An Analysis On The Impact Of Social Media On College Students Academic Performance

An Analysis on the impact of Social Media on College Students’ Academic Performance
An Empirical Paper
Presented to
Dr. Cesar Rufino
School of Economics
De La Salle University-Manila
In partial fulfillment of
The requirements for
Introduction of Econometrics
1st
Trimester, AY 2013-2014
Submitted by:
Ilagan, Ferdinand Tan
V24
September 6, 2013

2
An analysis on the impact of Social Media on
College Students’ Academic Performance | ILAGAN
TABLE OF CONTENTS
I. INTRODUCTION ..............................................................................................................4
A. BACKGROUND OF THE STUDY ......................................................................................... 4
B. STATEMENT OF THE PROBLEM........................................................................................ 6
C. OBJECTIVES OF THE STUDY ............................................................................................. 7
D. SIGNIFICANCE OF THE STUDY.......................................................................................... 7
E. SCOPE AND LIMITATION ................................................................................................. 8
II. THEORITICAL FRAMEWORK AND REVIEW OF RELATED LITERATURE...............................9
III. OPERATIONAL FRAMEWORK ......................................................................................12
A. HYPOTHESIZED MODEL OF SPECIFICATION USED IN THE STUDY ................................... 12
B. DESCRIPTION OF THE VARIABLES USED ......................................................................... 12
C. A‐PRIORI EXPECTATIONS ............................................................................................... 15
D. INTRODUCTION OF HYPOTHESIZED ECONOMETRIC MODEL ........................................ 17
IV. METHODOLOGY..........................................................................................................18
A. PRESENTATION OF DATA............................................................................................... 18
B. DATA GATHERING ......................................................................................................... 19
V. EMPIRICAL RESULTS AND INTERPRETATION.................................................................21
A. SUMMARY OF DATA...................................................................................................... 21
B. INITITAL REGRESSION.................................................................................................... 22
C. TEST FOR THE SIGNIFICANCE OF THE MODEL ............................................................... 25
D. TEST FOR VIOLATIONS................................................................................................... 26
E. CONCLUSION FOR THE TEST OF VIOLATIONS AND ADJUSTMENTS .............................. 31
VI. CONCLUSION AND RECOMMENDATIONS....................................................................35
VII. BIOLIOGRAPHY OR REFERENCES ................................................................................36

3
Abstract
The analysis behind the paper explores the impact of social media on college
students’ academic performance namely spent browsing social media in a day, using social
media in relation to academics, gender, spent time browsing social media in a week,
students’ CGPA. A survey with 30 teenage respondents, ages range from 16 to 22. The
survey was conducted to see which factors affect most in a students’ academic
performance. In this study, Dummy Variable was used on the gender of the students. The
Ordinary Least Squares or the OLS method of regression method was used in this study in
determining the results of the study. The model in this study will be regressed using
statistical programs that were discussed in class such as Stata12 and Gretl. After the
regression process, it showed that the significant variable to student’s CGPA is using social
media in‐relation to academics. The other independent variables seem to be insignificant in
the study.

4
I. Introduction
A. Background of the Study
Social media had been a massive part in every household. Technology had given us a
great deal and helped us with our tasks and made everything easy. We know that social
media sites like (Google+, twitter, Facebook and Multiply) had introduced us in many
methods that can connect people easier. Social media sites had been a popular means to
connect with other people over the Internet.
During the 1980’s home computers were becoming more common in every
households and social media sites were becoming more refined. The Internet relay chats, or
IRCs, were first used in 1988 and were popularized into the ‘90s. The first popular social
media site, Six Degrees, was created in 1997. The site enabled its users to upload a profile
and make friends with the other users (Hendricks, 2013). By 2006, Facebook and Twitter
both became available to users throughout the world. These sites still remain as the most
popular social network on the Internet. After the success of these social media sites, other
sites like Tumblr, Spotify, Foursquare and Pinterest began appearing to fill specific social
network niches. According to Drew Hendricks (2013), a tech, social and environmental
addict, “there are a tremendous variety of social networking sites, and many of them can be
linked to allow cross posting. This creates an environment where users can reach the
maximum number of people without sacrificing the intimacy of person‐to‐person
communication. We can only speculate about what the future of social networking may look

