Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.80.5.289-294
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Do Business Communication Courses Improve
Student Performance in Introductory Marketing?
Leah E. Marcal , Judith E. Hennessey , Mary T. Curren & William W. ROBERTS
To cite this article: Leah E. Marcal , Judith E. Hennessey , Mary T. Curren & William W. ROBERTS
(2005) Do Business Communication Courses Improve Student Performance in Introductory
Marketing?, Journal of Education for Business, 80:5, 289-294, DOI: 10.3200/JOEB.80.5.289-294
To link to this article: http://dx.doi.org/10.3200/JOEB.80.5.289-294
Published online: 07 Aug 2010.
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Do Business Communication
Courses Improve
Student Performance in
Introductory Marketing?
LEAH E. MARCAL
JUDITH E. HENNESSEY
MARY T. CURREN
WILLIAM W. ROBERTS
California State University, Northridge
Northridge, California
N
ationwide, faculty members have
lamented that students’ writing
skills are weak. This is a major concern
for marketing faculty members because
communication skills are considered
critical to marketing success. Extensive
research has shown that employers consider communication skills to be among
the most necessary critical skills for
marketing and business majors (Young
& Murphy, 2003). According to Smart,
Kelley, and Conant (1999), “Many
[marketing] professors who discussed
skill issues indicated that their schools
had been actively seeking to improve
student writing . . .” (p. 210). Completion of the business communications
course may be one way to ensure that
students have the knowledge and skills
needed to succeed in marketing courses.
Prerequisites are standard in college
curricula and establish the preconditions for course enrollment. Prerequisites may include specific courses, academic status, and tests of preparedness.
Such prerequisites perform two distinct,
yet related, functions. First, they can be
used as a filter that prevents program
continuation. Second, they can serve as
a measure of course preparedness. As a
filter, prerequisites may improve course
performance by eliminating weak students. As a measure of preparedness,
ABSTRACT. In this study, the
authors investigated whether completion of a business communications
course improved student performance
in an introductory marketing management course. Regression analysis indicated that students who completed the
communications course received higher grades than the otherwise comparable students. In addition, marketing
majors and students with high college
grade point averages (GPAs) earned
better grades in the marketing course.
After controlling for college GPA, the
authors found that log-linear analysis
supported the regression findings by
showing a partial association between
the grades in marketing and completion of the communications course.
valid prerequisites should increase the
likelihood for success.
As preparation, prerequisites signal
the set of entering skills that are
required for successful course completion. Designated prerequisites are part
of program design and course sequencing. With pressure from regional and
international accrediting agencies for
increased program assessment, the
effect of prerequisites on student outcomes becomes increasingly important.
Currently, the State of California mandates validation and reevaluation of
community-college course prerequisites.1 In this study, we used regression
and categorical modeling techniques to
determine whether a business communications course should be a requirement
for students who enroll in the marketing
course.
Literature Review
Numerous researchers have analyzed
the effects of quantitative prerequisites
on course performance. Analysis of student performance in introductory economics dominates the literature. For
example, Anderson, Benjamin, and Fuss
(1994) found that a high school calculus
course was significant in predicting performance in basic economics. Cohn,
Cohn, and Bradley (1998) also found
math skills were important but questioned math as a prerequisite, arguing
that evidence from other courses or SAT
performance could suffice. Ely and Hittle (1990) found that performance in
business finance was improved by completion of accounting courses and was
not influenced by mathematical background. Marcal and Roberts (2001)
found that students who satisfy a business statistics requirement received
higher grades in the financial management course.
Most of the remaining studies investigate how course or individual characteristics affect student success. For examMay/June 2005
289
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ple, Henebry (1997) considered the
importance of class schedule and found
that students were more likely to pass a
financial management course if it met
more than once a week. Horvath,
Beaudin, and Wright (1992) investigated
gender differences in course persistence
and found that female students were less
likely to persist in the introductory economics course sequence. In one of the
few studies analyzing student outcomes
in introductory marketing, Borde (1998)
found that students with high college
GPAs earned better grades, whereas
community college transfer students
and students with employment commitments earned lower grades.
Method
The sample for this project consisted
of students from a large public university in Southern California. California
State University at Northridge (CSUN)
has over 26,000 students and almost
6,000 business majors. In our study, we
analyzed course outcomes for students
enrolled in Introductory to Marketing
Management (MKT 304) over a 3-year
period. This course is a typical 3-unit,
semester-long, junior business marketing class designed to increase students’
knowledge of marketing management’s
role in a firm’s business strategy. Completion of the required, 3-unit Business
Communications course (Buscom) prior
to enrollment in MKT 304 is encouraged. Buscom introduces students to
techniques for developing oral and written communication skills adapted to
business situations. Marketing faculty
members often assert that these communication skills are essential for comple-
tion of assignments in the marketing
course. However, only 42% of the students in this sample had completed the
business communications course before
taking MKT 304.
The analysis sample consisted of
3,280 students who earned a grade in
MKT 304 between spring 1996 and fall
1998. We collected data on student
characteristics before their enrollment
in this course and collected their grades
in the course at the semester’s end. The
average age of students enrolled in
MKT 304 was just under 26 (see Table
1). Course enrollment was balanced in
terms of gender: Forty-eight percent of
the students were women. The mean
college GPA was 2.7 after the students’
completion of 100 units.
