Table Of ContentMajor Re-selection Advising and Academic Performance
Deborah McKenzie, University of South Florida
Tony Xing Tan, University of South Florida
Edward C. Fletcher, University of South Florida
Andrea Jackson-Williams, University of South Florida
We sought to determine whether receiving major undergraduate years (Beggs et al., 2008; Gordon,
re-selection (MRS) advising benefits undergrad- 2007) and a 6-year degree attainment rate below
uate students’ grade-point averages (GPAs). We 60% among American college students who enroll
used a quasi-experimental nonequivalent control in 4-year colleges (Bettinger, Boatman, & Long,
group design to compare a treatment group (n ¼ 2013; Cuseo, 1991).
219) of undergraduates who changed their Academic advising designed to help students
majors after receiving MRS advising with a transition from one major to another contributes to
control group (n ¼ 206) who changed majors students’ academic progression, persistence with
without advising during the same semester as the re-selected majors, and retention (Campbell &
treatment group. Findings showed that, on Nutt, 2008; Gordon & Steele, 1992; Hunter &
average, students who received MRS experienced
White, 2004; Mayhall & Burg, 2002; Metzner,
no change in their program GPA but an increase
1989; Steele, 1994; Steele, Kennedy, & Gordon,
in their semester GPA; however, the control group
1993; Steingass & Sykes, 2008). For instance, in a
experienced a decrease in program and semester
recent study on the effect of centralized advising,
GPAs. Multiple regression analysis confirmed
Kot (2014) found that first-year students who
that MRS advising had a positive effect on
received centralized advising had earned higher
posttest semester GPAs (b ¼.33, p , .001) and
grade-point averages (GPAs) and experienced
program GPAs (b ¼.28, p , .001). Implications
lower attrition rates than peers who did not receive
for student advising are discussed.
any advising during the same period. Kot em-
[doi:10.12930/NACADA-15-029] ployed the propensity score matching technique to
estimate the impact of centralized academic
KEY WORDS: academic performance, major
advising on 2,745 undergraduates’ first-year GPAs
changers, major re-selection advising, nonequiv-
and second-year enrollment behaviors. Data from
alent control group design
students who accessed centralized advising were
matched with those who received no advising over
Research has shown that when selecting
the same two semesters. Findings showed that
academic majors, undergraduates take into consid-
students who used centralized academic advising
eration their academic interests, aptitude, the
earned higher first-term, second-term, and first-
psychological and social benefits associated with
year cumulative GPAs and more enrolled for their
a major, postgraduation employment prospects,
second year than students who had not seen an
and the appropriate education for their chosen
advisor.
occupations (Allen & Robbins, 2008; Beggs,
The burden for receiving useful advising does
Bantham, & Taylor, 2008). Changes in any of
not fall solely to students. Some colleges and
these factors might lead students to re-select an
academic major. In an alternative scenario, some universities provide inadequate advising opportu-
undergraduates declare a major after minimal nities to connect students’ interests (e.g., career
considerations of relevant circumstances (Mor- goals) with appropriate academic majors (Feldt et
timer, Zimmer-Gembeck, Holmes, & Shanahan, al., 2011). To provide an effective advising
2002). As a result of either situation, students may program, planners and administrators must recog-
lack confidence in their original choice and nize that students at different stages of their
commitment to their declared major such that they academic career need different types of advising.
subsequently need to change to a different major. For instance, first-year students looking to declare
Both of these decision-making processes may a major likely require different conversations and
contribute to the 35–75% of undergraduates exercises than second- or third-year students who
changing their majors at least once during their experienced failure in their selected program and
NACADA Journal Volume 37(1) 2017 15
McKenzie et al.
