08832323.2015.1027164
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Predicting MBA Student Success and Streamlining
the Admissions Process
William R. Pratt
To cite this article: William R. Pratt (2015) Predicting MBA Student Success and
Streamlining the Admissions Process, Journal of Education for Business, 90:5, 247-254, DOI:
10.1080/08832323.2015.1027164
To link to this article: http://dx.doi.org/10.1080/08832323.2015.1027164
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Date: 11 January 2016, At: 19:25
JOURNAL OF EDUCATION FOR BUSINESS, 90: 247–254, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1027164
Predicting MBA Student Success and Streamlining
the Admissions Process
William R. Pratt
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:25 11 January 2016
Clarion University of Pennsylvania, Clarion, Pennsylvania, USA
Within this study the author examines factors commonly employed as master of business
administration applicant evaluation criteria to see if these criteria are important in
determining an applicant’s potential for success. The findings indicate that the Graduate
Management Admissions Test (GMAT) is not a significant predictor of student success when
considering factors such as undergraduate grade point average and work experience.
Furthermore, the results suggest that prior findings in support of the GMAT are the result of
missing variables in the model specification. Our results show that undergraduate grade point
average alone can be employed as an admission criterion and indicator of potential success
in lieu of the GMAT; adopting this criterion instead can streamline the admission process
while minimizing student expenses. Within the discussion section the author offers
suggestions for reducing the need for the GMAT score information in the admissions process.
Keywords: admission criteria, GMAT, MBA, waiver
Traditionally, the approach employed to assess a business graduate school applicant involves an official Graduate Management Admissions Test (GMAT) score(s),
undergraduate transcript, resume, letters of recommendation, and a personal essay(s). The GMAT is a standardized exam that is widely used by more than 6,000
management programs worldwide (Graduate Management Admission Council, 2014). The purpose of the
GMAT is to provide business schools with a standardized metric of comparison that other metrics do not possess. However, over the past 25C years the GMAT has
received much criticism and contention such as: claims
of incorrect score reporting over a 10-month period,
wide scale cheating, low explanation of student outcomes, and that the GMAT disadvantages minority students (Dowling, 2009; Fairtest, 2001, 2008; Gropper,
2007; Hechinger, 2008; Tanguma, Serviere-Munoz, &
Gonzalez, 2012). Given such reports, it is important to
ask is the GMAT a necessary admissions criterion?
Within this study I explore this question and to identify
possible alternatives for the admissions process.
Correspondence should be addressed to William R. Pratt, Clarion University of Pennsylvania, Department of Finance, 840 Wood Street, Still
Hall, Office 325, Clarion, PA 16214, USA. E-mail: williamrpratt@gmail.
com
The prior research employs explanatory variables such
as the GMAT scores and undergraduate grade point average
(UGPA) in investigation of which measures are linked to
student success. The results from these studies are mixed in
findings of significance, the covariates employed, and the
response variable(s) used to measure of success. In addition, the majority of studies have not identified or recommended criteria in lieu of the GMAT, and the purpose of
this study is to see if a viable alternative(s) exists.
The empirical findings of this study indicate that the
GMAT is not a consistent significant predictor of student
graduation, whereas UGPA and work experience offer
information on the probability of student outcomes. I find
that years of work experience will tend to result in a lower
probability of graduation, consistent with the prior report of
Gropper (2007), who noted a negative relationship between
work experience and overall master of business administration (MBA) performance. Specifically, I note a nonlinear
relationship between work experience and student
graduation.
The remainder of the article is organized as follows. In
the next section I provide a review of the extant literature,
followed by hypotheses to be tested. The next section
describes the data and methodology employed. I finish with
a presentation of the results with a discussion of limitations
and a conclusion.
248
W. R. PRATT
REVIEW OF LITERATURE
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Admissions Criteria
Typically most graduate schools and researchers employ
admissions criteria such as the GMAT, undergraduate
GPA, work experience, and age as predictors of performance in MBA programs. Some research suggests additional explanatory factors such as gender, undergraduate
field of study, and time since graduation, with some
linking outcomes to the type of program enrollment
(traditional, executive MBA [EMBA], online, 11month).
Studies employing the GMAT can be categorized as
either tests of significance where the GMAT is a predictor
of student outcomes or as an analysis of variance explained.
In studies examining the GMAT, the results are widely
mixed. The literature appears to be equally divided on the
GMAT as a predictor of student success, with varying definitions of success. For instance, Gropper (2007) found that
the GMAT can explain first-year MBA student GPA, however with respect to overall program performance the
GMAT lacks statistical significance. Supporting Gropper’s
findings, a number of studies report significance with first
year performance (Kuncel, Crede, & Thomas, 2005, 2007).
A common finding of predictor studies points to the GMAT
being an important factor, but less than factors such as
UGPA (Borde, 2007; Fairfield-Sonn, Kolluri, Singamsetti,
& Wahab, 2010; Sulaiman & Mohezar, 2006; Yang & Lu,
2001). Related in finding, Ahmadi, Raiszadeh, and Helms
(1997) examined the variance explained by multiple predictors, noting that UGPA accounts for 27% of outcome variance and 18% for the GMAT—comparable findings are
observed in other studies (Paolillo, 1982; Truell, Zhao,
Alexander, & Hill, 2006). Among others, Arnold et al.
(1996) and Seigert (2008) suggested that both the GMAT
and UGPA should be employed when assessing graduate
applicants.
Contrary to the prior, a number of studies report that other
factors are more important than UGPA or GMAT and often
note that one or both lack statistical significance (Christensen,
Nance, & White, 2011; Fish & Wilson, 2009). Adams and
Hancock (2000) reported that amount of work experience is
most related to student success relative to other predictors.
Researchers have suggested that work experience may also
proxy for age or time since undergraduate studies. A study
funded by the Graduate Management Admission Council
suggested that time has a decay effect on the ability of UGPA
to explain MBA success, such that the GMAT becomes a
valuable indicator for assessing those who have been away
from school for a number of years (Talento-Miller & Guo,
2009)—similar findings are reported by Peiperl and Trevelyan (1997). Braunstein (2006) noted that work experience
is a better predictor for students whose undergraduate degree
is not business related. From these reports, I can expect to find
either a positive and negative relationship associated with
work experience, and an undergraduate discipline-dependent
relationship.
Similar to Braunstien (2006), a sizeable amount of
research identifies the type of undergraduate degree as a
potential predictor of success in a MBA program. Sulaiman
and Mohezar (2006) found that undergraduate discipline is
a predictor of MBA success. Moses (1987) also found that
accounting majors are more likely to be successful in certain coursework due to their exposure to frequent reading
of business publications and accounting knowledge. Similarly, Christensen et al. (2011) found support for accounting as a significant determinant, though only at the 10%
level; however, they do identify performance in undergraduate economics and statistics as statistically significant
determinants of success. Gropper (2007) offered similar
evidence of undergraduate degree type lending to success
in a MBA program.
