08832323.2015.1110552
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Faculty salary as a predictor of student outgoing
salaries from MBA programs
Karla R. Hamlen & William A. Hamlen
To cite this article: Karla R. Hamlen & William A. Hamlen (2016) Faculty salary as a predictor of
student outgoing salaries from MBA programs, Journal of Education for Business, 91:1, 38-44,
DOI: 10.1080/08832323.2015.1110552
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Date: 11 January 2016, At: 18:55
JOURNAL OF EDUCATION FOR BUSINESS
2016, VOL. 91, NO. 1, 38–44
http://dx.doi.org/10.1080/08832323.2015.1110552
Faculty salary as a predictor of student outgoing salaries from MBA programs
Karla R. Hamlena and William A. Hamlenb
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
a
Cleveland State University, Cleveland, Ohio, USA; bState University of New York at Buffalo, Buffalo, New York, USA
ABSTRACT
KEYWORDS
The authors’ purpose was to investigate the predictive value of faculty salaries on outgoing salaries of
master of business administration (MBA) students when controlling for other student and program
variables. Data were collected on 976 MBA programs using Barron’s Guide to Graduate Business Schools
over the years 1988–2005 and the Princeton Review’s The Best 295 Business Schools 2014 edition. A
hierarchical linear regression analysis was conducted with student and program characteristics as
control variables, faculty salary as the predictor variable, and average outgoing salary as the
dependent variable. In general, higher faculty salaries were associated with higher starting salaries for
MBA students upon graduation. Potential explanations and limitations are discussed.
faculty salary; MBA students;
utgoing salary; regression
With rising numbers of graduate school applicants
despite fluctuations in the economy ( Graduate Management Admission Council [GMAC], 2012), faculty and
administrators in business schools continually seek to
identify factors that most impact student outcomes upon
graduation to offer competitive programs of high quality.
There is disagreement, however, over which factors are
indicative of the quality of a master of business administration (MBA) program, and whether these translate to
improved career-related outcomes upon graduation. Student characteristics have been widely used for measuring
program quality, but these may be more highly indicative
of the rigor of admissions than of the program itself. It
has been suggested that economic attainment upon graduation may be a better outcome to consider when looking for ways to assess and improve MBA programs
(Tracy & Waldfogel, 1997). In addition, the role of faculty salary in student outcomes has not been as widely
investigated as some other student and program variables. The purpose of this study was to determine student,
faculty, and program characteristics that predict the
starting salary of graduates from MBA programs and, in
particular, the relationship between faculty salary and
outgoing starting salary, when controlling for other student and program characteristics. The research questions
to be addressed are the following:
Research Question 1 (RQ1): Do faculty salaries predict
outgoing salary of MBA program graduates, above
and beyond the predictive value of other program
and student variables?
CONTACT Karla R. Hamlen
Cleveland, OH 44115, USA.
© 2016 Taylor & Francis Group, LLC
k.hamlen@csuohio.edu
RQ2: Do these relationships differ based on quality of
the students entering the program, with quality
measured by incoming student GMAT (Graduate
Management Admissions Test) scores?
Significance
Previous research addressing the assessment of MBA programs has pointed toward the use of economic attainment
upon graduation as a more viable outcome variable than
most student characteristics for evaluation and measurement
of MBA programs. As recommended by previous researchers, in the present study the outcomes of MBA programs
were measured using the outgoing salary of graduates. A
pre-existing and publicly available data set was used, allowing
for a large sample. This study adds to the research base in
this field by incorporating a larger sample size than most
previous research, by utilizing a dependent variable that better captures workplace outcomes rather than academic success within the program itself, and also by highlighting the
predictive value of faculty salaries when controlling for many
other potential factors relating to outgoing student salaries.
Related literature
Justification for evaluating an MBA program
through outgoing salaries
One challenge in evaluating MBA programs is that the
quality of students admitted to top programs can skew
Cleveland State University, Department of Curriculum & Foundations, 2121 Euclid Avenue,
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
JOURNAL OF EDUCATION FOR BUSINESS
the outcome variables. While student characteristics such
as scores on the GMAT and prior grade point average
(GPA) may predict success in an MBA program (Kass,
Grandzol, & Bommer, 2012; Siegert, 2008; TalentoMiller & Rudner, 2008), they do not necessarily predict
actual business workplace competencies such as decision-making and leadership skills (Kass et al., 2012).
The precedent for measuring career success of business school graduates by their salaries dates at least as far
back as the 1970s (e.g., Gutteridge, 1973). Tracy and
Waldfogel (1997) posited that evaluation of the quality
of MBA programs is better done through graduate performance in the market—outgoing salaries—than
through subjective opinions or incoming student characteristics. Ray and Jeon (2008) also named MBA graduate
earnings a measure of success of an MBA program, based
on their finding that rankings are often a reflection of
inputs (student and program characteristics). They demonstrated that some lower-ranked programs utilize their
resources more efficiently to result in higher outgoing
student salaries, in relation to incoming student characteristics and program resources, than their higher-ranked
counterparts.
Predictor and control variables
Faculty salary was the primary predictor of interest in
this study, and several other variables were included as
control variables, based on the possibility that they may
also be predictors of MBA program outcomes, as demonstrated in previous studies.
Faculty salary
Research has shown that higher education institutions
tend to use faculty salary to reward faculty activities that
enhance university prestige, such as research, obtaining
grants, and particularly publication in top-tier journals.
This appears to be equally true regardless of whether the
institution’s mission emphasizes teaching or research
(Braxton, Luckey, & Helland, 2002; Fairweather, 2005;
Gomez-Mejia & Balkin, 1992; Melguizo & Strober,
2007). Faculty rank has also been shown to be directly
proportional to faculty research productivity (White,
James, Burke, & Allen, 2012), and those of a higher faculty rank are more likely to have more experience and
seniority than their counterparts at other ranks (Strathman, 2000). Furthermore, it has been suggested that faculty quality is directly related to research grant
performance, and that obtaining research grants not
only increases faculty salaries but also allows for more
university resources in the form of equipment, personnel, and technology (Mohr, 1999). In general, faculty feel
that a greater value and recognition is placed on research
39
achievements as opposed to teaching achievements
(Alpay & Verschoor, 2014). Interestingly, higher base
faculty salaries tend to be related to spending fewer hours
in the classroom teaching among comprehensive and
doctoral granting institutions, with higher salary returns
due to greater teaching hours being a rare occurrence in
any type of institution (Fairweather, 2005).
There is little research exploring connections between
faculty quality or faculty salaries and MBA program outcomes. Student ratings of faculty have been significantly
related to the students’ likelihood of recommending their
MBA program to others (Bruce & Edgington, 2008), and
there is a positive relationship between the value of the
MBA degree—partially measured by expected outgoing
salaries of graduates—and student likelihood of recommending the program (Bruce & Edgington, 2008). The
present study is an investigation of the predictive value
of faculty salary when value is assessed by outgoing salary of graduates of an MBA program. Faculty salary is
particularly valuable as a predictor variable when, as in
many cases, other measures of faculty quality, such as
research productivity and prestige, are unavailable.
