08832323.2012.757541
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
The Role of Predictor Courses and Teams on
Individual Student Success
Lori Jo Baker-Eveleth , Michele O’Neill & Sanjay R. Sisodiya
To cite this article: Lori Jo Baker-Eveleth , Michele O’Neill & Sanjay R. Sisodiya (2014) The
Role of Predictor Courses and Teams on Individual Student Success, Journal of Education for
Business, 89:2, 59-70, DOI: 10.1080/08832323.2012.757541
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Date: 11 January 2016, At: 20:30
JOURNAL OF EDUCATION FOR BUSINESS, 89: 59–70, 2014
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2012.757541
The Role of Predictor Courses and Teams
on Individual Student Success
Lori Jo Baker-Eveleth
University of Idaho, Moscow, Idaho, USA
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Michele O’Neill
California State University-Chico, Chico, California, USA
Sanjay R. Sisodiya
University of Idaho, Moscow, Idaho, USA
Research suggests that diverse environments enhance conscious modes of thought, resulting
in greater intellectual engagement and active thinking. Ordinal and multinomial logistic regression results indicate that accounting courses and business law classes are useful predictors
of subsequent performance. Odds ratio estimates indicate that students who perform poorly
in these predictor classes are more than four times as likely to perform poorly in subsequent
classes. Academic and gender diversity were not found to be significantly related to student
performance.
Keywords: academic diversity, gender diversity, predictors, student performance, teams
Predictor courses have been used as a mechanism to determine student achievement in college courses (Kuncel,
Crede, & Thomas, 2007; Yang & Lu, 2001). While much
research has been done on the role of predictor courses
for business students (Borde, 1998; McMillan-Capehart &
Adeyemi-Bello, 2008; Sulaiman & Mohezar, 2006), little research has been done on student performance in predictor
courses and the forecasting of success in highly integrated
business courses.
This gap in the literature needs to be studied because
the Association to Advance Collegiate Schools of Business (AACSB) strongly encourages integrated education
(AACSB, 2011). Perhaps the traditional predictor courses,
typically taught as stand-alone courses, may not adequately
prepare students for an integrated business curriculum. In this
study we investigated the helpfulness of not only predictor
courses, but also academically and gender diverse teams on
student achievement in an integrated business curriculum.
Correspondence should be addressed to Lori Jo Baker-Eveleth, University of Idaho, Department of Business and Economics, 875 Campus
Drive, P. O. Box 443161, Moscow, ID 83844-3161, USA. E-mail: leveleth@
uidaho.edu
INTEGRATED CURRICULUM
AND STUDENT TEAMS
Many undergraduate business programs use grade point averages (GPAs) in specific courses to predict performance,
but this often produces mixed results (Al-Twaijry, 2010;
Borde, 1998). Predicting performance is increasingly challenging when considering the desire to integrate educational
programs. In particular, when considering integrated undergraduate business curriculum (IUBC), functional courses are
team-taught rather than taught using single discipline approaches. This form of integration challenges not only evaluating performance, but also predicting student success.
In addition, the business community looks for employees
with team skills and an understanding of cross-functional
interactions (Athavale, Davis, & Myring 2008). Combining
the two, schools seek opportunities to expose students to diverse business environments to enhance conscious modes of
thought, resulting in greater intellectual engagement and active thinking (Gurin, Dey, Hurtado, & Gurin, 2002). While
preparing students to work in diverse teams may better prepare them for the work environment (Ely & Thomas, 2001),
forecasting success in a team (oral communication, leadership, facilitation) is not always consistent with performance
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
60
L. J. BAKER-EVELETH ET AL.
measures of GPA (Alshare, Lane, & Miller, 2011; Joseph,
Soon, Chang, & Slaughter, 2010).
Thus, we consider the role that teams which are diverse
in academic experience and gender may play in enhancing
student success in an integrated program. Diversity as a concept is multidimensional, whereby some consider diversity
to include race, gender, age, disability, sexual orientation,
and national orientation (Shore et al., 2009), others say that
diversity can also include communication style, networks,
and knowledge (Ely & Thomas, 2001). In particular, Ely and
Thomas defined cultural differences in a group as arising
from the “life experiences, knowledge, and insights” from
the individuals (p. 265). Based on the previous description,
this study, focused on academic (major) and gender as measures of diversity on teams, asks these questions: can a working environment of a diverse team lead to enhanced student
performance. To what extent does exposure to a diverse environment improve individual performance?
DATA AND METHODOLOGY
To explore these questions, we gathered three years of data
from a residential land-grant university. The IUBC sequence
of courses at this institution includes six sequential modules
beginning with a short introductory module focusing on team
management concepts, where students form teams and work
together for two additional modules. Before starting the last
three modules, students may change teams; therefore, we
examine student performance and team configurations during
each of five modules (labeled herein Modules I, II, III, IV,
and V). In this study team configurations were for academic
majors (eight possible) and gender representing two proxies
for diverse teams.
For the predictor classes, we examined whether the
courses indicate success in such a junior level integrated
course (see Table 1 for a description of courses). The college
uses a traditional set of predictor classes covering introductory accounting (two courses labeled herein I and II), business
law, statistics, and economics (two courses labeled herein I
and II, or a single class worth more credits labeled herein
III). The course deliverables include exams and regularly
collected assignments.
University-level data were gathered on grades, gender,
and major for students who had enrolled in any of Modules
I–V from fall 2008 through spring 2011 and were taught by a
five-person faculty team. This request produced 284 unique
students and provided complete results for 202 students.
Grades were recorded as A, B, C, D, or F. There were
84 instances of grade P for passing in one or more predictor
classes for transfer students, and because the original score
was not on record these instances were recoded to a C. Because students must achieve a minimum GPA in predictor
courses before being admitted into the junior level sequence
of courses, some repeated one or more of their predictor
TABLE 1
Descriptions of Predictor Courses
Predictor
course
Description
Accounting I
Overview of the nature and purpose of general purpose
financial statements provided to external decision makers
Accounting II Intro to cost behavior and managerial use of accounting
information for planning, control, and performance
evaluation
Business law Law and its relationship to society; legal framework of
business enterprises; court organization and operation;
private property and contracts as basic concepts in a free
enterprise system
Statistics
Introduction to methods including design of statistical
studies, basic sampling methods, descriptive statistics,
probability and sampling distributions; inference in
surveys and experiments, regression, and analysis of
variance
Economics I
Organization and operation of American economy; supply
and demand, money and banking, macroeconomic
analysis of employment, aggregate output and inflation,
public finance, and economic growth
Economics II Microeconomic principles governing production, price
relationships, and income distribution
Economics III Introduction to the principles of economics, covering both
micro and macro concepts, theory, analysis, and
applications
classes. The college admitted students upon earning the necessary GPA in predictor courses.
Because the predictor courses included the option to take
either two economics classes (I and II) or a single economics
course worth more credit hours (III), we eventually analyzed
the data according to whether students took the two-course
economics sequence of predictors (Predictor Group 1) or
one-course economics sequence (Predictor Group 2). The
final data set contained 202 students in Modules I and II, 178
students in Modules III and IV, and 177 students in Module
V. The drop in observations after Module II results because
students performing poorly in Modules I and II often drop
the IUBC and do not enroll in Modules III and V.
To develop the two measures of team diversity (academic
and gender), we analyzed the self-selected student teams
listed for each module for size, mix of men and women,
and mix of declared majors. The college offered eight majors: accounting, economics, finance, information systems
(IS), management and human resources (MHR), marketing,
operations management (OM), and professional golf management. There were four frequent combinations of double majors: accounting-finance, economics-finance, IS-OM,
and MHR-marketing. Because there was no way to determine which major in the double majors was more appropriate to use for a representative major, we kept track of
the majors involved in the double majors when developing the measure for academic diversity, which is described
subsequently.
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ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
A student is deemed to have been on a gender diverse
team in a given module if the proportion of male (M) and
female (F) students for the various team sizes (N) was any
of the following: N of 2 has 1M/1F; N of 3 has 2M/1F or
2F/1M; N of 4 has 2M/2F; N of 5 has 2M/3F or 3M/2F; N of
6 has 2M/4F, 3M/3F, or 4M/2F. For all other proportions, the
student was considered not to be on a gender diverse team.
The measure for academic diversity uses the variety of
majors represented on a team as a proxy. When a team member had a double major, it subsumed any matching single
majors and counted for two majors if no others matched. For
example, if a team member was an accounting-finance double major, another member was a finance major, and another
member was a Marketing major, we counted three majors
as being represented (accounting, finance, marketing); on
the other hand, if the accounting-finance double major was
teamed with two marketing majors, we counted three majors as being represented (accounting, finance, marketing).
A student then was deemed to have been on an academically
diverse team in a given module if the proportion of different
majors to total majors represented for the various team sizes
was any of the following: N of 2 has 2/2; N of 3 has 2/3; N
of 4 has 3/4 or 4/4; N of 5 has 3/5, 4/5, or 5/5; N of 6 has 3/6,
4/6, 5/6, or 6/6. For all other proportions, the student was
considered not to be on an academically diverse team.
Cross-tabulations were performed and two measures
of ordinal association calculated, gamma and Somers’ d.
Gamma can be interpreted as the percentage reduction in
error achieved when ranking the dependent variable (here,
grades in each of the five modules) by knowing the independent variable (here, grades in the various predictor classes).
Because gamma is a symmetrical measure (meaning that the
same value is calculated even if the independent variable
is switched for the dependent variable), we also report
Somers’ d, which in its original form is an asymmetrical
measure.
To explore deeper for possible relationships among performance in the predictor courses, exposure to diverse teams,
and performance in each of the five integrated modules, we
conducted ordinal regression analysis, which accounts for an
ordered dependent variable (Chen & Hughes, 2004; Noruˇsis,
2011). The four grade categories were ranked and coded in
the following order: A = 1, B = 2, C = 3, D/F = 4. Ordinal regression analysis models—with an adaptation to the
underlying variable’s distribution via a link function—the
cumulative probability of an event (e.g., a student scoring a
C in Module II) along with all the events ordered before it
(e.g., scoring either a B or A). The reference category then
to which the estimated coefficients are compared is the last
one ranked, here the combined scores of D/F.
To determine the most appropriate link function and model
formulation, we made various comparisons and performed
tests (Chen & Hughes, 2004). The complementary log-log
was determined to be the most appropriate link function.
Even then, the assumption of parallel lines was not met except
for three instances: modeling Module I grades with either
61
group of predictors and modeling Module III grades with
Predictor Group 1.
Because the ordinal regression methodology could not be
used to analyze most of the data, we relaxed the normality,
linearity, and homogeneity of variance assumptions (Garson,
2011; Schwab, 2011). We then analyzed the data as though it
were a nominal variable. Doing so allowed us to apply multinomial logistic regression analysis. SPSS software (ver. 19,
Chicago, IL) also allowed us to change the reference grade
category for each of the modules from D/F (used in ordinal)
to A.