must find a new major to remain enrolled in who receive MRS advising outperform major
college. changers who do not receive any advising? We
Because students who need to re-select their included two groups of undergraduates who
majors are particularly vulnerable for leaving matriculated at approximately the same time and
college without a degree (e.g., dropping out or changed their majors at the same point in their
academic dismissal), some postsecondary institu- college careers. However, without random group
tions allocate academic advising resources to assignments, we did not have pretest sampling
respond to the specific needs of major changers. data for the two groups. Students in the treatment
A typical program features a centralized major re- group changed their majors after receiving MRS
selection (MRS) advising office that provides advising. The students in the control group
advising to students who request it; however, little changed their majors without receiving MRS
research has been conducted to describe specific advising. This research design provided an
characteristics of MRS advising and whether they opportunity to infer the effect of MRS advising
benefit students who received it. Therefore, for the on students’ programs and term GPAs while
current study, we described a centralized MRS as controlling for a host of covariates, including age,
well as compare the academic performances of gender, transfer status, and racial background.
undergraduates who changed their majors after
receiving MRS advising with a peer group who Participants
changed majors without receiving any advising. On Most participants were in their third year
the basis of existing literature, we hypothesized (78.3%). The groups also included sophomores
that students who selected majors after receiving (16.2%) and seniors (5.5%) during the 2013-2014
MRS advising would outperform their peers who academic year. The treatment group included all
changed their majors without receiving advising. 219 students who received MRS advising during
the summer and fall semesters of 2012. Prior to
Methods
receiving MRS advising, these students had
Context completed an average of 28.9 credit hours (SD
The study was conducted at a comprehensive, ¼ 10.5) and had formally declared an academic
public, research university serving more than major at the university. Subsequent to receiving
48,000 students. The first-to-second year reten- MRS advising, all treatment group students had
tion rate was 89%, and 67% had graduated within selected and declared a different major. The
6 years. Similar to descriptions in the literature control group was randomly drawn from the
(e.g., Gordon & Steele, 1992; Osipow, 1983), at undergraduate population who had matriculated
the studied university, second- and third-year at approximately the same time as students
students in good academic standing (defined as a assigned to the treatment group. It included 206
2.0 GPA or higher) need MRS advising because undergraduates who had declared a major and
they have discovered new interests or experienced then changed to a different major during the same
one or more academic challenges (e.g., failure to period as the treatment group students; however,
complete prerequisites for a declared major). control group students received no advising.
Students who want or need to change their majors Similar to those in the treatment group, students
are encouraged, but not required, to meet with an in the control group had completed an average of
MRS advisor and to consult with advisors in both 28.7 credit hours (SD ¼8.9) when they changed
their current college and the one(s) of interest. In their majors.
other words, undergraduates may declare their
major by completing a major declaration online Data Source
or on paper by submitting the proper form to the Upon approval from the Institutional Review
college of choice. After college staff process the Board, we obtained the following data directly
information and update the student records from the university registrar reporting system for
system, students may register for courses in their students in both groups: demographic informa-
new college. tion (e.g., age, gender, ethnic and racial back-
grounds); transfer status (i.e., whether or not the
Research Design student transferred into the university); previously
We used a quasi-experimental nonequivalent declared major and currently declared major; and
control group design (per Fife-Schaw, 2012) to GPAs for each semester as well as for the
address the research question: Do major changers students’ programs of study for each semester.
16 NACADA Journal Volume 37(1) 2017
Major Re-selection Advising
In addition, students in the treatment group filled Handbook of Career Advising (Hughey, Nelson,
out an in-take form that included a checklist of Damminger, & McCalla-Wriggins, 2009). Spe-
reasons for major re-selection prior to meeting cifically, they exhibit a solid understanding of
with their MRS advisor. The students could student development as well as learning and
choose all the reasons that applied to them as career development. Furthermore, they apply
well as write in additional reasons. No informa- extensive knowledge about all academic pro-
tion was available on the reasons for major grams and curriculum requirements at the 13
change among those in the control group because colleges of this university rather than an individ-
they did not request MRS advising. Finally, ual college or a program. Highly motivated, MRS
information on the characteristics of the MRS advisors demonstrate effectiveness in working
office was provided by the program manager of with students to achieve their goals. Advisors
the MRS office. who handle other types of advising need might
In data analyses, pretest semester GPAs were consider MRS advisors to be generalists.
calculated by averaging the students’ GPAs from Philosophically, MRS advising is guided by
all semesters before the time period the treatment the principles of developmental advising (e.g.,
group students received MRS advising (Summer Grites, 2013; Grites & Gordon, 2000) and the
and Fall 2012). Posttest semester GPAs were notion that one discovers vocational options
determined from students’ GPAs of the semester through a gradual process (Gottfredson, 2005).