Results
Ordered Probit Analysis
We estimated an ordered probit
model to determine whether students
who completed the business communications course obtained higher grades in
the marketing class. The specification
for the model was as follows:
MKT304* = â’x + å, and å ~ N[0,1],
where MKT304* is the unobserved continuous grade scale that underlies the
students’ course grades, and x is the
vector of explanatory variables. The letter grades were coded so that F = 0, D =
1, C = 2, B = 3, and A = 4.2 These
observed grades were related to the
unobserved grading scale in the following manner:
MKT304 = 0 if MKT304* ≤ 0,
MKT304 = 1 if 0 < MKT304* ≤ ì1,
MKT304 = 2 if ì1 < MKT304* ≤ ì2,
MKT304 = 3 if ì2 < MKT304* ≤ ì3,
MKT304 = 4 if ì3 < MKT304*.
The µ’s are threshold parameters that
provide the ranking in the model and are
estimated with the beta coefficients.
The estimation results (µ and β)
allow a calculation of the conditional
probability that a student receives a particular letter grade given her characteristics (x). The probabilities for each of
the five letter grades are as follows:
Prob(MKT304 = 0) = Ö(–â´x),
Prob(MKT304 = 1) = Ö(ì1 – â´x) –
Ö(– â´x),
Prob(MKT304 = 2) = Ö(ì2 – â´x) –
Ö(ì1 – â´x),
Prob(MKT304 = 3) = Ö( ì3 – â´x) –
Ö(ì2 – â´x),
Prob(MKT304 = 4) = 1 – Ö(ì – â´x),
3
where Φ is the cumulative standard normal distribution.
We assumed that student performance in the marketing course would be
influenced by personal characteristics,
past achievement in college courses,
completion of the business communications course, and choice of major. We
included information on each student’s
age, gender, and equal-opportunity and
nonresident-alien status in the regression. College grade point average and
total units completed comprised past
achievement in college courses. We distinguished accounting and marketing
majors from other business majors (see
Table 1).
We report the regression results in
Table 2.3 Our findings indicate that
TABLE 1. Variable Definitions and Descriptive Statistics (N = 3,280)
Variable
MKT 304
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
290
Definition
Grade in Introduction to Marketing Management
Student’s age in years when enrolled in MKT 304
Indicates the student is a female
Indicates participation in the Equal Opportunity Program
Indicates the student is a nonresident alien
College grade point average prior to MKT 304 enrollment
Number of completed college credit hours prior to MKT 304
Student completed the business communications course
Indicates the student is an accounting major
Indicates the student is a marketing major
Journal of Education for Business
%
M
SD
2.45
25.65
0.96
5.32
2.72
100.48
0.50
24.78
48.1
11.7
7.8
42.0
26.5
11.8
TABLE 2. Ordered Probit Analysis of MKT 304 Grade
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Variable
Constant
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
Mu(1)
Mu(2)
Mu(3)
Sample size
Log likelihood (Ln)
Restricted Ln
χ2
r
SE
t
–2.690
0.007*
–0.006
–0.167**
–0.292**
1.564**
0.006**
0.190**
0.049**
0.213**
0.999
2.397
3.869
0.147
0.003
0.039
0.062
0.069
0.043
0.001
0.043
0.045
0.064
0.045
0.050
0.058
3,280
–3,685.04
–4,461.62
1,553.16
–18.35
1.94
–0.16
–2.67
–4.20
36.61
6.86
4.41
1.09
3.31
22.21
47.56
66.79
M of X
25.65
0.48
0.12
0.08
2.72
100.48
0.42
0.27
0.12
Actual versus F predicted grades
MKT 304 grade
Actual
frequency
Predicted
probability
0
1
2
3
4
0.030
0.117
0.361
0.359
0.133
0.008
0.074
0.421
0.428
0.070
Marginal effects of the regressors
Variable
Equals 0
Equals 1
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
–0.0002
0.0001
0.0038
0.0067
–0.0359
–0.0001
–0.0044
–0.0011
–0.0049
–0.0009
0.0008
0.0215
0.0376
–0.2015
–0.0007
–0.0245
–0.0063
–0.0275
MKT 304 grade
Equals 2
Equals 3
–0.0017
0.0015
0.0412
0.0721
–0.3865
–0.0014
–0.0471
–0.0122
–0.0527
0.0018
–0.0016
–0.0442
–0.0774
0.4151
0.0015
0.0505
0.0131
0.0566
Equals 4
0.0009
–0.0008
–0.0222
–0.0389
0.2088
0.0008
0.0254
0.0066
0.0285
Note. Dependent variables = MKT 304 grade.
*p = .05 (two-tailed). **p = .01 (two-tailed).
completion of the business communications course (Buscom) improved student performance in marketing. The
marginal effects suggest that students
who completed Buscom before the marketing course were 7.6% more likely to
receive a grade of A or B in MKT 304
than otherwise comparable students.
Development of skills in the communications course may have helped the stu-
dents complete the classroom discussions, case analyses, and written and
oral presentations that are typically
required in the marketing course. Alternatively, students with weak communication skills may have delayed taking
Buscom. If so, completion of business
communications may signal better students who would have received higher
grades in marketing without the course.
There were some other interesting
results. First, we expected older and
more experienced college students to
obtain higher grades in MKT 304. Each
year of age increased the likelihood of
receiving better grades in the marketing
course. The number of completed units
also had a positive effect on student success in marketing.
