Download Factors Affecting GPA

Factors Affecting GPA
BUS 302
Professor Fang
Camille Legaspi
Raphielle Perez
Allison Remigio
July Sengsourya
1
INTRODUCTION
Research Question
Our research question is: What factors affect GPA? One of our friends was the
inspiration for choosing the related question of what factors affect GPA. We wanted to know
what exactly affected GPA. We went about forming research questions that pertained to that. We
wanted to see what really affects a person’s grades in school.
This question has value because it encompasses all the different factors that would affect
someone going to school. It helps students prioritize their own schedule to know their limits in
school and work. It can help new students by showing them that it is important to balance out
your school schedule with work, family and other important things.
Research Plan
In our research plan, we plan to survey 100 college students at Cal State San Marcos. We want
our surveys to be short and concise and include questions about factors that affect a student’s
GPA. As a group, we will brainstorm factors that are important such as: work hours, units taken,
study hours, living arrangements, motivation of getting a higher GPA, plans after graduation, and
the importance of getting a 3.0 or greater.
The theory we are going to use would be the hypothesis theory. The hypothesis theory
provides us with a structural analytical method for making decisions of this type. To formulate
the hypothesis we used the null and alternative hypotheses. The null hypothesis is a statement
about the sample values that we tested. The null hypothesis will be rejected only if the data
provided has contradictory evidence. The alternative hypothesis includes all the values not
covered by the null hypothesis. The alternative hypothesis is to be true if the null hypothesis is
rejected.
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Predictions
Before conducting the survey we believe that there will be strong and positive
correlations between working hours and the decrease in GPA, study hours and the increase in
GPA, unemployed students will have a higher GPA verses students who are employed. The
factor with the weakest correlation we predict will be commute time and it’s affect on GPA. The
more hours students spend working takes away from time that can be put towards their studies.
This is why we believe working hours and employed students will have a decrease affect on
GPA. Commute time affects each and every student, but the amount of effect we believe is
minimal.
METHODOLOGY
Research Procedure
Once we agreed on a research question, we decided that the best way to answer our
question was to conduct a survey. We felt that a survey would be the simplest and easiest
research method. Due to time constraints, we agreed to personally hand out surveys instead of
sending them to people through the Internet or mail. By handing out the surveys, we could get
responses immediately because we would not have to wait until respondents mailed the surveys
back to us or respond electronically. Also, we were guaranteed that for every survey we handed
out, we would get a completed survey back since surveys sent through mail or email have a
higher chance of getting ignored. It was also convenient to hand out the surveys personally
because we could just give them to classmates and acquaintances. Therefore, our sampling
technique was based on convenience sampling.
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After we made the decision to conduct a survey, we began to brainstorm the factors that
would most likely affect GPA. With these factors in mind, each of us separately came up with
questions that relate to factors that affect GPA. We then combined our questions and removed
duplicate and irrelevant questions because we wanted to keep the ones that were significant.
After submitting the rough draft of our survey to the teacher, we further revised our questions so
that they would be more focused on the research question. During this process, we reworded the
questions so that respondents would know exactly how to respond. After revising the survey, we
were left with the 16 questions that we felt were the most relevant. It was really important that
our questionnaire be short (15-20 questions) and fit one page because we did not want to
overwhelm and discourage our respondents.
After we created our survey, we began discussion on how we were going to execute our
research. We decided to have a sample size of 100 respondents because we felt that it would be
feasible, considering our time constraints. To further expedite the surveying process, each
member of the group was to hand 25 surveys. This way, each person could hand the surveys out
to their respective classmates and acquaintances. After each person had their surveys filled out,
he/she inputted the results into Microsoft Excel. It was easier to input the results of 25 surveys
individually than inputting 100 surveys at once. We then combined our results to begin
analyzing.
Outcome
There were many advantages and disadvantages of our research methodology. It was
advantageous because it was efficient and quick. It was convenient because we handed out the
surveys to whomever we knew and was near. The convenience factor and the fact that it took
less than one minute to fill out the survey enabled us to get results immediately. However,
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because the respondents were put on the spot, many answered our questions with estimations.
For instance, some did not know their GPA at the top of their head. Had they been on a
computer, they could have looked up more accurate information regarding their GPA on
SMART Web. Some answered the question hastily, possibly because of bad timing or because
they wanted to rush through the survey process. Finally, the fact that we kept the survey short
helped because everyone was willing to participate and, with exception of seven people,
completed the entire survey.