after receiving MRS advising (Spring 2013). In practice, the 3-I process—inquire, inform,
Pretest program GPAs were calculated from integrate—proposed by Gordon (2006) was
cumulative GPAs earned in program-specific incorporated into the advisors’ interactions with
courses in all semesters before the students students. The MRS advisor carefully studies the
received MRS advising. Posttest program GPAs in-take form (see Appendix) filled out by the
were obtained from Spring 2013 semester grades student and then provides individualized, student-
after students received MRS advising. centered, collaborative, and goal-orientated ad-
vising. In addition to discussing the key infor-
Results mation provided by the student on the in-take
Characteristics of Major Re-selection Advising form, the MRS-trained advisor probes into
According to the program manager, the MRS additional issues deemed important for engaging
office is staffed with two full-time MRS advisors students in reflection on their academic history,
and two part-time graduate assistants. The MRS strengths, and weaknesses and in evaluating steps
advisors were trained to recognize that many necessary for their academic progress and
second- and third-year students in need of new personal growth. Equally important, the MRS
majors were at an elevated risk for leaving the advisor works with the students to consider more
college without a degree (e.g., being dismissed or than the linear connection between an academic
dropping out), and advisors accepted a vital role major and a postgraduation career and think
in promoting student retention. Similar to other about gaining transferable skills (e.g., critical
types of academic advisors, the MRS advisors thinking).
only work with students who request advising to
re-select majors and serve as liaisons between Demographics
students and mental health counseling profes- Table 1 summarizes the demographics of the
sionals (e.g., Kadar, 2001; Robbins, 2012). two groups. No significant differences in age,
MRS advisors have acquired a set of skills gender distribution, or percentage of transfer
unlike advisors who do not specialize in major students were found; however, a significant group
changers. MRS advisors identified as seasoned difference was found in the distribution of ethnic
staff members with training in both career and and racial backgrounds between the two groups
mental health counseling. A requirement for (v 2 ¼13.60, p ¼.002).
employment as an MRS advisor, a background Post hoc analyses showed a significantly
in mental health counseling applies directly to the higher percentage of White students in the
many students who arrive at the MRS office with treatment group (52.5%) than in the control
a sense of urgency, frustration, defeat, and group (37.4%): v 2 ¼ 9.81, p ¼ .002. However,
preexisting mental health conditions (e.g., de- the percentage of Black students in the treatment
pression). MRS advisors also demonstrate key group (20.1%) was significantly lower than in the
career advising competencies outlined in the control group (30.6%): v2 ¼6.20, p ¼.012. The
NACADA Journal Volume 37(1) 2017 17
McKenzie et al.
Table 1. Demographics of the treatment and control groups (N ¼425)
Demographic Treatment Group (n ¼ 219) Control Group (n ¼ 206) Statistic
Mean age (years) 22.7 (SD ¼ 6.0) 22.8 (SD ¼ 2.8) t ¼ .16
Gender n (%) n (%) v 2 ¼ 1.24
Female 136 (62.1) 117 (56.8)
Male 73 (37.9) 89 (43.2)
Race/Ethnicity v 2 ¼ 13.60**
Asian 16 (7.3) 27 (13.1)
Black 44 (20.1) 63 (30.6)
Hispanic 44 (20.1) 39 (18.9)
White 115 (52.5) 77 (37.4)
Transfer student v2 ¼ .09
Yes 84 (38.4) 82 (39.1)
No 135 (61.6) 124 (60.9)
Note. **p , .01.
percentage of Asian students was significantly the control group. The program GPA of the
lower in the treatment group (7.3%) than in the treatment group (M ¼ 2.85, SD ¼ .60) was also
control group (13.1%): v 2 ¼3.93, p ¼.047. significantly higher than that of the control group
(M ¼2.75, SD ¼.46): t ¼2.07, p ¼.004. The mean
Reasons for Major Re-selection and Mean GPAs for both groups correspond to a B� .