5
in the next decade or even 100 years from now, but it seems clear that it will exist in some
form for as long as humans are alive.”
Given the history of social media, there are still many social media sites appearing
on the Internet. The most famous social media site now is Facebook. Facebook, which was
invented by Mark Zuckerberg launched a website called “Thefacebook” on February 2004.
The website was named from a printed manual that was handed out every year in Harvard
University containing the pictures of all students and staff of the university, so they can get
to know each other. The site was still in simple form and its purpose was to be an exclusive
tool for the Harvard students to talk to one another over the Internet. After a month, the
website extended to the other three Ivy League schools across the country. It later spread to
all the Ivy League and Boston schools, and then most universities across the United Sates. In
2005, all high schools could join Facebook, and in the later part of 2006 it was opened to
everybody with e‐mail address and background information. The company then turned the
website’s name to Facebook in 2005 after they bought the domain name facebook.com and
paid $200,000.
After the success in launching the website, Facebook had been able to expand as a
company by opening its share of the company to the market, allowing investors to invest in
the company and by venture capital. Because of that, Facebook had gotten many offers to
be bought out from top companies such as Google and Yahoo and other top companies in
the market (Singer, 2010).
Social media had become a big part to every people. Nowadays, almost all people
have social media accounts. Social media sites had been used in almost all areas from

6
business markets to academics. Students had used social media sites as tools to help them
upload lecture notes to groups and relay messages from teachers to their classmates. Social
media sites especially, Facebook, let people interact each other. Since it helped us with
almost everything from messaging now to video calls, social media site isn’t as well known
as they were in the ‘90s. With these social media sites around us, it only leaves us to
question whether social media sites affect the students’ grade. Are social media sites an
effective tool to boost a student’s academics in class? These are just some question this
study is providing us.
B. Statement of the Problem
In 1997 when social media was first created, it was not as popular as today. In the
past few year social media sites had been modified, upgraded and had become user
friendly. This research seeks to answer whether there is a relationship between the
students’ CGPA to ages 16 to 22 and the other variables in this study such as spent time
browsing social media in a week, spent browsing social media in a day, using social media in
relation to academics and spent time studying in a week.

7
C. Significance of the Study
Given the background of the study, the creation of social media had changed the
lives of many people mainly students and there are many arguments on how social media
have a negative effect to students and leads to lower their academic performance. In the
academic world, everyone viewed social media as a distraction and how influences badly
influences students like us. Some studies also said how social media sites lead the student to
procrastination in their academics. This study will provide information on the impact of
social media to college students and how social media can be used in‐relation with their
academics.
The study will also entice readers with simple understanding on the impact of social
media to college students’ academic performance. Although the model and how the factors
affecting the study may prove to be simple, the method and the model will be the results
and the basis of simplification on the analysis on the impact of social media to college
students’ academic performance.
D. Objectives of the Study
With the given background of the study, the statement of the problem and, the
significance of the study, this study would like to achieve these two objectives:
a. To find out which factor has a massive effect towards the students’ CGPA by using a
multiple regression analysis.
b. To find out whether there is a significant relationship with the dependent variable, which is
students’ CGPA to the independent variables and the dummy variable.

8
E. Scope and Limitations
For this study, the scope covers 30 randomly selected teenagers that are ages 16 to
22. The sample size may not represent enough results to show how it affects a student’s
academic performance compared to other paper that has massive respondents. The other
limitation of this paper is that, the paper only focused on frequently used social media sites
like (Facebook and Twitter). The factors that are in this study are the spent time browsing
social media in a day, spent time browsing social media in a week, students’ CGPA, spent
time studying in a week, using social media in‐relation to studies and gender as a Dummy
Variable. Even though there are other factors that can affect a student’s academic
performance, the paper had some limitations on its variables.
The conclusion is that study may show results but it cannot be used to generalize;
however, it is true only for the respondents in the study.

