Traditional classroom instruction (i.e., lecturing) is generally
considered to be highly effective in terms of transferring knowledge
and, as such, has steadfastly remained the dominant method in higher
education, particularly for those areas that are considered more
quantitative. For example, studies have shown that the "chalk and
talk" method of instruction is still most popular in finance
courses (e.g., Saunders, 2001; Farooqi & Saunders, 2004; Iqbal,
Farooqi & Saunders, 2006), as well as courses in related disciplines
such as economics (Becker & Watts, 1996; Becker & Watts, 2001).
An argument could be made that one of the primary objectives of
higher education is preparation for a professional career upon
graduation. There is naturally more to this preparation than knowledge
attainment. The application of this knowledge to "real world"
situations is a skill that has historically been largely left to the
students' own devices, and often insufficiently. However, in recent
years, there has been much discussion on the use of classroom games and
simulations as a way to fill this gap.
The use of games in instruction is far from a new concept, as there
is evidence as early as the 1940s (e.g., Chamberlain, 1948) of their use
in Economics courses. However, Holt (1999) shows there has been much
more emphasis over the last couple of decades, due in large part to the
rapid rise in technology, which allows easier integration into the
classroom. In addition, part of the reasoning behind the low levels of
use was there was little research documenting a benefit in student
learning from classroom simulations; however, recent works, including
Cebula & Toma (2002), have addressed this latter issue, finding a
positive influence from "bringing course material to life."
More generally, Harter & Harter (2010) find that stock market
simulations can significantly increase financial literacy among high
school students, and Moffit, Skull & McKinney (2010) find that
students completing an equity trading game believe their knowledge
levels have improved, as has their interest in the topic.
However, to our knowledge, none of the existing studies document
whether performance in such games (beyond just their simple use) impacts
these outcomes. In particular, do students who perform well (i.e., earn
an above average return) in such portfolio simulations have a greater
interest in the field than those whose performance lags either their
peers or the market? This question is particularly relevant, as
anecdotal evidence suggests that some professors have been reluctant to
implement such games for fear that a poor performance will dissuade
students from pursuing a career in the field. Recent research (e.g.,
Waggle & Moon, 2011) finds that only approximately 30% of all
undergraduate investment analysis courses use some type of stock market
simulation as an aide in understanding the material being taught.
Although this is the particular issue on which we focus, we believe our
study provides other contributions as well.
First, there is surprisingly little research done on the use of
classroom games in finance courses, whereas there is abundance in the
area of economics and other disciplines. This is particularly
interesting given the nature of finance, in that it lends itself readily
to the real world application these games are designed to provide.
Second, several previous studies examine games in general and in often
very short-term (a single class period, for example) situations. The
simulation examined in this study is a very realistic setting that
covers an entire semester and should therefore provide more accurate
The final contribution of the study revolves around the
students' perspective of such games. From the instructor's
perspective, the increased entertainment value could result in more
favorable instructor evaluations as the games enhance student learning
and make the class more enjoyable. However, we examine an alternative
option for benefit-- the clarification of student perception of the
discipline. In addition to questioning the student on knowledge increase
as a result of the course, they are also queried on their interest
levels in working in the profession. While both of these overlap with
previous studies, we also survey students on their interest in managing
their own money later in life. Since courses are often taken as
electives by students with different majors, we believe this question
gets more at the heart of the impact of the use of games.
We find that the experience of taking the course had a positive
influence on student interest, knowledge, and experience; however, in
contrast, we find no consistent relation between simulation returns,
market returns, or market volatility and changes in interest, likelihood
of future management of money, or knowledge levels. Thus, the prevalence
of benefits documented in prior literature, combined with the lack of
negative side effects from poor student performance on such simulations,
suggests that the use of such investment simulations is warranted.
