tiểu luận kinh tế lượng ANАLYZING THЕ ЕFFЕCTS OF FАCTORS ON THЕ TЕRM GPA

FOREIGN TRADE UNIVERSITY FACULTY OF
BANKING AND FINANCE
-----❧❧•❧❧-----

FINANCIAL ECONOMETRICS
MIDTERM REPORT

ANАLYZING THЕ ЕFFЕCTS OF FАCTORS
ON THЕ TЕRM-GPA
Team members:
Nguyễn Kỳ Mi

Nguyễn Minh Ngọc

1617340058
1613340067

Nguyễn Hương Quỳnh

1613340078

Nguyễn Phương Thảo

1613340088

Class: KTEE310.1
Instructor: Mrs. Pham Thuy Quynh
Hanoi, 12/2019


Table of Contents

ABSTRACT................................................................................................................................................... 1
INTRODUTION …………………………………………………………………….…………….. 2
Literature review....................................................................................................................4

I.
1.

Overview of the determinants on GPA............................................................................4

2.

Related published researches............................................................................................4

3.

Research hypothesis...........................................................................................................6

4.

Methodology........................................................................................................................6

II.

Specification of the regression model on term-GPA......................................................7

1.

Theoretical model specification........................................................................................7

2.

Interpretation of the data..................................................................................................8

III.

Estimated results of regression model on term-GPA..................................................11

1.

Regression analysis...........................................................................................................11

2.

Sample regression model.................................................................................................13

3.

Testing for possible problem with the model................................................................14

4.

Fixing the model...............................................................................................................16

5.

Hypothesis testing.............................................................................................................16

6.

Analyzing the prigpa model............................................................................................17

IV.

Recommendations............................................................................................................19

1.

For the student..................................................................................................................19

2.

For the researchers...........................................................................................................20

CONCLUSION................................................................................................................................... 21
REFERENCES.................................................................................................................................... 22

APPENDIX ………………………………………………………………………………………...23


Table of Figures:
Table 2.1: Summary of number of observations, mean, standard deviation, max, min of
model’s variables ……………………………………………………………………………….. 9
Table 2.2: Correlation between variables of the model ………………………………… 10
Table 3.1: The testing regression result from STATA …………………………………... 11
Table 3.2: The t-test result of gender from STATA ……………………………………..

12

Table 3.3: The regression result after removing dummy variables from STATA….. 13
Table 3.4: The VIF test result from STATA ………………………………………………. 14

Table 3.5: The Breusch – Pagan test result from STATA...……………………………… 14
Graphic 3.1: Histogram of the residuals of the model ……………………………… 15
Table 3.6: The Ramsey reset test result from STATA …………..…...…………………… 15
Table 3.7: The fixed regression model result from STATA …………………………….. 16


ABSTRACT
Economеtrics is thе quаntitаtivе аpplicаtion of stаtisticаl аnd mаthеmаticаl modеls
using dаtа to dеvеlop thеoriеs or tеst еxisting hypothеsеs in еconomics аnd to forеcаst
futurе trеnds from historicаl dаtа. It subjеcts rеаl-world dаtа to stаtisticаl triаls аnd
thеn compаrеs аnd contrаsts thе rеsults аgаinst thе thеory or thеoriеs bеing tеstеd. Thе
mеthods аrе rеliеd on stаtisticаl infеrеncеs to quаntify аnd аnаlyzе еconomic thеoriеs
by lеvеrаging tools such аs frеquеncy distributions, probаbility, аnd probаbility
distributions, stаtisticаl infеrеncе, corrеlаtion аnаlysis, simplе аnd multiplе rеgrеssion
аnаlysis, simultаnеous еquаtions modеls, аnd timе sеriеs mеthods.
Sincе thе dеvеlopmеnt of thе Economеtrics mеthods, еconomists hаvе аppliеd thе
mеthod in mаny situаtions to mеаsurе еconomic rеlаtionships in thе rеаl world. It is of
great importance that we, economics student, should learn econometrics to support our
study and future work. Therefore, we would like to present our econometrics report
about “Analyzing effects of factors on the Term-GPA”.
This rеport would not bе possiblе without thе hеlp аnd guidаncе from our lеcturе.
Thеrеforе, first аnd forеmost, wе I wаnt to еxprеss our hеаrtfеlt thаnk to our Lеcturе
Phаm Thuy Quynh for offеring us еnormous guidаncе аnd support throughout thе
coursе of Finаnciаl еconomеtrics. Duе to thе limitеd timе, it is difficult for us to аvoid
mаking mistаkеs. Thеrеforе, wе sincеrеly hopе to rеcеivе commеnts from our lеcturе
so thаt wе cаn mаkе аn improvеmеnt in our аssignmеnt.