9
II. Theoretical Framework and Review of Related Literature
In the general perception in the Philippines, the Philippine public and private higher
universities differ in their cost structures in every degree program they offer (Rufino,
2006). A lot of students are searching for the perfect colleges or Universities to attend, and
many others are also questioning if academics is an important factor and if they should
bother with further pursuing their education. There are various colleges and Universities
offering different courses/ programs that can be an interest to every one student’s that
were thinking of proceeding to a higher education and different programs offered by
colleges or Universities that can help you to earn a degree without requiring years of
schooling. Lot of people may also think that academics is as not as important in finding a
job or anything about earning money. However, unless you are born into a right family and
doesn’t bother working and taking a job or win millions in a lottery education is an
important a key factor in improving yourself and securing your future (N.A., 2010). This
short article is related to my study, since academics is an important part in every individual.
It is evident that the CGPA of a student can affect the performance of a student.
Academic Performance
The academic performance is based on the grading system such as De La Salle
University, which is shown below:
GRADING POINT DESCRIPTION
4.0 EXCELLENT
3.5 SUPERIOR

10
3.0 VERY GOOD
2.5 GOOD
2.0 SATISFACTORY
1.5 FAIR
1.0 PASSED
0.0 FAILED
CGPA
Through the Cumulative grade point average (CGPA), it can be shown that it is a
massive factor in affecting a student’s academic performance. According to Erdem, Şentürk,
& Arslan (2007), after an individual graduate, there are employers that use CGPA as basis
and most of them prefer candidates with a higher.
Gender
It is known that males and females think differently. According to David Sadker (1994),
“Sitting in the same classroom, reading the same textbook, listening to the same teacher,
boys and girls receive very different type of education. It is true that upon entering school,
girls perform equal to or much better than of the boys on every measure of achievements,
but when the girls graduate from high school or college, they have fallen behind.”

11
Source: Gender and Academic Performance in English Communication Courses in a Japanese
University.
The study shows how males and female respond in the academic workforce. According
to John Westby (2006), the results proved that in terms of academic performance in all
levels, females had higher academic performance than males. It also shows that in every
level female student had higher average percentile than the male student. But, there are
also other factors that can affect academic performance not only gender.

12
III. Operational Framework
A. The Hypothesized Model Specification used in the Study
THE MODEL OF THE IMPACT OF SOCIAL MEDIA ON COLLEGE STUDENTS ACADEMIC
PERFORMANCE:
CGPA = ß1 + ß2 Ge+ ß3 Sw + ß4 Sd +ß5 Stra + U
B. Description of the Variable Used
Before the results on the effect of the factors used in the study be discussed, it is
important to be familiar to the definitions, measured used and symbols that were used. In
this study, the dependent variable is the student’s CGPA. The independent variables are
gender, spent browsing social media in a day, using social media in relation to academics,
gender, spent time browsing social media in a week. The model specification, definitions
and measurements are shown below:
VARIABLE LABEL MEASUREMENT DEFINITION
CUMULATIVE
GRADE POINT
CGPA Using 1.0 as the
lowest and 4.0 as
The variable measured using
the grading system in

13
AVERAGE (CGPA) the highest universities such as De La
Salle University.
GENDER Ge Male or Female
(Dummy Variables:
Female – 1 and
Male – 0)
The variable used to
determine the gender of a
student in a university.
SOCIAL MEDIA SITE
ACCESSED IN A
WEEK
Sw In hours
measure how long a student
spends time accessing a
social media site in a week.
SOCIAL MEDIA SITE
ACCESSED IN A DAY
Sd In hours
spends time accessing the
social media site in a day.
SOCIAL MEDIA IN‐
RELATION TO Stra In hours

14
STUDIES
(STUDY –RELATED
ACTIVITIES)
spends time using a social
media site in‐relation to
studying.
All of the variable in the study were categorized into two groups, the independent
variables (regressor) and the dependent variable (regressand), which can be shown below:
INDEPENDENT VARIABLE:
Gender
Social Media Accessed in a
week
Social Media Accessed in a
day
Social Media In- Relation to
studies
DEPENDENT
VARIABLE:
Cumulative Grade
Point Average
(CGPA)

15
C. A‐Priori Expectations
Regressor
(Independent Variable)
Intuition Algebraic Sign of the
A‐Priori Expectation
Gender
According to Valjeaner Ford
(2008), Gender does have an
effect on students’
academic; there are schools
that proved that gender
does affect students’
academic performance.
+
(POSITIVE RELATIONSHIP)
SOCIAL MEDIA ACCESSED IN
A WEEK
According to Claire Wright
(2010), “Facebook and other
social network sites do hold
a threat to the learning
ability of students, due to
the fact that many use these
sites for the sole use to keep
in contact with friends and
to procrastinate from their
educational activities.”
‐
(NEGATIVE RELATIONSHIP)