GMAT and GPA scores
The GMAT is generally used as the primary admissions
exam required for business schools, designed to function
as an aptitude test for business school. Many researchers
have provided evidence that the GMAT successfully predicts success in MBA programs, when success is measured
by academic performance (Koys, 2010; Kuncel, Crede, &
Thomas, 2007; Oh, Schmidt, Shaffer, & Le, 2008), and
even more so when combined with undergraduate grade
point average to predict academic performance in an
MBA program (Braunstein, 2002; Talento-Miller & Rudner, 2005; Truell, Zhao, Alexander, & Hill, 2006).
Gender
There has historically been a gender gap within graduate
business programs, where men tend to score higher than
women in entrance measures. Hancock (1999) noted
gender differences in GMAT scores among MBA students, but no difference in academic achievement, while
others assert that gender does not significantly predict
academic performance in an MBA program (Truell
et al., 2006; Yang & Lu, 2001). This issue requires the
inclusion of gender as a control variable in most analyses
of academic performance.
Age
In most previous research, age has been included as a
control variable but has not generally been shown to be
related to academic performance in an MBA program
(e.g., Wright & Palmer, 1997; Yang & Lu, 2001). Most
40
K. R. HAMLEN AND W. A. HAMLEN
studies, however, measure performance through academic achievement within the program. As this study
measures performance through outgoing salaries upon
graduation, age becomes more important because it may
be related to work experience and salary expectations.
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Other control variables
Other variables that were hypothesized to play a role in
differences in program outcomes were whether the
school was public or private, numbers of full-time and
part-time faculty, numbers of full-time and part-time
students, percent of international students, percent of
statistically underrepresented minority students, and
cost of the school.
Research methodology
Data source
A sample of 976 MBA programs were selected from Barron’s Guide to Graduate Business Schools (Miller & Pollack, 2007) over the span of years 1988–2005 and from
the Princeton Review’s The Best 295 Business Schools
2014 edition ( Princeton Review, 2013). All of the schools
for which the guide provided complete information for
the variables in this study were included in the analysis.
Of the resulting sample, approximately 38% were public,
and 62% private. Statistics were reported by administrators from each business school. Validity of the data was
investigated by comparing faculty salaries in the sample
to salary data collected by national and international
organizations. For the year 1995, business faculty full
professor salaries in the sample did not differ significantly from the mean provided by the College University
and Personnel Association (Howe, 1996) for either public universities, t(95) D 0.928, p > .05 or for private universities, t(54) D ¡1.093, p > .05. For the year 2013, the
mean faculty salary of the sample data was not significantly different from the corresponding mean reported
by the Association to Advance Collegiate Schools of
Business (2013), t(28) D 1.401, p > .05, supporting the
validity of the data.
Variables
Data were recorded for each school on the following variables: Average faculty salary (full professor in that program), GMAT and undergraduate GPA scores upon
entry to the program, average student age in the MBA
program, average outgoing salary of MBA graduates,
percent of international students, percent of statistically
underrepresented minority students, percent of women,
number of full-time faculty, number of part-time faculty,
number of full-time students, number of part-time students, tuition cost, and total cost. To ensure normality of
the data, several variables were transformed to the natural log of the original data. This was done for each of the
continuous, or nondiscrete, variables. Additionally, faculty salaries were adjusted for cost of living based on
consumer price index for that year. Faculty salaries were
measured by the average salary of a full professor in that
program, for consistency of comparisons across
institutions.
Findings
A linear regression was performed with average outgoing
salary (natural log) as the dependent variable and all of
the aforementioned predictor variables as independent
and control variables. Multicollinearity statistics were
examined, and it was apparent that tuition cost was
highly related to several other variables, so this was
removed from the analysis. Additionally, the relationships between numbers of full time and part time faculty
and numbers of full time and part time students created
multicollinearity so, of these, only numbers of full time
faculty and numbers of part time students were included
in the analysis. After these corrections were made, all
VIF values were under 2.5, and all tolerance levels were
over 0.4, so multicollinearity was not a problem. Once
these were resolved, a hierarchical regression was performed to determine the extent to which faculty salaries
predict outgoing student salary from MBA programs,
above and beyond the student characteristic and school
characteristic variables. The first step of the model
included all student characteristic variables (GMAT
scores, GPA, age, percent international students, women,
minority students, and number of part-time students).
The second step of the model added in school characteristics (public or private, number of full time faculty). The
final step added in the primary independent variable of
interest, faculty salary.
Student and school characteristic variables
The first step of the model, with student characteristic
variables as independent variables (GMAT scores, GPA,
age, percent international students, women, minority
students, and number of part-time students), was significant, F(7, 632) D 67.937, p < .001 (R2 D .432, adjusted
R2 D .426). This indicates that the model was a good fit
and that approximately 43% of the variability in outgoing salaries of MBA graduates can be explained by variation in the student characteristic variables included in
the model. The second step of the model, adding school
characteristics (public or private, number of full-time
JOURNAL OF EDUCATION FOR BUSINESS
Table 1. Hierarchical regression analysis predicting outgoing salaries of MBA students.
Step/variable
B
SE B
b
GMAT
1.805 0.117 .569
GPA
¡0.389 0.192 ¡.069
Age
0.137 0.091 .046
International 0.041 0.011 .117
students
Women
0.005 0.033 .005
Minority
0.046 0.011 .126
PT students
0.039 0.007 .189
Step 2
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Faculty salary
R2 R2 (adj.) DR2
67.937 .432 .426
(df D 7, 622)
Step 1
FT Faculty
Public
Step 3
F
56.779 .451 .443 .019
(df D 9, 622)
0.038 0.017 .092
¡0.087 0.020 ¡.147
175.701 .739 .734 .288
(df D 10, 622)
0.924 0.035
.749
p < .05. p < .01. p < .001. Dependent variable: average salary (Ln).
faculty) into the model as additional independent variables, was also significant, F(9, 632) D 56.779, p < .001
(R2 D .451, adjusted R2 D .443). While this model still
significantly predicts variability in outgoing salaries from
MBA programs, the school characteristic variables added
little explanatory value above and beyond that of the student characteristics from the first step.
41
value to corresponding steps of the model including all
schools. In general, higher average full professor salaries
are associated with higher starting salaries for MBA students upon graduation, b D 15.749, p < .001.
The result of the regression analysis for schools that
have average GMAT scores of less than or equal to 560
can be found in Table 3. Overall, the three steps of the
regression model were significant. Among MBA programs with an average GMAT score of less than or equal
to 560, the primary predictor of outgoing salaries is average faculty salary. In agreement with the previous two
analyses, faculty salary is positively associated with student outgoing salaries, b D 0.698, p < .001.
As an additional check to ensure that the significance
of faculty salary was not related to tuition of the institution, universities were separated into two groups based
on the median natural log of the reported total cost of
attending, with total cost adjusted based on Consumer
Price Index in the same manner as faculty salaries. For
both groups, the overall regressions were significant and
there was a positive relationship between faculty salary
and outgoing salary: higher cost group, b D 0.725, p <
.001; lower cost group, b D 0.690, p < .001.
Discussion
Faculty salaries
Faculty salary
In the third and final step, the faculty salaries variable
was added to the regression. This step was also significant, F(10, 632) D 175.701, p < .001(R2 D .739, adjusted
R2 D .734). See Table 1 for complete results. From Step 2
to Step 3, the explanatory value increased from approximately 45% to approximately 74% of the variation in
outgoing salaries from MBA programs being explained
by the variables in the model. This shows that faculty salaries significantly predict variation in outgoing salaries
from MBA programs, above and beyond the included
student and school characteristic variables, and adding
the variable faculty salaries contributes substantially
more explanatory value to the model (DR2 D .288).