Occasionally, multinomial logistic regression analysis
produces unreasonable estimates. Often, such results are due
to multicollinearity, predictor categories having zero observations, or complete separation or bifurcation of the data,
which drives the maximum likelihood estimates to infinity.
Standard errors exceeding 2.0 indicate such problems, even if
the estimates look reasonable (Schwab, 2011). Analyzing the
data with multinomial logistic regression revealed problems
with one or more standard errors or estimated implausible coefficients for eight of the 10 possible full regression models.
Eliminating the academic diversity measure, which had a low
frequency of nondiversified teams (discussed subsequently),
resolved estimation issues for five of the models.
To check the usefulness of the estimated models we calculated pseudo R2. However, because the levels are not as
meaningful as those for traditional R2 measures, we also
checked the accuracy of each model’s classification estimation. Even if no relationship existed between the independent
and dependent variables, by chance alone we could predict
correctly some of the time, which dependent variables belonged with which group of independent variables. To address this, we compared the model’s computed classification
percentage accuracy rate to a modified or by chance percentage accuracy rate: the sum of the model’s squared marginal
percentages, which is then increased by 25%. Comparing the
rates allowed us to conclude that a model is useful when its
classification accuracy rate is at least a 25% improvement
over classification by chance alone (Schwab, 2011).
RESULTS
Descriptive frequency distributions are presented on team
sizes and academic majors. Table 2 reports changes in team
size frequencies across the five modules. Data collected show
teams ranged in size from a low of two members, formed only
once for only two modules, to a high of six members, which
occurred in all modules. Five members to a team was the
most common size, occurring 44.6–52% of the time. Table 3
reports the distribution of majors and four most common
double majors declared at the start of Modules I and III,
which was when teams reformed for the second semester.
The most commonly declared majors were accounting, marketing, finance, and MHR. The frequency of these majors
ranged from a low of 12.0% (MHR, Module I) to a high of
19.7% (accounting, Module III). The percentage change in
62
L. J. BAKER-EVELETH ET AL.
TABLE 2
Team Sizes Across the Five Modules
Module
I
II
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III and IV
V
TABLE 3
Distribution of Declared Majors Across Beginning of
Semesters When Teams Form
Team size
Frequency
%
Cumulative%
3
4
5
6
Total
3
4
5
6
Total
2
4
5
6
Total
4
5
6
Total
1
11
25
19
56
1
11
26
18
56
1
8
25
17
51
8
26
16
50
1.8
19.6
44.6
34.0
100.0
1.8
19.6
46.4
32.2
100.0
2.0
15.7
49.0
33.3
100.0
16.0
52.0
32.0
100.0
1.8
21.4
66.0
100.0
1.8
21.4
67.8
100.0
2.0
17.7
66.7
100.0
16.0
68.0
100.0
each declared major suggests students may be affected by the
diversity of business topics. After students complete Modules
I and II, the double major of IS-OM gained 157.1% majors
(from 0.7% to 1.8%). The second highest positive gain was in
the double major economics-finance at 63.6% (from 1.1% to
1.8%). Students declaring nonbusiness majors experienced
the largest decline at 22.2% (from 1.9% to 1.4%).
Results for the two variables measuring exposure to a diverse team are presented in Table 4. Teams were academically
diverse all the time, occurring no less frequently than 95.7%
of the time (Module V). There was a small chance a student
would not be exposed to an academically diverse team and
50% chance a student was exposed to a gender-diverse team.
These formed at a frequency ranging from 53.1% (Module
V) to 56.7% (Module I).
Table 5 presents cross-tabulations and measures of ordinal association. All gamma and Somers’ d calculations were
significant at 5% or higher for all predictor classes in each of
the five modules. Gamma scores ranged from a low of 0.253
(economics I and Module V) to a high of 0.629 (economics
III and Module II), indicating that a moderate to fairly strong
positive relationship exists between scores earned in the predictor classes and scores earned in the modules. Accounting
I had the most consistently strong relationship, with four of
five gamma scores exceeding 0.500 (Modules I–III and V).
Economics I and II showed the weakest relationships with
all gamma scores below 0.450 for all five modules. Somers’
d measures ranged from 0.161 (economics I and Module V)
to 0.444 (economics III and Module I), indicating that these
predictor classes somewhat to moderately positively related
with influencing the module grades.
Results for the three estimated models that met the parallel lines assumption of ordinal regression are reported in
Tables 6 (Module I with Predictor Group 1), 7 (Module I
Module I
%
Cumul.
Accounting
Marketing
Finance
MHR
Economics
19.7
14.8
13.0
12.0
8.8
19.7
34.5
47.5
59.5
68.3
MHR and
Mark.
Acct. and
Finance
PGMMarketing
Info Sys.
7.7
Accounting
Marketing
Finance
MHR
MHR and
Mark.
76.0 Economics
5.6
81.6 POM
POM
Nonbusiness
Econ. and
Finance
Info Sys.
and POM
4.9
4.9
4.9
1.9
1.1
0.7
Module III
86.5 PGMMarketing
91.4 Acct. and
Finance
96.3 Info Systems
98.2 Econ. and
Finance
99.3 Info Sys. and
POM
100.0 Non-business
%
%
Cumulative change
18.0
16.9
13.4
13.0
7.7
18.0
34.9
48.3
61.3
69.0
−8.6
14.2
3.1
8.3
0.0
7.0
76.0
−20.5
5.3
81.3
8.2
4.9
86.2
0.0
4.6
90.8
−17.9
4.2
1.8
95.0
96.8
−14.3
63.6
1.8
98.6
157.1
1.4
100.0
−22.2
Note. Acct. = accounting; Econ. = economics; Info Sys. = information
systems; Mark. = marketing; MHR = management and human resources;
PGM-Marketing = professional golf management-marketing; POM = production operations management.
with Predictor Group 2), and 8 (Module III with Predictor
Group 1). As expected because the reference grade category
is a D or F, the signs of all statistically significant coefficients are negative, indicating that a student was less likely
to perform at the reference grade level. Also as desired, the
chi-square statistics for model fit are significant while those
for the test of parallel lines are insignificant. The three measures of pseudo R2 are reported as well.
Table 6 shows that students earning As or Bs in Accounting I are significantly less likely to earn Ds or Fs in Module I (significance = .000). Students earning As in statistics
were significantly less likely to earn Ds or Fs in the module
(.010). No other predictor courses’ grades are statistically
TABLE 4
Changes in Two Measures of Diversity Across the Five
Modules
Module
I
II
III and IV
V
Team is academically diverse % Team is gender diverse %
Yes
No
Yes
No
Yes
No
Yes
No
98.2
1.8
98.2
1.8
95.8
4.2
95.7
4.3
Yes
No
Yes
No
Yes
No
Yes
No
56.7
43.3
54.9
45.1
54.8
45.2
53.1
46.9
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
TABLE 5
Cross-Tabulation Percentage Frequency Results for Performance in Predictor Classes Vis-a-Vis
the Five Modules
`
Module I
A
Acct. I
Gamma
Somers’ d
Acct. II
Gamma
Somers’ d
Bus. law
Gamma
Somers’ d
Statistics
Gamma
Somers’ d
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
Module II
Module III
Module IV
Module V
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
86
52
14
40
0
8
0
0
0.503∗
0.347∗
100
70
0
24
0
4
0
2
0.617∗
0.435∗
77
46
23
43
0
11
0
0
0.440∗
0.305∗
73
48
14
38
14
14
0
0
0.504∗
0.354∗
29
44
26
1
20
46
31
3
27
39
34
0
5
58
32
5
16
56
28
0
27
55
18
0
21
38
38
3
21
43
32
5
37
35
24
4
32
36
32
0
34
37
28
0
36
18
46
0
24
54
21
1
21
51
23
4
22
49
28
1
15
62
15
8
22
53
23
1
21
42
34
3
31
46
21
1
19
44
31
6
22
52
26
0
11
41
48
0
18
45
37
0
12
42
46
6
30
46
24
0
14
43
43
0
79
48
16
40
5
11
0
1
0.418∗
0.265∗
89
61
11
27
0
8
0
4
0.504∗
0.326∗
78
33
19
52
3
15
0
0
0.372∗
0.241∗
72
40
25
42
3
18
0
0
0.488∗
0.315∗
29
45
26
0
20
40
34
6
70
54
30
37
0
9
0
0
0.529∗
0.367∗
86
60
14
26
0
12
0
2
0.453∗
0.310∗
63
42
26
48
11
10
0
0
0.430∗
0.296∗
66
38
17
43
17
19
0
0
0.339∗
0.231∗
25
44
29
2
35
34
29
1
70
45
30
41
0
13
0
1
0.522∗
0.351∗
83
63
13
27
0
10
1
1
0.616∗
0.431∗
66
40
30
48
4
12
0
0
0.460∗
0.315∗
67
39
23
45
9
16
0
0
0.514∗
0.355∗
25
42
33
0
0
53
47
0
93
52
28
0
7
37
49
58
0
10
23
42
0
1
0
0
0.591∗
0.376∗
86
59
43
25
14
29
30
33
0
9
26
42
0
3
1
0
0.420∗
0.263∗
73
39
26
21
19
48
48
57
8
14
23
21
0
0
2
0
0.367∗
0.230∗
73
38
28
15
19
44
41
38
8
18
31
46
0
0
0
0
0.385∗
0.240∗
(Continued on next page)
63
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64
TABLE 5
Cross-Tabulation Percentage Frequency Results for Performance in Predictor Classes Vis-a-Vis
the Five Modules (Continued)
`
Module I
A
Econ. I
Gamma
Somers’ d
Econ. II
Gamma
Somers’ d
Econ. III
Gamma
Somers’ d
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
Module II
Module III
Module IV
Module V
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
92
20
8
51
0
27
0
2
0.306∗
0.215∗
83
38
17
34
0
28
0
0
0.283∗
0.194∗
100
54
0
34
0
10
0
2
0.627∗
0.419∗
10
54
36
0
21
48
28
3
4
54
42
0
19
50
25
6
20
55
25
0
25
42
33
0
20
40
40
0
13
53
27
7
23
30
47
0
25
30
40
5
30
34
34
2
8
42
50
0
25
31
42
2
0
50
50
0
31
52
7
10
10
43
38
10
24
41
24
10
0
50
42
8
35
35
19
11
22
56
22
0
27
50
17
7
25
75
0
0
73
22
18
45
9
31
0
2
0.253∗∗
0.161∗∗
70
42
20
31
10
27
0
0
0.422∗
0.266∗
69
52
25
34
6
10
0
3
0.357∗
0.215∗
11
61
27
2
17
37
44
2
67
21
27
49
7
29
0
1
0.328∗
0.218∗
71
35
21
30
7
35
0
0
0.308∗
0.201∗
83
49
17
34
0
13
0
3
0.485∗
0.293∗
8
57
33
2
33
37
26
4
65
20
23
53
12
24
0
3
0.298∗
0.211∗
75
36
19
37
6
27
0
0
0.397∗
0.279∗
74
49
16
42
10
8
0
1
0.410∗
0.264∗
8
50
42
0
20
34
45
1
72
23
17
51
11
24
0
1
0.433∗
0.297∗
67
40
22
29
11
31
0
0
0.395∗
0.267∗
77
51
19
39
4
7
0
3
0.629∗
0.444∗
28
47
19
6
50
50
0
0
Note: For ease of exposition dependent variables are in columns, independent variables are in rows, and rounded whole percentages are reported except for gamma and Somers’ d. Some column frequencies
do not sum to 100 due to rounding.