We included gender in the regression
because some researchers have found that
male gender was a significant predictor of
student success in economics and finance
courses (Anderson et al., 1994; Borde,
Byrd, & Modani, 1998). However, Borde
(1998) found that gender did not influence student performance in the introductory marketing course. Our results also
suggest that gender did not affect marketing grades. The coefficient on female
gender was small, negative, and statistically insignificant.
CSUN has a large minority enrollment. Many of these students speak
English as a second language. Our best
measures to capture this population were
participation in the university’s Equal
Opportunity Program (EOP) and status
as a nonresident alien. Many EOP participants are first-generation college students and frequently come from homes
where English is seldom spoken. Our
findings indicate that EOP participants
and students with nonresident alien status earned lower grades in marketing.
Students with higher college grade
point averages (GPA) earned better
grades in MKT 304. The coefficient on
college GPA was large, positive, and
statistically significant. Moreover, the
marginal effects indicate that holding a
higher GPA reduced the probability of
receiving a C, D, or F, while substantially increasing the probability of
receiving an A or B. This finding is consistent with previous pedagogical
research in marketing and economics
courses (Borde, 1998; Brasfield, Harrison, & McCoy, 1993; Von Allmen,
1996) and confirms that previous success is a good predictor of future success in college courses.
College GPA was our best measure of
student ability. The regression did not
include Scholastic Aptitude Test (SAT)
scores or high school GPA because 74%
of the students in our sample were missing this information.4 However, in sepaMay/June 2005
291
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rate regression results (not reported),
the inclusion of SAT scores and high
school GPA did not alter these findings
in a substantive manner. In particular,
the marginal effect of Buscom was still
strong, positive, and statistically significant.5 If completion of business communications simply signals more capable students, then the inclusion of SAT
scores should substantially reduce the
marginal effect of Buscom.
Finally, the ordered probit regression
accounted for the presence of account-
ences among probabilities, but it is
poorly suited for testing for independence. In contrast, log-linear models
treat all variables as “dependent.” These
models emphasize model building,
goodness-of-fit tests, and estimation of
cell frequencies in a contingency table.
It is easy to test for independence with
log-linear models.
The log-linear model analyzes the
frequency counts of observations falling
into each cross-classification category
in a contingency table. Because the cell
However, our results suggest that accounting
majors earn grades similar to those of other
business majors in MKT 304.
ing and marketing majors in the MKT
304 course. CSUN faculty members
often have stated that accounting majors
perform better than other business
majors in nonaccounting business
courses. Accounting majors need to
earn a higher GPA (3.0) than other business students (2.0) to enter the program
and remain in good standing. However,
our results suggest that accounting
majors earn grades similar to those of
other business majors in MKT 304. Our
results also indicate that marketing
majors were 8.5% more likely to earn an
A or B in MKT 304 than otherwise
comparable business majors. This is not
surprising, because these students have
selected themselves into the marketing
major on the basis of their ability and
interest in the subject matter.
Log-Linear Analysis
We checked the ordered probit
regression results with a more conservative hierarchical log-linear modeling
procedure. Like linear regression, the
ordered probit model makes a clear distinction between the dependent variable
and the explanatory variables. This
model is well suited for testing hypotheses about the coefficients and differ292
Journal of Education for Business
counts in a table are not identical, a randomly chosen observation has different
probabilities of belonging to the various
cells. Log-linear models propose that
the logarithm of a cell probability can
be modeled as a linear combination of
parameters. The dependent variable is
the number of observations (frequency)
in a cell (Upton, 1991).
The procedure estimates maximum
likelihood parameters of hierarchical
log-linear models using the NewtonRaphson Method (Bishop, Feinberg, &
Holland, 1995). Through this procedure, one can examine the ordered
impacts of model variables proceeding
from the independence model to the
highest-way interaction (i.e., saturated
model), while exploring each model in
between for goodness-of-fit.
In Table 3, we present the log-linear
results. Each of the five models contains
the two variables of primary interest—
MKT 304 (M) and Buscom (B)—along
with college GPA. We confined our
attention to these three variables to produce a contingency table having very few
cells with zero counts.6 We selected college GPA as the third variable because it
is our best measure of student ability and
it had the largest effect on MKT 304
grades in the ordered probit results.
Log-linear analysis will not handle
continuous variables, so we “categorized” college GPA. The variable G
indicates whether a student has a college GPA that exceeds 2.72, which was
the median GPA in our analyzed sample.7 We explored numerous alternative
categories for G. Unfortunately, any
expansion in the number of categories
for G produced frequency tables with
several cells having very few counts.
The first model in Table 3 includes
main effects without interactions between
the variables. This “independence”
model indicates that there was no association between the three variables M, G,
and B. The likelihood ratio (L.R.)
shows the “goodness-of-fit.” Here, it was
1,080.1 units, which means that the independence model could not be accepted.
Model 2 includes main effects with
an interaction between G and B with no
effect on M. This model indicates that G
and B were associated but that neither
variable was associated with M. The
large L.R. statistic shows that this is a
poor model for predicting the observed
cell frequencies.
Model 3 includes main effects with
an interaction between G and M with no
effect of B on M. Model 3 is a significant improvement over Model 2
(1,039.8 – 112.5 with 4 degrees of freedom). However, the goodness-of-fit at
112.5 is not a good fit. The improvement of fit, however, does suggest that
the two variables M and G should not be
treated as independent.