16
SOCIAL MEDIA ACCESSED IN
A DAY
According to Sanzhar
Nayzabekov (2012), “due to
the fact that academic
procrastination and
motivational problems seem
to be correlated, these
problems can be overcome
by creating a positive
attitude toward academic
tasks.”
‐
(NEGATIVE RELATIONSHIP)
SOCIAL MEDIA IN‐RELATION
TO STUDIES
(STUDY –RELATED
ACTIVITIES)
It seems that when students
access social media sites, it
has a negative effect in
academic performance
however, through social
media sites students have
access to announcement of
tasks and notes uploaded
through social media sites
help motivate students to
study and achieve well on
assignments.
+
(POSITIVE RELATIONSHIP)

17
D. Introduction Of Hypothesized Econometric Model
It is known that everything has an explanation and meaning, economists creates
economic theories using data and analyzes the data and explains it in a simple way for
everyone to understand. The data are statistics that were made and observe from real
events. Furthermore, the data are transformed into econometric models using tools and
statistical Softwares. Given the tools, data are regressed to form results relevant to the
study. The Ordinary Least Squares (OLS) is one of the methods used to interpret the data.
The Ordinary Least Squares (OLS) is used in regression analysis; the use of OLS is intuitively
appealing and mathematically simpler to understand than the method of maximum
likelihood (Gujarati & Porter, 2009).

18
IV. Methodology
A. Presentation of Data
The data that was used in this study are as follows: CGPA as the regressand, and gender,
social media accessed in a day, social media accessed in a week and social media in‐relation
in studies as the regressors. The data used in the study was taken from 30 random college
students.
GE SW SD STRA CGPA
1 10 4 2 3.0
1 10 4 2 3.0
0 10 4 2 3.0
0 10 6 1 3.0
0 10 4 3 3.0
0 10 4 4 3.0
0 8 2 1 3.0
1 10 4 2 3.0
1 8 2 4 3.0
1 6 6 4 3.0
1 10 4 3 3.0
1 4 8 3 3.0
0 6 2 2 2.5
1 10 2 2 3.0
0 4 1 3 3.0
1 10 10 3 3.0
0 10 4 1 2.5
0 10 6 3 3.0
0 10 10 3 3.0
1 10 4 2 3.0
1 10 4 3 3.0
0 6 2 1 2.5
1 10 2 1 3.0
0 10 6 4 3.0
1 10 2 2 3.0
0 4 2 4 3.5

19
1 10 2 2 3.5
1 2 1 1 3.0
0 6 1 1 3.0
1 10 4 2 3.0
B. Data Gathering

20
The data set resulted from the survey that was gathered from 30 randomly selected
college students the ages from 16 to 22. The age used was chosen because that’s mostly is
the age range of students that are studying in colleges and universities. The respondents
answered 6 questions that the researcher thought to be important in the study. The
dependent variable was the respondent’s estimated CGPA.

21
V. Empirical Results and Interpretation
A. Summary of Data
The model that was used in the data is linear‐linear. There are 30 observation gathered
from the survey. Using Stata, the model below showed the summarized data that was taken
from the survey.
Using the table above, it shows the variables that were used in the study, the total
number of observations or the respondents, the standard deviation of the dependent
variable and the independent variables and the minimum and the maximum values of the
result s that was summarized from the data. The average number of students spent time
browsing social media sites are 8.466667 and a standard deviation of 2.445733. The least of
students spending time using social media in a week is 2 and at a maximum of 10. The
average students that spent time browsing social media sites are 3.9 with a standard
deviation of 2.397556. The average students that use social media in‐relation to studies
resulted to 2.3666667 with a standard deviation of 1.033352. The least consumption among
the observations is 1 while the maximum is at 4.