∗ p < .05. ∗∗ p < .01.
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 6
Ordinal Regression Results for Module I With
Predictor Group 1 Classes
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Class/item
Acct. I
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. I
Econ. I
Econ. I
Econ. II
Econ. II
Econ. II
Econ. II
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
65
TABLE 7
Ordinal Regression Results for Module I With
Predictor Group 2 Classes
Category
Estimate
SE
Signif.
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
A
B
C
A
B
C
D/F
Yes
No
Yes
No
−17.900
−18.081
−17.772
0a
−0.416
−0.667
0.218
0a
−1.002
−0.777
−0.131
0a
−0.932
−0.468
0a
−0.533
−0.034
0a
−0.707
−0.888
−1.042
0a
−0.652
0a
0.279
0a
35.330
44.337
.255
.282
.125
0.371
0.318
0.000
—
0.692
0.701
0.722
—
1.308
1.342
1.361
—
0.362
0.306
—
0.415
0.289
—
1.128
1.106
1.109
—
0.916
—
0.252
—
.000
.000
—
—
.547
.341
.762
—
.444
.563
.923
—
.010
.126
—
.199
.906
—
.531
.422
.347
—
.477
—
.269
—
.009
.160
aParameter
is set to zero because it is redundant because it is the comparison category.
significantly related to scores earned in Module I, including
diversity measures.
Table 7 reveals that students taking predictor classes in
group 2 who earned As or Bs in accounting II or statistics
were significantly less likely to earn Ds or Fs in Module I
(significance = .000, .000, .036, and .040, respectively). A
student earning an A in business law was also significantly
less likely to earn a D or F (.026). The diversity measures
have no significant relationships with Module I performance.
Table 8 shows earning an A in accounting I was associated with being less likely to earn a D or F in Module III
(significance = .015). There was a marginally significant
relationship for those earning a B in the predictor class
(.077). A student earning an A or B in economics II was
less likely to earn a D or F in the module (.000 and .000,
respectively). However, given that the standard errors for
business law were marginally larger than 2.0, all results
must be interpreted with caution.
Multinomial regression results for the five models able to
be estimated are presented in Tables 9–13. Panel A of the
tables reports likelihood ratio significance tests, which indi-
Class/item
Acct. I
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. III
Econ. III
Econ. III
Econ. III
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
Category
Estimate
SE
Signif.
A
B
C
D/F
A
B
C
D/F
A
B
C
A
B
C
A
B
C
D/F
Yes
No
Yes
No
−0.070
−0.013
1.386
0a
−19.179
−17.510
−17.751
0a
−1.378
−0.706
0a
−0.903
−0.921
0a
0.448
0.592
1.151
0a
0.565
0a
0.261
0a
68.416
11.399
.566
.633
.372
1.465
1.439
1.480
—
0.614
0.612
0.000
—
0.618
0.565
—
0.430
0.449
—
0.837
0.816
0.944
—
1.172
—
0.367
—
.962
.993
.349
—
.000
.000
—
—
.026
.211
—
.036
.040
—
.592
.468
.223
—
.630
—
.477
—
.000
.999
aParameter
is set to zero because it is redundant because it is the comparison category.
cate for each of the predictor courses and the gender diversity
measure whether there is a significant relationship between
that variable and the particular module’s grades. Panel B
reports, according to each module grade, the estimated coefficients for each predictor class and the gender diversity
variable, and their odds ratios. All these estimates are in reference to scoring an A in the module while the grades in the
predictor classes are ranked A = 1, B = 2, C = 3, D/F = 4;
thus, significant positive coefficients have odds ratios exceeding 1.0, indicating that as performance in a predictor course
declined (grade is lower and the rank is higher), the odds of
scoring a grade in the module other than an A increased.
Results for Module II grades with classes from Predictor
Group 1 presented in Panel A of Table 9 indicate a significant relationship between performances in Accounting
I and Business Law vis-`a-vis performance in the module
(significance = .041 and .013, respectively). Performance in
statistics was marginally related (.072). Panel B reveals that
business law can distinguish scores of C in Module II from
scores of A (.026) and can marginally distinguish scores of
D or F in the module from scores of A (.099). Economics I
can marginally differentiate scores of C from A (.077), and
accounting I can distinguish scores of D or F from A (.019).
None of the predictor classes helps predict which students
66
L. J. BAKER-EVELETH ET AL.
TABLE 9
Multinomial Logistic Regression Results for Module II
With Predictor Group 1 Classes
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
TABLE 8
Ordinal Regression Results for Module III With
Predictor Group 1 Classes
Class/item
Category
Estimate
SE
Signif.
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. I A
Econ. I B
Econ. I C
Econ. II
Econ. II
Econ. II
Econ. II
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
A
B
C
A
B
C
D/F
A
B
C
D/F
A
B
C
−1.106
0.004
0a
A
B
C
D/F
Yes
No
Yes
No
−1.604
−0.990
0a
1.394
1.592
1.501
0a
0.473
0.475
1.617
0a
−0.271
0.220
0a
0.741
0.480
0.662
0.560
.015
.077
1.231
1.234
1.265
.258
.197
.235
2.014
2.024
2.042
.814
.814
.428
0.573
0.499
.636
.659
−19.633
−19.510
−18.921
0a
0.014
0a
−0.324
0a
42.601
43.031
.334
.361
.158
Panel A: Likelihood ratio tests
χ2
Effect
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
Signif.
8.277
3.905
10.834
7.002
4.057
0.031
3.428
.041
.272
.013
.072
.255
.999
.330
Panel B: Parameter estimates
.136
.993
0.582
0.492
0.000
.000
.000
0.881
.987
0.421
.442
.001
.138
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
C
aParameter is set to zero because it is redundant because it is the comparison category.
tend to score a B in the module from those who tend to score
an A. The measure for gender diversity is insignificant across
all grade breaks. The corresponding odds ratios for the significant variables indicate that a student who performs poorly in
business law is 6.5 times more likely to score a C in Module II
than an A—Exp(B) = 6.509—and over 4.5 times more likely
to score a D or F (4.550). A student scoring poorly in economics I is over 4.5 more times likely to score a C rather than
an A (4.574); scoring poorly in accounting I suggests that one
will be over eight times as likely to score a D or F (8.221).
Table 10 presents results for Module II using Predictor
Group 2 courses. Panel A shows that business law is significantly related to performance in the module (significance =
.040), but accounting II is more useful (.001) than accounting I. Panel B reveals findings similar to those in Table 9;
namely, none of the courses can usefully distinguish module scores of B from A and the gender diversity measure
is never significant. Accounting II can differentiate between
students scoring Cs (.007) or Ds or Fs (.019) from those scoring As. Such students are nearly 10 times as likely to perform
thus—Exp(B) = 9.926 and 9.827, respectively. Business law
can discern (significance = .030) between students earning
D/F
Estimate, B
SE
Signif.
Odds
ratio,
Exp(B)
0.842
−0.504
0.826
0.026
1.196
0.100
−1.258
0.750
0.698
0.794
0.577
0.799
0.631
0.925
.261
.470
.298
.964
.134
.874
.174
2.322
0.604
2.284
1.026
3.308
1.105
0.284
0b
—
—
—
1.300
0.030
1.873
0.944
1.520
0.108
−0.610
0.797
0.717
0.841
0.641
0.859
0.666
1.013
.103
.967
.026
.141
.077
.871
.547
3.668
1.030
6.509
2.570
4.574
1.114
0.543
0b
—
—
—
2.107
−0.122
1.515
0.660
0.994
0.128
−0.648
0.896
0.784
0.918
0.757
0.966
0.792
1.183
.019
.876
.099
.383
.304
.871
.584
8.221
0.885
4.550
1.936
2.703
1.137
0.523
0b
—
—
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
a D or F from those earning an A, estimating that as students perform worse in the course they are nearly 10 times
more likely to score a D or F in the module rather than an
A—Exp(B) = 9.807. Statistics can marginally discern (significance = .081) scores of D or F from A, estimating that
such students are 4.231 times as likely to do so.
Results for Module III grades vis-`a-vis Predictor Group
1 are reported in Table 11. Again, business law significantly
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 10
Multinomial Logistic Regression Results for Module II
With Predictor Group 2 Classes
TABLE 11
Multinomial Logistic Regression Results for Module III
With Predictor Group 1 Classes
Panel A: Likelihood ratio tests
Panel A: Likelihood ratio tests
χ2
Effect
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
Signif.
2.223
15.946
8.326
3.609
3.709
1.265
.527
.001
.040
.307
.295
.737
Effect
χ2
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
4.816
2.594
7.822
1.802
4.521
3.154
3.963
67
Signif.
.186
.459
.050
.615
.210
.368
.265
Panel B: Parameter estimates
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Panel B: Parameter estimates
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
C
D/F
Estimate, B
SE
Odds
ratio,
Signif. Exp(B)
0.709
0.481
0.000
0.406
0.184
0.504
0b
0.617
0.704
0.539
0.423
0.503
0.659
—
.250
.494
.999
.338
.714
.445
—
2.032
1.617
1.000
1.501
1.202
1.655
—
0.301
2.295
0.296
0.808
1.019
−0.162
0b
0.819
0.844
0.808
0.615
0.645
0.946
—
.713
.007
.714
.189
.114
.864
—
1.351
9.926
1.345
2.244
2.772
0.851
—
1.049
2.285
2.283
1.442
0.755
−0.212
0b
0.980
0.978
1.050
0.827
0.773
1.179
—
.285
.019
.030
.081
.329
.857
—
2.855
9.827
9.807
4.231
2.127
0.809
—
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
C
D/F
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
relates to performance in the module (significance = .050).
Panel B results indicate that only economics I appears able
to discern among the various grades (significance = .094 and
.061 for grades B and C, respectively). The corresponding
odds of doing so are 4.049 and 5.160. The gender diversity
measure remains insignificant.
