Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.83.3.159-164

Journal of Education for Business

ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20

A Structural Equation Model for Predicting
Business Student Performance
James J. Pomykalski , Paul Dion & James L. Brock
To cite this article: James J. Pomykalski , Paul Dion & James L. Brock (2008) A Structural
Equation Model for Predicting Business Student Performance, Journal of Education for
Business, 83:3, 159-164, DOI: 10.3200/JOEB.83.3.159-164
To link to this article: http://dx.doi.org/10.3200/JOEB.83.3.159-164

Published online: 07 Aug 2010.

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A฀Structural฀Equation฀Model฀for฀Predicting฀
Business฀Student฀Performance฀
JAMES฀J.฀POMYKALSKI฀
PAUL฀DION฀
JAMES฀L.฀BROCK฀
SUSQUEHANNA฀UNIVERSITY฀
SELINSGROVE,฀PENNSYLVANIA฀

ABSTRACT. In฀this฀study,฀the฀authors฀
developed฀a฀structural฀equation฀model฀that฀
accounted฀for฀79%฀of฀the฀variability฀of฀a฀

student’s฀final฀grade฀point฀average฀by฀using฀
a฀sample฀size฀of฀147฀students.฀The฀model฀is฀
based฀on฀student฀grades฀in฀4฀foundational฀
business฀courses:฀introduction฀to฀business,฀
macroeconomics,฀statistics,฀and฀using฀databases.฀Educators฀and฀administrators฀can฀
use฀a฀valid฀structural฀equation฀model฀as฀(a)฀
a฀criterion฀for฀admitting฀students฀to฀business฀school,฀(b)฀an฀advising฀tool฀to฀suggest฀
to฀underperforming฀students฀that฀a฀major฀
other฀than฀business฀should฀be฀considered,฀
and฀(c)฀a฀tool฀to฀suggest฀that฀mentoring฀is฀
necessary.฀
Keywords:฀business,฀grade฀point฀average,฀
macroeconomics,฀structural฀equation฀model
Copyright฀©฀2008฀Heldref฀Publications

฀

C

an฀educators฀predict฀the฀success฀of฀

seniors฀in฀an฀undergraduate฀business฀ program฀ by฀ early฀ (i.e.,฀ freshman฀
year)฀ performance฀ variables?฀ If฀ valid฀
performance฀ predictors฀ can฀ be฀ identified,฀ how฀ can฀ the฀ educators฀ apply฀ the฀
predictors฀ to฀ increase฀ student฀ success฀
and฀program฀quality?฀The฀first฀of฀these฀
questions฀ is฀ the฀ primary฀ focus฀ of฀ this฀
article,฀ in฀ which฀ we฀ describe฀ a฀ structural฀equation฀model.฀Although฀the฀second฀question฀deserves฀more฀discussion,฀
three฀ applications฀ are฀ readily฀ apparent.฀
First,฀ using฀ the฀ valid฀ performance฀ predictors,฀ academic฀ advisors฀ can฀ assist฀
underperforming฀students฀early฀in฀their฀
college฀careers฀in฀choosing฀majors฀outside฀the฀business฀school,฀if฀appropriate.฀
Second,฀these฀predictors฀identify฀at-risk฀
students฀for฀mentoring.฀Third,฀these฀predictors฀could฀also฀lead฀to฀an฀increase฀in฀
student฀ quality฀ because฀ only฀ students฀
who฀have฀achieved฀some฀threshold฀performance฀ level฀ would฀ be฀ accepted฀ into฀
the฀program.
For฀ an฀ Association฀ to฀ Advance฀ Collegiate฀Schools฀of฀Business฀International฀
(AACSB)–accredited฀ business฀ program฀
at฀a฀small,฀private,฀liberal฀arts฀university,฀
the฀ success฀ of฀ students฀ (measured฀ by฀

4-year฀graduation฀rates,฀job฀placements,฀
etc.)฀ is฀ paramount฀ to฀ the฀ mission.฀ The฀
number฀ of฀ business฀ school฀ students฀ is฀
restricted฀to฀a฀proportion฀of฀total฀enrollments฀ to฀ maintain฀ the฀ liberal฀ arts฀ character฀ of฀ the฀ institution.฀ Administrators฀