22
Using the summarized results, we can analyze the data given the variables used in
the study. One of the variable answered by the respondents seemed very far from each
other. It only tells us that there are maybe some other factors that that has an impact to
CGPA that were not included in the variable given in the study.
B. Initial Regression
In the summarized linear model, we solved for the mean, standard deviation, the
minimum and maximum of each of the variable. By using the Ordinary Least Squares (OLS)
Regression Model to regress the data that we obtained using survey as the method, we can
obtain the regressed model. By putting all the dependent and independent variable to Stata
and check using gretl, the following results were obtained:
In the given OLS regressed model data, in order to determine if a variable is
significant, the p‐value must be less than or equal to 0.05. From the result, it is shown that
out of the four variables in the study there is one variable namely stra satisfied the

23
condition p ≤ 0.05. Given the results, stra is proven to be a significant factor in the study
with a P> |t| = 0.042.
The remaining three variables namely, Ge, Sw, and Sd proved to be an insignificant
factor in the study. But these variables cannot be simply removed or dropped in the study,
because it will cause a variable bias towards the study.
Estimated Initial Ordinary Least Squares Regression Model Specification:
ESTIMATED ORDINARY LEAST SQUARES REGRESSION MODEL (OLS)
CGPA = 2.759559 + 0.1000628 Ge + 0.0038396 Sw – 0.148423 Sd +
0.0827253 Stra
The estimated Ordinary Least Squares regression model was derived from the
initial regression analysis. From the estimated model, we can determine the significant and
insignificant variables. The constant variable ß1, deemed to be significant with a P>|t| =
0.000 and the coefficient of the constant variable is 2.759559, which can be interpreted as
the measure of how likely student will earn a Cumulative Grade Point Average given that all
other variables were to be equal to zero. The Ge variable is not statistically significant in this
model with a P>|t| = 0.184. Since p‐value is greater than 0.05, the variable results to be
insignificant. The coefficient of the variable is 0.1000628. The ß2 shows that there is a
negative change in the probability that CGPA would be equal to 1 for a unit change in Ge.
Given gender as a Dummy variable it resulted, for female to be 1 and for male to be 0.

24
The spent time using social media in a week variable, Sw variable is not
statistically significant in this model with a P>|t| = 0.814. The variable results deemed to be
insignificant. The coefficient of the variable is 0.0038396. Even though, the coefficient is
positive the p‐value is greater than 0.05. The ß3 shows there is a negative change in the
probability that CGPA would be equal to 1 for a unit change in Sw.
The spent time using social media in a day variable, Sd variable, which is also
insignificant in which the P>|t| = 0.406. The result shows that the coefficient of the variable
is consistent which is stated with a negative a‐priori expectation. The coefficient is resulted
to – 0.148423. The ß4 shows that there is a negative effect on the probability on the
students’ academics or there would be a small chance that CGPA would be equal to 1.
The social media in‐relation to studies appears to be a significant variable in the
model, which the P>|t| = 0.042. The p‐value of the variable is less than 0.05 it only tells us
that significance of Stra to the model. The ß5 variable with a coefficient amount to
0.0827253 is similar with the positive a‐priori expectation. It only shows that there is a
0.0827253 increase in the chance that the dependent variable, CGPA would be equal to one
given that there is a unit increases in Stra.
The R2
measures the impact of the variability from the exogenous variables to the
endogenous variables and the R2
is the coefficient of multiple determinations. The R2
tends
to fall between 0.2 and 0.6. Using the result in the model, the value of R2
is 0.2086. The
result tells us that there is only 20.86% of the data explained by the model. The adjusted R2
reduces biases in the model. The adjusted R in the model is 0.0819, which only shows that
there are only 8.19% of the data explained in the model.

25
C. Test for the Significance of the Model
In testing whether the entire model is significant, from the initial regression of the
model. This test is also called as the Joint test of significance or the collective test. The test
was generated using the initial regression model, which can be shown below:
Given the results, the reader should have a clear understanding of what the symbols in
the model are used. In the SS column, it shows the results of the Regressed sum of squares
(SSR), Residual sum of Squares (SSE) and the Total sum of squares (SST). The df is the
degrees of freedom in the model. In order to determine if the model is significant, the Prob>
F must be a value lesser than 0.05. Since the concept for the ANOVA testing is to be able to
reject the null hypothesis, which declares that the model lacks explanatory variable that
could affect the model thus, to accept the alternative hypothesis where at least one variable
is not equal to zero and at least one variable has an explanatory power in the model. Using
our results from the model, it shows that the F‐statistic is 0.1938. Even if the F‐statistics
proved to be higher than 0.05,it shows that the other 3 remaining variables does not have
an explanatory power in the model also, since the social media in‐relation to studies
variable deemed to be significant, in only shows that there is at least one explanatory power
and it also proves that the variable deemed to be a significant factor in the model even
though it exceeded 0.05.