Module III’s grades relative to courses in Predictor Group
2 are shown in Table 12. As before, Panel A shows that performance in business law is related to performance in the module (significance = .049), and now both accounting courses
are marginally related (.086 and .089, respectively). Panel
B reveals that business law differentiates between students
scoring Ds or Fs rather than As (.063) and that accounting
Odds
ratio,
Signif. Exp(B)
Estimate, B
SE
0.119
1.203
−0.695
−0.253
1.398
0.991
−0.778
0.833
0.862
0.786
0.580
0.835
0.788
0.863
.887
.163
.377
.663
.094
.209
.367
1.126
3.329
0.499
0.776
4.049
2.695
0.459
0b
—
—
—
0.829
0.960
0.367
−0.432
1.641
1.210
−1.469
0.856
0.880
0.820
0.631
0.875
0.809
0.927
.333
.276
.655
.494
.061
.135
.113
2.292
2.612
1.443
0.649
5.160
3.352
0.230
0b
—
—
—
0.975
1.055
−0.108
0.140
1.192
1.312
−0.512
0.893
0.902
0.859
0.669
0.919
0.843
0.994
.275
.242
.900
.834
.195
.119
.606
2.651
2.872
0.898
1.150
3.294
3.713
0.599
0b
—
—
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
II distinguishes between those scoring a C rather than an A
(.047). The estimated odds of doing so are 8.397 and 9.232,
respectively. The gender diversity measure remains insignificant and none of the courses can discern students scoring Bs
from those scoring As.
Module V scores modeled with Predictor Group 2 are
reported in Table 13. Panel A shows that Accounting I
appears significantly relates to performance in the module
68
L. J. BAKER-EVELETH ET AL.
TABLE 12
Multinomial Logistic Regression Results for Module III
With Predictor Group 2 Classes
TABLE 13
Multinomial Logistic Regression Results for Module V
With Predictor Group 2 Classes
Panel A: Likelihood ratio tests
Panel A: Likelihood ratio tests
Effect
χ2
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
6.585
6.507
7.880
3.114
0.439
4.008
Signif.
.086
.089
.049
.374
.932
.261
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Panel B: Parameter estimates
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
C
D/F
χ2
Effect
Signif.
10.070
2.016
5.620
4.424
2.937
3.390
.018
.569
.132
.219
.401
.335
Panel B: Parameter estimates
Odds
ratio,
Signif. Exp(B)
Module
gradea
Class/
item
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Estimate, B
SE
−0.420
1.393
−0.122
0.048
0.018
0.055
0b
0.640
1.046
0.658
0.498
0.522
0.803
—
.512
.183
.853
.923
.972
.945
—
0.657
4.025
0.886
1.049
1.018
1.057
—
B
0.885
2.223
−0.151
−0.794
0.115
−0.735
0b
0.769
1.120
0.821
0.736
0.629
0.991
—
.250
.047
.854
.280
.855
.458
—
2.423
9.232
0.860
0.452
1.122
0.479
—
C
0.976
1.588
2.128
0.509
−0.369
−2.126
0b
1.080
1.271
1.144
0.986
0.844
1.455
—
.366
.212
.063
.606
.662
.144
2.654
4.896
8.397
1.664
0.691
0.119
—
D/F
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
(significance = .018). Panel B shows that the course further
differentiates among students earning Cs (.043) or Ds or Fs
(marginally at .066) rather than As. business law can discern
differences among student performance, namely those
scoring Cs (.060). The respective odds of such performances
are 10.197, 23.178, and 6.727. As always, gender diversity
is still insignificant, both generally and for all grades, and
none of the predictor courses is helps to discern module
scores of B from A.
Table 14 compares the classification accuracy rates to the
modified or by chance accuracy rates. All the original classification rates improve upon the modified rates, suggesting
Odds
ratio,
Signif. Exp(B)
Estimate, B
SE
1.134
0.263
1.029
0.474
−0.166
−0.577
0b
1.085
0.856
0.921
0.547
0.698
0.958
—
.296
.758
.264
.387
.812
.547
—
3.108
1.301
2.799
1.606
0.847
0.562
—
2.322
0.528
1.906
−0.338
−0.151
−1.541
0b
1.148
0.920
1.013
0.707
0.756
1.072
—
.043
.566
.060
.633
.842
.151
—
10.197
1.695
6.727
0.713
0.860
0.214
—
3.143
1.578
2.317
−1.014
−2.142
−1.729
0b
1.710
1.311
1.471
1.393
1.715
1.698
—
.066
.229
.115
.467
.212
.309
—
23.178
4.844
10.146
0.363
0.117
0.177
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
that all of the models are useful at predicting grade rankings
in the estimated modules.
DISCUSSION
To sum, exposure to academically diverse or gender diverse teams did not significantly affect individual student
performance in an integrated curriculum. Only one model
found the first economics course of a two-class sequence in
a predictor group significantly useful in predicting performance in later modules. The second economics course was
useful one time, and an alternative single economics course
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 14
Model Classification Percentage Accuracy Rates
Versus Modified Accuracy Rates
Module
II
III
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
V
Predictor group
Modified accuracy
Classification accuracy
1
2
1
2
2
44.2
44.2
37.3
51.0
51.6
57.5
61.0
54.3
67.1
61.6
worth more credit hours included in a different predictor
group was never significant. Both regression analyses found
the Business Law predictor course useful. The first accounting course of the two-course sequence was the next most
frequently significant predictor course followed by statistics.
While relatively few of the classes comprising either set of
predictor groups were significantly related to follow-up performance, the multinomial regression methods reveal that the
odds ratios for those courses that are significantly related exceed 4.0. Students and faculty should monitor performance
in select predictor courses because students that do not perform well are four times more likely not to perform well in
the junior year compulsory business modules.
Of the five modules with grade rankings modeled here,
Module II grades were significantly related to more predictor
courses than other modules’ grades, at six instances. Module I
grades were related five times, Module III had four instances,
and Module V showed only two significant relations. Module
IV grades were never significantly related to performance in
either of the predictor groups.
While all the modules teach typical introductory material
for five business areas (finance, IS, MHR, marketing, and
OM) in a highly integrated fashion, each module has a particular focus. Module I covers the variety of systems within
which businesses operate. Module II highlights the planning
and implementation processes needed at the strategic levels.
Module III teaches tools used to do quantitative and qualitative forecasting, while Module IV covers the management of
firm resource. Finally, Module V considers the major operating decisions related to product, pricing, and distribution.
One interpretation of our results, then, is that topics and techniques covered in introductory accounting, business law, and
to a lesser degree statistics are a better preparation for understanding the systemic qualities of the business environment
and the variety of planning required in firms.
As for the two diversity measures lacking any explanatory power, perhaps there was not enough variation in the
academic diversity measure because over 95% of teams were
considered diverse. A larger sample size or different proxy
for diversity could be helpful. Perhaps exposure to different
genders by the time students are juniors in college no longer
has the same effect as earlier. Alternatively, in capturing
how diverse knowledge bases on a team affects grade perfor-
69
mance, perhaps better proxies would reflect communication
skills, coping skills, or team management skills.
CONCLUSION
We investigated whether predictor courses and academicallyand gender-diverse teams influence student performance in a
compulsory yearlong integrated business course that records
grades for five modules. Observations are collected over three
years. These results provide insight not only to what predictor courses might be helpful in enhancing student success
in an integrated program, but also to whether diverse team
environments increase individual student success.
Cross-tabulation analysis suggests predictor courses are
useful in reducing the prediction error when ranking grades
in each module. Gamma scores indicated a moderate to fairly
strong positive relationship between performance in the predictor courses and performance in each module. Performance
in the first of a two-course accounting sequence of predictor classes was strongly associated with subsequent scores
in the modules. The directional measure, Somers’ d, indicated the courses are moderately helpful in predicting later
performance in the modules.
The proxy measures of diversity were based on the
makeup of self-determined student teams that vary in size
and include degrees of academic and gender diversity. The
majority of the time students were exposed to academically
diverse teams and over half the time to gender diverse teams.
More in-depth analysis was attempted with ordinal regression to try to use any information contained in the ranked
nature of grades. Results suggest that the two accounting
courses and the statistics predictor course are useful in estimating later performance in the first module. Neither of the
diversity measures was statistically significant.
Data constraints cause only two of the five modules to be
estimable with ordinal regression (some assumptions are relaxed, academic diversity measure dropped, and multinomial
logistic regression applied). Three modules are estimable under this analysis and show that the first accounting course and
a predictor course in business law are most consistently related to later performance in the modules. Also, when significant, predictor courses can better distinguish among students
performing at the C, D, or F grade levels (as opposed to
scoring As) than those earning Bs.
We considered academic and gender differences when
evaluating team diversity. While we had predicted that
gender diversity at the team level would enhance success,
the measure remained insignificant. This result could be
driven by a number of factors, including the likelihood that
gender diversity might not be as critical as it once was when
promoting novelty in thought because students are increasingly more exposed to gender diverse environments. This in
no way suggests that gender diversity is not important, rather,
that gender diversity is more of a norm in team environments.
The selection of measurements for team diversity and the
70
L. J. BAKER-EVELETH ET AL.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
results create a limitation of the study and an opportunity for
additional research. The construct of diversity at the team
level is multidimensional and includes more than academic
and gender diversity; thus, we suggest future researchers
consider alternative measures of team diversity in enhancing
student success. Our student diversity (residential, landgrant university), which is common to other universities and
colleges, is somewhat limited. An extension of this present
study would be to validate these results in a different
context and to explore additional facets of diversity. An
additional opportunity could be to investigate appropriate
predictor courses that may help students develop stronger
communication and interpersonal skills prior to entering the
integrated business course.
REFERENCES
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managerial-accounting courses. Journal of Education for Business, 85,
311–322.
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skills in information systems (IS) curricula: Perspectives of IS educators
and students. Journal of Education for Business, 86, 186–194.
Association to Advance Collegiate Schools of Business. (2011). Eligibility procedures and accreditation standards for business accreditation.
Retrieved from http://www.aacsb.edu/accreditation/standards.asp
Athavale, M., Davis, R., & Myring, M. (2008). The integrated business
curriculum: An examination of perceptions and practices. Journal of Education for Business, 83, 295–301.
Borde, S. F. (1998). Predictors of student academic performance in the
introductory marketing course. Journal of Education for Business, 73,
302–306.
Chen, C., & Hughes, J. Jr. (2004). Using ordinal regression model to analyze
student satisfaction questionnaires. IR Applications, 1, 1–12.
Ely, R. J., & Thomas, D. A. (2001). Cultural diversity at work: The effects of diversity perspectives on work group processes and outcomes.
Administrative Science Quarterly, 46, 229–273.
Garson, G. D. (2011). Logistic regression. From Statnotes: Topics in multivariate analysis. Retrieved from http://faculty.chass.ncsu.
edu/garson/PA765/logistic.htm
Gurin, P., Dey, E. L., Hurtado, S., & Gurin, G. (2002). Diversity and higher
education: Theory and impact on educational outcomes. Harvard Educational Review, 72, 330–366.