MBA Programs
With advances in technology and an increasing demand
for management training, business schools often offer
multiple methods of instruction to meet the needs of
working professionals. Differing from the traditional
two-year face-to-face MBA, students may pursue a
MBA in a part-time, online, or executive track setting.
It is important to note that students choose their specific
program track, hence method of instruction may be
linked to student characteristics. Hobbs and Gropper
(2013) identified that the characteristics of students
entering into an EMBA differ from students entering
into the traditional face-to-face program—typically an
EMBA requires a minimum of five years of work experience. Guy and Lownes-Jackson (2013) noted that faceto-face students typically have better pre-and postinstruction test results in assessment of a single MBA
course. Davis (2014) noted a similar difference in student performance in online versus traditional. Edward
(2006) suggested the difference is a result of condensing
the traditional two-year program to into intensive formats with new methods of content delivery and structure. As the program types vary, it is not surprising that
predictors vary by program type (Carver & King, 1994;
Fish & Wilson, 2009; Siegert, 2008). However, Taher
et al. (2011) suggested that differences in success are
the result individual personality type and learning
approaches. Their report suggests that it is necessary to
allow for variation by program type.
HYPOTHESES
The functional role of the admissions process is to assess
applicants’ potential for success and fit. Understandably the
PREDICTING MBA STUDENT SUCCESS
result of the admissions process will have a significant
impact on an applicant’s future and incorporating poor criterion could have a negative impact on the applicant’s
future. Considering the significant impact the admissions
process will have on an applicant’s future, it is necessary to
ensure that the admission criterion correctly serve as an
information source of success. A summary of our hypotheses is the following:
Hypothesis 1 (H1): The GMAT is a positive predictor
of MBA student success.
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H2: Undergraduate GPA is a positive predictor of
MBA student success.
H3: Work experience is positive predictor of MBA
student success.
H4: Having an undergraduate business degree would
increase the likelihood of success.
The first research question is, is the GMAT a predictor
of MBA student success? Within the extant literature the
reports are widely mixed. In Kuncel et al. (2005, 2007) and
Siegert (2008) reported that the GMAT is a significant and
important predictor of a student’s potential, whereas Adams
and Hancock (2000) and Christensen (2011) did not find
such a relationship. Per H1, I expect to find a positive relationship between GMAT and graduation, such that the
probability of graduating will increase with an increase in
GMAT score.
Our second research question is interested in the quality
of the information conveyed in undergraduate GPA—is
undergraduate GPA a predictor of MBA student success?
Similar to H1, the literature is mixed in findings, either
identifying UGPA as positively related (Borde, 2007), or,
similar to Seigert (2008), failing to find statistical significance—I am not aware of any studies that report an inverse
(negative) relationship between UGPA and graduating
(Christensen et al., 2011; Fish & Wilson, 2009). Per H2, I
expect UGPA to be positively related to graduation, such
that the probability of graduating will increase with an
increase in UGPA.
For H3, I state that work experience is a positive predictor of MBA success. As in the report of Adams and Hancock (2000), I expect to find that work experience is a
predictor of MBA success, such that an increase in work
experience will lend to a greater probability of success.
I also examine the ability of students having an
undergraduate degree in business versus students that do
not. Consistent with the prior reports of Braunstien
(2006), Sulaiman and Mohezar (2006), and Christensen
et al. (2011), I expect to find that having an undergraduate business degree will increase the likelihood of success—H4.
249
DATA
The analysis employs data on 271 business graduate students that attended an Association to Advance Collegiate Schools of Business–accredited MBA program.
The data employed include information on the students’
undergraduate major, UGPA, graduate GPA, years of
professional work experience, the program track the student applied for (traditional, 11-month program, EMBA,
or online), indication if the student was suspended from
the program, and GMAT score if available. The program employs a requirement of foundation coursework
that is designed to ensure students have a foundation
knowledge before attempting coursework from the MBA
core classes. The foundation coursework is usually fulfilled by an undergraduate degree in business; therefore
foundation coursework is usually typically only required
of students who do not have an undergraduate degree in
business.
During the data collection period, the program employed
a policy of case-by-case GMAT waiver, so the sample
includes GMAT scores for 170 students.1 Hence 101 students were admitted without GMAT scores. Within the
analysis I use the sample of 170 students who have GMAT
scores to attain the regression estimates, I later employ the
larger sample of 271 students to predict the probability of
graduating using only UGPA.
Descriptive Statistics
Table 1 provides the descriptive statistics. The total sample employed is 271, with 170 having GMAT data. The
sample GMAT scores range from 320 to 740 with a mean
of 483. In terms of percentiles of all GMAT test takers,
the sample ranges from the fifth percentile to the 97th and
the mean of the GMAT score is approximately the 27th
percentile of all test takers. Undergraduate GPAs range
from a low of 2.08 to 4.00, the average UGPA entering
into the program is 3.33. Work experience ranges from
zero to 29 years—98 of the 271 individuals reported they
had no professional work experience. The second quartile
includes 38 observations that have one or two years of
work experience. The third quartile spans from three to
seven years and the fourth quartile spans from eight to
29 years. Thirteen percent of all students (N D 271) entering into the graduate program report having double majored in their undergraduate studies.
The response variable graduate indicates if a student
successfully completed the MBA program. The value of
.96 indicates that 4% of the sample was unsuccessful in the
MBA program. The statistic MBA GPA indicates the performance in the 10 core courses of the program—the sample of students who took the GMAT have a mean MBA
250
W. R. PRATT
TABLE 1
Descriptive Statistics
n D 170
GMAT score
Undergraduate GPA
Years of work experience
Double major (1 D yes)
Business major (1 D yes)
Graduate (1 D yes)
MBA GPA
n D 271
M
SD
Min
Max
M
SD
Min
Max
483
3.33
1.63
0.18
0.69
0.96
3.50
73
0.39
3.14
320
2.08
0
740
4.00
17
—
3.31
5.14
0.13
0.64
0.96
3.52
—
0.38
6.52
—
2.08
0
—
4.00
29
0.428
0.47
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Note: GMAT D Graduate Management Admissions Test; GPA D grade point average; MBA D master of business administration.
GPA of 3.50 and the mean GPA increases to 3.52 when the
pool receiving waivers is added to the main sample.
RESEARCH METHOD
Prior findings indicate that the type of MBA program may
affect the student’s likelihood of success. To account for
this effect, I employed a multilevel model with a random
intercept for program type (traditional, EMBA, online, 11month). The response variable yij [Graduate D 1] uses logit
as the link function. With student-level covariates xi and
zeta j accounting for the type of MBA program:
ð2Þ
0
ð2Þ
vs. .963 predicted) and when employing the probit link
the probability of success is 86%, 10% lower than observed
in the sample. In addition, the probit estimates were found
to have the same coefficient sign and similar levels of significance, such that the information is consistent—as
expected the logit and probit differ in the fitted tail values
as a result of the respective cumulative distribution function
(CDF).