In ancillary results, we do find that males experience a larger
increase in interest in the material than females. The same is true for
graduate students relative to undergraduate students. Finance majors
experience a larger increase in their likelihood of continuing to manage
their own (real) money in the future, and students with higher course
grades are likely to have higher changes in experience levels and
The literature relevant to the history of games in the business
classroom is large and developed, particularly with respect to economics
courses. Chamberlain (1948) is credited with the first application of
games in a classroom setting. Specifically, Chamberlain, using doctoral
students at Harvard, allowed the students to circle the room and
negotiate trades with others. Some individuals were designated buyers,
while some where sellers; the interaction of the two groups led to
further understanding of how markets work. Others, including Smith
(1962), quickly built upon this, and the use of games in economics
courses became relatively widespread. Davis & Holt (1993) and Kagel
& Roth (1995) survey the work done on the topic to that point.
Brauer & Delemester (2001) extend the survey by completing a more
comprehensive review of the existing games for Economics courses.
Fels (1993) brings to light the fact that, although not unusual,
the use of games prior to the mid 1990s never became common-place. The
two reasons suggested by Fels (1993) were high costs of creation and
relatively low documentation of significant student benefit in terms of
increased knowledge attainment. The first issue has been largely
overcome due to the rapid rise in technology and related computer-based
simulations available at reasonable costs. The implementation of
easy-to-use simulations such as the Stock Market Game (SMG) has led to
the more evolved and involved electronic simulations available today.
Also, the ease of use of such programs make the opportunity cost for the
instructor minimal. See Wood, O'Hare & Andrews (1992) and Bell
(1993) for early examinations of SMG. Complete information on the
program can be found at www.stockmarketgame.com.
The second issue is more involved, but it too has been largely
resolved, with the dominant conclusion that classroom games do provide
benefit for the students. Frank (1997) found that students who
experienced a classroom environment using games performed better on
multiple choice tests than did counterparts in classrooms without games.
Dickie (2006) finds evidence that also supports this contention. Gremmen
& Potters (1997) and Biel & Delemeester (1999) find that
students that experienced games learned more about the economic model
than those who did not. Mullin & Sohan (1999) and Yandell (2004)
find no significant difference in test results dependent upon the use of
games; however, they find that students generally are more satisfied
with the course if there is a game involved.
Fraas (1980) finds that the student's pre-course level of
knowledge was a significant contributing factor in the effectiveness of
the games. Students that had little prior knowledge received more
benefit from the games than those students with higher starting
knowledge levels. Tsigaris (2008) suggests there is a double dividend
from experimental games. The instructor, assuming they are utility
maximizers, should perhaps incorporate games in order to increase their
course evaluations. In addition, the students may benefit from increased
knowledge the real-world application of material provides. Tsigaris
(2008) also states that the intensity of the simulation is an important
element in the effectiveness of the classroom game. Cebula & Toma
(2002) find empirical support for both of these notions.
While evidence on experimental games in economics courses is
abundant, the same is not true in finance. Unfortunately, until recently
the use of such games in finance courses has been much less examined,
due in part to the slow acclimation of the discipline to computer-based
simulations. In fact, Clinebell & Clinebell (1995) show finance
courses were often slow to use computers in their instruction despite
being available, yet Devasagayam & Hyat (2007) find evidence that
supports the use of computer simulations as a pedagogical device in a
cross-disciplinary study of finance and marketing courses. Foster et al.
(2004) and Helliar et al. (2000) find more specific evidence that a
market-share game can improve student learning in undergraduate finance
Some examples of past literature in the area are only peripherally
related to the finance classroom. For example, Breen & Boyd (1976)
present an early programming guide for creating simulations that would
be applicable in money and banking classes. Also, Bell's (1993)
version of the uncomputerized SMG was primarily designed for investment
analysis, as stated by the author. There is also very little evidence on
the effectiveness of these experimental games in helping students
clarify their opinions on disciplines as a whole, perhaps as a viable
career option. An exception is Sherman, Sebora & Digman (2008) who
find that the use of experimental methods generally increase the impact
of the course on students choice of becoming an entrepreneur.
There are a few notable exceptions that are similar in nature to
the current study. King & Jennings (2004) find that the inclusion of
trading simulation increases student learning. Ascioglu & Kugle
(2005) implement a surveying technique to examine the influence of
simulations on student enjoyment and learning objectives. Lekvin (2005)
examines whether there is a relationship between trading ability (i.e.,
performance) and academic performance (i.e., grade) and largely finds
success in either is independent of the other.