GPAs Posttest GPA. The mean semester GPA for the
According to information gathered from in- treatment group (M ¼ 2.86, SD ¼ .83) was
take forms completed by students in the treatment significantly higher than that for the control group
group, students need to change majors for (M ¼2.22, SD ¼.71): t ¼7.91, p , .001. These
multiple reasons. Using techniques proposed by means correspond to a B� for the treatment group
Creswell (2013), we applied content analysis to and a C for the control group. The mean program
the treatment group’s reasons for major re- GPA for the treatment group (M ¼2.84, SD ¼.57)
selection and to the self-identified barriers to was also significantly higher than that for the
their academic progress. More specifically, we control group (M ¼2.48, SD ¼.36): t ¼7.85, p ,
identified recurring terms in student responses .001. These means correspond to a B� for the
and used them as coding categories, which we treatment group and a Cþfor the control group.
subsequently transformed into emerging themes. Changes in GPA after MRS. As shown in
Results showed that loss of interest in the Figure 1, after receiving MRS advising, students in
previous major (n ¼79; 40.1%), difficulties with the treatment group experienced a significant
courses in the previous major (n ¼ 56; 28.4%), increase in semester GPAs: pretest, M ¼2.73, SD
failure to meet minimum GPA requirements of ¼.67; posttest, M ¼2.86, SD ¼.83; paired t ¼2.39,
the academic program (n ¼29; 14.7%), failure to p ¼.018. The GPAs correspond to Bs according to
meet some or all of the prerequisites of a desired the university grading guidelines. However, the
major (n ¼11; 5.58%), denial of admission into a treatment group experienced no changes in mean
desired major (n ¼ 7; 3.55%), and other issues program GPAs: pretest, M ¼ 2.85, SD ¼ .60;
(e.g., family finance, mental health; n ¼ 66; posttest, M ¼2.83, SD ¼.56; paired t ¼.61, p ¼.54.
33.5%) were primary reasons for changing On the contrary, students in the control group
majors. Because the control group participants experienced a significant decrease in semester
did not receive MRS, no information was GPAs: pretest, M ¼2.58, SD ¼.49; posttest, M ¼
available on their reasons for changing majors. 2.22, SD ¼.71; paired t ¼ 6.39, p ¼ .001. This
Pretest GPA. The mean semester GPA of the corresponds to a decrease from Cþ to C. The
treatment group was significantly higher (M ¼ control group also experienced a significant
2.73, SD ¼.67) than that of the control group (M ¼ decrease in program mean GPAs: pretest, M ¼
2.58, SD ¼.49): t ¼2.70, p ¼.007. According to 2.74, SD ¼.46; posttest: M ¼2.47, SD ¼.35; paired
university grading guidelines, the averages corre- t ¼10.76, p ¼.001. This mean average corresponds
spond to a B� for the treatment group and a Cþfor to a drop from a B� to Cþ.
18 NACADA Journal Volume 37(1) 2017
Major Re-selection Advising
Figure 1. Change in undergraduate GPAs between pretest and posttest
Multiple Regression Analysis MRS advising, showed a higher posttest mean
Simple correlation analyses revealed that program GPA (b ¼ .28, p , .001) than control
semester GPAs were highly correlated with group students, who did not receive MRS.
program GPAs prior to MRS advising (r ¼.85, Overall, these variables explained 56.6% of the
p , .001) and after MRS advising (r ¼.74, p , variance in students’ posttest program GPAs.
.001). However, the students’ pretest semester
GPAs only moderately correlated with posttest Discussion
semester GPAs (r ¼.35, p , .001). The control We examined the effect of MRS advising on
group pretest program GPAs were highly corre- undergraduate semester and program GPAs. We
lated with posttest program GPAs (r ¼.69, p , compared a group of undergraduates who changed
.001). their majors after receiving MRS advising with a
We conducted simultaneous multiple regres- group of randomly selected undergraduate major
sion analyses for posttest semester GPAs and changers during the same period but who received
program GPAs respectively, with group member- no advising. The study yielded several informative
ship (treatment vs. control group) as the key findings.