I also perform 100,000 simulations using the Stata command GLLASIM. The simulation output employing the
logit link results in a mean estimate of .9631 (s D .0005)
for the intercept only model and .9631 (s D .0171) when
undergraduate GPA is employed. I identify logit as the link
function that better represents the data.
Logit[Prob.yij D 1 j xij ; zj Þ] D xij b C zj
Assuming independence across programs:
ð2Þ
zj j xij » N 0; cð2Þ
Using Stata 13 (Stata Corp., College Station, TX), I
employed the GLLAMM command, which maximizes the
log likelihood via Newton’s method to attain the estimates
presented in the Results section—refer to Rabe-Hesketh
and Scrondal (2003, 2008) for a more in-depth discussion.
Assessing the Link Function
Prior research has identified potential bias in logistic regression when examining rare events. Typically the rare occurrence is observed less than 1% of the time. I assess the link
function by comparing the descriptive statistics of the sample with predicted values that are attained from the regression. If the link function is correctly specified, then I expect
the predicted probability of success to be similar to that of
the sample 96%—refer to the descriptive statistics in
Table 1.
I find that the predicted probabilities using logit as consistent with the sample. The predicted probability of success differs from the actual sample by only .3% (.96 sample
RESULTS
The results of our analysis are presented in Tables 2 and 3.
In Tables 2 and 3, columns 1–4 examine predictors without
employing GMAT scores. Columns 5–9 examine predictor
with the use of GMAT scores. Table 2 reports the estimates
for the sample of students who have GMAT scores
(n D 170). The total sample of students consists of two
groups of students, either those that have a GMAT waiver
(n D 101) or those that having GMAT scores (N D 170),
resulting in a total sample size of 271 students. Table 2 estimates differ from the prior table in for columns 1–4 N D
271, as GMAT scores are not employed and columns 5–9 n
D 170, since GMAT scores are utilized—note that the
reported values in columns 5–8 are identical in Tables 2
and 3.
Column 1 of Tables 2 and 3 employs no covariates,
therefore the intercept value is the mean or average
expected probability of graduating for the sample once the
coefficient is exponentiated and converted into a probability. In columns 2–7, UGPA is a significant predictor of student success at the 95% level or better in support of H2—I
reject the null hypothesis.
The covariates work experience and work experience
squared are reported in columns 3–6. Each predictor is
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TABLE 2
Regression Estimates for Response Variable (Is Graduate [1 D yes, 0 D no])
(1)
Coefficient
Constant
(2)
OR
3.496 (0.158)***
(3)
Coefficient
OR
Coefficient
¡1.504 (1.347)
UGPA
(4)
OR
Coefficient
¡1.335 (2.591)
1.557 (0.389)** 4.745
Work experience2
OR
¡1.956 (4.029)
1.755 (0.610)**
5.783
¡0.842 (0.079)*** 0.431
0.086 (0.029)** 1.090
Work experience
(5)
Business major
Double major
1.992 (0.870)*
7.330
¡0.953 (0.082)*** 0.386
0.099 (0.025)*** 1.104
0.293 (1.303)
1.340
¡1.067 (0.551)
0.344
GMAT score
Enroll
Log likelihood
0.000 (0.000)
0.000 (0.000)
¡22.558
0.000 (0.000)
¡21.598
0.000 (0.000)
¡19.588
Coefficient
(6)
OR
OR
Coefficient
(8)
OR
¡8.130 (2.052)***
¡7.585 (1.651)***
¡4.028 (1.907)*
2.119 (0.153)*** 8.326
¡1.062 (0.237)*** 0.346
1.932 (0.185)*** 6.905
¡0.952 (0.268)*** 0.386
1.115 (0.427)** 3.050
0.981
¡0.019 (0.081)
0.106 (0.008)*** 1.112
1.197
0.180 (1.113)
0.094 (0.007)*** 1.099
¡0.949 (0.423)*
0.387
0.013 (0.009)
1.013
0.000 (0.000)
¡19.238
Coefficient
(7)
0.013 (0.007)
1.013
0.000 (0.000)
¡18.010
¡18.275
Coefficient
(9)
OR
¡4.326 (2.529)
1.200 (0.472)*
OR
¡1.496 (1.190)
3.320
0.009 (0.003)** 1.009
0.009 (0.003)** 1.009
0.000 (0.000)
0.000 (0.000)
¡20.797
Coefficient
0.011 (0.002)*** 1.011
0.000 (0.000)
¡20.808
¡21.307
Note: n D 170. Robust standard errors are Huber/White. Columns 1–4 report coefficient estimates that do not employ GMAT scores. Columns 5–9 report coefficient estimates that do employ GMAT scores. GMAT D Graduate Management Admissions Test; OR D odds
ratio; UGPA D undergraduate grade point average.
*p .05, ** p .01, *** p .001.
TABLE 3
Regression Estimates With Full Sample (N D 271)—Response Variable Is Graduate (1 D yes, 0 D no)
(1)
Coefficient
Constant
3.262 (0.224)***
UGPA
(2)
OR
Coefficient
(3)
OR
¡0.663 (1.161)
OR
0.6123 (2.121)
1.218 (0.385)** 3.379
Work experience
Work experience2
Coefficient
(4)
0.031
Coefficient
0.6223998 (2.363)
0.628
¡0.476 (0.163)**
¡0.872
n
Log likelihood
0.000 (0.000)
271
271
¡42.809
¡41.767
0.000 (0.000)
Coefficient
OR
Coefficient
0.000 (0.000)
(9)
OR
¡8.130 (2.052)***
0.180 (1.113)
Coefficient
OR
¡1.496 (1.190)
3.320
1.197
0.387 (0.423)*
0.013 (0.009)
0.000 (0.000)
OR
2.922
0.418 (0.082)*** ¡0.949
GMAT score
Enroll
Coefficient
1.462
¡0.465 (157)**
Double major
OR
(8)
1.032 (0.005)***
1.072 (0.403)**
Business major
Coefficient
(7)
0.380 (0.646)
2.978
0.031
OR
(6)
¡7.585 (1.651)***
¡4.028 (1.907)*
¡4.326 (2.529)
2.119 (0.153)*** 8.326
1.932 (0.185)*** 6.905
1.115 (0.427)** 3.050
1.200 (0.472)*
0.981
0.621 ¡1.062 (0.237)*** 0.346 ¡0.952 (0.268)*** 0.386 ¡0.019 (0.081)
0.106
1.112 (0.008)*** 0.094
1.099 (0.007)***
1.091 (0.445)*
1.031 (0.006)***
(5)
0.000 (0.000)
1.013
0.013 (0.007)
0.000 (0.000)
1.013
0.009 (0.003)** 1.009
0.009 (0.003)** 1.009
0.011 (0.002)*** 1.011
0.000 (0.000)
0.000 (0.000)
0.000 (0.000)
271
271
170
170
170
170
170
¡38.679
¡38.093
¡18.010
¡18.275
¡20.797
¡20.808
¡21.307
Note: n D 170. Robust standard errors are Huber/White. Columns 1–4 report coefficient estimates that do not employ GMAT scores—these values are attained using the larger data set of N D 271.. Columns 5–9 report coefficient estimates that do employ GMAT scores—
hence the sample size is limited to n D 170. GMAT D Graduate Management Admissions Test; OR D odds ratio; UGPA D undergraduate grade point average.