Finally, Moffit, Stull & McKinney (2010) is most similar to the
current study. Specifically, they examine pre-and post- simulation
knowledge via testing, as well as pre- and post-surveys gathering data
on the students and their opinions on the simulation. This latter is
very similar to what we do in the current study. They find that students
benefit from the simulation, as grades on the post-exams are
significantly higher than on the pre-exams, which suggests an increased
understanding of fundamental financial knowledge. They find that
approximately 60% of students in the study find the simulation a
knowledge-increasing process, while over 80% find the simulation
increases their interest in the subject. The Moffit, Stull &
McKinney (2010) study does differ from this one in several ways. First,
they examine the simulation independent of a classroom. Second, like
previous studies, they do not examine the influence of simulation
performance on the survey results, but rather just whether the
participation influences the respondents' opinions. Third, the
current study utilizes a larger sample over a longer period of time,
which allows for examination of differing market conditions. Fourth, our
simulation allows for trading of a wide range of securities, whereas the
Moffit, Stuff & McKinney (2010) simulation allows only equity
We believe the addition of investment performance is a valuable
extension to the literature. For example, researchers in behavioral
finance have widely documented the "snakebite effect," which
suggests that investors who experience a painful loss (or otherwise
unsuccessful investment) are less likely to invest going forward
(Nofsinger, 2011). Thus, some professors may likely have avoided the use
of such simulations, or at least not had extensive classroom discussion
on the results, for fear of dissuading students with poor performance
from pursuing a career. Thus, we believe the current study complements
and extends the existing literature by examining this particular
Data are collected via a survey method at the beginning and end of
ten courses in upper level Investment Analysis (over the course of three
years) at two four-year Universities. Butler University is a private
University located in Indianapolis, IN, while the College of Charleston
is a public University located in Charleston, SC. The courses are very
similar in nature, as both instructors use the same text, employ
approximately the same teaching style, and compute grades based upon
very similar components and weighting. For instance, both instructors
use the simulation as a determinant of the student's grade in much
the same way. Specifically, students are graded based upon completion of
assignments and explanation of their trading activity, and not on their
performance. Both instructors provide a very modest amount of extra
credit for performance superior to that of the market (the S&P 500
over the equivalent time period). Thus, the motivation for students to
participate in the simulation should be roughly equivalent between the
two instructors. On the beginning survey, students were asked a sampling
of questions that served as controls for the study, including class
level and major. Also, and more importantly, students were asked to
subjectively rate themselves (on a scale of one to ten) in four
1. Interest in pursuing a career in the field of investments.
2. Likelihood of managing their own investments after graduation.
3. Level of experience with investments such as stocks, mutual
funds, and options.
4. Level of knowledge with respect to investments such as stocks,
mutual funds, and options.
The surveys were administered, then collected by the professors and
sealed until the end of each respective course to retain anonymity. At
that time, the students were again asked to rate themselves in each of
the four categories above. The study then focuses on the differing
levels of ratings provided by the students on the two surveys. At the
end of the respective course, the instructor compiles all data from the
surveys. In addition, the student's return on the simulation
contest is computed. Both instructors use StockTrak, a widely-used
online investment simulation company. Finally, the grade, rounded to the
nearest whole percent is recorded for each student respondent. Results
of summary statistics are presented in Tables 1 and 2.
Table 1 first presents averages for the total sample. The majority
of students taking the courses were male, which is typical of most
finance courses. In addition, approximately two-thirds of students
completing the survey were seniors at the time of course completion,
while a slightly higher percentage was Finance majors. The College of
Charleston did not have a Finance major during the sample period.
Instead, students can choose to have a Finance concentration with a
Business Administration major. While admittedly not the same, the
requirements for the concentration are relatively consistent with the
requirements for the major at Butler University. Thus, for the sake of
this study, we assume they are equivalent.
Approximately 16% of the respondents were graduate (MBA) students,
while less than 7% took the class during summer session. The average
StockTrak Return was just over 9%, covering approximately 12 weeks
during each semester. This represents an average of about 2 percent in
excess of the S&P 500 over the equivalent period of time.