predictor. We controlled for students’ demograph- First, information from the MRS in-take
ic profile information (age, gender, ethnic and obtained from the treatment group undergraduates,
racial background), transfer status, and corre- who sought MRS advising before changing their
sponding pretest semester and program GPAs majors, showed that loss of interest and poor
(Table 2). academic performance with previous majors com-
As presented in Table 2, regression results for prised the two main reasons for changing a major.
posttest semester GPA showed that, with con- These factors likely influenced each other. If
trolled demographic covariables and pretest GPA, interest proves an important factor in students’
students who received MRS had higher posttest major selection as suggested (e.g., DeMarie &
semester GPAs (b ¼.35, p , .001) than students Aloise-Young, 2003; Malgwi, Howe, & Burnbay,
who did not receive MRS. Overall, the variables 2005), then loss of interest might lead to academic
explained 26.3% of the variance in students’ disengagement, which contributes to poor academ-
posttest semester GPAs. Regression results ic performance. However, poor academic perfor-
showed that, with controlled demographic vari- mance might also serve as a precursor for losing
ables and pretest program GPA (b ¼ .67, p , interest as well as involuntary major re-selection
.001), treatment group students, who had received (Allen & Robbins, 2008). In addition, Asian and
NACADA Journal Volume 37(1) 2017 19
McKenzie et al.
Table 2. Multiple regression predicting posttest semester and program GPAs (N ¼425)
Posttest Semester GPA Posttest Program GPA
Characteristics B b B b
Age .004 .02 .004 .04
Female .12 .07 .04 .04
Male Ref.(0) Ref.(0) Ref.(0) Ref.(0)
Nontransfer student �.19 �.11* �.03 �.03
Transfer student Ref.(0) Ref.(0) Ref.(0) Ref.(0)
Asian .20 .07 �.02 �.01
Black .05 .02 �.04 �.04
Hispanic .02 .01 �.04 �.03
White Ref.(0) Ref.(0) Ref.(0) Ref.(0)
Pretest GPA .46 .33*** .63 .67***
MRS advising (yes) .58 .35*** .28 .28***
MRS advising (no) Ref.(0) Ref.(0) Ref.(0) Ref.(0)
F 21.06*** 54.88***
R2 .263 .566
Note. MRS ¼Major re-selection advising. For posttest semester GPA, corresponding pretest semester
GPAs were used; for posttest program GPA, corresponding pretest program GPAs were used.
*p , .05. **p , .01. ***p , .001.
Black students who utilized MRS advising were advising strategies affect students’ academic per-
underrepresented in the sample. Prior research has formances.
shown students of different ethnic and racial Second, all undergraduates seeking it can
backgrounds hold different perceptions on the receive MRS advising. Therefore, the students
importance of academic advising (e.g., Kot, who chose to receive it before changing their
2014; Smith & Allen, 2006). However, more majors resemble those chosen by random sample
research is needed to understand the factors with regard to the independent variable. Because
associated with the underutilization of academic access did not affect either group, the higher mean
advising services among Asian and Black students. pretest semester and program GPAs of the
treatment group over those of the control group
We obtained data on the control group, such as
suggest nonaccess factors affected the choices to
demographic information, previous and current
use MRS or not.
majors, pretest and posttest GPAs, from the
According to the literature, students who
registrar’s reporting system, but we could not
demonstrate better academic performances may
obtain qualitative data on students’ decisions about
seek help more readily than those who perform less
seeking advising when selecting their new majors
well (Alexitch, 2002). They also may not doubt the
or the ways this group made sense of declining
quality of the advising (Metzner, 1989). Perhaps
academic performances after changing their ma-
those in the treatment group, with the higher mean
jors. Because of the further decline in their GPAs,
GPA, perceived that MRS advising could help
the control group students may need to select
them in selecting a new major. This speculation
another major again in subsequent semesters, or
comports with the literature suggesting that
they may drop out of or be dismissed from the
utilization of university resources are positively
university. More research on their experiences
associated with academic performance (e.g., Rob-
would inform efforts to engage them proactively bins et al., 2009). Research has also shown that
before they leave college by choice or by academic students possess widely different perceptions of the
failure. In addition, because other intervention benefits of advising (Christian & Sprinkle, 2013).