Joseph, D., Soon, A. N. G., Chang, R. H. L., & Slaughter, S. A. (2010).
Practical intelligence in IT: Assessing soft skills of IT professionals.
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Kuncel, N. R., Crede, A. F.,
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
The Role of Predictor Courses and Teams on
Individual Student Success
Lori Jo Baker-Eveleth , Michele O’Neill & Sanjay R. Sisodiya
To cite this article: Lori Jo Baker-Eveleth , Michele O’Neill & Sanjay R. Sisodiya (2014) The
Role of Predictor Courses and Teams on Individual Student Success, Journal of Education for
Business, 89:2, 59-70, DOI: 10.1080/08832323.2012.757541
To link to this article: http://dx.doi.org/10.1080/08832323.2012.757541
Published online: 17 Jan 2014.
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Date: 11 January 2016, At: 20:30
JOURNAL OF EDUCATION FOR BUSINESS, 89: 59–70, 2014
C Taylor & Francis Group, LLC
Copyright
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2012.757541
The Role of Predictor Courses and Teams
on Individual Student Success
Lori Jo Baker-Eveleth
University of Idaho, Moscow, Idaho, USA
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Michele O’Neill
California State University-Chico, Chico, California, USA
Sanjay R. Sisodiya
University of Idaho, Moscow, Idaho, USA
Research suggests that diverse environments enhance conscious modes of thought, resulting
in greater intellectual engagement and active thinking. Ordinal and multinomial logistic regression results indicate that accounting courses and business law classes are useful predictors
of subsequent performance. Odds ratio estimates indicate that students who perform poorly
in these predictor classes are more than four times as likely to perform poorly in subsequent
classes. Academic and gender diversity were not found to be significantly related to student
performance.
Keywords: academic diversity, gender diversity, predictors, student performance, teams
Predictor courses have been used as a mechanism to determine student achievement in college courses (Kuncel,
Crede, & Thomas, 2007; Yang & Lu, 2001). While much
research has been done on the role of predictor courses
for business students (Borde, 1998; McMillan-Capehart &
Adeyemi-Bello, 2008; Sulaiman & Mohezar, 2006), little research has been done on student performance in predictor
courses and the forecasting of success in highly integrated
business courses.
This gap in the literature needs to be studied because
the Association to Advance Collegiate Schools of Business (AACSB) strongly encourages integrated education
(AACSB, 2011). Perhaps the traditional predictor courses,
typically taught as stand-alone courses, may not adequately
prepare students for an integrated business curriculum. In this
study we investigated the helpfulness of not only predictor
courses, but also academically and gender diverse teams on
student achievement in an integrated business curriculum.
Correspondence should be addressed to Lori Jo Baker-Eveleth, University of Idaho, Department of Business and Economics, 875 Campus
Drive, P. O. Box 443161, Moscow, ID 83844-3161, USA. E-mail: leveleth@
uidaho.edu
INTEGRATED CURRICULUM
AND STUDENT TEAMS
Many undergraduate business programs use grade point averages (GPAs) in specific courses to predict performance,
but this often produces mixed results (Al-Twaijry, 2010;
Borde, 1998). Predicting performance is increasingly challenging when considering the desire to integrate educational
programs. In particular, when considering integrated undergraduate business curriculum (IUBC), functional courses are
team-taught rather than taught using single discipline approaches. This form of integration challenges not only evaluating performance, but also predicting student success.
In addition, the business community looks for employees
with team skills and an understanding of cross-functional
interactions (Athavale, Davis, & Myring 2008). Combining
the two, schools seek opportunities to expose students to diverse business environments to enhance conscious modes of
thought, resulting in greater intellectual engagement and active thinking (Gurin, Dey, Hurtado, & Gurin, 2002). While
preparing students to work in diverse teams may better prepare them for the work environment (Ely & Thomas, 2001),
forecasting success in a team (oral communication, leadership, facilitation) is not always consistent with performance
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
60
L. J. BAKER-EVELETH ET AL.
measures of GPA (Alshare, Lane, & Miller, 2011; Joseph,
Soon, Chang, & Slaughter, 2010).
Thus, we consider the role that teams which are diverse
in academic experience and gender may play in enhancing
student success in an integrated program. Diversity as a concept is multidimensional, whereby some consider diversity
to include race, gender, age, disability, sexual orientation,
and national orientation (Shore et al., 2009), others say that
diversity can also include communication style, networks,
and knowledge (Ely & Thomas, 2001). In particular, Ely and
Thomas defined cultural differences in a group as arising
from the “life experiences, knowledge, and insights” from
the individuals (p. 265). Based on the previous description,
this study, focused on academic (major) and gender as measures of diversity on teams, asks these questions: can a working environment of a diverse team lead to enhanced student
performance. To what extent does exposure to a diverse environment improve individual performance?
DATA AND METHODOLOGY
To explore these questions, we gathered three years of data
from a residential land-grant university. The IUBC sequence
of courses at this institution includes six sequential modules
beginning with a short introductory module focusing on team
management concepts, where students form teams and work
together for two additional modules. Before starting the last
three modules, students may change teams; therefore, we
examine student performance and team configurations during
each of five modules (labeled herein Modules I, II, III, IV,
and V). In this study team configurations were for academic
majors (eight possible) and gender representing two proxies
for diverse teams.
For the predictor classes, we examined whether the
courses indicate success in such a junior level integrated
course (see Table 1 for a description of courses). The college
uses a traditional set of predictor classes covering introductory accounting (two courses labeled herein I and II), business
law, statistics, and economics (two courses labeled herein I
and II, or a single class worth more credits labeled herein
III). The course deliverables include exams and regularly
collected assignments.
University-level data were gathered on grades, gender,
and major for students who had enrolled in any of Modules
I–V from fall 2008 through spring 2011 and were taught by a
five-person faculty team. This request produced 284 unique
students and provided complete results for 202 students.
Grades were recorded as A, B, C, D, or F. There were
84 instances of grade P for passing in one or more predictor
classes for transfer students, and because the original score
was not on record these instances were recoded to a C. Because students must achieve a minimum GPA in predictor
courses before being admitted into the junior level sequence
of courses, some repeated one or more of their predictor
TABLE 1
Descriptions of Predictor Courses
Predictor
course
Description
Accounting I
Overview of the nature and purpose of general purpose
financial statements provided to external decision makers
Accounting II Intro to cost behavior and managerial use of accounting
information for planning, control, and performance
evaluation
Business law Law and its relationship to society; legal framework of
business enterprises; court organization and operation;
private property and contracts as basic concepts in a free
enterprise system
Statistics
Introduction to methods including design of statistical
studies, basic sampling methods, descriptive statistics,
probability and sampling distributions; inference in
surveys and experiments, regression, and analysis of
variance
Economics I
Organization and operation of American economy; supply
and demand, money and banking, macroeconomic
analysis of employment, aggregate output and inflation,
public finance, and economic growth
Economics II Microeconomic principles governing production, price
relationships, and income distribution
Economics III Introduction to the principles of economics, covering both
micro and macro concepts, theory, analysis, and
applications
classes. The college admitted students upon earning the necessary GPA in predictor courses.
Because the predictor courses included the option to take
either two economics classes (I and II) or a single economics
course worth more credit hours (III), we eventually analyzed
the data according to whether students took the two-course
economics sequence of predictors (Predictor Group 1) or
one-course economics sequence (Predictor Group 2). The
final data set contained 202 students in Modules I and II, 178
students in Modules III and IV, and 177 students in Module
V. The drop in observations after Module II results because
students performing poorly in Modules I and II often drop
the IUBC and do not enroll in Modules III and V.
To develop the two measures of team diversity (academic
and gender), we analyzed the self-selected student teams
listed for each module for size, mix of men and women,
and mix of declared majors. The college offered eight majors: accounting, economics, finance, information systems
(IS), management and human resources (MHR), marketing,
operations management (OM), and professional golf management. There were four frequent combinations of double majors: accounting-finance, economics-finance, IS-OM,
and MHR-marketing. Because there was no way to determine which major in the double majors was more appropriate to use for a representative major, we kept track of
the majors involved in the double majors when developing the measure for academic diversity, which is described
subsequently.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
A student is deemed to have been on a gender diverse
team in a given module if the proportion of male (M) and
female (F) students for the various team sizes (N) was any
of the following: N of 2 has 1M/1F; N of 3 has 2M/1F or
2F/1M; N of 4 has 2M/2F; N of 5 has 2M/3F or 3M/2F; N of
6 has 2M/4F, 3M/3F, or 4M/2F. For all other proportions, the
student was considered not to be on a gender diverse team.
The measure for academic diversity uses the variety of
majors represented on a team as a proxy. When a team member had a double major, it subsumed any matching single
majors and counted for two majors if no others matched. For
example, if a team member was an accounting-finance double major, another member was a finance major, and another
member was a Marketing major, we counted three majors
as being represented (accounting, finance, marketing); on
the other hand, if the accounting-finance double major was
teamed with two marketing majors, we counted three majors as being represented (accounting, finance, marketing).
A student then was deemed to have been on an academically
diverse team in a given module if the proportion of different
majors to total majors represented for the various team sizes
was any of the following: N of 2 has 2/2; N of 3 has 2/3; N
of 4 has 3/4 or 4/4; N of 5 has 3/5, 4/5, or 5/5; N of 6 has 3/6,
4/6, 5/6, or 6/6. For all other proportions, the student was
considered not to be on an academically diverse team.
Cross-tabulations were performed and two measures
of ordinal association calculated, gamma and Somers’ d.
Gamma can be interpreted as the percentage reduction in
error achieved when ranking the dependent variable (here,
grades in each of the five modules) by knowing the independent variable (here, grades in the various predictor classes).
Because gamma is a symmetrical measure (meaning that the
same value is calculated even if the independent variable
is switched for the dependent variable), we also report
Somers’ d, which in its original form is an asymmetrical
measure.
To explore deeper for possible relationships among performance in the predictor courses, exposure to diverse teams,
and performance in each of the five integrated modules, we
conducted ordinal regression analysis, which accounts for an
ordered dependent variable (Chen & Hughes, 2004; Noruˇsis,
2011). The four grade categories were ranked and coded in
the following order: A = 1, B = 2, C = 3, D/F = 4. Ordinal regression analysis models—with an adaptation to the
underlying variable’s distribution via a link function—the
cumulative probability of an event (e.g., a student scoring a
C in Module II) along with all the events ordered before it
(e.g., scoring either a B or A). The reference category then
to which the estimated coefficients are compared is the last
one ranked, here the combined scores of D/F.
To determine the most appropriate link function and model
formulation, we made various comparisons and performed
tests (Chen & Hughes, 2004). The complementary log-log
was determined to be the most appropriate link function.
Even then, the assumption of parallel lines was not met except
for three instances: modeling Module I grades with either
61
group of predictors and modeling Module III grades with
Predictor Group 1.