Since several of the variables may be related to the
rank of the school and the type of students it attracts,
the regression was conducted again, with the data separated by GMAT scores. The median GMAT score for all
of the schools was 560, so the schools were divided into
two groups: those with average GMAT scores greater
than 560, and those with average GMAT scores less
than or equal to 560. The result of the regression analysis for schools that have average GMAT scores greater
than 560 can be found in Table 2. Overall, the three
steps of the regression model were all significant, with
each step adding proportionally similar explanatory
The relationship between average faculty salary and outgoing student salary is the primarily relationship of interest, and it was shown to be the strongest, both in terms of
level of significance, and also because it was recurrent in
each analysis. As faculty salaries were adjusted uniformly
based on Consumer Price Index for each year, faculty
salary does not reflect a difference based on cost of living,
Table 2. Schools that have average GMAT scores greater than
560.
Step/variable
B
SE B
b
F
R2 R2 (adj.) DR2
.431 .416
28.243
(df D 7, 258)
Step 1
GMAT
GPA
Age
International
students
Women
Minority
PT students
Step 2
2.953 0.293 .553
¡0.819 0.323 ¡.127
0.108 0.106 .048
0.026 0.019 .067
FT faculty
Public
Step 3
0.061 0.023 .148
¡0.015 0.028 ¡.029
¡0.039 0.040 ¡.048
0.029 0.018 .080
0.035 0.009 .187
23.244
.447 .428 .016
(df D 9, 258)
.715 .704 .268
64.634
(df D 10, 258)
Faculty salary 0.917 0.059 .698
p < .05. p < .01. p < .001. Dependent variable: average salary (Ln).
K. R. HAMLEN AND W. A. HAMLEN
42
Table 3. Schools that have average GMAT scores less than or
equal to 560.
Step/variable
B
SE B
b
GMAT
1.388 0.213 .352
GPA
¡0.217 0.239 ¡.047
Age
0.098 0.159 .030
International 0.044 0.015 .157
students
Women
¡0.100 0.054 .091
Minority
0.037 0.015 .122
PT students
0.040 0.009 .224
Step 2
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Faculty salary
R2 R2 (adj.) DR2
.202 .187
12.897
(df D 7, 353)
Step 1
FT faculty
Public
Step 3
F
.251 .232 .049
13.212
(df D 9, 353)
0.042 0.025 .114
¡0.135 0.028 ¡.255
.651 .641 .400
65.915
(df D 10, 353)
0.903 0.045
MBA programs with higher paid senior faculty are more
likely to have faculty with strong publication records,
thus giving the students’ MBA degrees greater prestige
and higher value. Higher research productivity could be
evidence of greater notoriety, expertise, or greater levels
of university support, among other possibilities. While
previous research supports these concepts, and use of the
control variables rule out that faculty salaries are purely
indicative of program competitiveness and selectivity, it
is also possible that the faculty salaries variable is capturing another variable that was not measured in this analysis, or a combination of other variables. Possibilities
include type of MBA program, location, and networking
connections of the institution, faculty, and students.
.762
p < .05. p < .01. p < .001.
Dependent variable: average salary (Ln).
and based on the final analysis, faculty salary also does
not reflect a difference based on tuition collected by the
institution. Determining the reason for this relationship
is not straightforward, but previous research supports
various possibilities: Higher faculty salaries may be indicative of greater faculty research productivity (see Fairweather, 2005; Melguizo & Strober, 2007), greater faculty
expertise/quality (Ehrenberg, McGraw, & Mrdjenovic,
2006), or motivation (Comm & Mathaisel, 2003), or
some other combination of these factors.
The predominant finding from research on faculty
salaries is that faculty salaries, beyond differences in cost
of living, relate most powerfully to research productivity,
including publications and obtaining research grants
(Braxton et al., 2002; Fairweather, 2005; Gomez-Mejia &
Balkin, 1992; Melguizo & Strober, 2007; White et al.,
2012). Furthermore, it has been suggested that faculty
quality is directly related to research grant performance,
and that obtaining research grants not only increases faculty salaries but also allows for more university resources
in the form of equipment, personnel, and technology
(Mohr, 1999). In general, faculty members at all types of
institutions tend to feel that a greater value and recognition is placed on research achievements as opposed to
teaching achievements (Alpay & Verschoor, 2014). Interestingly, rather than rewarding teaching, higher base faculty salaries tend to be related to spending fewer hours
in the classroom teaching among comprehensive and
doctoral granting institutions, with higher salary returns
due to greater teaching hours being a rare occurrence in
any type of institution (Fairweather, 2005).
Based on this evidence, one interpretation of the
results of the present study is that outgoing salaries from
an MBA program are strongly related to the publication
record of the faculty in that program. In other words,
Limitations
While separating schools based on cost confirms the positive relationship between faculty salary and outgoing
student salary, each method of dividing the data comes
with its own set of limitations. For example, some
schools use subsidies to recruit better students who cannot afford the tuition, and administrators’ self-reports on
total tuition are not always valid. This touches on a larger
limitation to this study, which is that it relies on data
reported by administrators, which is not always valid or
reliable (e.g., Hossler, 2000). While faculty salaries were
shown to be valid based on comparisons to outside data
sources, it is not possible to verify all of the data provided
in these guidebooks. Other limitations to this study
include lack of additional variables by which to analyze
the sample, such as type of MBA program and connections between faculty and businesses, and the possibility
of confounding variables that were not considered in the
analysis.
Conclusions
There have been several studies investigating the qualities of an MBA program that relate to student achievement and performance as measured by GPA (Kass et al.,
2012; Siegert, 2008; Talento-Miller & Rudner, 2008).
There have been fewer studies investigating factors of an
MBA program that relate to the outcome of salary upon
graduation from the program. This study adds to literature by using the outcome variable of outgoing salary, by
focusing on a less-researched predictor—faculty salary,
and by utilizing a public data set, thereby employing a
large sample size that includes many different programs
across the United States.
The primary finding of this study was a significant,
positive relationship between faculty salary in an
MBA program and student salary upon graduation
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JOURNAL OF EDUCATION FOR BUSINESS
43
from the program. This relationship was significant
when including all programs in the sample, as well as
both for programs with higher average GMAT scores
and for programs with lower GMAT scores when
split into separate groups. This was an exploratory
correlational study, so cause-effect relationships cannot be established. While there are several possible
explanations for this relationship, it highlights the
importance of faculty in MBA programs. According
to previous research, programs that value faculty
more are also more likely to support faculty research
efforts, which can bolster a program’s reputation,
thus increasing employer demand for the program’s
graduates (Kranzler, Grapin, & Daley, 2011). Additionally, researchers have determined that faculty
research productivity is significantly related to student
learning, providing another link between faculty quality and student outcomes (Galbraith & Merrill, 2012).
to important issues for consideration among MBA
program faculty and administrators. While the focus
has traditionally been on student success within
a program, this study, among others, points to
the importance of considering career and financial factors upon graduation from the program. Traditionally, there has been an emphasis on admitting
the best students to programs to bolster the success
rate of the program. It is important for coordinators
and administrators to consider additional factors
other than student characteristics, such as valuing
faculty research achievement and support, when
looking for ways to foster a successful program.
