The Academic Success Inventory for College Students ...

The Academic Success Inventory for College Students ...

The Academic Success Inventory for College Students: Exploratory Factor Analysis and Applicability
Erica Mathis, B.S., Brianna Werner, B.A., and Emily Bullock-Yowell, Ph.D.
Psychology Department, The University of Southern Mississippi

Introduction
In the existing higher education literature, academic success has typically been
measured by grade point average and exam results (Duff, Boyle, Dunleavy, & Ferguson,
2004; Diseth 2003). However, some research has asserted that academic success should
be considered as a more complex construct (Welles, 2010). Often, students decide or are
forced to leave college before degree completion because of their academic success.
These students often leave due to reasons not exclusive to academic performance. Such
reasons include: lack of motivation, lack of career decidedness, lack of support, external
conflict from friends and family, demands of extracurricular activities, and even lack of
self-confidence (Prevatt et al., 2011). Therefore, it is important to assess variables outside
of GPA and course grades to determine what unaddressed aspects of academic success
influence students decisions to leave a degree program before completion.
To address this issue, researchers have developed a self-report measure that assesses
a variety of factors posited to be components of academic success. The Academic Success
Inventory for College Students (ASICS; Prevatt et al., 2011) currently includes ten subscales
which are labeled as the posited factors. These subscales include: Skills, Quality of
Instruction, Career Decidedness, External Motivation/Future, Confidence in Abilities,
Personal Adjustment, Concentration and Self-Regulation, Socializing, Internal
Motivation/Interest, and Lack of Anxiety. The current study sought to explore the measure
in order to determine if there were indeed ten factors represented in the measure. The
current study also had a broader goal of adding to the literature regarding the ASICS, as
there is more literature needed to support the measure and aid in giving the measure
more visibility to practitioners in college settings.
Known correlations with the ASICS
The ASICS total score is:
directly correlated with scores on a measure of positive automatic thoughts (Mathis,
Bullock-Yowell, Leuty, & Nicholson, 2016).
inversely correlated with scores on a measure of negative career thoughts (Mathis et al.,
2016).
positively correlated with scores on a measure of academic major satisfaction (Mathis et
al., 2016).
positively correlated with GPA (Mathis et al., 2016).
For women, the ASICS subscales career decidedness and socializing are positively
correlated with interest profile elevation (Wooten & Bullock-Yowell, 2015).

Hypothesis
An exploratory factor analysis will reveal less than ten factors that assessed the overall
construct of academic success.

Methods

Discussion and Future Directions

Through the SONA research system, 368 undergraduate students (300 female, 68 male; M
age 20.5;63.3% White Non-Hispanic, 31.5% Black Non-Hispanic, 2.2% American
Indian/Alaskan Native, 1.1% Asian/Pacific Islander, 1.1% Other, .8% Hispanic; M GPA 3.04)
were recruited to complete a websurvey in exchange for course credit.
Measures:
Demographics questionnaire
Academic Success Inventory for College Students (ASICS; Prevatt et al., 2011)-50 Items
ASICS Scales

M

Total Score
.88
3077
General Academic Skills
Perceived Instructor Efficacy
Career Decidedness
External Motivation/Future
External Motivation/Current
Personal Adjustment
Concentration
.81
46
Socializing
.83
76
Internal Motivation/Confidence
Lack of Anxiety
.82
37

SD
497
.89
68
.42
61
.86
77
.87
61
.53
74
.83
62
20
21
.82
57
20

17
23
22
24
15
24

17

Range across all 14 99.

Results
An exploratory factor analysis using principal axis factoring with a direct oblimin oblique
rotation was conducted in order to extract items for the factor structure. Skewness and
kurtosis were assessed. The number of factors that were extracted was determined by
eigenvalues (Kaiser, 1958), Cattells scree test (Cattell, 1966), parallel analysis (Horn, 1965),
and minimum average partial (Velicer, 1976). The number of factors to be retained was
informed by these analyses and reasoning assessed through past scientific knowledge.
Cattells scree test suggested that there were 11 factors, the parallel analysis suggested
that there were 10 factors, and the revised minimum average partial suggested that there
were 9 factors. Therefore, it was concluded that the hypothesis was not supported and, in
fact, a 10 factor stucture was supported in the ASICS.

The ASICS is an important measure for assessing college students success in
a more global manner than relying on GPA and course grades alone.
Conducting more research on and including this measure would likely
increase the visibility of the measure to practitioners in a college or university
setting.
While there appears to be some weak evidence for 9 factors, the current
study found that there are likely 10 factors represented on the ASICS.
More research is needed to confirm 10 factors, specifically in a more diverse
college sample.
Additionally, future research should consider the effects of incentivizing
participants through course credit. This could lead students to falsely believe
that they should answer in an overly positive manner.

References
Cattell, B.R. (1966). The scree test for number of factors. Multivariate Behavioral Research, 1(2). 245-276.
Kaiser, H.F. (1958). The varimax criterion of analytic rotation in factor analysis. Psychometrika, 23, 187 -200.
Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the depression anxiety stress scales. Sydney: Psychology
Foundation.
Mathis, E.L., Bullock-Yowell, E., Leuty, M., and Nicholson, B. (2016). Congruence with College Major in Light
of Cognitive Influence and Work Roles. (Unpublished masters thesis) University of Southern Mississippi,
Hattiesburg, Mississippi.
National Center for O*NET Development. Interest Profiler (IP) Short Form. O*NET Resource Center. Retrieved
from https://www.onetcenter.org.org/IPSF.htlm
Prevatt, F., Li, H., Welles, T., Festa-Dreher, D., Yelland, S., & Lee, J. (2011).The Academic Success
Inventory for College Students: Scale Development and Practical Implications for Use with Students. Journal
Of College Admission, (211), 26-31.
Swanson, J. L., & Hansen, J. C. (1986). A clarification of Holland's construct of differentiation: The
importance of score elevation. Journal Of Vocational Behavior, 28(2), 163-173. doi:10.1016/00018791(86)90049-7
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations.
Psychometrika, 41(3), 321 327.
Welles, T. L. (2010). An analysis of the Academic Success Inventory for College Students: construct validity
and factor scale invariance. Unpublished doctoral dissertation, Florida State University, Tallahassee, FL.
Wooten, E. & Bullock-Yowell, E. (2015). Does overall level of career interest relate to student academic
success. Poster presentation at the USM Undergraduate Research Symposium, University of Southern
Mississippi, Hattiesburg, MS.

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