26
D. Test for Violations
Since the model that was used in the study uses a linear‐linear model, there are some
assumptions that are sometimes violated throughout the study. For this research, three of
the different tests will be used to the model. The tests that will be used in this research are
Multicollinearity test, heteroscedasticity test, and Misspecification test.
Test for Multicollinearity
Multicollinearity is the variables that the independent variables are intercorrelated to
each other, but not perfectly. Multicollinearity is one of the violations that may lead to the
rejection of the model. Having a multicollinearity in the model also indicates that there is a
relationship among the variables and must be highly avoided. When a certain model
experiences mutlicollinearity, the Ordinary Least Squares Model (OLS) will still be linear and
unbiased but it also tells us that the variance is high and there will also be a high R2
in the
initial regression model. The method used to check for multicollinearity is the Variance
Inflation Factor (VIF) test. The data was computed using Stata, and is shown below:
In the VIF test, all the variables should be less than 10 for the model to experience the
assumptions of non‐multicollinearity and tolerable multicollinearity variables in the specific
model. Based on the given results, the highest VIF given in the results is the sd with a

27
variance inflation factor (VIF) of 1.31. The other variables in the VIF test show a value less
than 10. The result tells us that the model exhibits tolerable multicollinearity and proves
that there is no existence of dangerous multicollinearity. By not having any dangerous
multicollinearity, it means that the model is not rejected and will not be subjected to any
remedies.
Test for Heteroscedasticity
The data that was used in the study is a cross‐sectional data. It is required to test for
violations like homoscedasticity in the study. Without testing the model, the research might
lead to variable biases, wrong transformation of data, dangerous multicollinearity in the
model, outliers in the data. If there are violations in the data, the Ordinary Least Squares
Model (OLS) will not be Best Linear Unbiased Estimator (BLUE). If the OLS model is proved
not to be BLUE, then the model must undergo remedies to fix the data that would lead to a
better accurate result. The results were obtained using Stata and undergone Gretl as to
check for homoscedasticity. There are two tests performed to check for homoscedasticity.
The results of the white’s test (imtest, white) are shown below:

28
From the given results, it shows that the p‐value of Chi2
is greater than the 95%
confidence interval. This also shows the null hypothesis of having a homoscedasticity in the
model can be accepted and we should reject the alternative hypothesis of
heteroscedasticity. There is another way to check the homoscedasticity of the model; the
test is called the Breusch‐Pagan‐ Godfrey test. The results for the Breusch‐Pagen‐Godfrey
test were performed using Stata. Using the Stata, the following results were acquired:
By using the results obtained from the Breusch‐Pagan‐Godfrey test, the p‐value
amounting to 0.3558 and greater than the significance level of 0.05. It can be also be
explained as to accept the null hypothesis of having a homoscedasticity in the model and
reject the alternative hypothesis of heteroscedasticity. It can also be concluded that not all
variances in the model are equal to zero and some of them have explanatory power.
Analyzing both tests performed to the model, it explains that there are non‐existing
heteroscedasticity in the model and it does not have any violations towards the model.

29
Test for Misspecification
Misspecification happens when the economic theory is incomplete; the model’s
estimated parameters will most likely be biased (Borghers & Wessa, 2000). There are six
kinds of misspecification that can be neglected in a model. In this study, the researcher will
only test 3. These three misspecification that will be tested are: Omission of a relevant
variable(s), Inclusion of any unnecessary variable(s), and Adoption of the wrong functional
form. It is important to check for any misspecification and need to take measurements for it
to be avoided. Using Stata, the omission of a relevant variable(s) can be tested using the
Ramsey RESET. This test analyzes the model and check if there are omissions of relevant
variables. The result for the Ramsey RESET can be shown below:
In determining if there are any omitted variables used in the model in using the
Ramsey RESET, when the p‐value tested is less than or equal to 0.05, then null hypothesis
with no omitted variables should be rejected and the accept the alternative hypothesis with
omitted variable. From the given result of the Ramsey Regression Error Specification test,
we can see that the p‐value is 0.4849. It can be concluded that we should accept the null
hypothesis with no omitted variables and reject the alternative hypothesis. Given the
Ramsey Regression Error Specification test, it can also be used to examine if there are