Model 4 includes main effects and all
two-way interactions. This model indicates that there was a partial association
between M and G, controlling for B;
and a partial association between M and
B, controlling for G. This model most
closely resembles our ordered probit
regression. The improvement of fit
(112.5 – 3.6 with 4 degrees of freedom) suggests that the two variables M
and B should not be treated as independent. The L.R. statistic—3.6—is low,
and the goodness-of-fit is not significantly different from zero, which means
that this model is acceptable.
Model 5 is the saturated model, which
adds the hypothesis that there is an interaction of G and B in their effects on M.
In general, it is not a good idea to accept
the saturated model when a simpler
TABLE 3. Hierarchical Log-Linear Models (N = 3,280)
Model
1. Independence
2. No effects on M
3. No effect of B on M
4. All two-way effects
5. Saturated
Label
df
Likelihood
ratio
p value
M, G, B
M, GB
MG, GB
MG, GB, MB
MGB
13
12
8
4
0
1,080.1
1,039.8
112.5
3.6
0.0
0.00
0.00
0.00
0.47
1.00
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Note. Each model is represented by a label referring to the three dependent variables M, G, and B.
Conditionally dependent variables appear together in the label with no comma between them. The
five models are hierarchical because a model that contains higher order effects must also contain
lower order effects. Because model 5 allows MGB, it must also allow M, G, B, MG, GB, and MB.
M = grade in MKT 304 (possible values: 0, 1, 2, 3, 4); G = student’s college GPA ≥ 2.72 (possible
values: 0, 1); B = student completed the business communication course (possible values: 0, 1).
model will fit the data. The improvement
of fit from adding the three-way interaction was not significant (3.6 – 0 with 4
degrees of freedom), which led us to
conclude that Model 4 is our best model.
Conclusion
In this study, we employed regression
and categorical modeling techniques to
determine whether a business communications course improved student performance in introductory marketing. Our
results indicate that students who completed the business communications
course beforehand earned better grades
in MKT 304 than those who did not.
The evidence suggests that a business
communications prerequisite would
improve student performance in the
marketing course.
After we collected the data for this
study, the business communications
course was reclassified as a freshmanlevel class. Additionally, all lower division business requirements, including
the business communications course,
must now be completed before business
students can take any junior-level core
class, including MKT 304. These new
rules have been in effect for only 1 year.
The minors in marketing, who are nonbusiness majors, will continue to take
MKT 304 without the business communications course. If the business communications course leads to a better
grade in MKT 304, then student grades
should improve for the business students only. In the future, researchers
should focus on analysis of the relative
change in grades predicted as (a) a test
of the validity of the models proposed
and (b) a test of the extent to which the
models proposed can be generalized. In
other words, do our results apply to
nonbusiness majors as well?
There are administrative costs as well
as costs to students whenever prerequisites are instituted and enforced. In this
study, we established an approach for
quantifying the value of a proposed prerequisite. It seems reasonable to recommend that instructors and curriculum
designers conduct similar studies when
they are considering the introduction of
new prerequisites.
NOTES
1. California Code of Regulations §55201.
2. Actual grades include plus and minus grades.
We collapsed the grades onto this 5-point scale for
several reasons. First, plus/minus grading is not
uniformly applied by the faculty members. Second, including plus/minus grades does not produce the continuous grade scale necessary for linear regression. Third, the use of plus/minus grades
creates 12 grade categories and is therefore cumbersome to report. Moreover, the findings are similar with the use of 12 grade categories.
3. The estimated coefficients of the explanatory
variables in an ordered probit regression are not
the marginal effects normally interpreted in a linear regression model. If we let Pj represent the
probability of receiving a j grade (e.g., j = 0 is an
F), then calculation of the marginal effects is:
∂Pj/∂xi = [f(µj-1 – β’xi) – f(µj – β’xi)] × β,
where f is the standard normal density. It is
clear that the marginal effects will vary with the
values of x. The data in Table 2 contain the marginal effects calculated at the means of the regressors (x). It is worth noting that the marginal effects
are multiples of the coefficient vector. Thus, the
magnitudes of the marginal effects are likely to be
very different from the beta coefficients. See
Greene (1993, pp. 672–676) for a discussion of
this regression technique.
4. More than 70% of our students transfer from
local community colleges, and the university does
not require SAT scores or high school GPAs for
these students.
5. Performing the identical regression reported in
Table 2 on the smaller sample of students who have
SAT scores and high school GPAs (n = 855), we
found that students who completed the business
communications course were 4.0% more likely to
receive an A or a B in MKT 304. Adding two variables—combined SAT score and high school
GPA—to this regression suggests that students who
completed the communications course were 3.5%
more likely to earn an A or B in MKT 304. The
impact of Buscom may be lower (4.0 versus 7.6)
for this smaller sample (n = 855) because these students entered CSUN as freshmen and had better
communication skills than students who had to
transfer from local community colleges.
6. It is important to limit the number of variables
and the number of categories for each variable to
produce a table having very few cells with zero
counts. Tables with zero counts lead to insensitive
tests of the hypotheses and numerous theoretical
problems. Please see the article by Upton (1991)
for further discussion of the log-linear model.
7. It is the convention in log-linear analysis to
name variables with only one letter.
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Do Business Communication Courses Improve
Student Performance in Introductory Marketing?