Recommendations
References
Because this research is correlational, explanations
behind significant relationships are purely theoretical,
and based on previous research. While further research
should be done to confirm these findings and to delve
into the underlying causes of these relationships, it
appears that higher faculty salary, possibly indicative of
faculty quality and support from the university, is indeed
related to higher student salary attainment upon graduating from MBA programs. This does not provide proof
that paying faculty more, or valuing faculty and their
productivity more highly, would result in higher outgoing salaries for students, but careful consideration of faculty pay and motivation is warranted. Future research
would add to these findings by including additional factors, such as the specific types of MBA programs being
assessed, looking into levels of formal and informal networking connections between MBA faculty and employers, and by utilizing different analyses that will allow for
the incorporation of school reputations and rankings as
independent or control variables.
Collecting valid and reliable data on MBA programs and students can be challenging. Researchers
would benefit if programs would collect uniform
data from students regarding these variables as well
as additional variables such as prior work experience
and career goals. The validity of administratorreported data has been called into question (Hossler,
2000), so either verifying the validity and reliability
of these data to a greater extent or utilizing additional data sources may be beneficial in understanding these relationships more thoroughly. Despite the
limitations of the data sources, these findings point
Alpay, E., & Verschoor, R. (2014). The teaching researcher:
Faculty attitudes toward the teaching and research roles.
European Journal of Engineering Education, 39, 365–376.
Association to Advance Collegiate Schools of Business. (2013).
Business school data trends 2013. Retrieved from http://
www.aacsb.edu/~/media/AACSB/Publications/data-trendsbooklet/2013-data-trends.ashx
Braxton, J. M., Luckey, W., & Helland, P. (Eds.). (2002). Institutionalizing a broader view of scholarship through Boyer’s
four domains. In ASHE-ERIC higher education report (Vol.
292). San Francisco, CA: Jossey-Bass/Wiley Periodicals.
Braunstein, A. W. (2002). Factors determining success in a
graduate business program. College Student Journal, 36,
471–477.
Bruce, G., & Edgington, R. (2008). Factors influencing wordof-mouth recommendations by MBA students: An examination of school quality, educational outcomes, and value
of the MBA. Journal of Marketing for Higher Education, 18,
79–101.
Comm, C. L., & Mathaisel, D. F. X. (2003). A case study of the
implications of faculty workload and compensation for
improving academic quality. International Journal of Educational Management, 17, 200–210.
Ehrenberg, R. G., McGraw, M., & Mrdjenovic, J. (2006). Why
do field differentials in average faculty salaries vary across
universities? Economics of Education Review, 25, 241–248.
Fairweather, J. S. (2005). Beyond the rhetoric: Trends in the
relative value of teaching and research. Journal of Higher
Education, 76, 401–422.
Galbraith, C. S., & Merrill, G. B. (2012). Faculty research productivity and standardized student learning outcomes in a
university teaching environment: A Bayesian analysis of
relationships. Studies in Higher Education, 37, 469–480.
Gomez-Mejia, L. R., & Balkin, D. B. (1992). Determinants of
faculty pay: An agency theory perspective. Academy of
Management Journal, 35, 921–955.
Graduate Management Admission Council (GMAC). (2012).
2012 application trends survey report. Retrieved from http://
Acknowledgments
Dr. Hamlen passed away on April 15, 2015.
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
44
K. R. HAMLEN AND W. A. HAMLEN
www.gmac.com/»/media/Files/gmac/Research/admissionsand-application-trends/2012-application-trends-surveyreport.pdf.
Gutteridge, T. G. (1973). Predicting career success of graduate
business school alumni. The Academy of Management Journal, 16, 129–137.
Hancock, T. (1999). The gender difference: Validity of standardized admission tests in predicting MBA performance.
Journal of Education for Business, 75, 91–93.
Hossler, D. (2000). The problem with college rankings. About
Campus, 5, 20–24.
Howe, R. D. (1996). Salary-trend studies of faculty for the years
1992–1993 and 1995–1996 in the following academic disciplines/major fields: Accounting, art, general…geology. College and University Personnel Association. Washington,
DC: Appalachian Consortium, Inc.
Kass, D., Grandzol, C., & Bommer, W. (2012). The GMAT as a
predictor of MBA performance: Less success than meets the
eye. Journal of Education for Business, 87, 290–295.
Koys, D. (2010). GMAT versus alternatives: Predictive validity
evidence from central Europe and the middle east. Journal
of Education for Business, 85, 180–185.
Kranzler, J. H., Grapin, S. L., & Daley, M. L. (2011). Research
productivity and scholarly impact of APA-accredited school
psychology programs: 2005–2009. Journal of School Psychology, 49, 721–738.
Kuncel, N. R., Crede, M., & Thomas, L. L. (2007). A meta-analysis of the predictive validity of the Graduate Management
Admissions Test (GMAT) and undergraduate grade point
average (UGPA) for graduate student academic performance. Academy of Management Learning and Education,
6, 51–68.
Melguizo, T., & Strober, M. H. (2007). Faculty salaries and the
maximization of prestige. Research in Higher Education, 48,
633–667.
Miller, E., & Pollack, N. F. (2007). Barron’s guide to graduate
business schools (15th ed.). Hauppage, NY: Barron’s Educational Series, Inc.
Mohr, L. B. (1999). The impact profile approach to policy
merit. Evaluation Review, 23, 212–249.
Oh, I., Schmidt, F. L., Shaffer, J. A., & Le, H. (2008). The
Graduate Management Admission Test (GMAT) is more
valid than we thought: A new development in metaanalysis and its implications for the validity of the
GMAT. Academy of Management Learning and Education, 7, 563–570.
Princeton Review. (2013). The best 295 business schools (2014
ed.). Framingham, MA: TPR Education IP Holdings.
Ray, S., & Jeon, Y. (2008). Reputation and efficiency: A nonparametric assessment of America's top-rated MBA programs. European Journal of Operational Research, 189(1),
245–268.
Siegert, K. O. (2008). Executive education: Predicting student
success in Executive MBA programs. Journal of Education
for Business, 83, 221–226.
Strathman, J. G. (2000). Consistent estimation of faculty rank
effects in academic salary models. Research in Higher Education, 41, 237–250.
Talento-Miller, E., & Rudner, L. M. (2005). GMAT validity
study summary report for 1997 to 2004. GMAC research
reports. McLean, VA: Graduate Management Admission
Council.
Talento-Miller, E., & Rudner, L. M. (2008). The validity of
Graduate Management Admission Test scores: A summary
of studies conducted from 1997 to 2004. Educational and
Psychological Measurement, 68, 129–138.
Tracy, J., & Waldfogel, J. (1997). The best business schools: A
market-based approach. Journal of Business, 70, 1–31.
Truell, A. D., Zhao, J. J., Alexander, M. W., & Hill, I. B. (2006).
Predicting final student performance in a graduate
business program: The MBA. Delti Pi Epsilon Journal, 48,
144–152.
White, C. S., James, K., Burke, L. A., & Allen, R. S. (2012).
What makes a “research star”? Factors influencing the
research productivity of business faculty. International
Journal of Productivity & Performance Management, 61,
584–602.
Wright, R. E., & Palmer, J. C. (1997). Examining performance
predictors for differentially successful MBA students. College Student Journal, 31, 276.
Yang, B., & Lu, X. R. (2001). Predicting academic performance in management education: An empirical investigation of MBA success. Journal of Education for
Business, 77(1), 15–20.