*p .05, ** p .01, *** p .001.
251
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252
W. R. PRATT
significant at the 99th percent level or better, though not in
support of H3—I am unable reject the null hypothesis. Differing from prior reports, I observe that in general work
experience has a negative influence on a student’s probability of success, though the impact of the negative effect
decreases with time as seen in the predictor work experience squared. These findings potentially counter TalentoMiller and Guo’s (2009) report of a decay effect in the
application of undergraduate grades as a predictor of student success. If I consider work experience as a proxy for
the amount of time since graduation, then I can identify
why a negative sign was observed—that is, perhaps the
quality of information supplied by undergraduate GPA
depreciates with time. Of course, if information depreciation is occurring I would expect the UGPA to capture this
effect, as well, I should observe a significant interaction
between work experience and UGPA, and I do not. Instead
there are other reasonable explanations such as the responsibilities of life increase after college graduation, coinciding with work experience or disconnects between theory
and practical experience. Again, I tested for variable interaction and did not find a significant relationship or a change
in other covariates sign or level of significance. It is also
worthy to note that squaring the work experience covariate
is statistically important and I did not observe this approach
in prior studies; however, it is probable that a prior study
has applied this approach. I discuss work experience in column seven when addressing the results of the predictor
GMAT score.
The indicator variables business major (students having
an undergraduate business degree D 1) and double major
(double majored in undergraduate studies D 1) are reported
in columns 4 and 5. Having an undergraduate business
degree does not appear to convey any useful information at
the standard level, hence the results do not support H4—I
am unable reject the null hypothesis. This finding also suggests that leveling courses do serve their purpose. Double
major is significant at the 90th percentile in column 4 and
at the 95th percentile in column 5. The only notable difference between Tables 2 and 3 is observed in the variable
double major where significance is greater in column four
of Table 3. Unexpectedly, students that double majored
have a lower probability of success, I also test for interactions with other covariates and no statistical significance
was observed.
Columns 5–9 report the estimates for when GMAT
scores are incorporated. In columns 7–9 I find that GMAT
scores are statistically significant; however, in columns 5
and 6 GMAT scores are no longer statistically significant.
The results suggest that GMAT information is significant
only when a model is misspecified—testing for misspecification is addressed in the next paragraph. The results provide only partial support for H1—I am unable to
confidently reject the null hypothesis.
Testing for Misspecification
With the results pointing towards possible misspecification,
I assess the possible specification error by regressing the
linear predicted value and square of the predicted value on
the response variable. If the model is correctly specified the
predicted value will not be statistically significant when
regressed on the response variable—that is, the test has a
null hypothesis that the model is misspecified. As well, the
squared value should not provide any useful information.
The misspecification test was performed for the model estimated in columns 3 (UGPA, work experience) and 8
(UGPA, GMAT). The specification test for column 3 indicated that the model is not misspecified and that I do not
have omitted variables; however, the same test model presented in column 8 indicates that the model is misspecified
and that there is at least one omitted variable.
In addition I compare the fit of the model estimated in
column 3 (UGPA, work experience) with that of column 6
(UGPA, work experience, GMAT) via likelihood ratio test.
The likelihood ratio test measures if the difference in model
fit is statistically better when additional variables are
employed. The test reveals that adding the GMAT does not
statistically improve upon the model of column 3, chisquared probability D .1057. A similar result is found when
comparing the log likelihood of columns 2 and 8—again,
the GMAT does not statistically improve the model fit: chisquared probability D .2087.2
DISCUSSION OF STUDY LIMITATIONS
The study is chiefly limited by a sample that includes graduate students from a single university; therefore these findings may only be representative of this sample and may not
apply to other institutions. This limitation provides an
opportunity for further research. Researchers may be interested in applying the analysis of this study to a data set of
multiple universities. Additionally, other topics of interests
could include variation across university characteristics
such as accreditation, population, and system affiliation.
CONCLUSION
The findings of this study expand on the extant literature by
providing empirical evidence that the GMAT is not a consistent predictor of student success in a MBA program. The findings suggest that prior support for the GMAT may be
attributable to an under specified model. Furthermore, this
finding reveals that the GMAT is not a dependable predictor
of student success, hence it is not a necessary requirement for
evaluating MBA program applicants. Instead the results show
that undergraduate GPA and work experience are more
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PREDICTING MBA STUDENT SUCCESS
appropriate measures for evaluation given that these measures
convey as much if not more information than the GMAT and
adding the GMAT to these measures does not statistically
improve prediction accuracy. Rather, I find that the GMAT
only statistically improves on models I observe as having
specification error. I fail to find evidence that having an
undergraduate degree in a business or business-based discipline (e.g., accounting, finance, management) lends to statistically significant improved probability of success. As well, I
do not find evidence that predictors of success differ by the
type of MBA program (traditional, 11-month program,
EMBA, or online) an applicant pursues.
Again, as these findings indicate that GMAT scores are not
a necessary source of information for evaluating applicants,
therefore Admissions committees may want to reduce student
financial expense and streamline their admissions process/criteria by identifying methods for reducing their need for the
GMAT requirement. Streamlining the admissions process/
criteria maybe achieved by multiple methods, for example,
(a) Estimating the historic probability(ies) of success in a program and then establishing criteria such as a minimum undergraduate GPA that predicts the desired probable level of
success, or (b) If a program is targeting a minimum GMAT
score, then a committee may want to identify the probability
of success associated with the targeted GMAT score and then
identify an equivalent probability of success based on other
criteria (e.g., UGPA, work experience). To be specific the first
method identifies an ideal probability of student success, say
95%. After identifying the target level of success, then ascertain the undergraduate GPA that is associated with a 95%
probability of success. If I assume that candidates with 3.25
undergraduate GPA have a 95% probability of success, I can
then say the target level of .95 is comparable to the UGPA
of 3.25. With respect to the second method, assume there
is University X that wants to target a minimum GMAT
score of 620. Under this method X would identify the probability of success given the 620 GMAT score and then identify
criteria that are able to provide an equivalent probability of
success.
NOTES
1 Twelve of the GMAT scores are converted Graduate
Record Examination (GRE) scores—the analysis was
assessed with and without the converted GRE scores
and there was not a significant change in the data or
results. None of the students having converted scores
were unsuccessful in the program.
2 The likelihood chi-squared ratio is attained by subtracting the difference of the two models and multiplying by two [LR Chi2(1 df) D 2*(–18.275 –
–19.588) D 2.616]. The chi-squared probability is calculated using the measured chi-squared ratio and the
difference in degrees of freedom.