strive฀to฀focus฀resources฀on฀students฀who฀
have฀ the฀ highest฀ probability฀ of฀ success.฀
Historically,฀ any฀ student฀ could฀ declare฀
himself฀ or฀ herself฀ a฀ business฀ major฀ on฀
admission.฀ However,฀ the฀ university฀ is฀
considering฀ a฀ policy฀ that฀ prevents฀ students฀ from฀ declaring฀ majors฀ until฀ their฀
sophomore฀year—which฀would฀represent฀
a฀ move฀ that฀ could฀ enable฀ the฀ business฀
school฀ to฀ establish฀ selective฀ admission฀
criteria.฀ Currently,฀ the฀ business฀ school฀
requires฀ each฀ student฀ to฀ take฀ the฀ same฀
foundation฀courses฀for฀majors฀in฀business฀
administration,฀ economics,฀ or฀ accounting.฀ Business฀ majors฀ can฀ choose—during฀the฀junior฀year—an฀area฀of฀emphasis฀
within฀the฀business฀administration฀major.฀
The฀current฀list฀of฀areas฀of฀emphasis฀that฀

students฀ can฀ select฀ includes฀ entrepreneurship,฀ finance,฀ global฀ management,฀
human฀ resource฀ management,฀ information฀systems,฀and฀marketing.
In฀ spring฀ 2006,฀ first-year฀ business฀
majors฀took฀business฀foundation฀courses฀
that฀ included฀ introduction฀ to฀ business,฀
macroeconomics,฀ statistics,฀ and฀ using฀
databases.฀ We฀ used฀ students’฀ grades฀
from฀ these฀ courses฀ and฀ their฀ Scholastic฀ Assessment฀ Test฀ (SAT)฀ scores,฀
high฀ school฀ performance,฀ and฀ gender฀
to฀ construct฀ an฀ initial฀ model฀ to฀ predict฀
senior฀grade฀point฀average฀(GPA).฀Later฀
versions฀ of฀ the฀ model฀ eliminated฀ SAT฀
scores,฀ high฀ school฀ performance,฀ and฀
gender฀ because฀ these฀ were฀ not฀ statistically฀significant.
January/February฀2008฀

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Review฀of฀Literature
A฀ review฀ of฀ business฀ literature฀
revealed฀only฀one฀study฀in฀which฀coursework฀was฀used฀to฀predict฀the฀success฀of฀
business฀students.฀Using฀senior฀GPA฀as฀
a฀ dependent฀ variable,฀ Brown,฀ McCormick,฀ and฀ Abraham฀ (2002)฀ found฀ that฀
a฀ single฀ course—principles฀ of฀ macroeconomics—served฀as฀a฀good฀indicator฀
of฀overall฀student฀success.฀Their฀model฀
accounted฀ for฀ 36%฀ of฀ the฀ variance฀ in฀
GPA,฀and฀they฀concluded฀that฀success฀in฀
the฀macroeconomics฀course฀was฀highly฀
correlated฀ with฀ success฀ in฀ any฀ undergraduate฀business฀major฀field฀(accounting,฀ economics,฀ finance,฀ information฀
systems,฀marketing,฀or฀management).฀
Predictions฀based฀solely฀on฀students’฀
American฀ College฀ Test฀ (ACT)฀ scores฀
have฀shown฀mixed฀results.฀Brown฀et฀al.฀
(2002)฀ and฀ Laband฀ and฀ Piette฀ (1995)฀
showed฀that฀ACT฀scores฀had฀little฀to฀no฀
impact.฀ However,฀ Naumann,฀ Bandalos,฀
and฀ Gutkin฀ (2003)฀ found฀ that฀ a฀ student’s฀ACT฀score฀was฀a฀solid฀predictor฀
of฀success฀but฀that฀using฀a฀group฀of฀selfregulated฀ learning฀ variables฀ (SRLVs)฀

enhanced฀ the฀ prediction.฀ The฀ SRLVs฀
include฀ both฀ motivational฀ variables฀
(intrinsic฀ goal฀ setting,฀ expectancy฀ for฀
success฀ beliefs,฀ and฀ self-efficacy)฀ and฀
strategy฀variables฀(study฀strategies,฀goal฀
setting,฀and฀time฀management).
Ballard฀and฀Johnson฀(2004)฀studied฀the฀
link฀between฀students’฀basic฀math฀skills฀
and฀ their฀ performance฀ in฀ a฀ beginning-฀
level฀ microeconomics฀ course,฀ which฀
is฀ typically฀ a฀ more฀ quantitative฀ course฀
than฀ macroeconomics.฀ Their฀ findings฀
revealed฀ that฀ the฀ students’฀ scores฀ on฀ a฀
faculty-designed,฀basic฀math-skills฀quiz฀
were฀“positively฀and฀significantly฀related฀to฀student฀performance”฀(p.฀15).฀
Aside฀from฀the฀work฀of฀Brown฀et฀al.฀
(2002),฀efforts฀to฀predict฀student฀success฀
by฀using฀previous฀coursework฀in฀undergraduate฀ business฀ programs฀ are฀ lacking.฀Additional฀ studies฀ that฀ incorporate฀
courses฀in฀addition฀to฀macroeconomics฀
for฀such฀prediction฀seem฀promising.