The average level of beginning ratings in the Interest and Manage
categories are relatively high, at 7.6 and 8.6, respectively. This is as
expected given they have enrolled in an upper level investments course,
indicating a preexisting interest in the topic. Also predictably, the
average beginning levels of Experience and Knowledge are relatively low
at 4.1 and 5.0, respectively. The rating in two of the four categories
increased, with Interest and Manage slightly decreasing. Since this
value can be predictably biased by very low or high starting values, we
also calculate the percentage change for each student in each category.
The average of these percentage changes implies the average student
experiences a substantial increase in perceived knowledge and
experience, with a smaller increase in interest. The lone decrease is in
the likelihood they will manage their own money in the future. This may
be a result of students gaining a more complete knowledge of the time
and energy involved in such an endeavor.
The remainder of Table 1 examines the sample, segmented by general
return levels. For example, we segment the sample at the median return
of each course. Then we combine all above and below median returns to
create the subsamples. We find that students that experienced above
median returns also had significantly higher ending levels of interest
and likelihood of future management. The only other significant variable
is Male, indicating males are more likely to generate an above median
return. Most importantly, it does not appear as though the percentage
change in any of the four knowledge or interest levels is related to
their performance in the simulation contest.
To more closely examine the issue, we also segment the sample by
isolating those individuals who generated the returns in the highest
quartile for each section. These are the students one would expect to
have the most positive feedback from the process. However, we again find
no significant difference in the changes in any of the four categories.
We do find that students with higher initial levels of interest,
experience, and likelihood of managing their money to be more likely to
generate the highest returns, possibly because they spend the most time
actually trading in the simulation. Male students and those who have
chosen Finance as their major are also more likely to generate the
highest returns relative to their peers. In unreported results, the
bottom quartile of returns was also segmented from the sample. It could
be hypothesized that those with the lowest returns experienced the most
dramatic change in the survey response categories, particularly if the
"snakebite" effect is present. However, we find no significant
differences in any of the variables, again indicated no relationship
between simulation performance and knowledge or interest levels.
Table 2 examines the sample segmented by student characteristics.
Males have higher levels of all four categories of ratings in both the
beginning and end of the semester. However, the only category where the
percentage change in survey answers is significantly larger is interest
in the profession. Thus, it largely appears the differences based upon
gender is due to the inherent nature of the students and has little to
do with the experiences of the classroom. Seniors experience a larger
increase in the likelihood of personal money management and experience
than underclassmen or graduate students. Finance majors have higher
levels of ratings in each category, but much like gender, the
differences seem to predate the class experience. The only exception to
this is that Finance majors see a higher increase in the likelihood of
managing their own money, while non-Finance majors experience a
decrease. The difference is significant and is logical given their
chosen majors. Finally, the results are interesting when segmenting by
median grade. For students that earn an above median grade, their ending
rating is larger in all four categories than those earning below median
grades. However, there are no significant differences in the change
To more completely examine the significant contributors to the four
surveying categories, we consider two variations of the following basic
Dep = [alpha] + [[beta].sub.1]STRet + [[beta].sub.2]SPDev +
[[beta].sub.3]SPRet + [[beta].sub.4]Male + [[beta].sub.5]MBA +
[[beta].sub.6]Senior + [[beta].sub.7]FinMajor + [[beta].sub.8]Summer +
[[beta].sub.9]Grade + [[beta].sub.10]Ins1 + [[beta].sub.11]ClassSz +
where the dependent variable represents rating values for each of
the four categories of survey questions. The first variation of the
model uses traditional ordinary least squares (OLS) regression analysis
with the percentage changes (i.e., IntCh, ManCh, ExpCh, and KnowCh) in
each rating variable as the dependent variables. STRet is each
student's holding period return over the StockTrak period. SPDev
and SPRet are the standard deviation and return of the S&P 500 over
the equivalent time period, respectively.
Male, MBA, Senior, FinMajor, and Summer are all dummy variables
designed to control for student or course specific characteristics that
could influence changes in the dependent variables. Grade is the
student's final grade, rounded to the nearest whole percentage.