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Faculty salary as a predictor of student outgoing
salaries from MBA programs
Karla R. Hamlen & William A. Hamlen
To cite this article: Karla R. Hamlen & William A. Hamlen (2016) Faculty salary as a predictor of
student outgoing salaries from MBA programs, Journal of Education for Business, 91:1, 38-44,
DOI: 10.1080/08832323.2015.1110552
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Date: 11 January 2016, At: 18:55
JOURNAL OF EDUCATION FOR BUSINESS
2016, VOL. 91, NO. 1, 38–44
http://dx.doi.org/10.1080/08832323.2015.1110552
Faculty salary as a predictor of student outgoing salaries from MBA programs
Karla R. Hamlena and William A. Hamlenb
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
a
Cleveland State University, Cleveland, Ohio, USA; bState University of New York at Buffalo, Buffalo, New York, USA
ABSTRACT
KEYWORDS
The authors’ purpose was to investigate the predictive value of faculty salaries on outgoing salaries of
master of business administration (MBA) students when controlling for other student and program
variables. Data were collected on 976 MBA programs using Barron’s Guide to Graduate Business Schools
over the years 1988–2005 and the Princeton Review’s The Best 295 Business Schools 2014 edition. A
hierarchical linear regression analysis was conducted with student and program characteristics as
control variables, faculty salary as the predictor variable, and average outgoing salary as the
dependent variable. In general, higher faculty salaries were associated with higher starting salaries for
MBA students upon graduation. Potential explanations and limitations are discussed.
faculty salary; MBA students;
utgoing salary; regression
With rising numbers of graduate school applicants
despite fluctuations in the economy ( Graduate Management Admission Council [GMAC], 2012), faculty and
administrators in business schools continually seek to
identify factors that most impact student outcomes upon
graduation to offer competitive programs of high quality.
There is disagreement, however, over which factors are
indicative of the quality of a master of business administration (MBA) program, and whether these translate to
improved career-related outcomes upon graduation. Student characteristics have been widely used for measuring
program quality, but these may be more highly indicative
of the rigor of admissions than of the program itself. It
has been suggested that economic attainment upon graduation may be a better outcome to consider when looking for ways to assess and improve MBA programs
(Tracy & Waldfogel, 1997). In addition, the role of faculty salary in student outcomes has not been as widely
investigated as some other student and program variables. The purpose of this study was to determine student,
faculty, and program characteristics that predict the
starting salary of graduates from MBA programs and, in
particular, the relationship between faculty salary and
outgoing starting salary, when controlling for other student and program characteristics. The research questions
to be addressed are the following:
Research Question 1 (RQ1): Do faculty salaries predict
outgoing salary of MBA program graduates, above
and beyond the predictive value of other program
and student variables?
CONTACT Karla R. Hamlen
Cleveland, OH 44115, USA.
© 2016 Taylor & Francis Group, LLC
k.hamlen@csuohio.edu
RQ2: Do these relationships differ based on quality of
the students entering the program, with quality
measured by incoming student GMAT (Graduate
Management Admissions Test) scores?
Significance
Previous research addressing the assessment of MBA programs has pointed toward the use of economic attainment
upon graduation as a more viable outcome variable than
most student characteristics for evaluation and measurement
of MBA programs. As recommended by previous researchers, in the present study the outcomes of MBA programs
were measured using the outgoing salary of graduates. A
pre-existing and publicly available data set was used, allowing
for a large sample. This study adds to the research base in
this field by incorporating a larger sample size than most
previous research, by utilizing a dependent variable that better captures workplace outcomes rather than academic success within the program itself, and also by highlighting the
predictive value of faculty salaries when controlling for many
other potential factors relating to outgoing student salaries.
Related literature
Justification for evaluating an MBA program
through outgoing salaries
One challenge in evaluating MBA programs is that the
quality of students admitted to top programs can skew
Cleveland State University, Department of Curriculum & Foundations, 2121 Euclid Avenue,
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
JOURNAL OF EDUCATION FOR BUSINESS
the outcome variables. While student characteristics such
as scores on the GMAT and prior grade point average
(GPA) may predict success in an MBA program (Kass,
Grandzol, & Bommer, 2012; Siegert, 2008; TalentoMiller & Rudner, 2008), they do not necessarily predict
actual business workplace competencies such as decision-making and leadership skills (Kass et al., 2012).
The precedent for measuring career success of business school graduates by their salaries dates at least as far
back as the 1970s (e.g., Gutteridge, 1973). Tracy and
Waldfogel (1997) posited that evaluation of the quality
of MBA programs is better done through graduate performance in the market—outgoing salaries—than
through subjective opinions or incoming student characteristics. Ray and Jeon (2008) also named MBA graduate
earnings a measure of success of an MBA program, based
on their finding that rankings are often a reflection of
inputs (student and program characteristics). They demonstrated that some lower-ranked programs utilize their
resources more efficiently to result in higher outgoing
student salaries, in relation to incoming student characteristics and program resources, than their higher-ranked
counterparts.
Predictor and control variables
Faculty salary was the primary predictor of interest in
this study, and several other variables were included as
control variables, based on the possibility that they may
also be predictors of MBA program outcomes, as demonstrated in previous studies.
Faculty salary
Research has shown that higher education institutions
tend to use faculty salary to reward faculty activities that
enhance university prestige, such as research, obtaining
grants, and particularly publication in top-tier journals.
This appears to be equally true regardless of whether the
institution’s mission emphasizes teaching or research
(Braxton, Luckey, & Helland, 2002; Fairweather, 2005;
Gomez-Mejia & Balkin, 1992; Melguizo & Strober,
2007). Faculty rank has also been shown to be directly
proportional to faculty research productivity (White,
James, Burke, & Allen, 2012), and those of a higher faculty rank are more likely to have more experience and
seniority than their counterparts at other ranks (Strathman, 2000). Furthermore, it has been suggested that faculty quality is directly related to research grant
performance, and that obtaining research grants not
only increases faculty salaries but also allows for more
university resources in the form of equipment, personnel, and technology (Mohr, 1999). In general, faculty feel
that a greater value and recognition is placed on research
39
achievements as opposed to teaching achievements
(Alpay & Verschoor, 2014). Interestingly, higher base
faculty salaries tend to be related to spending fewer hours
in the classroom teaching among comprehensive and
doctoral granting institutions, with higher salary returns
due to greater teaching hours being a rare occurrence in
any type of institution (Fairweather, 2005).
There is little research exploring connections between
faculty quality or faculty salaries and MBA program outcomes. Student ratings of faculty have been significantly
related to the students’ likelihood of recommending their
MBA program to others (Bruce & Edgington, 2008), and
there is a positive relationship between the value of the
MBA degree—partially measured by expected outgoing
salaries of graduates—and student likelihood of recommending the program (Bruce & Edgington, 2008). The
present study is an investigation of the predictive value
of faculty salary when value is assessed by outgoing salary of graduates of an MBA program. Faculty salary is
particularly valuable as a predictor variable when, as in
many cases, other measures of faculty quality, such as
research productivity and prestige, are unavailable.
GMAT and GPA scores
The GMAT is generally used as the primary admissions
exam required for business schools, designed to function
as an aptitude test for business school. Many researchers
have provided evidence that the GMAT successfully predicts success in MBA programs, when success is measured
by academic performance (Koys, 2010; Kuncel, Crede, &
Thomas, 2007; Oh, Schmidt, Shaffer, & Le, 2008), and
even more so when combined with undergraduate grade
point average to predict academic performance in an
MBA program (Braunstein, 2002; Talento-Miller & Rudner, 2005; Truell, Zhao, Alexander, & Hill, 2006).