1

INTRODUCTION
Purposе of thе Rеsеаrch
Grаding in еducаtion is thе procеss of аpplying stаndаrdizеd mеаsurеmеnts of vаrying
lеvеls of аchiеvеmеnt throughout thе coursе. аs wе аll know, onе of thе indicаtors thаt
highlight thе univеrsity studеnts’ quаlificаtion is thе аcаdеmic pеrformаncе, which is
mostly mеаsurеd by thе cumulаtivе Grаdе Point Avеrаgе (GPA). GPA is thе аvеrаgе of
аll finаl grаdеs for coursеs within а progrаm, wеightеd by thе unit vаluе of еаch of
thosе coursеs. Nowаdаys, mаny еmployеrs usе GPA аs а stаndаrd of rеquirеmеnt to
scrееn out job cаndidаtеs, еspеciаlly whеn rеcruiting frеsh grаduаtе cаndidаtеs аnd
thеy mostly prеfеr cаndidаtеs with а highеr GPA.
Mаny еxtеrnаl fаctors аrе considеrеd аs dеtеrminаnts of thе studеnts’ GPA such аs
gеndеr, living conditions, incomе lеvеl of thе fаmily, sociаl еnvironmеnt, typе аnd
quаlity of еducаtionаl institutions, аnd so on. At thе sаmе timе, thеrе hаvе bееn
numеrous studiеs in thе fiеld of fаctors thаt hаvе аn еffеct on studеnt’s аcаdеmic
succеss. As thе Grаdе Point Avеrаgе (GPA) bееn usеd аs а unit of mеаsurе to аssеss thе
аcаdеmic pеrformаncе of thе studеnts, it is importаnt to idеntify аnd undеrstаnd thе
fаctors thаt influеncе thе GPA of studеnts. Thеrеforе, wе would likе to prеsеnt our
еconomеtrics Rеport аbout “Anаlyzing thе еffеcts of fаctors on thе tеrm-GPA”. In
this rеsеаrch, wе wаnt to put our mаin focus on idеntifying fаctors thаt hаvе аn impаct
on thе univеrsity studеnt’s term GPA, spеcificаlly studеnts’ cumulative GPA, numbеr of
clаssеs studеnts аttеnd, homеwork turnеd in by studеnts, аnd ACT scorе. In ordеr to sее
how еаch fаctor аffеcts thе tеrm-GPA, wе would likе to build thе еconomic rеgrеssion
modеl to find thе quаntitаtivе rеlаtionship, which contributеd to thе initiаl аccеptаncе
of thе fаctors аffеcting GPA.

2


Objеctivеs аnd Scopе of thе Rеsеаrch
Objеctivеs of Rеsеаch: In this rеport, our mаin rеsеаrch objеctivе is to idеntify thе

fаctor thаt contributеs to а studеnt’s аcаdеmic pеrformаncе аt thе univеrsity аnd to
whаt еxtеnt do thеy аffеct thе studеnt’s аcаdеmic pеrformаncе.
Scopе of Rеsеаrch: To mееt with our objеctivеs, wе would likе to put our focus on
univеrsity studеnts, using thе collеctеd stаtisticаl dаtе from аll sеcond-yеаr studеnts in
Dеnison univеrsity during thе fаll tеrm of 2010.
Rеsеаrch findings
According to thе rеsults, fаctors such аs studеnts’ finаl tеst scorеs, thе numbеr of
clаssеs studеnts аttеnd, homеwork turnеd in by studеnts, аnd ACT scorе cаn hаvе аn
impаct on thе Grаdе Point аvеrаgе.
Structurе of thе Rеport
Our report is divided into four parts, which is
Chаptеr 1: Literature view with the overview of the determinants on GPA and research
hypothesis, as well as the methodology used to conduct this research.
Chаptеr 2: Modеl spеcificаtion and model problem solving.
Chаptеr 3: Analysis of the prigpa regression result.
Chapter 4: Recommendations for students for better academic result and for future
researches.

3


I.Literature review
1. Overview of the determinants on GPA
Researchers suggested that intelligence accounts for only 25% of observed variance in
grades. It follows that other variables also have an influence on academic performance.
Moreover, the academic success and retention of students, particularly during their first
year, are major concerns for colleges and universities (Noble and Sawyer, 1987; Ting,
2001; Sander, Pike and Saupe, 2002). Stakeholders are increasingly attentive to the
academic success of students as a measure of the effectiveness of higher education.
These concerns continue to challenge researchers exploring student characteristics that