Ins1 is a dummy variable used to identify one of the two instructors
teaching the courses in which the survey were administered and is used
to control for any instructor specific impact on the results. ClassSz is
the final control variable, measured as the number of students
completing the course in which the survey was administered.
Table 3 presents results for the model above. The only significant
influence on the change in interest in the profession is for MBA
students, suggesting perhaps those farther along in their education
career better hone in on their career aspirations. Finance majors are
more likely to experience a significant increase in the likelihood of
managing their investments in the future, which is another unsurprising
result. On the other hand, Finance majors seem to experience a
significantly lower amount of knowledge increase. This is also logical,
as these students are likely to have the most pre-course knowledge and,
in turn, have less of a "blank slate" than non-Finance majors
who may have only had one prior Finance class.
The dependent variable that results in the most significant
relations is the change in experience. Interestingly, higher levels of
market returns result in lower levels of experience change, suggesting
that the best learning may occur in down markets, particularly when the
money lost was not your own. Males and seniors obtain lower levels of
increase in experience than their counterparts. Finally, students that
obtain higher grades have a larger perceived increase in experience,
which is as would be expected. Perhaps most importantly from Table 3, we
find that the student's return on the simulation contest is
unrelated to their change in any of the four rating categories. This
suggests that students' perception of financial material and/or the
Finance profession is not altered by their performance on the simulated
investment environment. While this does not mean that knowledge does not
increase as a result of the simulation, it is an interesting extension
of the discussion of benefits from such classroom activity.
In an attempt to more precisely examine the situation, Table 4
presents logistic regression results designed to capture variables that
significantly relate to a positive change in any of the four response
variables. Thus, whereas Table 3 examines the dependent variables as
continuous, Table 4 collapses them into dummy variables where the
respective variable equals 1 if the ending value for the response
variable is larger than the beginning value, 0 otherwise. There is a
marginally significant negative relation between the volatility of the
market and an increase in interest, which is logical if one assumes
individuals relate stable conditions to positive interest. Students with
higher grades are more likely to have increases in interest and
perception of knowledge attainment.
Finance majors are less likely to experience an increase in
knowledge, which is again consistent with the notion that they have a
higher starting level of knowledge. Students with larger StockTrak
returns are more likely to have increases in experience levels, although
the level of significance is relatively small. Consistent with the
results above, the only surprising result is a negative relation between
market returns and experience changes. One would expect that students
would feel they had a large increase in experience during positive
Using a surveying technique, we examine student opinions in upper
level and graduate level investment analysis courses. The study
specifically focuses on the interaction between the students'
returns on an investment simulation and their responses to four
variables: (1) perceived knowledge level, (2) interest level in the
discipline, (3) likelihood of managing money in the future, and (4)
perceived experience in the discipline. We find the levels of percentage
change in each of the four are unrelated to StockTrak (i.e., simulation)
As a whole, our results suggest that any concern over the snakebite
effect is unfounded, as there is no link between performance and
perceived interest or knowledge level. If anything, we find the opposite
as a lower market return (which would generally correspond to lower
absolute simulation performance) actually is associated with an increase
in perceived experience level. Thus, we suggest that simulations
continue to be used and that instructors not hesitate to make full use
of both rankings and performance in classroom discussion.
