This document summarizes a study examining whether having close peers in the same academic major affects a student's GPA. The author hypothesizes that being surrounded by peers in one's major program leads to better academic decisions and higher GPAs. A survey collected data on 71 students, including their major, years in major, close peers in major, SAT score, gender, major GPA and overall GPA. The author plans to use regression analysis to determine if these variables, particularly close peers in major, have a statistically significant impact on major GPA. Previous related studies found mixed results on peer influences, with some showing effects on GPA and others finding no difference between social and random peers.

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Choosing friends wisely: The impact of having close peers within one’s academic program.
Kathleen McCunney
Econ 3598
3/13/2014

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Introduction:
As students advance from high school to college, the floodgate of outside influence
opens. During grades eight through 12, students must maintain grades with outside pressures
consisting of mainly extra-curricular and weekend activities. However, the transition into the
diversified life of a college student introduces more friends, activities, and responsibilities. Do
these added influences, specifically by peers, affect a student’s academic ability (Grade Point
Average)?
A common cliché, ‘birds of a feather flock together’, implies that those with similar
characteristics are drawn to one another. If this were to hold true, it would be safe to assume that
college students of similar traits or interests would tend to attract one another. With that being
said, there could also be reason to believe that students with the same academic interests, that
being their major, would spend enough time together to be considered an influence on one
another. This leads to the question of whether or not having close peers (defined as spending 10
or more hours together) in the same major program at a university will influence their GPAs.
Having the answer to this question would be of significant importance to a university
aiming to increase the academic achievements of their students. If the amount of peers following
the same academic program as one student’s leads to them having a higher GPA than not having
any then it would be in the university’s interest to aim to group them together. This could be
done by assigning roommates freshman and sophomore year, allowing for reassignment if a
change of major occurs.

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Literature Review:
In a study done at Dartmouth, research found that peers tend to have an effect on GPA
throughout freshman year (Sacerdote, 2001). The study specified that the randomly assigned
roommates would affect each other’s GPA’s negatively at a decreasing rate as the years passed.
Their model puts GPA of student “j” as the dependent variable, controlled by three known
variables. The three known variables include the measure of high school academic ability of their
roommate, student “i”, high school academic ability of themselves, and GPA of student “i”. The
model allows for a general error term as well as an error term for each of the two academic
ability measurements. From there an OLS regression was ran finding that there is indeed a
freshman year effect on GPA.
An article by Li Han and Tao Li, published in the Economics Of Education Review, took
a similar approach to Sacerdote’s. They instead chose to investigate gender differences when it
came to peer influences. When they separated the results by male and female, they got slightly
different results than Sacerdote’s. Because their study was done in China, they used a variable
called “CET” which is comparable to the SAT in the United States. They found that when
concerning the GPA of women, their own CET scores had no significant effect. However, when
it came to women whose CET scores were in the bottom 50%, they found that peers with higher
CET scores had a positive effect on their GPA. The students with CET scores in the top 50%
suffered no negative influence on GPA. Because of this, Han and Li go as far to say that it would
be in a college’s best interest to mix female students who have proven to have different academic
abilities (Han and Li, 2007).
A separate study published in the Journal of Economic Perspectives concludes that living
with a binge drinker will have a negative effect on GPA (Kremer & Levy, 2008). The controls in

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this study were not strictly numerical, however. Kremer and Levy’s model was based around the
dependent variable, college GPA. They controlled for high school GPA, admissions test scores,
and family background. Their dummy variables included answers to the questions of whether
they drank before college. Ultimately, they argued that the college roommates’ inherent need for
bonding led to a decrease in GPA through increased binge drinking.
Lastly, an article published in the Journal of Public Economics found that social peers
have no greater influence on academic performance than a random peer (Foster, 2006). In his
study, Foster found that the coefficients of friend effects (.19 and -.06 for men and women
respectively) and random peers (.12 and -.05) varied so insignificantly that he could conclude
that there is no merit in attributing GPA to choice of friends.
Unlike Sacerdote, my research aims to find whether roommates with similar interests will
have an impressionable effect on GPA, as opposed to a random assignment. Therefore, my study
will not be time-lapsed. Because Temple does not currently assign roommates according to
academic interests, I cannot do a four year collection of data.
Although I am encouraged to see a study like Han and Li’s that shows there is a peer
influence on GPA, I will not be separating men from women in my study.
Unlike Kremer and Levy, I will not be controlling for a close peer’s need for bonding. I
will be analyzing whether being surrounded by those following similar academic programs will
lead to better or worse academic decisions.
The biggest difference between my study and Foster’s is that I group “peers” and
“friends” into the same category. Instead of separating social friends from academic peers, I