Gender
There has historically been a gender gap within graduate
business programs, where men tend to score higher than
women in entrance measures. Hancock (1999) noted
gender differences in GMAT scores among MBA students, but no difference in academic achievement, while
others assert that gender does not significantly predict
academic performance in an MBA program (Truell
et al., 2006; Yang & Lu, 2001). This issue requires the
inclusion of gender as a control variable in most analyses
of academic performance.
Age
In most previous research, age has been included as a
control variable but has not generally been shown to be
related to academic performance in an MBA program
(e.g., Wright & Palmer, 1997; Yang & Lu, 2001). Most
40
K. R. HAMLEN AND W. A. HAMLEN
studies, however, measure performance through academic achievement within the program. As this study
measures performance through outgoing salaries upon
graduation, age becomes more important because it may
be related to work experience and salary expectations.
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Other control variables
Other variables that were hypothesized to play a role in
differences in program outcomes were whether the
school was public or private, numbers of full-time and
part-time faculty, numbers of full-time and part-time
students, percent of international students, percent of
statistically underrepresented minority students, and
cost of the school.
Research methodology
Data source
A sample of 976 MBA programs were selected from Barron’s Guide to Graduate Business Schools (Miller & Pollack, 2007) over the span of years 1988–2005 and from
the Princeton Review’s The Best 295 Business Schools
2014 edition ( Princeton Review, 2013). All of the schools
for which the guide provided complete information for
the variables in this study were included in the analysis.
Of the resulting sample, approximately 38% were public,
and 62% private. Statistics were reported by administrators from each business school. Validity of the data was
investigated by comparing faculty salaries in the sample
to salary data collected by national and international
organizations. For the year 1995, business faculty full
professor salaries in the sample did not differ significantly from the mean provided by the College University
and Personnel Association (Howe, 1996) for either public universities, t(95) D 0.928, p > .05 or for private universities, t(54) D ¡1.093, p > .05. For the year 2013, the
mean faculty salary of the sample data was not significantly different from the corresponding mean reported
by the Association to Advance Collegiate Schools of
Business (2013), t(28) D 1.401, p > .05, supporting the
validity of the data.
Variables
Data were recorded for each school on the following variables: Average faculty salary (full professor in that program), GMAT and undergraduate GPA scores upon
entry to the program, average student age in the MBA
program, average outgoing salary of MBA graduates,
percent of international students, percent of statistically
underrepresented minority students, percent of women,
number of full-time faculty, number of part-time faculty,
number of full-time students, number of part-time students, tuition cost, and total cost. To ensure normality of
the data, several variables were transformed to the natural log of the original data. This was done for each of the
continuous, or nondiscrete, variables. Additionally, faculty salaries were adjusted for cost of living based on
consumer price index for that year. Faculty salaries were
measured by the average salary of a full professor in that
program, for consistency of comparisons across
institutions.
Findings
A linear regression was performed with average outgoing
salary (natural log) as the dependent variable and all of
the aforementioned predictor variables as independent
and control variables. Multicollinearity statistics were
examined, and it was apparent that tuition cost was
highly related to several other variables, so this was
removed from the analysis. Additionally, the relationships between numbers of full time and part time faculty
and numbers of full time and part time students created
multicollinearity so, of these, only numbers of full time
faculty and numbers of part time students were included
in the analysis. After these corrections were made, all
VIF values were under 2.5, and all tolerance levels were
over 0.4, so multicollinearity was not a problem. Once
these were resolved, a hierarchical regression was performed to determine the extent to which faculty salaries
predict outgoing student salary from MBA programs,
above and beyond the student characteristic and school
characteristic variables. The first step of the model
included all student characteristic variables (GMAT
scores, GPA, age, percent international students, women,
minority students, and number of part-time students).
The second step of the model added in school characteristics (public or private, number of full time faculty). The
final step added in the primary independent variable of
interest, faculty salary.
Student and school characteristic variables
The first step of the model, with student characteristic
variables as independent variables (GMAT scores, GPA,
age, percent international students, women, minority
students, and number of part-time students), was significant, F(7, 632) D 67.937, p < .001 (R2 D .432, adjusted
R2 D .426). This indicates that the model was a good fit
and that approximately 43% of the variability in outgoing salaries of MBA graduates can be explained by variation in the student characteristic variables included in
the model. The second step of the model, adding school
characteristics (public or private, number of full-time
JOURNAL OF EDUCATION FOR BUSINESS
Table 1. Hierarchical regression analysis predicting outgoing salaries of MBA students.
Step/variable
B
SE B
b
GMAT
1.805 0.117 .569
GPA
¡0.389 0.192 ¡.069
Age
0.137 0.091 .046
International 0.041 0.011 .117
students
Women
0.005 0.033 .005
Minority
0.046 0.011 .126
PT students
0.039 0.007 .189
Step 2
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Faculty salary
R2 R2 (adj.) DR2
67.937 .432 .426
(df D 7, 622)
Step 1
FT Faculty
Public
Step 3
F
56.779 .451 .443 .019
(df D 9, 622)
0.038 0.017 .092
¡0.087 0.020 ¡.147
175.701 .739 .734 .288
(df D 10, 622)
0.924 0.035
.749
p < .05. p < .01. p < .001. Dependent variable: average salary (Ln).
faculty) into the model as additional independent variables, was also significant, F(9, 632) D 56.779, p < .001
(R2 D .451, adjusted R2 D .443). While this model still
significantly predicts variability in outgoing salaries from
MBA programs, the school characteristic variables added
little explanatory value above and beyond that of the student characteristics from the first step.
41
value to corresponding steps of the model including all
schools. In general, higher average full professor salaries
are associated with higher starting salaries for MBA students upon graduation, b D 15.749, p < .001.
The result of the regression analysis for schools that
have average GMAT scores of less than or equal to 560
can be found in Table 3. Overall, the three steps of the
regression model were significant. Among MBA programs with an average GMAT score of less than or equal
to 560, the primary predictor of outgoing salaries is average faculty salary. In agreement with the previous two
analyses, faculty salary is positively associated with student outgoing salaries, b D 0.698, p < .001.
As an additional check to ensure that the significance
of faculty salary was not related to tuition of the institution, universities were separated into two groups based
on the median natural log of the reported total cost of
attending, with total cost adjusted based on Consumer
Price Index in the same manner as faculty salaries. For
both groups, the overall regressions were significant and
there was a positive relationship between faculty salary
and outgoing salary: higher cost group, b D 0.725, p <
.001; lower cost group, b D 0.690, p < .001.
Discussion
Faculty salaries
Faculty salary
In the third and final step, the faculty salaries variable
was added to the regression. This step was also significant, F(10, 632) D 175.701, p < .001(R2 D .739, adjusted
R2 D .734). See Table 1 for complete results. From Step 2
to Step 3, the explanatory value increased from approximately 45% to approximately 74% of the variation in
outgoing salaries from MBA programs being explained
by the variables in the model. This shows that faculty salaries significantly predict variation in outgoing salaries
from MBA programs, above and beyond the included
student and school characteristic variables, and adding
the variable faculty salaries contributes substantially
more explanatory value to the model (DR2 D .288).