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Table 1: Summary Statistics, Segmented by Return
Total Segment by Median Segment by Top Quartile
Above Below p-value Above Below p-value
N 194 97 97 47 147
IntB 7.6134 7.7959 7.4271 .2132 8.2553 7.4082 .0111
ManB 8.5619 8.6633 8.4583 .3585 9.0426 8.4082 .0082
ExpB 4.1392 4.3265 3.9479 .2319 4.3617 4.0680 .4395
KnowB 4.9639 5.0102 4.9167 .7356 5.2766 4.8639 .1960
IntE 7.4330 7.8980 6.9583 .0049 8.4894 7.0952 .0000
ManE 8.1856 8.4184 7.9479 .0745 8.9574 7.9388 .0001
ExpE 5.9072 6.0714 5.7396 .2260 6.3191 5.7755 .0809
KnowE 6.9175 7.0000 6.8333 .4228 7.1915 6.8299 .1178
IntCh .0203 .0375 .0028 .3391 .0603 .0076 .2106
ManCh -.0244 -.0040 -.0453 .2335 .0036 -.0334 .3052
ExpCh .9708 .9238 1.0187 .6464 1.0006 .9613 .9470
KnowCh .7126 .7227 .7024 .8974 .6626 .7286 .5581
Male .7320 .8367 .6250 .0008 .9149 .6735 .0000
MBA .1598 .1735 .1458 .6014 .1702 .1565 .8279
Senior .6685 .6702 .6667 .9595 .6591 .6714 .8816
FinMajor .6856 .7143 .6563 .3869 .7872 .6531 .0659
Summer .0670 .0714 .0625 .8047 .0638 .0680 .9199
Grade .8408 .8493 .8320 .1252 .8511 .8376 .3014
STRet .0928 .2013 -.0179 .0000 .3136 .0222 .0000
ExRet .0195 .1257 -.0889 .0000 .2390 -.0507 .0000
IntB (IntE) is the level of interest in pursuing a career in
investments (on a scale of 1 to 10) based upon responses from a survey
administered at the beginning (end) of the respective course. ManB
(ManE) is the likelihood of managing their investment portfolio (on a
scale of 1 to 10) based upon responses from a survey administered at
the beginning (end) of the respective course. ExpB (ExpE) is the level
of experience with investments such as stocks, mutual funds, and
options based upon a survey administered at the beginning (end) of the
respective course. KnowA (KnowE) is the level of knowledge with
respect to investments such as stocks, mutual funds, and options (on a
scale of 1 to 10) based upon a survey administered at the beginning
(end) of the respective course. IntCh, ManCh, ExpCh, and KnowCh is the
percentage change in the beginning and end values of each respective
survey variable. Male is a dummy variable equal to one if the student
was male, zero otherwise. MBA is dummy variable equal to one if the
student was enrolled as an MBA student, zero otherwise. Senior is a
dummy variable equal to 1 if the student was enrolled in their senior
year, zero otherwise. FinMajor is a dummy variable equal to 1 if the
student is a finance major, zero otherwise. Summer is a dummy variable
equal to 1 if the course was a summer course, zero otherwise. STRet is
the return on the simulated Stocktrak account over the investment
period. ExRet is the excess return on the simulated Stocktrak account
over the investment period, calculated as STRet minus the return on
the S&P 500 over the same time period. p-values are calculated
assuming unequal variances and test the differences between the Above
and Below columns.
Table 2: Summary Statistics, Segmented by Student Characteristics
Male MBA Senior
Yes No p Yes No p Yes No p
N 142 52 31 163 123 61
IntB 7.75 7.25 .14 7.16 7.70 .25 7.53 7.80 .38
ManB 8.68 8.21 .06 8.48 8.58 .80 8.59 8.48 .67
ExpB 4.39 3.44 .01 4.84 4.01 .12 4.23 3.97 .49
KnowB 5.16 4.42 .02 5.42 4.88 .24 4.98 5.02 .90
IntA 7.75 6.56 .00 7.58 7.40 .66 7.20 7.70 .17
ManA 8.42 7.54 .01 8.52 8.12 .23 7.95 8.52 .03
ExpA 6.13 5.29 .01 6.35 5.82 .10 5.78 6.11 .24
KnowA 7.08 6.46 .02 7.55 6.80 .00 6.76 7.23 .03
IntCh .05 -.06 .08 .16 -.01 .09 .00 .01 .74
ManCh -.01 -.07 .14 .06 -.04 .12 -.06 .05 .01
ExpCh .88 1.21 .21 .95 .98 .92 .82 1.30 .05
KnowCh .67 0.84 .41 .95 .67 .39 .61 .92 .14
Yes No p High Low p
N 125 59 94 90
IntB 8.05 6.71 .00 7.82 7.41 .17
ManB 8.67 8.29 .12 8.74 8.34 .08
ExpB 4.37 3.66 .03 4.19 4.09 .76
KnowB 5.22 4.51 .02 5.05 4.92 .65
IntA 7.78 6.49 .00 7.79 6.93 .01
ManA 8.50 7.39 .00 8.48 7.79 .01
ExpA 8.09 5.47 .02 6.21 5.56 .02
KnowA 7.11 6.49 .01 7.15 6.67 .02
IntCh .00 .00 .92 .01 -.01 .80
ManCh .01 -.10 .01 -.01 -.05 .27
ExpCh .90 1.15 .33 1.15 .80 .11
KnowCh .61 .92 .15 .84 .58 .14
Male, MBA, Senior, and FinMajor are dummy variables and are segmented
by that basis. Grade is segmented by the median value. Since grades
are reported rounded to the nearest whole percentage, there are
several grades at the median, which are excluded from this analysis.