Initial฀Model฀Design
We฀proposed฀to฀construct฀a฀predictive฀
model฀ of฀ overall฀ student฀ performance,฀
as฀gauged฀by฀senior฀GPA,฀by฀using฀two฀
classes฀of฀variables:฀student฀performance฀
and฀ student฀ aptitude.฀ Student฀ perfor160฀

Journal฀of฀Education฀for฀Business

mance฀variables฀are฀the฀final฀grades฀in฀the฀
four฀ freshman-year฀ foundation฀ courses:฀
introduction฀to฀business฀(indicated฀in฀the฀
Appendix฀as฀IBGRD),฀statistics฀(SGRD),฀
macroeconomics฀(MEGRD),฀and฀a฀database฀course฀that฀was฀called฀“using฀databases”฀ (UDGRD).฀ These฀ courses฀ are฀
consistent฀with฀the฀AACSB฀accreditation฀
guidelines฀ (2005),฀ and฀ many฀ AACSB฀
programs฀may฀require฀similar฀courses฀in฀
either฀the฀first฀year฀or฀the฀second฀year฀of฀
the฀program.฀Aptitude฀variables฀included฀
verbal฀ SAT฀ score฀ (VSAT),฀ math฀ SAT฀

score฀ (MSAT),฀ and฀ high฀ school฀ GPA฀
(HSGPA).฀Brown฀et฀al.฀(2002)฀also฀used฀
several฀of฀these฀measures.
We฀ included฀ two฀ additional฀ variables—high฀school฀rank฀(HSRANK)฀and฀
gender฀ (GNDR)—in฀ the฀ initial฀ model.฀
High฀school฀rank฀is฀a฀percentile฀measure฀
created฀as฀the฀ratio฀of฀high฀school฀class฀
rank฀ to฀ high฀ school฀ class฀ size.฀ This฀ is฀
the฀ same฀ procedure฀ used฀ by฀ Brown฀ et฀
al.฀(2002).฀Researchers฀have฀used฀gender฀
in฀ previous฀ studies฀ with฀ mixed฀ results.฀
Brown฀et฀al.฀found฀that฀gender฀was฀statistically฀insignificant,฀whereas฀Holahan,฀
Green,฀and฀Kelley฀(1983),฀Heath฀(1989),฀
and฀ Laband฀ and฀ Piette฀ (1995),฀ all฀ concluded฀ that฀ gender฀ does฀ play฀ a฀ role฀ in฀
student฀performance.
We฀ collected฀ the฀ data฀ through฀ the฀
university฀registrar’s฀office.฀We฀collected฀grades฀of฀all฀current฀business฀majors฀
from฀fall฀2004฀to฀spring฀2004.฀The฀model฀
sample฀ included฀ the฀ 147฀ students฀ who฀
completed฀ the฀ degree฀ requirements฀ of฀

the฀university฀in฀May฀2004.฀This฀sample฀
exhibited฀a฀profile฀consistent฀with฀those฀
of฀ other฀ recent฀ graduating฀ classes.฀The฀
Appendix฀presents฀the฀aptitude,฀performance,฀and฀dependent฀variables฀that฀we฀
initially฀considered฀in฀the฀study.
This฀analysis฀used฀structural฀equation฀
modeling฀(SEM),฀“a฀comprehensive฀statistical฀ approach฀ to฀ testing฀ hypotheses฀
about฀ relations฀ among฀ observed฀ and฀
latent฀ variables”฀ (Hoyle,฀ 1995,฀ p.฀ 4).฀
SEM฀ can฀ “answer฀ a฀ set฀ of฀ interrelated฀
research฀ questions฀ in฀ a฀ single,฀ systematic,฀ and฀ comprehensive฀ analysis฀ by฀
modeling฀the฀relationships฀among฀multiple฀ independent฀ and฀ dependent฀ constructs฀simultaneously”฀(Gefen,฀Straub,฀
&฀Boudreau,฀2000,฀p.฀4).
The฀ initial฀ structural฀ model,฀ known฀
as฀ a฀ path฀ model฀ (Hoyle,฀ 1995),฀ examines฀ the฀ effects฀ of฀ each฀ of฀ the฀ aptitude฀

variables฀ on฀ the฀ grades฀ in฀ each฀ of฀ the฀
four฀ foundation฀ courses.฀ In฀ addition,฀
the฀ analysis฀ measures฀ each฀ of฀ the฀ aptitude฀ variables฀ for฀ its฀ direct฀ impact฀ on฀
the฀senior฀GPA.฀Also,฀we฀examined฀the฀