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chose to only include the peers in the same major as someone, who they also spend 10 or more
hours a week with.
Hypothesis and Model:
My hypothesis states that the amount of close peers (spending 10 or more hours a week
with them) a student has that are in the same academic program (major) as them will affect their
GPA. My reasoning for this is based on how a peer group operates. There exists an incentive to a
certain degree to be in the loop within one’s group of peers. For example, missing a lecture that
multiple peers have attended comes with an opportunity cost. The cost of missing the lecture can
lead to confusion in group discussions, a lower grade than one’s peers, etc. My argument is that
the incentive to be involved in group discussion and homework groups, and maintaining similar
GPAs to peers will ultimately lead to more lectures attended and a higher level of care of the
material being studied.
Students also have the ability to measure benefits within their peer groups. The benefit of
having friends with the same assignments and exam schedule could be insurmountable. Having
multiple sources of knowledge, excluding a professor or tutor, can be seen as a benefit without
cost. I exclude professors and tutors because reaching them for questions can come at a cost to a
student. For example, going to a teacher’s office hour may cut into time used for a higher-utility
gaining activity. A student may choose to make their roommates or friends a group of people
they can ask academic questions to in order to maximize utility.

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My model will include the independent variables of number of years in major, number of
close peers within the same major, SAT score, overall GPA, and dummy variable of gender. My
dependent variable will be GPA within one’s major. My model will look like:
Y = β0 + β1X1 + β2X2 + β3X3 +β4X4 + B5X5 + µ
where Y is major GPA, β0 is a constant, β1, β2, β3, and β4 are arbitrary coefficients of years in
major (X1), number of close peers in same major, (X2), SAT out of 1600 (X3), and overall GPA
(X5), respectively. The β5 term is a coefficient, 0 for female and 1 for male, for X5, gender. The
last term, µ, represents the overall error term. I plan to run a regression using excel to find if any
of these variables are statistically significant in determining GPA.
Data:
My database consists of 71 observations. There are eight Economics majors, 17 Finance, 7
math, 6 Math Econ, 16 risk, and 1 of each of the following: Architecture, Facilities Management,
Entrepreneurship, Marketing, Accounting, Risk Management, International Business, Criminal
Justice, Education, Art History, Women Studies, Graphic/Interactive Design, Kinesiology, and
Undeclared. The data was constructed from a questionnaire consisting of the following seven
questions:
1) What is your major?
2) How many years have you been in your major?
3) How many close peers do you have within you major? (This is someone you would spend
10 or more hours a week with)
4) What was your SAT score out of 1600?
5) Do you define yourself as male or female?
6) What is your GPA within your major?
7) What is your overall GPA?

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The survey received 42 online responses. I had to eliminate 3 of those responses due to
missing answers. The online program I used for my questionnaire was SurveyMonkey. I chose
SurveyMonkey because they allow you to chose the type of question you are answering. For
example, question 2 was multiple choice but I was able to have a “if other, please explain”
comment box. I had one response that chose “if other” and in the comment box they explained
they’d only been in their current major for 1 semester (half of a year). For example, two of the
three surveys had no answer for number 6. This left me with a total of 39 online response. The
remaining 32 were collected in person from a hard-copy of the same survey. The students were
found in the following classes at Temple: Mathematical Economics, Global Development,
International Monetary Economics, Numerical Analysis, and Energy, Ecology, and the
Economy. I handed out hard copies prior to my classes beginning. For example, I collected 8
responses from my Global Development class and 17 from International Monetary Economics.
The other 7 were collected from the other classes I’ve mentioned.
Based on this data, my model looks like this:
GPA = β0 + β1*(years in major) + β2*(number of close peers in major) + β3*(SAT score out of
1600) + β4*(overall GPA) + β5*(0 for male, 1 for female) + µ.
In order to conceptualize the data I’ve received so far, I’ve arranged this summary of
statistics for all numerical observations:
Table 1 Mean Min Max
# Years in Major 2.56 0.5 5.00
# Close Friends 4.44 0.00 15.00
SAT 1200.56 1000.00 1420.00
Major GPA 3.25 1.80 4.00