Leah E. Marcal , Judith E. Hennessey , Mary T. Curren & William W. ROBERTS
To cite this article: Leah E. Marcal , Judith E. Hennessey , Mary T. Curren & William W. ROBERTS
(2005) Do Business Communication Courses Improve Student Performance in Introductory
Marketing?, Journal of Education for Business, 80:5, 289-294, DOI: 10.3200/JOEB.80.5.289-294
To link to this article: http://dx.doi.org/10.3200/JOEB.80.5.289-294
Published online: 07 Aug 2010.
Submit your article to this journal
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Do Business Communication
Courses Improve
Student Performance in
Introductory Marketing?
LEAH E. MARCAL
JUDITH E. HENNESSEY
MARY T. CURREN
WILLIAM W. ROBERTS
California State University, Northridge
Northridge, California
N
ationwide, faculty members have
lamented that students’ writing
skills are weak. This is a major concern
for marketing faculty members because
communication skills are considered
critical to marketing success. Extensive
research has shown that employers consider communication skills to be among
the most necessary critical skills for
marketing and business majors (Young
& Murphy, 2003). According to Smart,
Kelley, and Conant (1999), “Many
[marketing] professors who discussed
skill issues indicated that their schools
had been actively seeking to improve
student writing . . .” (p. 210). Completion of the business communications
course may be one way to ensure that
students have the knowledge and skills
needed to succeed in marketing courses.
Prerequisites are standard in college
curricula and establish the preconditions for course enrollment. Prerequisites may include specific courses, academic status, and tests of preparedness.
Such prerequisites perform two distinct,
yet related, functions. First, they can be
used as a filter that prevents program
continuation. Second, they can serve as
a measure of course preparedness. As a
filter, prerequisites may improve course
performance by eliminating weak students. As a measure of preparedness,
ABSTRACT. In this study, the
authors investigated whether completion of a business communications
course improved student performance
in an introductory marketing management course. Regression analysis indicated that students who completed the
communications course received higher grades than the otherwise comparable students. In addition, marketing
majors and students with high college
grade point averages (GPAs) earned
better grades in the marketing course.
After controlling for college GPA, the
authors found that log-linear analysis
supported the regression findings by
showing a partial association between
the grades in marketing and completion of the communications course.
valid prerequisites should increase the
likelihood for success.
As preparation, prerequisites signal
the set of entering skills that are
required for successful course completion. Designated prerequisites are part
of program design and course sequencing. With pressure from regional and
international accrediting agencies for
increased program assessment, the
effect of prerequisites on student outcomes becomes increasingly important.
Currently, the State of California mandates validation and reevaluation of
community-college course prerequisites.1 In this study, we used regression
and categorical modeling techniques to
determine whether a business communications course should be a requirement
for students who enroll in the marketing
course.
Literature Review
Numerous researchers have analyzed
the effects of quantitative prerequisites
on course performance. Analysis of student performance in introductory economics dominates the literature. For
example, Anderson, Benjamin, and Fuss
(1994) found that a high school calculus
course was significant in predicting performance in basic economics. Cohn,
Cohn, and Bradley (1998) also found
math skills were important but questioned math as a prerequisite, arguing
that evidence from other courses or SAT
performance could suffice. Ely and Hittle (1990) found that performance in
business finance was improved by completion of accounting courses and was
not influenced by mathematical background. Marcal and Roberts (2001)
found that students who satisfy a business statistics requirement received
higher grades in the financial management course.
Most of the remaining studies investigate how course or individual characteristics affect student success. For examMay/June 2005
289
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ple, Henebry (1997) considered the
importance of class schedule and found
that students were more likely to pass a
financial management course if it met
more than once a week. Horvath,
Beaudin, and Wright (1992) investigated
gender differences in course persistence
and found that female students were less
likely to persist in the introductory economics course sequence. In one of the
few studies analyzing student outcomes
in introductory marketing, Borde (1998)
found that students with high college
GPAs earned better grades, whereas
community college transfer students
and students with employment commitments earned lower grades.
Method
The sample for this project consisted
of students from a large public university in Southern California. California
State University at Northridge (CSUN)
has over 26,000 students and almost
6,000 business majors. In our study, we
analyzed course outcomes for students
enrolled in Introductory to Marketing
Management (MKT 304) over a 3-year
period. This course is a typical 3-unit,
semester-long, junior business marketing class designed to increase students’
knowledge of marketing management’s
role in a firm’s business strategy. Completion of the required, 3-unit Business
Communications course (Buscom) prior
to enrollment in MKT 304 is encouraged. Buscom introduces students to
techniques for developing oral and written communication skills adapted to
business situations. Marketing faculty
members often assert that these communication skills are essential for comple-
tion of assignments in the marketing
course. However, only 42% of the students in this sample had completed the
business communications course before
taking MKT 304.
The analysis sample consisted of
3,280 students who earned a grade in
MKT 304 between spring 1996 and fall
1998. We collected data on student
characteristics before their enrollment
in this course and collected their grades
in the course at the semester’s end. The
average age of students enrolled in
MKT 304 was just under 26 (see Table
1). Course enrollment was balanced in
terms of gender: Forty-eight percent of
the students were women. The mean
college GPA was 2.7 after the students’
completion of 100 units.