Since several of the variables may be related to the
rank of the school and the type of students it attracts,
the regression was conducted again, with the data separated by GMAT scores. The median GMAT score for all
of the schools was 560, so the schools were divided into
two groups: those with average GMAT scores greater
than 560, and those with average GMAT scores less
than or equal to 560. The result of the regression analysis for schools that have average GMAT scores greater
than 560 can be found in Table 2. Overall, the three
steps of the regression model were all significant, with
each step adding proportionally similar explanatory
The relationship between average faculty salary and outgoing student salary is the primarily relationship of interest, and it was shown to be the strongest, both in terms of
level of significance, and also because it was recurrent in
each analysis. As faculty salaries were adjusted uniformly
based on Consumer Price Index for each year, faculty
salary does not reflect a difference based on cost of living,
Table 2. Schools that have average GMAT scores greater than
560.
Step/variable
B
SE B
b
F
R2 R2 (adj.) DR2
.431 .416
28.243
(df D 7, 258)
Step 1
GMAT
GPA
Age
International
students
Women
Minority
PT students
Step 2
2.953 0.293 .553
¡0.819 0.323 ¡.127
0.108 0.106 .048
0.026 0.019 .067
FT faculty
Public
Step 3
0.061 0.023 .148
¡0.015 0.028 ¡.029
¡0.039 0.040 ¡.048
0.029 0.018 .080
0.035 0.009 .187
23.244
.447 .428 .016
(df D 9, 258)
.715 .704 .268
64.634
(df D 10, 258)
Faculty salary 0.917 0.059 .698
p < .05. p < .01. p < .001. Dependent variable: average salary (Ln).
K. R. HAMLEN AND W. A. HAMLEN
42
Table 3. Schools that have average GMAT scores less than or
equal to 560.
Step/variable
B
SE B
b
GMAT
1.388 0.213 .352
GPA
¡0.217 0.239 ¡.047
Age
0.098 0.159 .030
International 0.044 0.015 .157
students
Women
¡0.100 0.054 .091
Minority
0.037 0.015 .122
PT students
0.040 0.009 .224
Step 2
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Faculty salary
R2 R2 (adj.) DR2
.202 .187
12.897
(df D 7, 353)
Step 1
FT faculty
Public
Step 3
F
.251 .232 .049
13.212
(df D 9, 353)
0.042 0.025 .114
¡0.135 0.028 ¡.255
.651 .641 .400
65.915
(df D 10, 353)
0.903 0.045
MBA programs with higher paid senior faculty are more
likely to have faculty with strong publication records,
thus giving the students’ MBA degrees greater prestige
and higher value. Higher research productivity could be
evidence of greater notoriety, expertise, or greater levels
of university support, among other possibilities. While
previous research supports these concepts, and use of the
control variables rule out that faculty salaries are purely
indicative of program competitiveness and selectivity, it
is also possible that the faculty salaries variable is capturing another variable that was not measured in this analysis, or a combination of other variables. Possibilities
include type of MBA program, location, and networking
connections of the institution, faculty, and students.
.762
p < .05. p < .01. p < .001.
Dependent variable: average salary (Ln).
and based on the final analysis, faculty salary also does
not reflect a difference based on tuition collected by the
institution. Determining the reason for this relationship
is not straightforward, but previous research supports
various possibilities: Higher faculty salaries may be indicative of greater faculty research productivity (see Fairweather, 2005; Melguizo & Strober, 2007), greater faculty
expertise/quality (Ehrenberg, McGraw, & Mrdjenovic,
2006), or motivation (Comm & Mathaisel, 2003), or
some other combination of these factors.
The predominant finding from research on faculty
salaries is that faculty salaries, beyond differences in cost
of living, relate most powerfully to research productivity,
including publications and obtaining research grants
(Braxton et al., 2002; Fairweather, 2005; Gomez-Mejia &
Balkin, 1992; Melguizo & Strober, 2007; White et al.,
2012). Furthermore, it has been suggested that faculty
quality is directly related to research grant performance,
and that obtaining research grants not only increases faculty salaries but also allows for more university resources
in the form of equipment, personnel, and technology
(Mohr, 1999). In general, faculty members at all types of
institutions tend to feel that a greater value and recognition is placed on research achievements as opposed to
teaching achievements (Alpay & Verschoor, 2014). Interestingly, rather than rewarding teaching, higher base faculty salaries tend to be related to spending fewer hours
in the classroom teaching among comprehensive and
doctoral granting institutions, with higher salary returns
due to greater teaching hours being a rare occurrence in
any type of institution (Fairweather, 2005).
Based on this evidence, one interpretation of the
results of the present study is that outgoing salaries from
an MBA program are strongly related to the publication
record of the faculty in that program. In other words,
Limitations
While separating schools based on cost confirms the positive relationship between faculty salary and outgoing
student salary, each method of dividing the data comes
with its own set of limitations. For example, some
schools use subsidies to recruit better students who cannot afford the tuition, and administrators’ self-reports on
total tuition are not always valid. This touches on a larger
limitation to this study, which is that it relies on data
reported by administrators, which is not always valid or
reliable (e.g., Hossler, 2000). While faculty salaries were
shown to be valid based on comparisons to outside data
sources, it is not possible to verify all of the data provided
in these guidebooks. Other limitations to this study
include lack of additional variables by which to analyze
the sample, such as type of MBA program and connections between faculty and businesses, and the possibility
of confounding variables that were not considered in the
analysis.
Conclusions
There have been several studies investigating the qualities of an MBA program that relate to student achievement and performance as measured by GPA (Kass et al.,
2012; Siegert, 2008; Talento-Miller & Rudner, 2008).
There have been fewer studies investigating factors of an
MBA program that relate to the outcome of salary upon
graduation from the program. This study adds to literature by using the outcome variable of outgoing salary, by
focusing on a less-researched predictor—faculty salary,
and by utilizing a public data set, thereby employing a
large sample size that includes many different programs
across the United States.
The primary finding of this study was a significant,
positive relationship between faculty salary in an
MBA program and student salary upon graduation
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
JOURNAL OF EDUCATION FOR BUSINESS
43
from the program. This relationship was significant
when including all programs in the sample, as well as
both for programs with higher average GMAT scores
and for programs with lower GMAT scores when
split into separate groups. This was an exploratory
correlational study, so cause-effect relationships cannot be established. While there are several possible
explanations for this relationship, it highlights the
importance of faculty in MBA programs. According
to previous research, programs that value faculty
more are also more likely to support faculty research
efforts, which can bolster a program’s reputation,
thus increasing employer demand for the program’s
graduates (Kranzler, Grapin, & Daley, 2011). Additionally, researchers have determined that faculty
research productivity is significantly related to student
learning, providing another link between faculty quality and student outcomes (Galbraith & Merrill, 2012).
to important issues for consideration among MBA
program faculty and administrators. While the focus
has traditionally been on student success within
a program, this study, among others, points to
the importance of considering career and financial factors upon graduation from the program. Traditionally, there has been an emphasis on admitting
the best students to programs to bolster the success
rate of the program. It is important for coordinators
and administrators to consider additional factors
other than student characteristics, such as valuing
faculty research achievement and support, when
looking for ways to foster a successful program.