effects฀ of฀ the฀ grades฀ in฀ the฀ foundation฀courses฀on฀senior฀GPA.฀For฀example,฀ the฀ independent฀ aptitude฀ variable฀
VSAT฀may฀affect฀senior฀GPA฀directly฀or฀
through฀ each฀ of฀ the฀ foundation฀ course฀
grades.฀We฀measured฀the฀impact฀of฀each฀
of฀the฀aptitude฀variables฀on฀senior฀GPA฀
in฀two฀ways:฀through฀each฀of฀the฀performance฀variables฀and฀on฀senior฀GPA.฀In฀
addition,฀we฀measured฀the฀impact฀of฀the฀
final฀ grade฀ of฀ each฀ foundation฀ course฀
on฀ senior฀ GPA.฀ We฀ performed฀ all฀ the฀
analyses฀as฀part฀of฀the฀SEM฀approach.
Resultant฀Model
We฀used฀AMOS฀4.0,฀a฀SEM฀package฀
from฀ SPSS,฀ Inc.,฀ to฀ find฀ and฀ confirm฀
the฀ statistically฀ significant฀ interrelationships฀ that฀ form฀ the฀ resultant฀ path฀
model.฀ AMOS฀ is฀ a฀ covariance-based฀
technique.
Figure฀ 1฀ represents฀ the฀ final฀ resultant฀path฀model฀after฀the฀analysis฀using฀
AMOS.฀ The฀ values฀ on฀ the฀ paths฀ are฀
standardized฀coefficients,฀and฀the฀numbers฀ above฀ the฀ variables—represented฀
by฀boxes—are฀the฀R2฀values.
The฀model฀shows฀that฀the฀major฀predictors฀of฀a฀student’s฀senior฀GPA฀are฀the฀
final฀grades฀that฀the฀student฀receives฀in฀
the฀four฀foundation฀classes,฀with฀macroeconomics฀and฀statistics฀being฀the฀major฀
contributors.฀ The฀ student’s฀ final฀ grade฀
in฀macroeconomics฀was฀the฀key฀indicator฀in฀the฀Brown฀et฀al.฀(2002)฀study.
The฀ analysis฀ shows฀ that฀ the฀ model฀
fits฀the฀observed฀data,฀as฀the฀normed฀fit฀
index฀(NFI)฀is฀0.999,฀with฀Hoyle฀(1995)฀
considering฀ values฀ of฀ 0.9฀ or฀ greater฀ as฀
indicating฀ a฀ good฀ fit.฀ In฀ addition,฀ the฀
model฀ has฀ χ2(3,฀ N฀ =฀ 147)฀ =฀ 3.608,฀ p฀
=฀ .307.฀ Kline฀ (1998)฀ suggested฀ that฀ a฀
good฀ fit฀ occurs฀ when฀ the฀ ratio฀ of฀ chisquare฀to฀degrees฀of฀freedom฀is฀less฀than฀
3.฀However,฀because฀the฀distribution฀of฀
chi-square฀ is฀ such฀ that฀ E(χ2)฀ =฀ df,฀ the฀
ideal฀fit฀would฀be฀where฀the฀ratio฀of฀chisquare฀to฀degree฀of฀freedom฀is฀equal฀to฀
1฀(Maruyama,฀1997).฀Thus,฀the฀NFI฀and฀
chi-square฀tests฀indicate฀that฀the฀model฀
is฀a฀good฀representation฀of฀the฀data,฀and฀
the฀model฀fits฀the฀data.

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Introduction฀to
business

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0.24

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Business฀statistics

0.00

0.18

0.31
0.79

Verbal฀SAT

0.09

Senior฀GPA

0.47

0.33

0.30
Macroeconomics

0.23
0.36

Using฀databases

FIGURE฀1.฀The฀resultant฀path฀model฀from฀the฀analysis฀of฀data.฀The฀values฀on฀the฀paths฀are฀standardized฀coefficients,฀
and฀the฀number฀above฀the฀variable—represented฀by฀boxes—are฀the฀R 2฀values.

The฀resultant฀path฀model฀(see฀Figure฀
1)฀indicates฀that฀the฀grades฀in฀introduction฀ to฀ business,฀ statistics,฀ and฀ macroeconomics฀ are฀ influenced฀ by฀ the฀ student’s฀ verbal฀ ability—as฀ measured฀ by฀
the฀VSAT฀score.฀The฀model฀also฀depicts฀
the฀ interrelationships฀ among฀ the฀ variables฀ and฀ specifies฀ the฀ significance฀ of฀
these฀ interrelationships.฀ Table฀ 1฀ shows฀
that฀ each฀ variable฀ in฀ the฀ final฀ model,฀
with฀ the฀ exception฀ of฀ VSAT,฀ is฀ significant฀ at฀ p฀