Because the ordinal regression methodology could not be
used to analyze most of the data, we relaxed the normality,
linearity, and homogeneity of variance assumptions (Garson,
2011; Schwab, 2011). We then analyzed the data as though it
were a nominal variable. Doing so allowed us to apply multinomial logistic regression analysis. SPSS software (ver. 19,
Chicago, IL) also allowed us to change the reference grade
category for each of the modules from D/F (used in ordinal)
to A.
Occasionally, multinomial logistic regression analysis
produces unreasonable estimates. Often, such results are due
to multicollinearity, predictor categories having zero observations, or complete separation or bifurcation of the data,
which drives the maximum likelihood estimates to infinity.
Standard errors exceeding 2.0 indicate such problems, even if
the estimates look reasonable (Schwab, 2011). Analyzing the
data with multinomial logistic regression revealed problems
with one or more standard errors or estimated implausible coefficients for eight of the 10 possible full regression models.
Eliminating the academic diversity measure, which had a low
frequency of nondiversified teams (discussed subsequently),
resolved estimation issues for five of the models.
To check the usefulness of the estimated models we calculated pseudo R2. However, because the levels are not as
meaningful as those for traditional R2 measures, we also
checked the accuracy of each model’s classification estimation. Even if no relationship existed between the independent
and dependent variables, by chance alone we could predict
correctly some of the time, which dependent variables belonged with which group of independent variables. To address this, we compared the model’s computed classification
percentage accuracy rate to a modified or by chance percentage accuracy rate: the sum of the model’s squared marginal
percentages, which is then increased by 25%. Comparing the
rates allowed us to conclude that a model is useful when its
classification accuracy rate is at least a 25% improvement
over classification by chance alone (Schwab, 2011).
RESULTS
Descriptive frequency distributions are presented on team
sizes and academic majors. Table 2 reports changes in team
size frequencies across the five modules. Data collected show
teams ranged in size from a low of two members, formed only
once for only two modules, to a high of six members, which
occurred in all modules. Five members to a team was the
most common size, occurring 44.6–52% of the time. Table 3
reports the distribution of majors and four most common
double majors declared at the start of Modules I and III,
which was when teams reformed for the second semester.
The most commonly declared majors were accounting, marketing, finance, and MHR. The frequency of these majors
ranged from a low of 12.0% (MHR, Module I) to a high of
19.7% (accounting, Module III). The percentage change in
62
L. J. BAKER-EVELETH ET AL.
TABLE 2
Team Sizes Across the Five Modules
Module
I
II
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
III and IV
V
TABLE 3
Distribution of Declared Majors Across Beginning of
Semesters When Teams Form
Team size
Frequency
%
Cumulative%
3
4
5
6
Total
3
4
5
6
Total
2
4
5
6
Total
4
5
6
Total
1
11
25
19
56
1
11
26
18
56
1
8
25
17
51
8
26
16
50
1.8
19.6
44.6
34.0
100.0
1.8
19.6
46.4
32.2
100.0
2.0
15.7
49.0
33.3
100.0
16.0
52.0
32.0
100.0
1.8
21.4
66.0
100.0
1.8
21.4
67.8
100.0
2.0
17.7
66.7
100.0
16.0
68.0
100.0
each declared major suggests students may be affected by the
diversity of business topics. After students complete Modules
I and II, the double major of IS-OM gained 157.1% majors
(from 0.7% to 1.8%). The second highest positive gain was in
the double major economics-finance at 63.6% (from 1.1% to
1.8%). Students declaring nonbusiness majors experienced
the largest decline at 22.2% (from 1.9% to 1.4%).
Results for the two variables measuring exposure to a diverse team are presented in Table 4. Teams were academically
diverse all the time, occurring no less frequently than 95.7%
of the time (Module V). There was a small chance a student
would not be exposed to an academically diverse team and
50% chance a student was exposed to a gender-diverse team.
These formed at a frequency ranging from 53.1% (Module
V) to 56.7% (Module I).
Table 5 presents cross-tabulations and measures of ordinal association. All gamma and Somers’ d calculations were
significant at 5% or higher for all predictor classes in each of
the five modules. Gamma scores ranged from a low of 0.253
(economics I and Module V) to a high of 0.629 (economics
III and Module II), indicating that a moderate to fairly strong
positive relationship exists between scores earned in the predictor classes and scores earned in the modules. Accounting
I had the most consistently strong relationship, with four of
five gamma scores exceeding 0.500 (Modules I–III and V).
Economics I and II showed the weakest relationships with
all gamma scores below 0.450 for all five modules. Somers’
d measures ranged from 0.161 (economics I and Module V)
to 0.444 (economics III and Module I), indicating that these
predictor classes somewhat to moderately positively related
with influencing the module grades.
Results for the three estimated models that met the parallel lines assumption of ordinal regression are reported in
Tables 6 (Module I with Predictor Group 1), 7 (Module I
Module I
%
Cumul.
Accounting
Marketing
Finance
MHR
Economics
19.7
14.8
13.0
12.0
8.8
19.7
34.5
47.5
59.5
68.3
MHR and
Mark.
Acct. and
Finance
PGMMarketing
Info Sys.
7.7
Accounting
Marketing
Finance
MHR
MHR and
Mark.
76.0 Economics
5.6
81.6 POM
POM
Nonbusiness
Econ. and
Finance
Info Sys.
and POM
4.9
4.9
4.9
1.9
1.1
0.7
Module III
86.5 PGMMarketing
91.4 Acct. and
Finance
96.3 Info Systems
98.2 Econ. and
Finance
99.3 Info Sys. and
POM
100.0 Non-business
%
%
Cumulative change
18.0
16.9
13.4
13.0
7.7
18.0
34.9
48.3
61.3
69.0
−8.6
14.2
3.1
8.3
0.0
7.0
76.0
−20.5
5.3
81.3
8.2
4.9
86.2
0.0
4.6
90.8
−17.9
4.2
1.8
95.0
96.8
−14.3
63.6
1.8
98.6
157.1
1.4
100.0
−22.2
Note. Acct. = accounting; Econ. = economics; Info Sys. = information
systems; Mark. = marketing; MHR = management and human resources;
PGM-Marketing = professional golf management-marketing; POM = production operations management.
with Predictor Group 2), and 8 (Module III with Predictor
Group 1). As expected because the reference grade category
is a D or F, the signs of all statistically significant coefficients are negative, indicating that a student was less likely
to perform at the reference grade level. Also as desired, the
chi-square statistics for model fit are significant while those
for the test of parallel lines are insignificant. The three measures of pseudo R2 are reported as well.
Table 6 shows that students earning As or Bs in Accounting I are significantly less likely to earn Ds or Fs in Module I (significance = .000). Students earning As in statistics
were significantly less likely to earn Ds or Fs in the module
(.010). No other predictor courses’ grades are statistically
TABLE 4
Changes in Two Measures of Diversity Across the Five
Modules
Module
I
II
III and IV
V
Team is academically diverse % Team is gender diverse %
Yes
No
Yes
No
Yes
No
Yes
No
98.2
1.8
98.2
1.8
95.8
4.2
95.7
4.3
Yes
No
Yes
No
Yes
No
Yes
No
56.7
43.3
54.9
45.1
54.8
45.2
53.1
46.9
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
TABLE 5
Cross-Tabulation Percentage Frequency Results for Performance in Predictor Classes Vis-a-Vis
the Five Modules
`
Module I
A
Acct. I
Gamma
Somers’ d
Acct. II
Gamma
Somers’ d
Bus. law
Gamma
Somers’ d
Statistics
Gamma
Somers’ d
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
Module II
Module III
Module IV
Module V
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
86
52
14
40
0
8
0
0
0.503∗
0.347∗
100
70
0
24
0
4
0
2
0.617∗
0.435∗
77
46
23
43
0
11
0
0
0.440∗
0.305∗
73
48
14
38
14
14
0
0
0.504∗
0.354∗
29
44
26
1
20
46
31
3
27
39
34
0
5
58
32
5
16
56
28
0
27
55
18
0
21
38
38
3
21
43
32
5
37
35
24
4
32
36
32
0
34
37
28
0
36
18
46
0
24
54
21
1
21
51
23
4
22
49
28
1
15
62
15
8
22
53
23
1
21
42
34
3
31
46
21
1
19
44
31
6
22
52
26
0
11
41
48
0
18
45
37
0
12
42
46
6
30
46
24
0
14
43
43
0
79
48
16
40
5
11
0
1
0.418∗
0.265∗
89
61
11
27
0
8
0
4
0.504∗
0.326∗
78
33
19
52
3
15
0
0
0.372∗
0.241∗
72
40
25
42
3
18
0
0
0.488∗
0.315∗
29
45
26
0
20
40
34
6
70
54
30
37
0
9
0
0
0.529∗
0.367∗
86
60
14
26
0
12
0
2
0.453∗
0.310∗
63
42
26
48
11
10
0
0
0.430∗
0.296∗
66
38
17
43
17
19
0
0
0.339∗
0.231∗
25
44
29
2
35
34
29
1
70
45
30
41
0
13
0
1
0.522∗
0.351∗
83
63
13
27
0
10
1
1
0.616∗
0.431∗
66
40
30
48
4
12
0
0
0.460∗
0.315∗
67
39
23
45
9
16
0
0
0.514∗
0.355∗
25
42
33
0
0
53
47
0
93
52
28
0
7
37
49
58
0
10
23
42
0
1
0
0
0.591∗
0.376∗
86
59
43
25
14
29
30
33
0
9
26
42
0
3
1
0
0.420∗
0.263∗
73
39
26
21
19
48
48
57
8
14
23
21
0
0
2
0
0.367∗
0.230∗
73
38
28
15
19
44
41
38
8
18
31
46
0
0
0
0
0.385∗
0.240∗
(Continued on next page)
63
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
64
TABLE 5
Cross-Tabulation Percentage Frequency Results for Performance in Predictor Classes Vis-a-Vis
the Five Modules (Continued)
`
Module I
A
Econ. I
Gamma
Somers’ d
Econ. II
Gamma
Somers’ d
Econ. III
Gamma
Somers’ d
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
Module II
Module III
Module IV
Module V
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
92
20
8
51
0
27
0
2
0.306∗
0.215∗
83
38
17
34
0
28
0
0
0.283∗
0.194∗
100
54
0
34
0
10
0
2
0.627∗
0.419∗
10
54
36
0
21
48
28
3
4
54
42
0
19
50
25
6
20
55
25
0
25
42
33
0
20
40
40
0
13
53
27
7
23
30
47
0
25
30
40
5
30
34
34
2
8
42
50
0
25
31
42
2
0
50
50
0
31
52
7
10
10
43
38
10
24
41
24
10
0
50
42
8
35
35
19
11
22
56
22
0
27
50
17
7
25
75
0
0
73
22
18
45
9
31
0
2
0.253∗∗
0.161∗∗
70
42
20
31
10
27
0
0
0.422∗
0.266∗
69
52
25
34
6
10
0
3
0.357∗
0.215∗
11
61
27
2
17
37
44
2
67
21
27
49
7
29
0
1
0.328∗
0.218∗
71
35
21
30
7
35
0
0
0.308∗
0.201∗
83
49
17
34
0
13
0
3
0.485∗
0.293∗
8
57
33
2
33
37
26
4
65
20
23
53
12
24
0
3
0.298∗
0.211∗
75
36
19
37
6
27
0
0
0.397∗
0.279∗
74
49
16
42
10
8
0
1
0.410∗
0.264∗
8
50
42
0
20
34
45
1
72
23
17
51
11
24
0
1
0.433∗
0.297∗
67
40
22
29
11
31
0
0
0.395∗
0.267∗
77
51
19
39
4
7
0
3
0.629∗
0.444∗
28
47
19
6
50
50
0
0
Note: For ease of exposition dependent variables are in columns, independent variables are in rows, and rounded whole percentages are reported except for gamma and Somers’ d. Some column frequencies
do not sum to 100 due to rounding.