Recommendations
References
Because this research is correlational, explanations
behind significant relationships are purely theoretical,
and based on previous research. While further research
should be done to confirm these findings and to delve
into the underlying causes of these relationships, it
appears that higher faculty salary, possibly indicative of
faculty quality and support from the university, is indeed
related to higher student salary attainment upon graduating from MBA programs. This does not provide proof
that paying faculty more, or valuing faculty and their
productivity more highly, would result in higher outgoing salaries for students, but careful consideration of faculty pay and motivation is warranted. Future research
would add to these findings by including additional factors, such as the specific types of MBA programs being
assessed, looking into levels of formal and informal networking connections between MBA faculty and employers, and by utilizing different analyses that will allow for
the incorporation of school reputations and rankings as
independent or control variables.
Collecting valid and reliable data on MBA programs and students can be challenging. Researchers
would benefit if programs would collect uniform
data from students regarding these variables as well
as additional variables such as prior work experience
and career goals. The validity of administratorreported data has been called into question (Hossler,
2000), so either verifying the validity and reliability
of these data to a greater extent or utilizing additional data sources may be beneficial in understanding these relationships more thoroughly. Despite the
limitations of the data sources, these findings point
Alpay, E., & Verschoor, R. (2014). The teaching researcher:
Faculty attitudes toward the teaching and research roles.
European Journal of Engineering Education, 39, 365–376.
Association to Advance Collegiate Schools of Business. (2013).
Business school data trends 2013. Retrieved from http://
www.aacsb.edu/~/media/AACSB/Publications/data-trendsbooklet/2013-data-trends.ashx
Braxton, J. M., Luckey, W., & Helland, P. (Eds.). (2002). Institutionalizing a broader view of scholarship through Boyer’s
four domains. In ASHE-ERIC higher education report (Vol.
292). San Francisco, CA: Jossey-Bass/Wiley Periodicals.
Braunstein, A. W. (2002). Factors determining success in a
graduate business program. College Student Journal, 36,
471–477.
Bruce, G., & Edgington, R. (2008). Factors influencing wordof-mouth recommendations by MBA students: An examination of school quality, educational outcomes, and value
of the MBA. Journal of Marketing for Higher Education, 18,
79–101.
Comm, C. L., & Mathaisel, D. F. X. (2003). A case study of the
implications of faculty workload and compensation for
improving academic quality. International Journal of Educational Management, 17, 200–210.
Ehrenberg, R. G., McGraw, M., & Mrdjenovic, J. (2006). Why
do field differentials in average faculty salaries vary across
universities? Economics of Education Review, 25, 241–248.
Fairweather, J. S. (2005). Beyond the rhetoric: Trends in the
relative value of teaching and research. Journal of Higher
Education, 76, 401–422.
Galbraith, C. S., & Merrill, G. B. (2012). Faculty research productivity and standardized student learning outcomes in a
university teaching environment: A Bayesian analysis of
relationships. Studies in Higher Education, 37, 469–480.
Gomez-Mejia, L. R., & Balkin, D. B. (1992). Determinants of
faculty pay: An agency theory perspective. Academy of
Management Journal, 35, 921–955.
Graduate Management Admission Council (GMAC). (2012).
2012 application trends survey report. Retrieved from http://
Acknowledgments
Dr. Hamlen passed away on April 15, 2015.
Downloaded by [Universitas Maritim Raja Ali Haji] at 18:55 11 January 2016
44
K. R. HAMLEN AND W. A. HAMLEN
www.gmac.com/»/media/Files/gmac/Research/admissionsand-application-trends/2012-application-trends-surveyreport.pdf.
Gutteridge, T. G. (1973). Predicting career success of graduate
business school alumni. The Academy of Management Journal, 16, 129–137.
Hancock, T. (1999). The gender difference: Validity of standardized admission tests in predicting MBA performance.
Journal of Education for Business, 75, 91–93.
Hossler, D. (2000). The problem with college rankings. About
Campus, 5, 20–24.
Howe, R. D. (1996). Salary-trend studies of faculty for the years
1992–1993 and 1995–1996 in the following academic disciplines/major fields: Accounting, art, general…geology. College and University Personnel Association. Washington,
DC: Appalachian Consortium, Inc.
Kass, D., Grandzol, C., & Bommer, W. (2012). The GMAT as a
predictor of MBA performance: Less success than meets the
eye. Journal of Education for Business, 87, 290–295.
Koys, D. (2010). GMAT versus alternatives: Predictive validity
evidence from central Europe and the middle east. Journal
of Education for Business, 85, 180–185.
Kranzler, J. H., Grapin, S. L., & Daley, M. L. (2011). Research
productivity and scholarly impact of APA-accredited school
psychology programs: 2005–2009. Journal of School Psychology, 49, 721–738.
Kuncel, N. R., Crede, M., & Thomas, L. L. (2007). A meta-analysis of the predictive validity of the Graduate Management
Admissions Test (GMAT) and undergraduate grade point
average (UGPA) for graduate student academic performance. Academy of Management Learning and Education,
6, 51–68.
Melguizo, T., & Strober, M. H. (2007). Faculty salaries and the
maximization of prestige. Research in Higher Education, 48,
633–667.
Miller, E., & Pollack, N. F. (2007). Barron’s guide to graduate
business schools (15th ed.). Hauppage, NY: Barron’s Educational Series, Inc.
Mohr, L. B. (1999). The impact profile approach to policy
merit. Evaluation Review, 23, 212–249.
Oh, I., Schmidt, F. L., Shaffer, J. A., & Le, H. (2008). The
Graduate Management Admission Test (GMAT) is more
valid than we thought: A new development in metaanalysis and its implications for the validity of the
GMAT. Academy of Management Learning and Education, 7, 563–570.
Princeton Review. (2013). The best 295 business schools (2014
ed.). Framingham, MA: TPR Education IP Holdings.
Ray, S., & Jeon, Y. (2008). Reputation and efficiency: A nonparametric assessment of America's top-rated MBA programs. European Journal of Operational Research, 189(1),
245–268.
Siegert, K. O. (2008). Executive education: Predicting student
success in Executive MBA programs. Journal of Education
for Business, 83, 221–226.
Strathman, J. G. (2000). Consistent estimation of faculty rank
effects in academic salary models. Research in Higher Education, 41, 237–250.
Talento-Miller, E., & Rudner, L. M. (2005). GMAT validity
study summary report for 1997 to 2004. GMAC research
reports. McLean, VA: Graduate Management Admission
Council.
Talento-Miller, E., & Rudner, L. M. (2008). The validity of
Graduate Management Admission Test scores: A summary
of studies conducted from 1997 to 2004. Educational and
Psychological Measurement, 68, 129–138.
Tracy, J., & Waldfogel, J. (1997). The best business schools: A
market-based approach. Journal of Business, 70, 1–31.
Truell, A. D., Zhao, J. J., Alexander, M. W., & Hill, I. B. (2006).
Predicting final student performance in a graduate
business program: The MBA. Delti Pi Epsilon Journal, 48,
144–152.
White, C. S., James, K., Burke, L. A., & Allen, R. S. (2012).
What makes a “research star”? Factors influencing the
research productivity of business faculty. International
Journal of Productivity & Performance Management, 61,
584–602.
Wright, R. E., & Palmer, J. C. (1997). Examining performance
predictors for differentially successful MBA students. College Student Journal, 31, 276.
Yang, B., & Lu, X. R. (2001). Predicting academic performance in management education: An empirical investigation of MBA success. Journal of Education for
Business, 77(1), 15–20.