Results
Ordered Probit Analysis
We estimated an ordered probit
model to determine whether students
who completed the business communications course obtained higher grades in
the marketing class. The specification
for the model was as follows:
MKT304* = â’x + å, and å ~ N[0,1],
where MKT304* is the unobserved continuous grade scale that underlies the
students’ course grades, and x is the
vector of explanatory variables. The letter grades were coded so that F = 0, D =
1, C = 2, B = 3, and A = 4.2 These
observed grades were related to the
unobserved grading scale in the following manner:
MKT304 = 0 if MKT304* ≤ 0,
MKT304 = 1 if 0 < MKT304* ≤ ì1,
MKT304 = 2 if ì1 < MKT304* ≤ ì2,
MKT304 = 3 if ì2 < MKT304* ≤ ì3,
MKT304 = 4 if ì3 < MKT304*.
The µ’s are threshold parameters that
provide the ranking in the model and are
estimated with the beta coefficients.
The estimation results (µ and β)
allow a calculation of the conditional
probability that a student receives a particular letter grade given her characteristics (x). The probabilities for each of
the five letter grades are as follows:
Prob(MKT304 = 0) = Ö(–â´x),
Prob(MKT304 = 1) = Ö(ì1 – â´x) –
Ö(– â´x),
Prob(MKT304 = 2) = Ö(ì2 – â´x) –
Ö(ì1 – â´x),
Prob(MKT304 = 3) = Ö( ì3 – â´x) –
Ö(ì2 – â´x),
Prob(MKT304 = 4) = 1 – Ö(ì – â´x),
3
where Φ is the cumulative standard normal distribution.
We assumed that student performance in the marketing course would be
influenced by personal characteristics,
past achievement in college courses,
completion of the business communications course, and choice of major. We
included information on each student’s
age, gender, and equal-opportunity and
nonresident-alien status in the regression. College grade point average and
total units completed comprised past
achievement in college courses. We distinguished accounting and marketing
majors from other business majors (see
Table 1).
We report the regression results in
Table 2.3 Our findings indicate that
TABLE 1. Variable Definitions and Descriptive Statistics (N = 3,280)
Variable
MKT 304
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
290
Definition
Grade in Introduction to Marketing Management
Student’s age in years when enrolled in MKT 304
Indicates the student is a female
Indicates participation in the Equal Opportunity Program
Indicates the student is a nonresident alien
College grade point average prior to MKT 304 enrollment
Number of completed college credit hours prior to MKT 304
Student completed the business communications course
Indicates the student is an accounting major
Indicates the student is a marketing major
Journal of Education for Business
%
M
SD
2.45
25.65
0.96
5.32
2.72
100.48
0.50
24.78
48.1
11.7
7.8
42.0
26.5
11.8
TABLE 2. Ordered Probit Analysis of MKT 304 Grade
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Variable
Constant
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
Mu(1)
Mu(2)
Mu(3)
Sample size
Log likelihood (Ln)
Restricted Ln
χ2
r
SE
t
–2.690
0.007*
–0.006
–0.167**
–0.292**
1.564**
0.006**
0.190**
0.049**
0.213**
0.999
2.397
3.869
0.147
0.003
0.039
0.062
0.069
0.043
0.001
0.043
0.045
0.064
0.045
0.050
0.058
3,280
–3,685.04
–4,461.62
1,553.16
–18.35
1.94
–0.16
–2.67
–4.20
36.61
6.86
4.41
1.09
3.31
22.21
47.56
66.79
M of X
25.65
0.48
0.12
0.08
2.72
100.48
0.42
0.27
0.12
Actual versus F predicted grades
MKT 304 grade
Actual
frequency
Predicted
probability
0
1
2
3
4
0.030
0.117
0.361
0.359
0.133
0.008
0.074
0.421
0.428
0.070
Marginal effects of the regressors
Variable
Equals 0
Equals 1
Age
Female
EOP
Nonresident
College GPA
Total units
Buscom
Acctmaj
Mktmaj
–0.0002
0.0001
0.0038
0.0067
–0.0359
–0.0001
–0.0044
–0.0011
–0.0049
–0.0009
0.0008
0.0215
0.0376
–0.2015
–0.0007
–0.0245
–0.0063
–0.0275
MKT 304 grade
Equals 2
Equals 3
–0.0017
0.0015
0.0412
0.0721
–0.3865
–0.0014
–0.0471
–0.0122
–0.0527
0.0018
–0.0016
–0.0442
–0.0774
0.4151
0.0015
0.0505
0.0131
0.0566
Equals 4
0.0009
–0.0008
–0.0222
–0.0389
0.2088
0.0008
0.0254
0.0066
0.0285
Note. Dependent variables = MKT 304 grade.
*p = .05 (two-tailed). **p = .01 (two-tailed).
completion of the business communications course (Buscom) improved student performance in marketing. The
marginal effects suggest that students
who completed Buscom before the marketing course were 7.6% more likely to
receive a grade of A or B in MKT 304
than otherwise comparable students.
Development of skills in the communications course may have helped the stu-
dents complete the classroom discussions, case analyses, and written and
oral presentations that are typically
required in the marketing course. Alternatively, students with weak communication skills may have delayed taking
Buscom. If so, completion of business
communications may signal better students who would have received higher
grades in marketing without the course.
There were some other interesting
results. First, we expected older and
more experienced college students to
obtain higher grades in MKT 304. Each
year of age increased the likelihood of
receiving better grades in the marketing
course. The number of completed units
also had a positive effect on student success in marketing.
