Robbins & Patton, attending the Summer Bridge program (Fall Only
1985), MVS-VI scale (Holland et al., 1980), and group). The study was designed to help address
RCA measure (Gati et al., 2003). Means, standard the research gap on the differences across selected
deviations, and a correlation matrix depicting career development factors within student–athlete
relationships between measures of goal instability, groups.
vocational identity, and career decidedness are Despite previous findings on the influence of
presented in Tables 2 and 3. We found significant sport participation on a college student-athlete’s
positive correlations between the VI and GIS scales academic and career planning (Brown et al.,
(r ¼.48), indicating that high levels of vocational 2000; Linnemeyer & Brown, 2010; Murphy et
identity were associated with low levels of goal al., 1996), the results of the current study found
instability. In addition, we found significant average to high mean scores across all three
positive correlations between vocational identity measures used to assess the variables of interest:
and career decidedness as measured by the RCA (r goal setting, vocational identity, and career
¼ .41), indicating that high levels of vocational decidedness. Student–athlete scores for both
identity were associated with high career decided- groups on the GIS were higher than published
NACADA Journal Volume 37(1) 2017 31
Buzzetta et al.
Table 3. Correlations among variables Table 4. MANCOVA comparisons of student-
athlete groups by variable
Variable VI GI CD
Summer
VI 1.00 — —
Bridge Fall Only
GI .476** 1.00 —
CD .414** .102 1.00 (n ¼ 31) (n ¼ 61)
Variable M SE M SE F Sig.
Note. VI ¼Vocational identity (Holland et al.,
2008); GI ¼Goal instability (Robbins & VI 11.34 0.77 12.28 0.49 .977 .326
Patton, 1985); CD ¼Career decidedness GI 49.07 1.48 50.54 0.95 .648 .423
(Gati et al., 2003) CD 4.09 0.30 3.66 0.19 1.372 .245
**p,.01. Note. VI ¼Vocational identity (Holland et al.,
2008); GI ¼Goal instability (Robbins &
Patton, 1985); CD ¼Career decidedness
(Gati et al., 2003)
mean scores for college students enrolled in an
undergraduate career-planning course: M ¼45.6;
student-athletes can provide valuable information
SD ¼ 9.1 (Bertoch et al., 2014); however,
to support their academic and career advising
according to the data from Bertoch et al.,
process. Gordon (2006) stressed the importance
participants enrolled in an undergraduate career-
of advising programs that help students relate
planning course reported greater goal instability
their interests, skills, and abilities to work options.
than student-athletes in the current study. Previ-
The results from the RCA measure used in our
ous research has indicated that college student-
study support the importance of assisting college
athletes experience more difficulty in formulating
student-athletes in learning about options outside
academic and career plans compared to their
their sport. For instance, participants in this study
nonathlete peers (Kennedy & Dimick, 1987;
indicated that they were currently considering a
Martens & Cox, 2000; Shurts & Shoffner,
specific occupation but were interested in explor-
2004). In contrast to previous findings, our study
ing other options before they make a decision.
found that student–athlete participants in both the
Student-athletes who have identified an academic
Summer Bridge and Fall Only groups possessed
a sense of goal directedness. In addition, the or occupational choice may need to confirm or
findings indicate that while student-athletes were clarify the appropriateness of their choice by
considering a specific occupation they were also contrasting it with other alternatives as well as
interested in exploring other options before exploring the implications for their academic
making a career decision. planning. Advisors can work with student-
The nature of athletic participation, which athletes to help them expand their occupational
requires commitment to goal setting and achieve- alternatives and assist them in prioritizing their
ment to become a scholarship athlete at a Division I academic and occupational choices.
university, may explain our findings. Bailey (1993) To expose student-athletes to additional career
described a high school program for athletes that options and the relationship between fields of
included a focus on setting goals and learning study, college campuses can offer events, jointly
decision making. The initiative was based on the sponsored by the advising office and career
concept that athletes must make decisions about center, during times that fit with athletes’
ways to balance their time, where to attend college to schedules. An example includes a career transi-
pursue their sport, and handle challenges by coaches tion panel presentation (Buzzetta et al., 2011;
to establish performance goals in their sport. Bailey’s Lenz & Shy, 2003) where former student-athletes
results pointed to the importance of building on these describe how they used their academic back-
skills in the academic and career planning process, ground and preparation in the workforce or in
but they also suggested the need to include graduate school.