We could have reworded our questions to be clearer because some answered our
questions incorrectly. For instance, with our question, “What motivates you most in
getting/maintaining a high GPA (3.0 or greater)?” we wanted them to answer by choosing one of
the responses. However, some disregarded the word “most” and chose more than one answer.
This problem could have been avoided had we been clearer, by specifying that they choose one.
Also, since we passed out the surveys to classmates and acquaintances, the respondents
were characteristically too similar; our results were not as spread out. Therefore, our sample is
definitely not representative of the population in CSUSM.
The survey that was handed out to CSUSM students is shown on the next page.
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Age __________
Do you have children?
Gender __________
YES
Marital status __________
NO
What is your commute time to school? _____ minutes
Do you participate in extracurricular (school-related) activities?
YES
NO
If yes, how many hours do you spend on those activities a week? _____
Do you have a job?
YES
NO
If yes, how many hours do you work a week? _____
What is your cumulative GPA? _____
How many units are you taking this semester? _____
How many hours do you spend studying a week?____
Who do you live with?
a) Parents
b) Significant other and/or children
c) Roommates
d) Alone
What motivates you the most in getting/maintaining a high GPA (3.0 or greater)?
a) Acceptance to graduate school
b) Attention of future employer(s)
c) Outside motivation (i.e. pressure from parents, friends, etc.)
d) Competition with fellow students
e) Self-recognition
f) Other ____________________________________________________
What are your plans after graduating from CSUSM?
a) Enroll in graduate school
b) Start career
c) Focus on family
How important is it for you to get/maintain a high GPA (3.0 or greater)?
Not Important
Very Important
1
2
3
4
5
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RESULTS
The following are the results of our survey. The questions of the survey can be broken up
into three categories: demographics, school, and outside school. We felt that all the questions
pertaining to these categories are relevant to our research. We included demographic questions
because we wanted to know who our respondents were and to determine their validity. The
school and outside school related questions were intended to help us find a commonality between
students who have GPA’s of 3.0 or higher versus those who have GPA’s lower than 3.0.
Demographics
The mean, median, and mode age of our respondents is 23. Although the mean, median,
and mode of age are equal, the frequency distribution is right-skewed. Out of pure luck, the
amount of females and males that we surveyed were almost equal with 50 female respondents
and 43 male respondents. As expected—those surveyed were college students—the majority of
students was not married and did not have children. Only 13% were married and 11% had
children. According to the right-skewed frequency distribution of commute time, most students’
drive to school takes 10 to 20 minutes. Lastly, almost half of the students we survey live with
their parents. We asked questions regarding marital status, children, commute time, and living
situation because we wanted to know if there was a correlation between these factors and GPA.
We believe that differing priorities, distractions, responsibilities, and time constraints would
affect GPA.
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Respondent's Age
40
35
30
25
20
15
10
5
0
18 < 21 21 < 24 24 < 27 27 < 30 30 < 33 33 < 36 36 < 39 39 < 42
Age Range
Gender
52
50
48
46
44
42
40
38
Female
Male
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Marital Status
90
80
70
60
50
40
30
20
10
0
Married
Single
Children
90
80
70
60
50
40
30
20
10
0
Yes
No
9
Commute Time to School
35
30
25
20
15
10
5
0
0 < 10
10 < 20
20 < 30
30 < 40
40 < 50
50 < 60
Minutes
Living Situation
Significant
Other
25%
Alone
3%
Parent
s
Roommates
23%
10
60 < 70
70 < 80
School
The mean GPA of students is 2.99, which is close to the mode of 3.00 and the frequency
distribution is almost symmetrical. Most students are full-time students, with the majority taking
13-16 units. Among those we surveyed, most spend 6-11 hours studying for school a week.
Many believe that getting a GPA of 3.0 or higher is very important and most are motivated to
maintain a high GPA because they want to get noticed by future employers. Finally, 82% plan to
start their career after graduating from CSUSM.
These questions were asked because we wanted to know whether students’ GPA is
affected by the amount of units they are taking, the amount of time they spend studying, their
motivation to have a high GPA, and their future plans. We figured that students with high
GPA’s are those that are taking the right amount of units (12-14 units), spend a lot of time
studying, and are highly motivated. Students with low GPA’s are the ones taking too many
units, spend little time studying, and have low motivation.