programs (e.g., academic assistance) have exerted Therefore, the GPA differences between the two
significant and positive influences on students’ groups may reflect differences in the students’
GPAs and retention levels (e.g., Bahr, 2008; Pan, beliefs about the benefits of MRS. Furthermore,
Guo, & Bai, 2008), future studies should expand the lower performance of the control group may
the scope of this investigation into the ways other reflect other characteristics or issues (e.g.,
20 NACADA Journal Volume 37(1) 2017
Major Re-selection Advising
inadequate decision-making efficacy, poor academ- and advising. Because undergraduates at most 4-
ic preparedness) (Firmin & MacKillop, 2008). year U.S. institutions must declare majors upon
Third, treatment group t tests revealed a completing general education courses, advisors
significant increase in semester GPA but no who proactively engage students making their
difference in program GPA; control group t tests initial selection of major might reduce the
revealed a significant decrease in both semester instances of subsequent major re-selection. This
and program GPAs. However, multiple regression study also reinforces the need for specific strategies
analyses, in which the pretest GPA and demo- for helpings students select a program of study.
graphic variables were controlled, showed that Some identified in the academic advising literature
students who received MRS had earned higher include assurances that students learn about
posttest semester and program GPAs than students services available to assist them in selecting and
who did not receive MRS. These findings changing academic majors. These types of pro-
confirmed the hypothesis that MRS was associated grams may be of particular benefit to Asian and
in a positive way with students’ GPAs. The positive Black students who may be unaware of these
effect of advising has been well established (e.g., services. For students considered at a high risk for
Steingass, & Sykes, 2008). Findings from our academic failure, targeted intrusive advising (e.g.,
study lend further support to this body of literature.
Heisserer & Parette, 2002), instead of student-
Because MRS advisors were trained to utilize a
initiated voluntary advising, might yield better
developmental advising approach with students
outcomes. Proactively identifying at-risk individu-
whose chosen majors were no longer viable,
als who might benefit from MRS advising may
students who interacted with MRS advisors likely
also facilitate academic performance.
selected new majors that fit well with their
academic backgrounds, interests, and career aspi- References
rations. Students’ corresponding pretest GPAs
Alexitch, L. R. (2002). The role of help-seeking
significantly predicted posttest GPAs, suggesting
attitudes and tendencies in students’ prefer-
that previous academic performance was a signif-
ences for academic advising. Journal of
icant predictor of subsequent academic perfor-
College Student Development, 43(1), 5–19.
mance.
Allen, J., & Robbins, S. (2008). Prediction of
Taking these findings together, we concluded
college major persistence based on vocational
that MRS advising exerted a positive influence on
interests, academic preparation, and first-year
students’ GPAs. However, we caution against
academic performance. Research in Higher
generalizing the findings, but point out that they
Education, 49(1), 62–79.
add support to the literature showing that under-
Bahr, P. R. (2008). Cooling out in the community
utilization of resources and services is associated
college: What is the effect of academic
with lower academic performance of undergradu-
advising on students’ chances of success?
ates considered academically at risk (Robbins et
Research in Higher Education, 49(80), 704–
al., 2009).
732.
Limitations and Recommendations for Beggs, J. M., Bantham, J. H., & Taylor, S. T.
Practice (2008). Distinguishing the factors influencing
college students’ choice of major. College
Several limitations characterize this study. First
the two groups were not matched on pretest GPA or Student Journal, 42, 381–394.
ethnic and racial background. In an ideal design, Bettinger, E. P., Boatman, A., & Long, B. T.
both groups of students with identical previous and (2013). Student supports: Developmental edu-
current majors (e.g., all students changed their cation and other academic programs. The
major from psychology to social work) would have Future of Children, 23(1), 93–115.
allowed for a more straightforward interpretation of Campbell, S. M., & Nutt, C. L. (2008). Academic
the effect (or lack thereof) of MRS advising on advising in the new global century: Supporting
GPAs. In addition, limited qualitative data were student engagement and learning outcomes
available for elucidating the reasons students in the achievement. Peer Review, 10(1), 4–7.
control group chose not to utilize MRS advising Christian, T. Y., & Sprinkle, J. E. (2013). College
services. student perceptions and ideals of advising: An
Nonetheless, findings from our study offer exploratory analysis. College Student Journal,
some implications for undergraduate education 47(2), 271–291.