253
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Predicting MBA Student Success and Streamlining
the Admissions Process
William R. Pratt
To cite this article: William R. Pratt (2015) Predicting MBA Student Success and
Streamlining the Admissions Process, Journal of Education for Business, 90:5, 247-254, DOI:
10.1080/08832323.2015.1027164
To link to this article: http://dx.doi.org/10.1080/08832323.2015.1027164
Published online: 23 Apr 2015.
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Date: 11 January 2016, At: 19:25
JOURNAL OF EDUCATION FOR BUSINESS, 90: 247–254, 2015
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2015.1027164
Predicting MBA Student Success and Streamlining
the Admissions Process
William R. Pratt
Downloaded by [Universitas Maritim Raja Ali Haji] at 19:25 11 January 2016
Clarion University of Pennsylvania, Clarion, Pennsylvania, USA
Within this study the author examines factors commonly employed as master of business
administration applicant evaluation criteria to see if these criteria are important in
determining an applicant’s potential for success. The findings indicate that the Graduate
Management Admissions Test (GMAT) is not a significant predictor of student success when
considering factors such as undergraduate grade point average and work experience.
Furthermore, the results suggest that prior findings in support of the GMAT are the result of
missing variables in the model specification. Our results show that undergraduate grade point
average alone can be employed as an admission criterion and indicator of potential success
in lieu of the GMAT; adopting this criterion instead can streamline the admission process
while minimizing student expenses. Within the discussion section the author offers
suggestions for reducing the need for the GMAT score information in the admissions process.
Keywords: admission criteria, GMAT, MBA, waiver
Traditionally, the approach employed to assess a business graduate school applicant involves an official Graduate Management Admissions Test (GMAT) score(s),
undergraduate transcript, resume, letters of recommendation, and a personal essay(s). The GMAT is a standardized exam that is widely used by more than 6,000
management programs worldwide (Graduate Management Admission Council, 2014). The purpose of the
GMAT is to provide business schools with a standardized metric of comparison that other metrics do not possess. However, over the past 25C years the GMAT has
received much criticism and contention such as: claims
of incorrect score reporting over a 10-month period,
wide scale cheating, low explanation of student outcomes, and that the GMAT disadvantages minority students (Dowling, 2009; Fairtest, 2001, 2008; Gropper,
2007; Hechinger, 2008; Tanguma, Serviere-Munoz, &
Gonzalez, 2012). Given such reports, it is important to
ask is the GMAT a necessary admissions criterion?
Within this study I explore this question and to identify
possible alternatives for the admissions process.
Correspondence should be addressed to William R. Pratt, Clarion University of Pennsylvania, Department of Finance, 840 Wood Street, Still
Hall, Office 325, Clarion, PA 16214, USA. E-mail: williamrpratt@gmail.
com
The prior research employs explanatory variables such
as the GMAT scores and undergraduate grade point average
(UGPA) in investigation of which measures are linked to
student success. The results from these studies are mixed in
findings of significance, the covariates employed, and the
response variable(s) used to measure of success. In addition, the majority of studies have not identified or recommended criteria in lieu of the GMAT, and the purpose of
this study is to see if a viable alternative(s) exists.
The empirical findings of this study indicate that the
GMAT is not a consistent significant predictor of student
graduation, whereas UGPA and work experience offer
information on the probability of student outcomes. I find
that years of work experience will tend to result in a lower
probability of graduation, consistent with the prior report of
Gropper (2007), who noted a negative relationship between
work experience and overall master of business administration (MBA) performance. Specifically, I note a nonlinear
relationship between work experience and student
graduation.
The remainder of the article is organized as follows. In
the next section I provide a review of the extant literature,
followed by hypotheses to be tested. The next section
describes the data and methodology employed. I finish with
a presentation of the results with a discussion of limitations
and a conclusion.
248
W. R. PRATT
REVIEW OF LITERATURE
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Admissions Criteria
Typically most graduate schools and researchers employ
admissions criteria such as the GMAT, undergraduate
GPA, work experience, and age as predictors of performance in MBA programs. Some research suggests additional explanatory factors such as gender, undergraduate
field of study, and time since graduation, with some
linking outcomes to the type of program enrollment
(traditional, executive MBA [EMBA], online, 11month).
Studies employing the GMAT can be categorized as
either tests of significance where the GMAT is a predictor
of student outcomes or as an analysis of variance explained.
In studies examining the GMAT, the results are widely
mixed. The literature appears to be equally divided on the
GMAT as a predictor of student success, with varying definitions of success. For instance, Gropper (2007) found that
the GMAT can explain first-year MBA student GPA, however with respect to overall program performance the
GMAT lacks statistical significance. Supporting Gropper’s
findings, a number of studies report significance with first
year performance (Kuncel, Crede, & Thomas, 2005, 2007).
A common finding of predictor studies points to the GMAT
being an important factor, but less than factors such as
UGPA (Borde, 2007; Fairfield-Sonn, Kolluri, Singamsetti,
& Wahab, 2010; Sulaiman & Mohezar, 2006; Yang & Lu,
2001). Related in finding, Ahmadi, Raiszadeh, and Helms
(1997) examined the variance explained by multiple predictors, noting that UGPA accounts for 27% of outcome variance and 18% for the GMAT—comparable findings are
observed in other studies (Paolillo, 1982; Truell, Zhao,
Alexander, & Hill, 2006). Among others, Arnold et al.
(1996) and Seigert (2008) suggested that both the GMAT
and UGPA should be employed when assessing graduate
applicants.
Contrary to the prior, a number of studies report that other
factors are more important than UGPA or GMAT and often
note that one or both lack statistical significance (Christensen,
Nance, & White, 2011; Fish & Wilson, 2009). Adams and
Hancock (2000) reported that amount of work experience is
most related to student success relative to other predictors.
Researchers have suggested that work experience may also
proxy for age or time since undergraduate studies. A study
funded by the Graduate Management Admission Council
suggested that time has a decay effect on the ability of UGPA
to explain MBA success, such that the GMAT becomes a
valuable indicator for assessing those who have been away
from school for a number of years (Talento-Miller & Guo,
2009)—similar findings are reported by Peiperl and Trevelyan (1997). Braunstein (2006) noted that work experience
is a better predictor for students whose undergraduate degree
is not business related. From these reports, I can expect to find
either a positive and negative relationship associated with
work experience, and an undergraduate discipline-dependent
relationship.
Similar to Braunstien (2006), a sizeable amount of
research identifies the type of undergraduate degree as a
potential predictor of success in a MBA program. Sulaiman
and Mohezar (2006) found that undergraduate discipline is
a predictor of MBA success. Moses (1987) also found that
accounting majors are more likely to be successful in certain coursework due to their exposure to frequent reading
of business publications and accounting knowledge. Similarly, Christensen et al. (2011) found support for accounting as a significant determinant, though only at the 10%
level; however, they do identify performance in undergraduate economics and statistics as statistically significant
determinants of success. Gropper (2007) offered similar
evidence of undergraduate degree type lending to success
in a MBA program.