p-values are calculated assuming unequal variances and test the
differences between the Yes and No columns.
Table 3: Multivariate Regressions, OLS
Coef p-value Coef p-value
Intercept -.3109 .5727 -.3521 .2837
STRet .0005 .7555 .0011 .2124
SPDev -.0104 .1122 .0011 .7817
SPRet -.0158 .2090 .0030 .6887
Male .0675 .3303 .0237 .5655
MBA .1967 .0701 .0157 .8069
Senior .0597 .4625 -.0566 .2418
FinMajor .0098 .8888 .0790 .0595
Summer .1887 .1821 .0463 .5815
Grade .0031 .4529 .0019 .4367
Ins1 .1185 .1556 .0424 .3931
ClassSz .0086 .3991 .0021 .7297
N 194 194
Adj. R-Sq. .0229 .0310
Coef p-value Coef p-value
Intercept .2353 .9028 -.3184 .8388
STRet .0036 .4976 -.0001 .9807
SPDev -.0163 .4762 .0041 .8251
SPRet -.0797 .0701 .0045 .9450
Male -.5078 .0372 -.2322 .2391
MBA -.4288 .2573 .0684 .8239
Senior -.5858 .0400 -.2390 .3004
FinMajor -.3217 .1898 -.5329 .0080
Summer -.3710 .4522 -.2390 .3004
Grade .0279 .0568 .0199 .0936
Ins1 .5029 .0851 .2112 .3721
ClassSz .0015 .9665 -.0075 .7951
N 194 194
Adj. R-Sq. .0896 .0320
Ins1 is a dummy variable equal to 1 if the courses is taught by one of
the two instructors in which the survey was administered, zero
otherwise. ClassSz is the number of students in the respective class
in which the survey is administered. All other variables are as
Table 4: Multivariate Regressions: Logistic Models
Coef. p-value Coef. p-value
Intercept -5.7057 .0771 -2.7967 .4038
STRet .0017 .8356 .0075 .3584
SPDev -.0617 .0989 -.0067 .8663
SPRet -.0637 .3506 -.0350 .6453
Male -.3659 .3484 .0615 .8872
MBA .1305 .8228 -.8662 .1775
Senior -.1448 .7539 -.5477 .2299
FinMajor -.1671 .6691 .2756 .5209
Summer .9078 .2325 .8114 .3033
Grade .0507 .0317 .0272 .2821
Ins1 1.1940 .0210 .2348 .6421
ClassSz .0948 .1113 -.0171 .7859
N 194 194
% Conc 69.0 64.0
Coef. p-value Coef. p-value
Intercept 2.9451 .3277 -2.0429 .5479
STRet .0197 .0768 -.0053 .6147
SPDev -.0358 .3305 .0064 .8830
SPRet -.1418 .0416 -.0395 .6270
Male -.1384 .7185 -.7200 .1497
MBA -.8150 .1951 -.2751 .7068
Senior -.6252 .2148 -.1975 .7167
FinMajor .0533 .8937 -.8542 .0895
Summer -.1820 .8105 -.0746 .9393
Grade .0000 .9994 .0678 .0141
Ins1 .4792 .2899 .4466 .3999
ClassSz -.0033 .9521 -.0381 .5437
N 194 194
% Conc 67.5 68.6
PosIntCh, PosManCh, PosExpCh, or PosKnowCh are dummy variables equal
to 1 if the respective change variable was positive, indicating an
increased level in the response variable from class beginning to end.
All other variables are as previously defined.