Respondent's Cumulative GPA
35
30
25
20
15
10
5
0
2.00 <
2.25
2.25 <
2.50
2.50 <
2.75
2.75 <
3.00
3.00 <
3.25
Cumulative GPA
11
3.25 <
3.50
3.50 <
3.75
3.75 <
4.00
Units Students are Taking
40
35
30
25
20
15
10
5
0
4<7
7 < 10
10 < 13
13 < 16
16 < 19
Units
Hours Spent Studying per Week
35
30
25
20
15
10
5
0
1<6
6 < 11
11 < 16
16 < 21
21 < 26
Hours
12
26 < 31
31 < 36
36 < 41
Motivation
Other
2%
Gradute School
14%
Self
30%
Competition
0%
Future
Employer
s
Outside
13%
Future Plans
Focus on
Family
5%
Start Career
82%
13
Enroll in
Graduate
School
13%
Importance of GPA > 3.0
35
30
25
20
15
10
5
0
Not Very
Important
Not Imporant
Neutral
Imporant
Level of Importance
14
Very
Important
Outside School
We believe that the busier students are, the greater the chance that their GPA’s were
going to be lower than those that mostly focuses on school. So we felt it was important to ask
questions regarding students’ lives outside of school.
The majority of students do not participate in extracurricular activities. But for those
students that do participate in school-related (CSUSM clubs, fraternities, sports, etc.)
extracurricular activities, most spent 1-9 hours on those activities while one person spent 40
hours on these activities. In addition, most students are currently unemployed. However, for
those that do work, the majority works part-time (30 hours or less).
Participation in Extracurricular Activities
80
70
60
50
40
30
20
10
0
Yes
No
15
Hours Spent on Extracurricular Activities per Week
16
14
12
10
8
6
4
2
0
1<9
9 < 17
17 < 25
25 < 33
Hours
Employment
90
80
70
60
50
40
30
20
10
0
Yes
No
16
33 < 41
Hours Spent Working per Week
25
20
15
10
5
0
4 < 11
11 < 18
18 < 25
25 < 32
Hours
17
32 < 39
39 < 46
46 < 53
ANALYSIS
GPA
AGE
COMM TIME
WORK
HRS
EA HRS
GPA
AGE
COMM
TIME
EA HRS
WORK
HRS
1
0.04967062
1
-0.0382282
-0.0523229
-0.0378705
-0.123538
1
0.04036569
1
-0.1320484
0.12682592
-0.0267086
-0.0258425
UNITS
0.18181975
-0.2420766
0.05111876
0.18664791
STUDY
0.02034314
0.26456598
0.01320398
0.07834934
1
0.2636274
0.1096085
UNITS
STUDY
1
0.37468962
1
Lower 95.0%
Upper 95.0%
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.2621
R Square
0.0687
Adjusted R Square
0.0037
Standard Error
0.4177
Observations
93
ANOVA
df
Regression
SS
MS
F
1.0577
6
1.1071
0.1845
Residual
86
15.0038
0.1745
Total
92
16.1110
Coefficients
Standard Error
Intercept
2.3277
Age
0.4280
5.4385
0.0000
1.4769
3.1786
1.4769
3.1786
0.0160
0.0132
1.2109
0.4197
0.6853
0.9251
0.2293
-0.0102
0.0422
-0.0102
0.0422
0.6758
-0.0075
0.0049
-0.0075
0.0049
0.4950
-0.0257
0.0125
-0.0257
0.0125
0.3575
-0.0103
0.0038
-0.0103
0.0038
0.0494
0.0001
0.0739
0.0001
0.0739
0.3488
-0.0195
0.0069
-0.0195
0.0069
0.0031
-0.0066
0.0096
Work Hours
-0.0033
0.0035
0.0370
0.0186
-0.0063
0.0066
1.9930
0.9421
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Lower 95%
Upper
95%
P-value
-0.0013
Study Time
0.3944
t Stat
Commute Time
Extracurricular
Activities
Semester Units
Significance
F
According to the multiple regression analysis we ran through Microsoft Excel, our model
is not significant because the Significance F of 0.3944. Even though we knew that our model is
not significant we decided to examine each of the factors separately to see if they correlate.