∗ p < .05. ∗∗ p < .01.
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 6
Ordinal Regression Results for Module I With
Predictor Group 1 Classes
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Class/item
Acct. I
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. I
Econ. I
Econ. I
Econ. II
Econ. II
Econ. II
Econ. II
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
65
TABLE 7
Ordinal Regression Results for Module I With
Predictor Group 2 Classes
Category
Estimate
SE
Signif.
A
B
C
D/F
A
B
C
D/F
A
B
C
D/F
A
B
C
A
B
C
A
B
C
D/F
Yes
No
Yes
No
−17.900
−18.081
−17.772
0a
−0.416
−0.667
0.218
0a
−1.002
−0.777
−0.131
0a
−0.932
−0.468
0a
−0.533
−0.034
0a
−0.707
−0.888
−1.042
0a
−0.652
0a
0.279
0a
35.330
44.337
.255
.282
.125
0.371
0.318
0.000
—
0.692
0.701
0.722
—
1.308
1.342
1.361
—
0.362
0.306
—
0.415
0.289
—
1.128
1.106
1.109
—
0.916
—
0.252
—
.000
.000
—
—
.547
.341
.762
—
.444
.563
.923
—
.010
.126
—
.199
.906
—
.531
.422
.347
—
.477
—
.269
—
.009
.160
aParameter
is set to zero because it is redundant because it is the comparison category.
significantly related to scores earned in Module I, including
diversity measures.
Table 7 reveals that students taking predictor classes in
group 2 who earned As or Bs in accounting II or statistics
were significantly less likely to earn Ds or Fs in Module I
(significance = .000, .000, .036, and .040, respectively). A
student earning an A in business law was also significantly
less likely to earn a D or F (.026). The diversity measures
have no significant relationships with Module I performance.
Table 8 shows earning an A in accounting I was associated with being less likely to earn a D or F in Module III
(significance = .015). There was a marginally significant
relationship for those earning a B in the predictor class
(.077). A student earning an A or B in economics II was
less likely to earn a D or F in the module (.000 and .000,
respectively). However, given that the standard errors for
business law were marginally larger than 2.0, all results
must be interpreted with caution.
Multinomial regression results for the five models able to
be estimated are presented in Tables 9–13. Panel A of the
tables reports likelihood ratio significance tests, which indi-
Class/item
Acct. I
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. III
Econ. III
Econ. III
Econ. III
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
Category
Estimate
SE
Signif.
A
B
C
D/F
A
B
C
D/F
A
B
C
A
B
C
A
B
C
D/F
Yes
No
Yes
No
−0.070
−0.013
1.386
0a
−19.179
−17.510
−17.751
0a
−1.378
−0.706
0a
−0.903
−0.921
0a
0.448
0.592
1.151
0a
0.565
0a
0.261
0a
68.416
11.399
.566
.633
.372
1.465
1.439
1.480
—
0.614
0.612
0.000
—
0.618
0.565
—
0.430
0.449
—
0.837
0.816
0.944
—
1.172
—
0.367
—
.962
.993
.349
—
.000
.000
—
—
.026
.211
—
.036
.040
—
.592
.468
.223
—
.630
—
.477
—
.000
.999
aParameter
is set to zero because it is redundant because it is the comparison category.
cate for each of the predictor courses and the gender diversity
measure whether there is a significant relationship between
that variable and the particular module’s grades. Panel B
reports, according to each module grade, the estimated coefficients for each predictor class and the gender diversity
variable, and their odds ratios. All these estimates are in reference to scoring an A in the module while the grades in the
predictor classes are ranked A = 1, B = 2, C = 3, D/F = 4;
thus, significant positive coefficients have odds ratios exceeding 1.0, indicating that as performance in a predictor course
declined (grade is lower and the rank is higher), the odds of
scoring a grade in the module other than an A increased.
Results for Module II grades with classes from Predictor
Group 1 presented in Panel A of Table 9 indicate a significant relationship between performances in Accounting
I and Business Law vis-`a-vis performance in the module
(significance = .041 and .013, respectively). Performance in
statistics was marginally related (.072). Panel B reveals that
business law can distinguish scores of C in Module II from
scores of A (.026) and can marginally distinguish scores of
D or F in the module from scores of A (.099). Economics I
can marginally differentiate scores of C from A (.077), and
accounting I can distinguish scores of D or F from A (.019).
None of the predictor classes helps predict which students
66
L. J. BAKER-EVELETH ET AL.
TABLE 9
Multinomial Logistic Regression Results for Module II
With Predictor Group 1 Classes
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
TABLE 8
Ordinal Regression Results for Module III With
Predictor Group 1 Classes
Class/item
Category
Estimate
SE
Signif.
Acct. I
Acct. I
Acct. I
Acct. II
Acct. II
Acct. II
Acct. II
Bus. law
Bus. law
Bus. law
Bus. law
Statistics
Statistics
Statistics
Econ. I A
Econ. I B
Econ. I C
Econ. II
Econ. II
Econ. II
Econ. II
Acad. diverse
Acad. diverse
Gender diverse
Gender diverse
Model fit, χ 2
Test of parallel lines, χ 2
Pseudo R2: Cox and Snell
Nagelkerke
McFadden
A
B
C
A
B
C
D/F
A
B
C
D/F
A
B
C
−1.106
0.004
0a
A
B
C
D/F
Yes
No
Yes
No
−1.604
−0.990
0a
1.394
1.592
1.501
0a
0.473
0.475
1.617
0a
−0.271
0.220
0a
0.741
0.480
0.662
0.560
.015
.077
1.231
1.234
1.265
.258
.197
.235
2.014
2.024
2.042
.814
.814
.428
0.573
0.499
.636
.659
−19.633
−19.510
−18.921
0a
0.014
0a
−0.324
0a
42.601
43.031
.334
.361
.158
Panel A: Likelihood ratio tests
χ2
Effect
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
Signif.
8.277
3.905
10.834
7.002
4.057
0.031
3.428
.041
.272
.013
.072
.255
.999
.330
Panel B: Parameter estimates
.136
.993
0.582
0.492
0.000
.000
.000
0.881
.987
0.421
.442
.001
.138
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
C
aParameter is set to zero because it is redundant because it is the comparison category.
tend to score a B in the module from those who tend to score
an A. The measure for gender diversity is insignificant across
all grade breaks. The corresponding odds ratios for the significant variables indicate that a student who performs poorly in
business law is 6.5 times more likely to score a C in Module II
than an A—Exp(B) = 6.509—and over 4.5 times more likely
to score a D or F (4.550). A student scoring poorly in economics I is over 4.5 more times likely to score a C rather than
an A (4.574); scoring poorly in accounting I suggests that one
will be over eight times as likely to score a D or F (8.221).
Table 10 presents results for Module II using Predictor
Group 2 courses. Panel A shows that business law is significantly related to performance in the module (significance =
.040), but accounting II is more useful (.001) than accounting I. Panel B reveals findings similar to those in Table 9;
namely, none of the courses can usefully distinguish module scores of B from A and the gender diversity measure
is never significant. Accounting II can differentiate between
students scoring Cs (.007) or Ds or Fs (.019) from those scoring As. Such students are nearly 10 times as likely to perform
thus—Exp(B) = 9.926 and 9.827, respectively. Business law
can discern (significance = .030) between students earning
D/F
Estimate, B
SE
Signif.
Odds
ratio,
Exp(B)
0.842
−0.504
0.826
0.026
1.196
0.100
−1.258
0.750
0.698
0.794
0.577
0.799
0.631
0.925
.261
.470
.298
.964
.134
.874
.174
2.322
0.604
2.284
1.026
3.308
1.105
0.284
0b
—
—
—
1.300
0.030
1.873
0.944
1.520
0.108
−0.610
0.797
0.717
0.841
0.641
0.859
0.666
1.013
.103
.967
.026
.141
.077
.871
.547
3.668
1.030
6.509
2.570
4.574
1.114
0.543
0b
—
—
—
2.107
−0.122
1.515
0.660
0.994
0.128
−0.648
0.896
0.784
0.918
0.757
0.966
0.792
1.183
.019
.876
.099
.383
.304
.871
.584
8.221
0.885
4.550
1.936
2.703
1.137
0.523
0b
—
—
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
a D or F from those earning an A, estimating that as students perform worse in the course they are nearly 10 times
more likely to score a D or F in the module rather than an
A—Exp(B) = 9.807. Statistics can marginally discern (significance = .081) scores of D or F from A, estimating that
such students are 4.231 times as likely to do so.
Results for Module III grades vis-`a-vis Predictor Group
1 are reported in Table 11. Again, business law significantly
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 10
Multinomial Logistic Regression Results for Module II
With Predictor Group 2 Classes
TABLE 11
Multinomial Logistic Regression Results for Module III
With Predictor Group 1 Classes
Panel A: Likelihood ratio tests
Panel A: Likelihood ratio tests
χ2
Effect
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
Signif.
2.223
15.946
8.326
3.609
3.709
1.265
.527
.001
.040
.307
.295
.737
Effect
χ2
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
4.816
2.594
7.822
1.802
4.521
3.154
3.963
67
Signif.
.186
.459
.050
.615
.210
.368
.265
Panel B: Parameter estimates
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Panel B: Parameter estimates
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender diverse
yes
C
D/F
Estimate, B
SE
Odds
ratio,
Signif. Exp(B)
0.709
0.481
0.000
0.406
0.184
0.504
0b
0.617
0.704
0.539
0.423
0.503
0.659
—
.250
.494
.999
.338
.714
.445
—
2.032
1.617
1.000
1.501
1.202
1.655
—
0.301
2.295
0.296
0.808
1.019
−0.162
0b
0.819
0.844
0.808
0.615
0.645
0.946
—
.713
.007
.714
.189
.114
.864
—
1.351
9.926
1.345
2.244
2.772
0.851
—
1.049
2.285
2.283
1.442
0.755
−0.212
0b
0.980
0.978
1.050
0.827
0.773
1.179
—
.285
.019
.030
.081
.329
.857
—
2.855
9.827
9.807
4.231
2.127
0.809
—
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Gender
diverse no
C
D/F
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
relates to performance in the module (significance = .050).