additional information on ways student-athletes can Gordon (2006) highlighted the importance of
connect their self-knowledge to future options. using the advising process to help students
understand the skills needed to enter various
Implications for Practice work settings. Employer panel discussions in
Learning more about the career aspirations, which participants describe the nature of the work
goals, and decision-making status of college and tips on re´sume´s, interviews, and similar
32 NACADA Journal Volume 37(1) 2017
Comparing Student–Athlete Groups
topics might be offered in several discipline or Limitations and Future Research
industry areas (e.g., careers for liberal arts majors Several limitations are associated with our
or nonprofit and government careers) (Lenz & study. First, the two student–athlete groups
Shy, 2003). Additional workshops focused on significantly differed by gender. The same study
understanding academic and career planning on groups more alike by gender may have
(self-knowledge, options knowledge, connecting
produced different results. Both groups also
majors to occupations) and strategies for success-
differed by ethnicity, with the majority of
ful transitions after athletic participation, includ-
participants in the Fall Only group identifying
ing stories of student-athletes who translated their
as Caucasian and the majority of participants in
skills developed in sports to job options, may
the Summer Bridge group identifying as African
prove engaging for students. Materials can be
American (followed by Caucasian). In addition,
developed and shared on campus web sites that
having a larger number of college student-athletes
highlight the success stories of student-athletes
who have made successful career transitions. in the Summer Bridge group would have
A recent career-planning survey administered enhanced the statistical power and generalizabil-
to 131 freshman athletes attending a large ity of the findings. Furthermore, some partici-
southeastern university indicated that 58.8% pants in both groups specified that they had
desired more assistance in gaining experience completed one or more years of undergraduate
related to their major and career interests (Foster, work; others were transfer students from other
Buzzetta, & Lenz, 2013). Results from the current institutions, including community colleges, and
study reinforce these previous findings and point prior experiences at other colleges and universi-
to the important role that academic and career ties may have affected their responses to the
advisors can play in educating student-athletes measures used in the research. Last, data
about the opportunities available to them outside
collected from participants in this study may
of athletics (e.g., campus organizations, experi-
differ from the student–athlete populations at
ential learning opportunities such as internships
other types of schools, such as those in Division
or volunteer work, leadership training and
II or III as well as athletes in sports not identified
development).
by participants in our study.
Furthermore, our research indicates the need
Future researchers could explore a pretest–
to educate student-athletes on making effective
posttest control group design to assess the effects
career decisions (Linnemeyer & Brown, 2010;
Smallman & Sowa, 1996). The results of our of a Summer Bridge–type program and determine
research suggest that student-athletes in both the the degree to which it influences student–athlete
Summer Bridge and Fall Only groups were academic and career planning factors. Also,
interested in expanding their options prior to increasing the number of participants in future
making a career decision. Campus advisors can studies may enhance the validity and generaliz-
draw on various theoretical approaches to inte- ability of the results. As previous research has
grate the exploration of options with academic focused primarily on comparing college student-
and career decisions. Gordon (2006) highlighted athletes to their nonathlete peers, researchers may
several theoretical perspectives that advisors can benefit from extending the literature on student-
apply to their work with students, including the
athletes by examining career development char-
cognitive information processing (CIP) approach
acteristics with students in other campus student
(see also, Sampson, Reardon, Peterson, & Lenz,
organizations who experience similar demands,
2004). The CIP theory–based approach (Sampson
pressures, and time commitments (e.g., student
et al., 2004), which includes a model for
government associations, Greek organization
expanding and narrowing options and identifying
members, and performing arts students). In
a first choice, can assist student-athletes in their
addition, the differences that exist within stu-
career transition and development (Rodriguez,
dent–athlete groups need to be examined. Such an
2012; Wooten, 1994). Academic and career
advisors can use CIP theory (Peterson, Lenz, & exploration might include research across a
Sampson, 2003) to assist student-athletes with variety of demographic groups, sports programs
current career choices as well as in developing the (revenue and nonrevenue producing), playing
skills necessary for making future career choices statuses (varsity versus nonvarsity), and division
(Reardon, Lenz, Sampson, & Peterson, 2017). affiliations.
NACADA Journal Volume 37(1) 2017 33
Buzzetta et al.
Closing Remarks Brown, C., Glastetter-Fender, C., & Shelton, M.
In closing, academic advisors, in collaboration (2000). Psychosocial identity and career con-
with campus career advisors, can better assist the trol in college student-athletes. Journal of
student–athlete population by understanding its Vocational Behavior, 56, 53–62. doi:10.1006/
unique needs and career development factors, and jvbe.1999.1691
then using this information in designing and Buzzetta, M., Cisneros, S., & Zucker, M. (2011,
delivering services and programs that contribute November). Celebrating and becoming a
to student-athletes’ successes during their time on champion for diversity: Successful strategies
campus and in their future life roles. Furthermore, for career professionals. Career Convergence:
academic and career advisors need to know Web Magazine. Retrieved from https://www.
specific career resources targeted to this popula- ncda.org/aws/NCDA/pt/sd/news_article/
tion, including relevant career theories, decision- 52974/_PARENT/CC_layout_details/false
making models, assessments, occupational infor- Finch, B. L. (2007). Investigating college ath-
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