MBA Programs
With advances in technology and an increasing demand
for management training, business schools often offer
multiple methods of instruction to meet the needs of
working professionals. Differing from the traditional
two-year face-to-face MBA, students may pursue a
MBA in a part-time, online, or executive track setting.
It is important to note that students choose their specific
program track, hence method of instruction may be
linked to student characteristics. Hobbs and Gropper
(2013) identified that the characteristics of students
entering into an EMBA differ from students entering
into the traditional face-to-face program—typically an
EMBA requires a minimum of five years of work experience. Guy and Lownes-Jackson (2013) noted that faceto-face students typically have better pre-and postinstruction test results in assessment of a single MBA
course. Davis (2014) noted a similar difference in student performance in online versus traditional. Edward
(2006) suggested the difference is a result of condensing
the traditional two-year program to into intensive formats with new methods of content delivery and structure. As the program types vary, it is not surprising that
predictors vary by program type (Carver & King, 1994;
Fish & Wilson, 2009; Siegert, 2008). However, Taher
et al. (2011) suggested that differences in success are
the result individual personality type and learning
approaches. Their report suggests that it is necessary to
allow for variation by program type.
HYPOTHESES
The functional role of the admissions process is to assess
applicants’ potential for success and fit. Understandably the
PREDICTING MBA STUDENT SUCCESS
result of the admissions process will have a significant
impact on an applicant’s future and incorporating poor criterion could have a negative impact on the applicant’s
future. Considering the significant impact the admissions
process will have on an applicant’s future, it is necessary to
ensure that the admission criterion correctly serve as an
information source of success. A summary of our hypotheses is the following:
Hypothesis 1 (H1): The GMAT is a positive predictor
of MBA student success.
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H2: Undergraduate GPA is a positive predictor of
MBA student success.
H3: Work experience is positive predictor of MBA
student success.
H4: Having an undergraduate business degree would
increase the likelihood of success.
The first research question is, is the GMAT a predictor
of MBA student success? Within the extant literature the
reports are widely mixed. In Kuncel et al. (2005, 2007) and
Siegert (2008) reported that the GMAT is a significant and
important predictor of a student’s potential, whereas Adams
and Hancock (2000) and Christensen (2011) did not find
such a relationship. Per H1, I expect to find a positive relationship between GMAT and graduation, such that the
probability of graduating will increase with an increase in
GMAT score.
Our second research question is interested in the quality
of the information conveyed in undergraduate GPA—is
undergraduate GPA a predictor of MBA student success?
Similar to H1, the literature is mixed in findings, either
identifying UGPA as positively related (Borde, 2007), or,
similar to Seigert (2008), failing to find statistical significance—I am not aware of any studies that report an inverse
(negative) relationship between UGPA and graduating
(Christensen et al., 2011; Fish & Wilson, 2009). Per H2, I
expect UGPA to be positively related to graduation, such
that the probability of graduating will increase with an
increase in UGPA.
For H3, I state that work experience is a positive predictor of MBA success. As in the report of Adams and Hancock (2000), I expect to find that work experience is a
predictor of MBA success, such that an increase in work
experience will lend to a greater probability of success.
I also examine the ability of students having an
undergraduate degree in business versus students that do
not. Consistent with the prior reports of Braunstien
(2006), Sulaiman and Mohezar (2006), and Christensen
et al. (2011), I expect to find that having an undergraduate business degree will increase the likelihood of success—H4.
249
DATA
The analysis employs data on 271 business graduate students that attended an Association to Advance Collegiate Schools of Business–accredited MBA program.
The data employed include information on the students’
undergraduate major, UGPA, graduate GPA, years of
professional work experience, the program track the student applied for (traditional, 11-month program, EMBA,
or online), indication if the student was suspended from
the program, and GMAT score if available. The program employs a requirement of foundation coursework
that is designed to ensure students have a foundation
knowledge before attempting coursework from the MBA
core classes. The foundation coursework is usually fulfilled by an undergraduate degree in business; therefore
foundation coursework is usually typically only required
of students who do not have an undergraduate degree in
business.
During the data collection period, the program employed
a policy of case-by-case GMAT waiver, so the sample
includes GMAT scores for 170 students.1 Hence 101 students were admitted without GMAT scores. Within the
analysis I use the sample of 170 students who have GMAT
scores to attain the regression estimates, I later employ the
larger sample of 271 students to predict the probability of
graduating using only UGPA.
Descriptive Statistics
Table 1 provides the descriptive statistics. The total sample employed is 271, with 170 having GMAT data. The
sample GMAT scores range from 320 to 740 with a mean
of 483. In terms of percentiles of all GMAT test takers,
the sample ranges from the fifth percentile to the 97th and
the mean of the GMAT score is approximately the 27th
percentile of all test takers. Undergraduate GPAs range
from a low of 2.08 to 4.00, the average UGPA entering
into the program is 3.33. Work experience ranges from
zero to 29 years—98 of the 271 individuals reported they
had no professional work experience. The second quartile
includes 38 observations that have one or two years of
work experience. The third quartile spans from three to
seven years and the fourth quartile spans from eight to
29 years. Thirteen percent of all students (N D 271) entering into the graduate program report having double majored in their undergraduate studies.
The response variable graduate indicates if a student
successfully completed the MBA program. The value of
.96 indicates that 4% of the sample was unsuccessful in the
MBA program. The statistic MBA GPA indicates the performance in the 10 core courses of the program—the sample of students who took the GMAT have a mean MBA
250
W. R. PRATT
TABLE 1
Descriptive Statistics
n D 170
GMAT score
Undergraduate GPA
Years of work experience
Double major (1 D yes)
Business major (1 D yes)
Graduate (1 D yes)
MBA GPA
n D 271
M
SD
Min
Max
M
SD
Min
Max
483
3.33
1.63
0.18
0.69
0.96
3.50
73
0.39
3.14
320
2.08
0
740
4.00
17
—
3.31
5.14
0.13
0.64
0.96
3.52
—
0.38
6.52
—
2.08
0
—
4.00
29
0.428
0.47
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Note: GMAT D Graduate Management Admissions Test; GPA D grade point average; MBA D master of business administration.
GPA of 3.50 and the mean GPA increases to 3.52 when the
pool receiving waivers is added to the main sample.
RESEARCH METHOD
Prior findings indicate that the type of MBA program may
affect the student’s likelihood of success. To account for
this effect, I employed a multilevel model with a random
intercept for program type (traditional, EMBA, online, 11month). The response variable yij [Graduate D 1] uses logit
as the link function. With student-level covariates xi and
zeta j accounting for the type of MBA program:
ð2Þ
0
ð2Þ
vs. .963 predicted) and when employing the probit link
the probability of success is 86%, 10% lower than observed
in the sample. In addition, the probit estimates were found
to have the same coefficient sign and similar levels of significance, such that the information is consistent—as
expected the logit and probit differ in the fitted tail values
as a result of the respective cumulative distribution function
(CDF).