We included gender in the regression
because some researchers have found that
male gender was a significant predictor of
student success in economics and finance
courses (Anderson et al., 1994; Borde,
Byrd, & Modani, 1998). However, Borde
(1998) found that gender did not influence student performance in the introductory marketing course. Our results also
suggest that gender did not affect marketing grades. The coefficient on female
gender was small, negative, and statistically insignificant.
CSUN has a large minority enrollment. Many of these students speak
English as a second language. Our best
measures to capture this population were
participation in the university’s Equal
Opportunity Program (EOP) and status
as a nonresident alien. Many EOP participants are first-generation college students and frequently come from homes
where English is seldom spoken. Our
findings indicate that EOP participants
and students with nonresident alien status earned lower grades in marketing.
Students with higher college grade
point averages (GPA) earned better
grades in MKT 304. The coefficient on
college GPA was large, positive, and
statistically significant. Moreover, the
marginal effects indicate that holding a
higher GPA reduced the probability of
receiving a C, D, or F, while substantially increasing the probability of
receiving an A or B. This finding is consistent with previous pedagogical
research in marketing and economics
courses (Borde, 1998; Brasfield, Harrison, & McCoy, 1993; Von Allmen,
1996) and confirms that previous success is a good predictor of future success in college courses.
College GPA was our best measure of
student ability. The regression did not
include Scholastic Aptitude Test (SAT)
scores or high school GPA because 74%
of the students in our sample were missing this information.4 However, in sepaMay/June 2005
291
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rate regression results (not reported),
the inclusion of SAT scores and high
school GPA did not alter these findings
in a substantive manner. In particular,
the marginal effect of Buscom was still
strong, positive, and statistically significant.5 If completion of business communications simply signals more capable students, then the inclusion of SAT
scores should substantially reduce the
marginal effect of Buscom.
Finally, the ordered probit regression
accounted for the presence of account-
ences among probabilities, but it is
poorly suited for testing for independence. In contrast, log-linear models
treat all variables as “dependent.” These
models emphasize model building,
goodness-of-fit tests, and estimation of
cell frequencies in a contingency table.
It is easy to test for independence with
log-linear models.
The log-linear model analyzes the
frequency counts of observations falling
into each cross-classification category
in a contingency table. Because the cell
However, our results suggest that accounting
majors earn grades similar to those of other
business majors in MKT 304.
ing and marketing majors in the MKT
304 course. CSUN faculty members
often have stated that accounting majors
perform better than other business
majors in nonaccounting business
courses. Accounting majors need to
earn a higher GPA (3.0) than other business students (2.0) to enter the program
and remain in good standing. However,
our results suggest that accounting
majors earn grades similar to those of
other business majors in MKT 304. Our
results also indicate that marketing
majors were 8.5% more likely to earn an
A or B in MKT 304 than otherwise
comparable business majors. This is not
surprising, because these students have
selected themselves into the marketing
major on the basis of their ability and
interest in the subject matter.
Log-Linear Analysis
We checked the ordered probit
regression results with a more conservative hierarchical log-linear modeling
procedure. Like linear regression, the
ordered probit model makes a clear distinction between the dependent variable
and the explanatory variables. This
model is well suited for testing hypotheses about the coefficients and differ292
Journal of Education for Business
counts in a table are not identical, a randomly chosen observation has different
probabilities of belonging to the various
cells. Log-linear models propose that
the logarithm of a cell probability can
be modeled as a linear combination of
parameters. The dependent variable is
the number of observations (frequency)
in a cell (Upton, 1991).
The procedure estimates maximum
likelihood parameters of hierarchical
log-linear models using the NewtonRaphson Method (Bishop, Feinberg, &
Holland, 1995). Through this procedure, one can examine the ordered
impacts of model variables proceeding
from the independence model to the
highest-way interaction (i.e., saturated
model), while exploring each model in
between for goodness-of-fit.
In Table 3, we present the log-linear
results. Each of the five models contains
the two variables of primary interest—
MKT 304 (M) and Buscom (B)—along
with college GPA. We confined our
attention to these three variables to produce a contingency table having very few
cells with zero counts.6 We selected college GPA as the third variable because it
is our best measure of student ability and
it had the largest effect on MKT 304
grades in the ordered probit results.
Log-linear analysis will not handle
continuous variables, so we “categorized” college GPA. The variable G
indicates whether a student has a college GPA that exceeds 2.72, which was
the median GPA in our analyzed sample.7 We explored numerous alternative
categories for G. Unfortunately, any
expansion in the number of categories
for G produced frequency tables with
several cells having very few counts.
The first model in Table 3 includes
main effects without interactions between
the variables. This “independence”
model indicates that there was no association between the three variables M, G,
and B. The likelihood ratio (L.R.)
shows the “goodness-of-fit.” Here, it was
1,080.1 units, which means that the independence model could not be accepted.
Model 2 includes main effects with
an interaction between G and B with no
effect on M. This model indicates that G
and B were associated but that neither
variable was associated with M. The
large L.R. statistic shows that this is a
poor model for predicting the observed
cell frequencies.
Model 3 includes main effects with
an interaction between G and M with no
effect of B on M. Model 3 is a significant improvement over Model 2
(1,039.8 – 112.5 with 4 degrees of freedom). However, the goodness-of-fit at
112.5 is not a good fit. The improvement of fit, however, does suggest that
the two variables M and G should not be
treated as independent.