30
adoption of any wrong functional form. The result also tells us that the model that was used
in the study was correct.
This test will determine if there is any inclusion of unnecessary variables. This test will
check if the independent variables are significant. The Wald’s test will be used to check if
there exist such biases in the model. Using Stata, we can be able to calculate for the results
and it can be shown below:
By using the result given, it shows that the gender variable is insignificant, irrelevant,
and does not affect the students’ CGPA. Since its p‐value is greater than 0.05 then we
should accept the null hypothesis.
The spent time using Social Media sites in a week, Sw deemed to be insignificant
and the variable does not have an effect to the students’ CGPA performance. The Sw
variable’s p‐value is 0.8142 which is greater than 0.05. It only tells that we should accept the
null hypothesis and reject the alternative hypothesis.

31
By analyzing the given result, the spent time using social media sites in a day variable,
sd, resulted to be insignificant and does not affect the students’ CGPA. The sd variable’s p‐
value is 0.04059 which is greater than 0.05. The given p‐value tells us to accept null
hypothesis and reject the alternative hypothesis.
The use of social media in‐relation to studies variable, stra has a p‐value of 0.0423,
which is less than 0.05. The variable deemed to significant in the model. The result tells us
to reject the null hypothesis and accept the alternative hypothesis.
E. Conclusions for the Test of Violations and Adjustments
Based on the results derived from the given tests, there are only tolerable
multicollinearity in which the model can be used without subjecting to further remedies. By
using the two tests to conduct if there is homoscedasticity, it shows that there are no
homoscedasticity occurred in the model and there is no need to do remedy the model.

32
In the test for misspecification, the omission of relevant variables and adoption of the
wrong functional form was not violated, so there is no need to change anything in the
model.
Corrective Measurements and Final Regression
Since irrelevant variable bias was violated, it is not always possible to drop the
insignificant variable in the model. In the study, the researcher wants to improve the model
by omitting the variables and regressing the model again. The results of the final regression
model is shown below:
By dropping the spent time using social media sites in a week, sw, the significant
variable, stra had more explanatory power in the model. At the same time, stra’s p‐value is
less than 0.05. By looking at the R2
, it can tell if the data satisfies the goodness to fit in the
model. The R2
appeared to be having lesser goodness to fit as 20.68% are being explained in
the given model.

33
Test for Multicollinearity
The results from the VIF test demonstrated that there are no sign of dangerous
multicollinearity since the largest VIF in the model which is both sd and stra only happened
to 1.15 which is less 10. The results conclude that the final model is not rejected and not be
subjected to any remedies.
Test for Heteroscedasticity
The result from the Breusch‐Pagan‐Godfrey test shows that there were no
Heteroscedasticity committed in the final model since its p‐value of chi2
is 0.3688 being
greater than 0.05. The model concludes to accept the null hypothesis of having a
homoscedasticity in the model and reject the alternative hypothesis with heteroscedasticity.

34
Test for Misspecification
Using the results from the RAMSEY Regression Error Specification Test, we can see
that there are no omitted variable biases in the final model. Since the p‐value is 0.5388
which is greater than 0.05. It only proves that the null hypothesis with no omitted variable
should be accepted and the alternative hypothesis with omitted variable bias can be
rejected.
The outcome from the Multicollinearity test, Heteroscedasticity test, and the
Misspecification test shows that there were no assumptions that were violated and there
were no misspecifications in the study. In testing the final model, the remaining variable
used in the final model proved to be significant. Even though that the goodness to fit
appeared to be lower than the initial model. Given the two models, we can conclude that
the final model is more realistic and better than the initial model. We can also say in the
final model deemed the significant and have a better explanatory power towards the entire
model.