I also perform 100,000 simulations using the Stata command GLLASIM. The simulation output employing the
logit link results in a mean estimate of .9631 (s D .0005)
for the intercept only model and .9631 (s D .0171) when
undergraduate GPA is employed. I identify logit as the link
function that better represents the data.
Logit[Prob.yij D 1 j xij ; zj Þ] D xij b C zj
Assuming independence across programs:
ð2Þ
zj j xij » N 0; cð2Þ
Using Stata 13 (Stata Corp., College Station, TX), I
employed the GLLAMM command, which maximizes the
log likelihood via Newton’s method to attain the estimates
presented in the Results section—refer to Rabe-Hesketh
and Scrondal (2003, 2008) for a more in-depth discussion.
Assessing the Link Function
Prior research has identified potential bias in logistic regression when examining rare events. Typically the rare occurrence is observed less than 1% of the time. I assess the link
function by comparing the descriptive statistics of the sample with predicted values that are attained from the regression. If the link function is correctly specified, then I expect
the predicted probability of success to be similar to that of
the sample 96%—refer to the descriptive statistics in
Table 1.
I find that the predicted probabilities using logit as consistent with the sample. The predicted probability of success differs from the actual sample by only .3% (.96 sample
RESULTS
The results of our analysis are presented in Tables 2 and 3.
In Tables 2 and 3, columns 1–4 examine predictors without
employing GMAT scores. Columns 5–9 examine predictor
with the use of GMAT scores. Table 2 reports the estimates
for the sample of students who have GMAT scores
(n D 170). The total sample of students consists of two
groups of students, either those that have a GMAT waiver
(n D 101) or those that having GMAT scores (N D 170),
resulting in a total sample size of 271 students. Table 2 estimates differ from the prior table in for columns 1–4 N D
271, as GMAT scores are not employed and columns 5–9 n
D 170, since GMAT scores are utilized—note that the
reported values in columns 5–8 are identical in Tables 2
and 3.
Column 1 of Tables 2 and 3 employs no covariates,
therefore the intercept value is the mean or average
expected probability of graduating for the sample once the
coefficient is exponentiated and converted into a probability. In columns 2–7, UGPA is a significant predictor of student success at the 95% level or better in support of H2—I
reject the null hypothesis.
The covariates work experience and work experience
squared are reported in columns 3–6. Each predictor is
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TABLE 2
Regression Estimates for Response Variable (Is Graduate [1 D yes, 0 D no])
(1)
Coefficient
Constant
(2)
OR
3.496 (0.158)***
(3)
Coefficient
OR
Coefficient
¡1.504 (1.347)
UGPA
(4)
OR
Coefficient
¡1.335 (2.591)
1.557 (0.389)** 4.745
Work experience2
OR
¡1.956 (4.029)
1.755 (0.610)**
5.783
¡0.842 (0.079)*** 0.431
0.086 (0.029)** 1.090
Work experience
(5)
Business major
Double major
1.992 (0.870)*
7.330
¡0.953 (0.082)*** 0.386
0.099 (0.025)*** 1.104
0.293 (1.303)
1.340
¡1.067 (0.551)
0.344
GMAT score
Enroll
Log likelihood
0.000 (0.000)
0.000 (0.000)
¡22.558
0.000 (0.000)
¡21.598
0.000 (0.000)
¡19.588
Coefficient
(6)
OR
OR
Coefficient
(8)
OR
¡8.130 (2.052)***
¡7.585 (1.651)***
¡4.028 (1.907)*
2.119 (0.153)*** 8.326
¡1.062 (0.237)*** 0.346
1.932 (0.185)*** 6.905
¡0.952 (0.268)*** 0.386
1.115 (0.427)** 3.050
0.981
¡0.019 (0.081)
0.106 (0.008)*** 1.112
1.197
0.180 (1.113)
0.094 (0.007)*** 1.099
¡0.949 (0.423)*
0.387
0.013 (0.009)
1.013
0.000 (0.000)
¡19.238
Coefficient
(7)
0.013 (0.007)
1.013
0.000 (0.000)
¡18.010
¡18.275
Coefficient
(9)
OR
¡4.326 (2.529)
1.200 (0.472)*
OR
¡1.496 (1.190)
3.320
0.009 (0.003)** 1.009
0.009 (0.003)** 1.009
0.000 (0.000)
0.000 (0.000)
¡20.797
Coefficient
0.011 (0.002)*** 1.011
0.000 (0.000)
¡20.808
¡21.307
Note: n D 170. Robust standard errors are Huber/White. Columns 1–4 report coefficient estimates that do not employ GMAT scores. Columns 5–9 report coefficient estimates that do employ GMAT scores. GMAT D Graduate Management Admissions Test; OR D odds
ratio; UGPA D undergraduate grade point average.
*p .05, ** p .01, *** p .001.
TABLE 3
Regression Estimates With Full Sample (N D 271)—Response Variable Is Graduate (1 D yes, 0 D no)
(1)
Coefficient
Constant
3.262 (0.224)***
UGPA
(2)
OR
Coefficient
(3)
OR
¡0.663 (1.161)
OR
0.6123 (2.121)
1.218 (0.385)** 3.379
Work experience
Work experience2
Coefficient
(4)
0.031
Coefficient
0.6223998 (2.363)
0.628
¡0.476 (0.163)**
¡0.872
n
Log likelihood
0.000 (0.000)
271
271
¡42.809
¡41.767
0.000 (0.000)
Coefficient
OR
Coefficient
0.000 (0.000)
(9)
OR
¡8.130 (2.052)***
0.180 (1.113)
Coefficient
OR
¡1.496 (1.190)
3.320
1.197
0.387 (0.423)*
0.013 (0.009)
0.000 (0.000)
OR
2.922
0.418 (0.082)*** ¡0.949
GMAT score
Enroll
Coefficient
1.462
¡0.465 (157)**
Double major
OR
(8)
1.032 (0.005)***
1.072 (0.403)**
Business major
Coefficient
(7)
0.380 (0.646)
2.978
0.031
OR
(6)
¡7.585 (1.651)***
¡4.028 (1.907)*
¡4.326 (2.529)
2.119 (0.153)*** 8.326
1.932 (0.185)*** 6.905
1.115 (0.427)** 3.050
1.200 (0.472)*
0.981
0.621 ¡1.062 (0.237)*** 0.346 ¡0.952 (0.268)*** 0.386 ¡0.019 (0.081)
0.106
1.112 (0.008)*** 0.094
1.099 (0.007)***
1.091 (0.445)*
1.031 (0.006)***
(5)
0.000 (0.000)
1.013
0.013 (0.007)
0.000 (0.000)
1.013
0.009 (0.003)** 1.009
0.009 (0.003)** 1.009
0.011 (0.002)*** 1.011
0.000 (0.000)
0.000 (0.000)
0.000 (0.000)
271
271
170
170
170
170
170
¡38.679
¡38.093
¡18.010
¡18.275
¡20.797
¡20.808
¡21.307
Note: n D 170. Robust standard errors are Huber/White. Columns 1–4 report coefficient estimates that do not employ GMAT scores—these values are attained using the larger data set of N D 271.. Columns 5–9 report coefficient estimates that do employ GMAT scores—
hence the sample size is limited to n D 170. GMAT D Graduate Management Admissions Test; OR D odds ratio; UGPA D undergraduate grade point average.