Model 4 includes main effects and all
two-way interactions. This model indicates that there was a partial association
between M and G, controlling for B;
and a partial association between M and
B, controlling for G. This model most
closely resembles our ordered probit
regression. The improvement of fit
(112.5 – 3.6 with 4 degrees of freedom) suggests that the two variables M
and B should not be treated as independent. The L.R. statistic—3.6—is low,
and the goodness-of-fit is not significantly different from zero, which means
that this model is acceptable.
Model 5 is the saturated model, which
adds the hypothesis that there is an interaction of G and B in their effects on M.
In general, it is not a good idea to accept
the saturated model when a simpler
TABLE 3. Hierarchical Log-Linear Models (N = 3,280)
Model
1. Independence
2. No effects on M
3. No effect of B on M
4. All two-way effects
5. Saturated
Label
df
Likelihood
ratio
p value
M, G, B
M, GB
MG, GB
MG, GB, MB
MGB
13
12
8
4
0
1,080.1
1,039.8
112.5
3.6
0.0
0.00
0.00
0.00
0.47
1.00
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Note. Each model is represented by a label referring to the three dependent variables M, G, and B.
Conditionally dependent variables appear together in the label with no comma between them. The
five models are hierarchical because a model that contains higher order effects must also contain
lower order effects. Because model 5 allows MGB, it must also allow M, G, B, MG, GB, and MB.
M = grade in MKT 304 (possible values: 0, 1, 2, 3, 4); G = student’s college GPA ≥ 2.72 (possible
values: 0, 1); B = student completed the business communication course (possible values: 0, 1).
model will fit the data. The improvement
of fit from adding the three-way interaction was not significant (3.6 – 0 with 4
degrees of freedom), which led us to
conclude that Model 4 is our best model.
Conclusion
In this study, we employed regression
and categorical modeling techniques to
determine whether a business communications course improved student performance in introductory marketing. Our
results indicate that students who completed the business communications
course beforehand earned better grades
in MKT 304 than those who did not.
The evidence suggests that a business
communications prerequisite would
improve student performance in the
marketing course.
After we collected the data for this
study, the business communications
course was reclassified as a freshmanlevel class. Additionally, all lower division business requirements, including
the business communications course,
must now be completed before business
students can take any junior-level core
class, including MKT 304. These new
rules have been in effect for only 1 year.
The minors in marketing, who are nonbusiness majors, will continue to take
MKT 304 without the business communications course. If the business communications course leads to a better
grade in MKT 304, then student grades
should improve for the business students only. In the future, researchers
should focus on analysis of the relative
change in grades predicted as (a) a test
of the validity of the models proposed
and (b) a test of the extent to which the
models proposed can be generalized. In
other words, do our results apply to
nonbusiness majors as well?
There are administrative costs as well
as costs to students whenever prerequisites are instituted and enforced. In this
study, we established an approach for
quantifying the value of a proposed prerequisite. It seems reasonable to recommend that instructors and curriculum
designers conduct similar studies when
they are considering the introduction of
new prerequisites.
NOTES
1. California Code of Regulations §55201.
2. Actual grades include plus and minus grades.
We collapsed the grades onto this 5-point scale for
several reasons. First, plus/minus grading is not
uniformly applied by the faculty members. Second, including plus/minus grades does not produce the continuous grade scale necessary for linear regression. Third, the use of plus/minus grades
creates 12 grade categories and is therefore cumbersome to report. Moreover, the findings are similar with the use of 12 grade categories.
3. The estimated coefficients of the explanatory
variables in an ordered probit regression are not
the marginal effects normally interpreted in a linear regression model. If we let Pj represent the
probability of receiving a j grade (e.g., j = 0 is an
F), then calculation of the marginal effects is:
∂Pj/∂xi = [f(µj-1 – β’xi) – f(µj – β’xi)] × β,
where f is the standard normal density. It is
clear that the marginal effects will vary with the
values of x. The data in Table 2 contain the marginal effects calculated at the means of the regressors (x). It is worth noting that the marginal effects
are multiples of the coefficient vector. Thus, the
magnitudes of the marginal effects are likely to be
very different from the beta coefficients. See
Greene (1993, pp. 672–676) for a discussion of
this regression technique.
4. More than 70% of our students transfer from
local community colleges, and the university does
not require SAT scores or high school GPAs for
these students.
5. Performing the identical regression reported in
Table 2 on the smaller sample of students who have
SAT scores and high school GPAs (n = 855), we
found that students who completed the business
communications course were 4.0% more likely to
receive an A or a B in MKT 304. Adding two variables—combined SAT score and high school
GPA—to this regression suggests that students who
completed the communications course were 3.5%
more likely to earn an A or B in MKT 304. The
impact of Buscom may be lower (4.0 versus 7.6)
for this smaller sample (n = 855) because these students entered CSUN as freshmen and had better
communication skills than students who had to
transfer from local community colleges.
6. It is important to limit the number of variables
and the number of categories for each variable to
produce a table having very few cells with zero
counts. Tables with zero counts lead to insensitive
tests of the hypotheses and numerous theoretical
problems. Please see the article by Upton (1991)
for further discussion of the log-linear model.
7. It is the convention in log-linear analysis to
name variables with only one letter.
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