NACADA Journal Volume 37(1) 2017 21
McKenzie et al.
Creamer, E. G., & Scott, D. W. (2000). Assessing Heisserer, D. L., & Parette, P. (2002). Advising at-
individual advisor effectiveness. In V. N. risk students in college and university settings.
Gordon & W. R. Habley (Eds.), Academic College Student Journal, 36(1), 69–83.
advising. A comprehensive handbook (pp. Hughey, K. F., Nelson, D. B., Damminger, J. K.,
339–347). San Francisco, CA: Jossey-Bass. & McCalla-Wriggins, B. (Eds.). (2009). The
Creswell, J. W. (2013). Qualitative inquiry and handbook of career advising. San Francisco,
research design: Choosing among fve ap- CA: Jossey-Bass.
proaches. Thousand Oaks, CA: Sage. Hunter, M. S., & White, E. R. (2004). Could
Cuseo, J. B. (1991). The freshman orientation fixing academic advising fix higher education?
seminar: A research-based rationale for its About Campus, 9(1), 20–25.
value, delivery, and content (Monograph Kadar, R. S. (2001). A counseling liaison model
Series No. 4). Columbia: University of South of academic advising. Journal of College
Carolina, National Resource Center for The Counseling, 4(2), 174–178.
Freshman Year Experience. Kot, F. C. (2014). The impact of centralized
DeMarie, D., & Aloise-Young, P. (2003). College advising on first-year academic performance
and second-year enrollment behavior. Re-
students’ interest in their major. College
search in Higher Education, 55(6), 527–563.
Student Journal, 37, 462–469.
Malgwi, C. A., Howe, M. A., & Burnbay, P. A.
Feldt, R., Ferry, A., Bullock, M., Camarotti-
(2005). Influences on students choice of
Carvalho, A., Collingwood, M., Eilers, S., &
college major. Journal of Education for
Nurre, E. (2011). Personality, career indecision
Business, 80(5), 275–282.
and college adjustment in the first semester.
Mayhall, J., & Burg, J. E. (2002). Solution-
Individual Differences Research, 9(2), 107–
focused advising with the undecided student.
114.
NACADA Journal, 22(1), 76–84.
Fife-Schaw, C. (2012). Quasi-experimental de-
Metzner, B. S. (1989). Perceived quality of
sign. In G. M. Breakwell, J. A. Smith, & D. B.
academic advising: The effect on freshmen
Wright (Eds.), Research methods in psychol-
attrition. American Educational Research
ogy (pp. 76–91). London, England: Sage.
Journal, 26, 422–442.
Firmin, M. W., & MacKillop, L. M. (2008).
Mortimer, J. T., Zimmer-Gembeck, M. J.,
Frequent major changing: Extrinsic and intrin-
Holmes, M., & Shanahan, M. J. (2002). The
sic factors. NACADA Journal, 28(2), 5–13.
process of occupational decision making:
Gordon, V. N. (2006). Career advising: An
Patterns during the transition to adulthood.
academic advisor’s guide. San Francisco,
Journal of Vocational Behavior, 61(3), 439–
CA: Jossey-Bass.
465.
Gordon, V. N. (2007). The undecided college
Osipow, S. H. (1983). Theories of career
student: An academic and career advising
development. Englewood Cliffs, NJ: Prentice-
challenge (3rd ed.). Springfield, IL: Charles C
Hall.
Thomas.
Pan, W., Guo, S., & Bai, H. (2008). Do
Gordon, V. N., & Steele, G. E. (1992). Advising
intervention programs assist students to suc-
major changers: Students in transition. ceed in college? A multilevel longitudinal
NACADA Journal, 12(1), 22–27. study. College Student Journal, 42(1), 90–98.