We first analyzed whether there was a correlation between the number of units a person
is taking and the student’s cumulative GPA. Our hypothesis was that the more units students are
taking, the lower their GPA is going to be. We made this conclusion because overloading on
units can lead students to work less effectively because they have to worry about more classes.
Although the relationship is not significant, there apparently is a negative correlation between the
two factors. Therefore, our claim is somewhat.
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To further support our hypothesis we ran a t-test.
H 0: r = 0
H A: r ≠ 0
t Test for Differences in Two Means
Data
Hypothesized Difference
Level of Significance
Population 1 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Population 2 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Intermediate Calculations
Population 1 Sample Degrees of
Freedom
Population 2 Sample Degrees of
Freedom
Total Degrees of Freedom
Pooled Variance
Difference in Sample Means
t Test Statistic
0
0.05
93
2.99
0.418
93
12.67
2.814
92
92
184
4.04666
-9.68
32.813569
Two-Tail Test
Lower Critical Value
Upper Critical Value
p-Value
Reject the null hypothesis
1.9729405
1.9729405
8.106E-79
This t-test indicates that, however insignificant the correlation, there still is one.
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We then did a two tail test on one sample with the following results. This t test was for
hours worked. We hypothesized that the more hours worked means the less time for students to
study and it shows in the correlation. It shows that we should reject the null hypothesis, the
upper and lower critical values. There was no correlation, and therefore we should reject the null
hypothesis.
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t Test for Differences in Two Means
Data
Hypothesized Difference
Level of Significance
Population 1 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Population 2 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Intermediate Calculations
Population 1 Sample Degrees of
Freedom
Population 2 Sample Degrees of
Freedom
Total Degrees of Freedom
Pooled Variance
Difference in Sample Means
t Test Statistic
0
0.05
93
18.92
12.79
93
2.99
0.418
92
92
184
81.879412
15.93
12.004799
Two-Tail Test
Lower Critical Value
Upper Critical Value
p-Value
Reject the null hypothesis
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1.9729405
1.9729405
6.815E-25
The null and alternative hypotheses are:
H0 : r = 0
HA :r ≠ 0
Correlation = -0.0382282
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t Test for Hypothesis of the
t Test for Hypothesis of the Mean
Mean
Data
Data
Null Hypothesis
µ=
Null Hypothesis
0
Level of Significance
0.05
Sample Size
Sample Mean
µ=
0
Level of Significance
94
0.05
Sample Size
23.215054
94
Sample Mean
2.99
Sample Standard
Deviation
14.0103538
Sample Standard Deviation
Intermediate Calculations
Standard Error of the Mean
Degrees of Freedom
t Test Statistic
0.418
Intermediate Calculations
1.445057658
Standard Error of the Mean
93
0.043113408
Degrees of Freedom
16.06514029
t Test Statistic
Two-Tail Test
93
69.35197499
Two-Tail Test
-
-
Lower Critical Value
1.985801768
Lower Critical Value
1.985801768
Upper Critical Value
1.985801768
Upper Critical Value
1.985801768
p-Value
1.42657E-28
p-Value
Reject the null hypothesis
7.0767E-82
Reject the null hypothesis
In this analysis between commute time vs. GPA, we conducted a two tailed test. In this
two tailed test, we looked at the rejection region where it is split into the two tails of the
sampling distribution. The alpha level is spilt evenly among the two tails. We chose our level of
significance to be ∝= .05 . In our case since the null hypothesis will be rejected, that means that
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the sample mean is extremely larger or extremely smaller. From our data, the p-value of in GPA
is significantly smaller than commute time. This tells us that the smaller the p-value than the
probability is in the rejection region, then the null hypothesis is rejected. In both of these cases,
however are both rejected because they fall outside of the region of given hypothesis.
We believe there will be a strong correlation between the hours that a student spends
studying and their GPA. Below is the graph of sample data we collected.
To test out our sample of interest we formed the hypothesis :
Ho : r < 0
H A:r > 0
α=0.05
With a sample size of 93 our degree of freedom is 91. Below is a t-test that was performed for
these to sample means. By looking at the result of the PHStat we can see that the critical value
1.65; therefore our decision rule was:
If t > 1.65 reject the null hypothesis, otherwise do not reject.