Panel B results indicate that only economics I appears able
to discern among the various grades (significance = .094 and
.061 for grades B and C, respectively). The corresponding
odds of doing so are 4.049 and 5.160. The gender diversity
measure remains insignificant.
Module III’s grades relative to courses in Predictor Group
2 are shown in Table 12. As before, Panel A shows that performance in business law is related to performance in the module (significance = .049), and now both accounting courses
are marginally related (.086 and .089, respectively). Panel
B reveals that business law differentiates between students
scoring Ds or Fs rather than As (.063) and that accounting
Odds
ratio,
Signif. Exp(B)
Estimate, B
SE
0.119
1.203
−0.695
−0.253
1.398
0.991
−0.778
0.833
0.862
0.786
0.580
0.835
0.788
0.863
.887
.163
.377
.663
.094
.209
.367
1.126
3.329
0.499
0.776
4.049
2.695
0.459
0b
—
—
—
0.829
0.960
0.367
−0.432
1.641
1.210
−1.469
0.856
0.880
0.820
0.631
0.875
0.809
0.927
.333
.276
.655
.494
.061
.135
.113
2.292
2.612
1.443
0.649
5.160
3.352
0.230
0b
—
—
—
0.975
1.055
−0.108
0.140
1.192
1.312
−0.512
0.893
0.902
0.859
0.669
0.919
0.843
0.994
.275
.242
.900
.834
.195
.119
.606
2.651
2.872
0.898
1.150
3.294
3.713
0.599
0b
—
—
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
II distinguishes between those scoring a C rather than an A
(.047). The estimated odds of doing so are 8.397 and 9.232,
respectively. The gender diversity measure remains insignificant and none of the courses can discern students scoring Bs
from those scoring As.
Module V scores modeled with Predictor Group 2 are
reported in Table 13. Panel A shows that Accounting I
appears significantly relates to performance in the module
68
L. J. BAKER-EVELETH ET AL.
TABLE 12
Multinomial Logistic Regression Results for Module III
With Predictor Group 2 Classes
TABLE 13
Multinomial Logistic Regression Results for Module V
With Predictor Group 2 Classes
Panel A: Likelihood ratio tests
Panel A: Likelihood ratio tests
Effect
χ2
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
6.585
6.507
7.880
3.114
0.439
4.008
Signif.
.086
.089
.049
.374
.932
.261
Acct. I
Acct. II
Bus. law
Statistics
Econ. III
Gender diverse
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
Panel B: Parameter estimates
Module
gradea
Class/
item
B
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
C
D/F
χ2
Effect
Signif.
10.070
2.016
5.620
4.424
2.937
3.390
.018
.569
.132
.219
.401
.335
Panel B: Parameter estimates
Odds
ratio,
Signif. Exp(B)
Module
gradea
Class/
item
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Acct. I
Acct. II
Bus. law
Statistics
Econ. I
Econ. II
Gender
diverse yes
Estimate, B
SE
−0.420
1.393
−0.122
0.048
0.018
0.055
0b
0.640
1.046
0.658
0.498
0.522
0.803
—
.512
.183
.853
.923
.972
.945
—
0.657
4.025
0.886
1.049
1.018
1.057
—
B
0.885
2.223
−0.151
−0.794
0.115
−0.735
0b
0.769
1.120
0.821
0.736
0.629
0.991
—
.250
.047
.854
.280
.855
.458
—
2.423
9.232
0.860
0.452
1.122
0.479
—
C
0.976
1.588
2.128
0.509
−0.369
−2.126
0b
1.080
1.271
1.144
0.986
0.844
1.455
—
.366
.212
.063
.606
.662
.144
2.654
4.896
8.397
1.664
0.691
0.119
—
D/F
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
(significance = .018). Panel B shows that the course further
differentiates among students earning Cs (.043) or Ds or Fs
(marginally at .066) rather than As. business law can discern
differences among student performance, namely those
scoring Cs (.060). The respective odds of such performances
are 10.197, 23.178, and 6.727. As always, gender diversity
is still insignificant, both generally and for all grades, and
none of the predictor courses is helps to discern module
scores of B from A.
Table 14 compares the classification accuracy rates to the
modified or by chance accuracy rates. All the original classification rates improve upon the modified rates, suggesting
Odds
ratio,
Signif. Exp(B)
Estimate, B
SE
1.134
0.263
1.029
0.474
−0.166
−0.577
0b
1.085
0.856
0.921
0.547
0.698
0.958
—
.296
.758
.264
.387
.812
.547
—
3.108
1.301
2.799
1.606
0.847
0.562
—
2.322
0.528
1.906
−0.338
−0.151
−1.541
0b
1.148
0.920
1.013
0.707
0.756
1.072
—
.043
.566
.060
.633
.842
.151
—
10.197
1.695
6.727
0.713
0.860
0.214
—
3.143
1.578
2.317
−1.014
−2.142
−1.729
0b
1.710
1.311
1.471
1.393
1.715
1.698
—
.066
.229
.115
.467
.212
.309
—
23.178
4.844
10.146
0.363
0.117
0.177
—
aReference
category is grade A.
is set to zero because it is redundant because it is the comparison category.
bParameter
that all of the models are useful at predicting grade rankings
in the estimated modules.
DISCUSSION
To sum, exposure to academically diverse or gender diverse teams did not significantly affect individual student
performance in an integrated curriculum. Only one model
found the first economics course of a two-class sequence in
a predictor group significantly useful in predicting performance in later modules. The second economics course was
useful one time, and an alternative single economics course
ROLE OF PREDICTOR COURSES ON STUDENT SUCCESS
TABLE 14
Model Classification Percentage Accuracy Rates
Versus Modified Accuracy Rates
Module
II
III
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
V
Predictor group
Modified accuracy
Classification accuracy
1
2
1
2
2
44.2
44.2
37.3
51.0
51.6
57.5
61.0
54.3
67.1
61.6
worth more credit hours included in a different predictor
group was never significant. Both regression analyses found
the Business Law predictor course useful. The first accounting course of the two-course sequence was the next most
frequently significant predictor course followed by statistics.
While relatively few of the classes comprising either set of
predictor groups were significantly related to follow-up performance, the multinomial regression methods reveal that the
odds ratios for those courses that are significantly related exceed 4.0. Students and faculty should monitor performance
in select predictor courses because students that do not perform well are four times more likely not to perform well in
the junior year compulsory business modules.
Of the five modules with grade rankings modeled here,
Module II grades were significantly related to more predictor
courses than other modules’ grades, at six instances. Module I
grades were related five times, Module III had four instances,
and Module V showed only two significant relations. Module
IV grades were never significantly related to performance in
either of the predictor groups.
While all the modules teach typical introductory material
for five business areas (finance, IS, MHR, marketing, and
OM) in a highly integrated fashion, each module has a particular focus. Module I covers the variety of systems within
which businesses operate. Module II highlights the planning
and implementation processes needed at the strategic levels.
Module III teaches tools used to do quantitative and qualitative forecasting, while Module IV covers the management of
firm resource. Finally, Module V considers the major operating decisions related to product, pricing, and distribution.
One interpretation of our results, then, is that topics and techniques covered in introductory accounting, business law, and
to a lesser degree statistics are a better preparation for understanding the systemic qualities of the business environment
and the variety of planning required in firms.
As for the two diversity measures lacking any explanatory power, perhaps there was not enough variation in the
academic diversity measure because over 95% of teams were
considered diverse. A larger sample size or different proxy
for diversity could be helpful. Perhaps exposure to different
genders by the time students are juniors in college no longer
has the same effect as earlier. Alternatively, in capturing
how diverse knowledge bases on a team affects grade perfor-
69
mance, perhaps better proxies would reflect communication
skills, coping skills, or team management skills.
CONCLUSION
We investigated whether predictor courses and academicallyand gender-diverse teams influence student performance in a
compulsory yearlong integrated business course that records
grades for five modules. Observations are collected over three
years. These results provide insight not only to what predictor courses might be helpful in enhancing student success
in an integrated program, but also to whether diverse team
environments increase individual student success.
Cross-tabulation analysis suggests predictor courses are
useful in reducing the prediction error when ranking grades
in each module. Gamma scores indicated a moderate to fairly
strong positive relationship between performance in the predictor courses and performance in each module. Performance
in the first of a two-course accounting sequence of predictor classes was strongly associated with subsequent scores
in the modules. The directional measure, Somers’ d, indicated the courses are moderately helpful in predicting later
performance in the modules.
The proxy measures of diversity were based on the
makeup of self-determined student teams that vary in size
and include degrees of academic and gender diversity. The
majority of the time students were exposed to academically
diverse teams and over half the time to gender diverse teams.
More in-depth analysis was attempted with ordinal regression to try to use any information contained in the ranked
nature of grades. Results suggest that the two accounting
courses and the statistics predictor course are useful in estimating later performance in the first module. Neither of the
diversity measures was statistically significant.
Data constraints cause only two of the five modules to be
estimable with ordinal regression (some assumptions are relaxed, academic diversity measure dropped, and multinomial
logistic regression applied). Three modules are estimable under this analysis and show that the first accounting course and
a predictor course in business law are most consistently related to later performance in the modules. Also, when significant, predictor courses can better distinguish among students
performing at the C, D, or F grade levels (as opposed to
scoring As) than those earning Bs.
We considered academic and gender differences when
evaluating team diversity. While we had predicted that
gender diversity at the team level would enhance success,
the measure remained insignificant. This result could be
driven by a number of factors, including the likelihood that
gender diversity might not be as critical as it once was when
promoting novelty in thought because students are increasingly more exposed to gender diverse environments. This in
no way suggests that gender diversity is not important, rather,
that gender diversity is more of a norm in team environments.
The selection of measurements for team diversity and the
70
L. J. BAKER-EVELETH ET AL.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:30 11 January 2016
results create a limitation of the study and an opportunity for
additional research. The construct of diversity at the team
level is multidimensional and includes more than academic
and gender diversity; thus, we suggest future researchers
consider alternative measures of team diversity in enhancing
student success. Our student diversity (residential, landgrant university), which is common to other universities and
colleges, is somewhat limited. An extension of this present
study would be to validate these results in a different
context and to explore additional facets of diversity. An
additional opportunity could be to investigate appropriate
predictor courses that may help students develop stronger
communication and interpersonal skills prior to entering the
integrated business course.
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