Gottfredson, L. S. (2005). Applying Gottfredson’s Robbins, R. (2012). Everything you have always
theory of circumscription and compromise in wanted to know about academic advising
career guidance and counseling. In S. Brown (well, almost). Journal of College Student
& R. Lent (Eds.), Career development and Psychotherapy, 26, 216–226.
counseling: Putting theory and research to Robbins, S., Allen, J., Casillas, A., Akamigbo,
work (pp. 71–100). Hoboken, NJ: John Wiley A., Saltonstall, M., Campbell, R., & Gore, P.
& Sons. (2009). Associations of resource and service
Grites, T. (2013). Developmental academic ad- utilization, risk level, and college outcomes.
vising: A 40-year context. NACADA Journal, Research in Higher Education, 50(1), 101–
33(1), 5–15. 118.
Grites, T., & Gordon, V. (2000). Developmental Smith, C., & Allen, J. (2006). Essential functions
academic advising revisited. NACADA Jour- of academic advising: What students want and
nal, 29(1), 12–15. get. NACADA Journal, 26(1), 56–66.
22 NACADA Journal Volume 37(1) 2017
Major Re-selection Advising
Steele, G. E. (1994). Major-changers: A special Authors’ Notes
type of undecided student In V. N. Gordon
Deborah McKenzie is the Program Coordinator
(Ed.), Issues in advising the undecided college
for Major Re-selection at the University of
student (Monograph Series No. 15; pp. 85– South Florida. Contact her at [email protected].
92). Columbia: University of South Carolina,
National Resource Center for The Freshman- Tony Xing Tan is a professor of Educational
Year Experience. Psychology at the University of South Florida
Steele, G. E., Kennedy, G. J., & Gordon, V. N.
Edward C. Fletcher is an associate professor of
(1993). The retention of major changers: A
Career and Workforce Education at the Univer-
longitudinal study. Journal of College Student
sity of South Florida.
Development, 34, 58–62.
Steingass, S. J., & Sykes, S. (2008). Centralizing Andrea Jackson-Williams is a PhD student in
advising to improve student outcomes. Peer Educational Psychology at the University of
Review, 10(1), 18–20. South Florida.
NACADA Journal Volume 37(1) 2017 23
McKenzie et al.
Appendix. Major re-selection advising information form
Welcome to the TRansitional Advising 1. Why did you originally choose this major?
Center (TRAC)! Our advisors are here to help ______________________________________
you choose a new major based on your goals, 2. Who or what had any influence on your
interests, and academic abilities. Most often, decision? _____________________________
students need to re-select a major because they 3. Why are you no longer pursuing this major?
no longer meet the GPA requirement for their (Check all that apply)
original major or their career goals and interests u Did not meet GPA requirements
have changed. u Portfolio was denied
Students with ‘‘MJ’’ holds are prevented from u Having difficulty with courses
registering for classes until they declare a major. u Too many prerequisites/courses
Choosing a new major requires active partici- u Loss of interest in the field
pation by both the student and the advisor. u D/F Rule
During the major re-selection process, your u Dismissal/ARC Petition
advisor will explore the degree options available u Academic Probation
to you and may refer you to campus resources u Other:
that can further assist you in making an 4. How may the Major Re-Selection advisor
informed decision. assist you? _____________________________
After you have decided on a major, your 5. What are your career goals?_____________
TRAC advisor will assist you with the declaration 6. Have you ever visited USF’s Career Center for
process and provide contact information for your career exploration?
new for your new major. If you have an ‘‘MJ’’ u Yes
hold, it will be lifted after you officially declare u No
your new major with the appropriate college. 7. How frequently have you been meeting with
All degree plans and courses discussed with your academic advisor? __________________
your TRAC advisor must be confirmed by the 8. How would you describe your study habits?
advisor for your new major. You are expected to ______________________________________
meet with your new advisor immediately upon 9. What could you do to improve? _________
declaring your new major. 10. Please describe any external factors that may
Name: ________________________________ have interfered with your academic performance
Student ID#: __________________________ (i.e., illness, family emergency, first time away
E-mail address: ________________________ from home, etc.): _______________________
Phone: _______________________________ 11. Please cross off majors that you have no
Current Cumulative GPA: ________________ interest in and rank remaining majors based on
Previous Major: ________________________ your interest level
24 NACADA Journal Volume 37(1) 2017