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t Test for Differences in Two Means
Data
Hypothesized Difference
Level of Significance
Population 1 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Population 2 Sample
Sample Size
Sample Mean
Sample Standard Deviation
Intermediate Calculations
Population 1 Sample Degrees of
Freedom
Population 2 Sample Degrees of
Freedom
Total Degrees of Freedom
Pooled Variance
Difference in Sample Means
t Test Statistic
Upper-Tail Test
Upper Critical Value
p-Value
Reject the null hypothesis
0
0.05
93
12.65
7.71
93
2.99
0.418
92
92
184
29.809412
9.66
12.064988
1.6531771
2.266E-25
t= 12.065, thus 12.065 > 1.65, therefore we reject the null hypothesis. Because the null
hypothesis was rejected the sample showed there was a correlation between study hours and
GPA.
The following are the results to our qualitative data. Because we were working with nonquantitative data, we decided to analyze these factors differently. We decided to find the mean
GPA of each category to determine any trends.
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In this study, we predicted that living with parents would increase a student’s GPA. We
predicted this beacause living with parents, the student would not have to work as much to
generate enough income for rent. The student will likely spend more time on studying and or
extracircular activities rather than spending more time working. However, in our survey that we
conducted, we found out that our prediction was false. Living alone would result in a GPA of a
3.08. Followed by living with parents with a GPA of 3.01 , roomates with a GPA of 2.98 , and
living with a significant other with a GPA of 2.95. Living alone would increase a student’s GPA
due to the limited distrations avaliable in the environment.
We would like to believe that if there is motivation behind one’s action or goal, the better
the chances they are to obtain and achieve their goal. This is the reason why we choose this
question. In this case, the motivation for the student would be their future plans after graduating.
Having a plan will help one stay motivated and helps provide needed momentum throughout the
course of study. The distribution of average GPA for each individual future plan is shown
below.
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As you can see by the graph students whose future plan is to focus on their family had the
highest GPA average, followed by future plans of starting a career and then enrolling in Graduate
School. This was not the result we as a group thought we would see. Our assumption was that
the students who’s main goal was to focus on family would have had the lowest average GPA
and the students who wanted to start a career or enroll in Graduate School would be neck and
neck for the highest average GPA. We thought the future plan of focusing on family would have
the lowest average GPA because after graduation these individuals are not needing to compete to
get accepted elsewhere or nor will the be competing with future students for career posistions. A
reason for this outcome could be that people who are enrolling in Graduate School are taking on
more units and stress therefore taking a toll on their GPA or maybe it is something as simple as
the distribution on sample.
Next we examined a person’s motivation to get a high GPA and how it affects there
overall GPA.
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Our hypothesis was that the more motivation a student had he/she would have a higher
GPA then students that did not have any or little motivation. Above is the bar graph showing the
outcomes from graphing motivation vs. GPA. With motivation consisting of 5 different options,
most students with any form of motivation had a higher GPA then other students with less
motivation. Therefore we can conclude from the above results that any form of outside
motivation can help increase Student’s overall grades.
We then looked at how the level of importance affects GPA.
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We wanted to determine whether a person's GPA is dependent on how important
getting/maintaining high GPA is to them. Our conclusion is that people who view having a high
GPA will most likely have higher GPA's because they are more likely put more effort into
studying hard to get better grades. On the other hand, people that do not consider a high GPA to
be important will most likely take the attitude of just wanting to get by and pass their classes.
When students have this attitude, they will mostly likely have lower GPA’s. Surprisingly, the
highest mean GPA belonged to the group of people that do not consider a high GPA of being
important. However, as the level of importance increased, the more the mean GPA increased.
This further supports our theory that the more important it is for someone to have a high GPA,
the more likely their GPA will be high.
CONCLUSION
In this research, we surveyed 100 college students at Cal State San Marcos. We
surrounded our questions on motivational factors on getting a high GPA. We considered
important factors such as the amount of works hours in a given week, units taken during the
semester, study hours on an average week, living arrangements, motivation of getting a higher
GPA, and the importance of achieving a 3.0 or greater to the student. From our data, we
determined that the most of our factors had no significant correlation except for study hours vs.
GPA. This comparison looked to have a stronger correlation because the more hours spent
studying the higher the student’s GPA will be. The weakest correlation that we determined
would be commute time because it did not have any significance due to the miles driven verses
the student’s GPA. From our study, we can conclude that many factors can affect a student’s
GPA. The amount of study hours is most significant because the amount of hours a student
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studies, determines the grade received in each class and then the outcome at the end of semester
is to receive a high GPA.
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