*p .05, ** p .01, *** p .001.
251
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252
W. R. PRATT
significant at the 99th percent level or better, though not in
support of H3—I am unable reject the null hypothesis. Differing from prior reports, I observe that in general work
experience has a negative influence on a student’s probability of success, though the impact of the negative effect
decreases with time as seen in the predictor work experience squared. These findings potentially counter TalentoMiller and Guo’s (2009) report of a decay effect in the
application of undergraduate grades as a predictor of student success. If I consider work experience as a proxy for
the amount of time since graduation, then I can identify
why a negative sign was observed—that is, perhaps the
quality of information supplied by undergraduate GPA
depreciates with time. Of course, if information depreciation is occurring I would expect the UGPA to capture this
effect, as well, I should observe a significant interaction
between work experience and UGPA, and I do not. Instead
there are other reasonable explanations such as the responsibilities of life increase after college graduation, coinciding with work experience or disconnects between theory
and practical experience. Again, I tested for variable interaction and did not find a significant relationship or a change
in other covariates sign or level of significance. It is also
worthy to note that squaring the work experience covariate
is statistically important and I did not observe this approach
in prior studies; however, it is probable that a prior study
has applied this approach. I discuss work experience in column seven when addressing the results of the predictor
GMAT score.
The indicator variables business major (students having
an undergraduate business degree D 1) and double major
(double majored in undergraduate studies D 1) are reported
in columns 4 and 5. Having an undergraduate business
degree does not appear to convey any useful information at
the standard level, hence the results do not support H4—I
am unable reject the null hypothesis. This finding also suggests that leveling courses do serve their purpose. Double
major is significant at the 90th percentile in column 4 and
at the 95th percentile in column 5. The only notable difference between Tables 2 and 3 is observed in the variable
double major where significance is greater in column four
of Table 3. Unexpectedly, students that double majored
have a lower probability of success, I also test for interactions with other covariates and no statistical significance
was observed.
Columns 5–9 report the estimates for when GMAT
scores are incorporated. In columns 7–9 I find that GMAT
scores are statistically significant; however, in columns 5
and 6 GMAT scores are no longer statistically significant.
The results suggest that GMAT information is significant
only when a model is misspecified—testing for misspecification is addressed in the next paragraph. The results provide only partial support for H1—I am unable to
confidently reject the null hypothesis.
Testing for Misspecification
With the results pointing towards possible misspecification,
I assess the possible specification error by regressing the
linear predicted value and square of the predicted value on
the response variable. If the model is correctly specified the
predicted value will not be statistically significant when
regressed on the response variable—that is, the test has a
null hypothesis that the model is misspecified. As well, the
squared value should not provide any useful information.
The misspecification test was performed for the model estimated in columns 3 (UGPA, work experience) and 8
(UGPA, GMAT). The specification test for column 3 indicated that the model is not misspecified and that I do not
have omitted variables; however, the same test model presented in column 8 indicates that the model is misspecified
and that there is at least one omitted variable.
In addition I compare the fit of the model estimated in
column 3 (UGPA, work experience) with that of column 6
(UGPA, work experience, GMAT) via likelihood ratio test.
The likelihood ratio test measures if the difference in model
fit is statistically better when additional variables are
employed. The test reveals that adding the GMAT does not
statistically improve upon the model of column 3, chisquared probability D .1057. A similar result is found when
comparing the log likelihood of columns 2 and 8—again,
the GMAT does not statistically improve the model fit: chisquared probability D .2087.2
DISCUSSION OF STUDY LIMITATIONS
The study is chiefly limited by a sample that includes graduate students from a single university; therefore these findings may only be representative of this sample and may not
apply to other institutions. This limitation provides an
opportunity for further research. Researchers may be interested in applying the analysis of this study to a data set of
multiple universities. Additionally, other topics of interests
could include variation across university characteristics
such as accreditation, population, and system affiliation.
CONCLUSION
The findings of this study expand on the extant literature by
providing empirical evidence that the GMAT is not a consistent predictor of student success in a MBA program. The findings suggest that prior support for the GMAT may be
attributable to an under specified model. Furthermore, this
finding reveals that the GMAT is not a dependable predictor
of student success, hence it is not a necessary requirement for
evaluating MBA program applicants. Instead the results show
that undergraduate GPA and work experience are more
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PREDICTING MBA STUDENT SUCCESS
appropriate measures for evaluation given that these measures
convey as much if not more information than the GMAT and
adding the GMAT to these measures does not statistically
improve prediction accuracy. Rather, I find that the GMAT
only statistically improves on models I observe as having
specification error. I fail to find evidence that having an
undergraduate degree in a business or business-based discipline (e.g., accounting, finance, management) lends to statistically significant improved probability of success. As well, I
do not find evidence that predictors of success differ by the
type of MBA program (traditional, 11-month program,
EMBA, or online) an applicant pursues.
Again, as these findings indicate that GMAT scores are not
a necessary source of information for evaluating applicants,
therefore Admissions committees may want to reduce student
financial expense and streamline their admissions process/criteria by identifying methods for reducing their need for the
GMAT requirement. Streamlining the admissions process/
criteria maybe achieved by multiple methods, for example,
(a) Estimating the historic probability(ies) of success in a program and then establishing criteria such as a minimum undergraduate GPA that predicts the desired probable level of
success, or (b) If a program is targeting a minimum GMAT
score, then a committee may want to identify the probability
of success associated with the targeted GMAT score and then
identify an equivalent probability of success based on other
criteria (e.g., UGPA, work experience). To be specific the first
method identifies an ideal probability of student success, say
95%. After identifying the target level of success, then ascertain the undergraduate GPA that is associated with a 95%
probability of success. If I assume that candidates with 3.25
undergraduate GPA have a 95% probability of success, I can
then say the target level of .95 is comparable to the UGPA
of 3.25. With respect to the second method, assume there
is University X that wants to target a minimum GMAT
score of 620. Under this method X would identify the probability of success given the 620 GMAT score and then identify
criteria that are able to provide an equivalent probability of
success.
NOTES
1 Twelve of the GMAT scores are converted Graduate
Record Examination (GRE) scores—the analysis was
assessed with and without the converted GRE scores
and there was not a significant change in the data or
results. None of the students having converted scores
were unsuccessful in the program.
2 The likelihood chi-squared ratio is attained by subtracting the difference of the two models and multiplying by two [LR Chi2(1 df) D 2*(–18.275 –
–19.588) D 2.616]. The chi-squared probability is calculated using the measured chi-squared ratio and the
difference in degrees of freedom.
253
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