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Overall GPA 3.22 2.00 4.00
The number of observations is currently at 71. Being that the average SAT score is about
1200, it is safe to assume that the surveyed students are beginning with an already high academic
ability. It is also interesting that the average overall GPA is a few hundredths of a point lower
than Major GPA. Although there is not much difference between the two, I would’ve expected
the average overall GPA to be higher. Overall GPA can include non-major courses such as
General Education requirements. These classes are often less academically straining.
Empirical Results and Discussion
Upon running my regression I found these results:
Table 2 Coefficients Standard Error t Stat P-value
Intercept 0.54 0.56 0.97 0.33
# Years in Major -0.04 0.04 -1.15 0.25
# Close Friends 0.01 0.01 1.31 0.20
SAT (1600) 0.00 0.00 0.42 0.68
Overall GPA 0.78 0.10 7.91 0.00
Male (0) or Female (1) 0.1 0.09 1.15 0.26
My R-squared value was 0.49. Firstly, I found that the number of years within one’s
major has a negative effect on GPA (-0.04). This is a fairly reasonable concept. As someone
progresses through their major, it can be assumed that the classes they take become more and

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more challenging. For example, someone in an Economics major may begin by taking
Introduction to Microeconomics. As their academic program progresses, classes such as
econometrics may be introduced, which is much more demanding.
The coefficient for number of close peers is relatively small, at 0.01. The p-value for this
variable, 0.20, is not considered statistically significant. Based on this number alone, I cannot
reject the null hypothesis. This simply means I cannot reject the accusation that friends have no
effect on GPA. In fact, the only statistically significant p-value would be for overall GPA. The p-
value of 0.00 shows that there is 100% significance in the null hypothesis. The null hypothesis in
this case would be that overall GPA has no effect on Major GPA. This will be commented on
further in my conclusion.
As seen above in Table 2, there is a coefficient of 0.00 for the effect of SAT score on
major GPA. Coupled with the p-value of 0.68, this shows that there is a high chance that a
student’s SAT score will have no effect on their college GPA.
Overall, the results did not allow me to reject the null hypothesis that close peers within a
student’s major will not affect their GPA. From the information, it is more likely that the amount
of time spent in a major and a student’s overall GPA will be the most determining factor (of the
factors I chose to evaluate) when it comes to GPA. This is an interesting finding, given how it
can help universities.
Conclusion
Like I mentioned before, I cannot reject the null hypothesis in this study. However, there
is still much to be learned. For example, my findings somewhat correlate with Han and Li’s.

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Their study recognized the positive effects of mixing students with different abilities. Though I
aimed to prove differently, my results indicate that this could be true. With this information,
universities could base their roommate assignments on this. There is also something to be said
regarding the lack of effect SAT scores have on GPA. The SAT is a rule of thumb college’s base
some, if not most, of their acceptance decisions on. Assuming universities aim to have excelling
students with high GPA, this indicates their selection process may be misleading.
All of these conclusions are drawn based on the fact that this study is 100 per-cent
accurate. However, that is not a safe assumption. There were a couple of hardships in conducting
this research that should be mentioned. Firstly, this is a relatively small sample size. A sample
size of 71 compared to large scale university enrollment is miniscule. An ideal sample would’ve
been about 500 students with diversity amongst majors. I would’ve liked to find out if the GPAs
varied across majors.
Another possible discrepancy was the surveying process. Online surveys were difficult
because there were no forms of communication. If someone had trouble regarding a question,
there was no way to ask me about it. Communication was not a problem regarding hard-copy
handouts. However, they still were not easy. As a graduating senior, I am in my smallest class
sizes in five years. My last writing intensive class, Econ 3596 has only 6 students, myself
included. When I handed out the survey, my classmates were apprehensive when returning them.
One of my peers even said “but how is this 100% anonymous if you’re only collecting five
papers? You know our majors!” This was ultimately true. I explained they’d be added to a pile of
other paper responses but I could still feel the hesitation.
Overall, this research study was an interesting experience. The process was long and
meticulous but ultimately extremely beneficial to my Economics IQ. If I had more time, I would

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have surveyed a lot more students and done all of the surveys in a lecture setting, as opposed to
small classes. I also would have brought my questionnaire to Dr. Stull ahead of time. I originally
sent out a questionnaire which did not ask for a control for academic ability (SAT score). This
left me with only 2 weeks to gain as many responses as possible before presenting to my peers.
Time limits aside, I appreciated the findings of the study. This project, despite being unable to
reject the null hypothesis, was both useful and eye-opening.

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References:
Sacerdote, Bruce. 2001. “Peer effects with random assignment: results for Dartmouth
roommates.” Quarterly Journal of Economics
Kremer, M. & Levy, D. 2008. “Peer effects and Alcohol Use among College Students.” The
Journal of Economic Perspectives Vol 22, No. 3 pp 189-206.
Foster, Gigi. 2006. “It’s not your peers, and it’s not your friends: Some progress toward
understanding the educational peer effect mechanism” Journal of Public Economics Vol 90, No.
8-9 pp 1455-1475
Han, Li & Li, Tao. 2009. “The gender difference of peer influence in higher education”
Economics of Education Review Vol 28 pp 129-134