35
VI. Conclusions and Recommendations
Given all the results, there are one out of four factors that became significant on the
impact of social media to college students’ academic performance. Based on the
outcome of both the initial and the final model, the 30 randomly selected college
students’ ages from 16 to 22 stated that social media in‐relation to studies have a
significant effect in their CGPA. Since the most of the respondents’ CGPA ranges from
2.5 to 3.0, it can only explain that social media in‐relation to studies variable has a
positive effect in the study.
In the given result, gender seemed to be an insignificant variable in the study
because it has a negative relationship. But there are studies that gender had been
significant in students’ academic performance. Since it has a negative relationship the
researcher assumed that there are no any relationship for being male or female in the
study. The researcher can also assume that both male and female college students’ can
achieve high grades.
The sd and sw variable in this model, with the given a‐priori expectations resulted
on having both negative relationship in the model and deemed to be insignificant in the
study and its impact to Cumulative Grade Point Average of the students’ academic
performance. The two variables could be improved in future studies about this research
topic. The study only utilized 30 observations, since when all 107 observations are used,
it will deem no significant variable in the study. The future research about this topic
should give more factors that can also affect the Cumulative Grade Point Average (of the
college student to have a better presentation and explanation of data.

36
VII. Bibliography or References
1. Borghers, E., & Wessa, P. (2012). Online Econometrics Textbook ‐ Regression
Extensions ‐ Assumption Violation of Linear Regression ‐ Misspecification of Linear
Regression. Retrieved August 31, 2013, from
www.xycoon.com/misspecification.html
2. Erdem, C., Şentürk, I., & Arslan, C. (2007, September). Factors Affecting Grade Point
Average of University Students. Retrieved August 26, 2013, from
http://idak.gop.edu.tr/isenturk/calismalar/gpa.pdf
3. Ford, V. (2008). Does Gender Affect Students' Academic Performance in the
Classroom? Retrieved August 26, 2013, from
http://coedpages.uncc.edu/ncare/Preceedings2008/Abstracts/NCARE_2008_Procee
dings_Ford.htm
4. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. Singapore: McGraw‐Hill
Irwin.
5. Hendricks, D. (2013, May 8). The Complete History of Social Media: Then And Now.
Retrieved August 20, 2013, from http://smallbiztrends.com/2013/05/the‐complete‐
history‐of‐social‐media‐infographic.html
6. Nayzabekov, S. (2012, April 20). Negative impact of social networking sites on
academic performance of students | Sanzhar Nayzabekov ‐ Academia.edu.
Retrieved August 26, 2013, from
http://www.academia.edu/1810511/Negative_impact_of_social_networking_sites_
on_academic_performance_of_students

37
7. Rufino, C. (2006). Estimating the degree cost functions of the Philippines public and
private higher educational Institutions | Cesar Rufino ‐ Academia.edu.
Retrieved September 2, 2013, from
http://www.academia.edu/4209212/Estimating_the_degree_cost_functions_of_the
_Philippines_public_and_private_higher_educational_Institutions
8. Sadker, D. (1994). Gender Bias in Education. Retrieved August 26, 2013, from
http://www.edchange.org/multicultural/papers/genderbias.html
9. Singer, R. (2010). Facebook & OSS: Brief History of Facebook. Retrieved August 22,
2013, from http://terpconnect.umd.edu/~sqin/group/bgfb.html
10. The Importance of Academics. (2010). Retrieved August 21, 2013, from
http://benjyx.articlealley.com/the‐importance‐of‐academics‐1570084.html
11. Westby, J. (2006, March). Journal of The Institute for Second Language Development
‐ Gender Performance. Retrieved August 26, 2013, from
http://www.jisld.org/articles/gender‐performance.html
12. Wright, C. (2010). The use of social networks by students: the possible risks and
implications ‐ Claire Wright B.A. (Hons) Business and Marketing. Retrieved August 26,
2013, from
http://webcache.googleusercontent.com/search?q=cache:r1HpH86XutYJ:www.new
port.ac.uk/research/Journals/newresearcher/vol4/Documents/4NR1.doc+&cd=5&hl
=en&ct=clnk&client=safari

An Analysis On The Impact Of Social Media On College Students Academic Performance

Recommended

Recommended

More Related Content

Similar to An Analysis On The Impact Of Social Media On College Students Academic Performance

Similar to An Analysis On The Impact Of Social Media On College Students Academic Performance (20)

More from Allison Thompson

More from Allison Thompson (20)

Recently uploaded

Recently uploaded (20)

An Analysis On The Impact Of Social Media On College Students Academic Performance