contribute to academic success. According to recent studies, the leading contributing
factors to students’ academic success fall into three basic categories: (1) student
demographics, such as gender, race, age, and employment status (Gates & Creamer,
1984); (2) academic factors, such as high school GPA, placement test scores, or
remediation status (Hardin, 1990); and (3) non-cognitive factors such as motivation,
social integration, self-concept, and career readiness (Bean and Metzner, 1986; White
and Sedlacek, 1986).
2. Related published researches
Researchers has studied on student academic success and persistence since the
early 1970s. Tinto’s (1993) conceptual framework showed that student academic
performance and persistence are impacted by their characteristics which measured
by high school preparation and college admission test scores levels. The results of
some studies (White & Sedlacek, 1986; Boyer & Sedlacek, 1988; Tracey &
Sedlacek, 1989; McLaughlin, 2006) have proved that cognitive variables, such as
high school GPA, high school percentile rank and college admission test scores,
predict the academic success of college students.
Studies concerned impact of admission test scores and academic performance and
preparation in high school on students’ GPA have found standardized test scores is a
reliable predictor of students’ GPA (Pascarella et al, 1981; Bean & Bradley, 1986).
4


House and Keeley (1997) also discovered scores on admission tests, such as the
American College Testing (ACT), were a reliable predictor for Native-American
students’ performance. Rodriquez (1996) found similar results for MexicanAmerican students.
Howerver, Hood (1992) found that the ACT score was not a significant predictor of
academic success among 409 African-American students but the best cognitive
predictor among his sample were high school percentile rank. In his study of 54
first-generation and low- income students in a Midwestern public university, Ting
(1998) also found that the ACT composite score was not a significant predictor of

academic success as measured by students’ GPA in the first semester.
Since the results of studies reviewing the impact of cognitive variables on the
students’ GPA such as high school GPA, standardized academic test scores result in
have found mixed results (Houston, 1980; Hood, 1992; Riehl, 1994; Ting, 1998),
there is a growing concern that a combination of cognitive and non-cognitive
measures, such as School Aptitute Test scores and psychosocial variables, will
better predict students’ grades than cognitive measures alone (Pascarella and
Terenzni, 1991; Sedlacek, 1991; Hood, 1992; Ting, 1998). Hood (1992) explored
the extent in his study to how cognitive and non-cognitive variables predict
African-American male students’ GPA. He found that successful leadership
experience and demonstrated community service to be the best predictors of GPA
for first-generation and low-income students, which are consistent with Sedlacek
(1986) and Ting (1998)’ studies.
Trippi and Steward, 1988; Fuertes, Sedlacek and Liu, 1994, showed that self-concept
and self-appraisal were the best predictors of academic success for both EuropeanAmerican and African-American students. In another study, Tracey and Sedlacek
(1989) found two non-cognitive variables, community service and realistic selfappraisal, to be the best predictors of academic success for Asian Americans.

5


3. Research hypothesis
As can be seen, Term-GPA is influenced by many factors, both by the individual’s
performance in the classes and learning effort shown during the self-study period. Our
research objective is to identify factors which are responsible for students’ academic
performance at the university and to what extent do they affect students’ academic
performance.
The hypothesis of the study is:
Ho: Students’ GPA in a term in the university is not related to students’ cumulative GPA,
number of classes students attend, homework turned in by students, and ACT score.

4. Methodology
a. Method of deriving the model
To derive the model, we follow five steps. The first step was to perform a literature
review to determine whether any other studies have been conducted relating to above
hypothesis. The second step was to collect the overall data of university students. We
then identify which variables significantly related to Term-GPA. The fourth step was to
run the regression model with identified variables and test if the model meets any
violation. Prigpaly, we fixed the model and analyzed the prigpa regression result to
explain the relation between variables and draw conclusion for the null hypothesis.
These five steps were followed by an exploration of the data collected and also a
discussion of the implications of this research. At the end of this paper, conclusions
and recommendations for future research areas will be discussed.
b. Method of collecting and analyzing data
For the study, data were collected based on secondary sources. Information used for
analysis included variables found on statistics on study status of students of Denison
university, including gender, ACT score, prigpa-term score, GPA, cumulative GPA,

6


classes attended, homework turned in. The 680 subjects included in this study were all
second-year students in Denison university during the fall term of 2010.
Data was analyzed using descriptive analysis, correlation analysis and regression
analysis of sample from STATA software application.
c. Method of testing hypothesis
In order to test research hypothesis, we use the regression analysis. If the hypothesis with
the α level of significance is not rejected, then there is not enough evidence to reject the
hypothesis that students’ GPA in a term in the university is not related to students’
cumulative GPA and gender, number of classes students attend, homework done by the
student, and ACT score. In other word, students’ cumulative GPA and gender, number of

classes students attend, homework done by the student, and ACT score do not affect the
term-GPA. On the other hand, if the hypothesis Ho is rejected, it means that there is a
relationship between the term-GPA and students’ cumulative GPA, number of classes
students attend, homework turned in by students, and ACT score.

II.

Specification of the regression model on term-GPA

1. Theoretical model specification
After studying related public researches, we came up with a population regression
model, which is as follows:
PRM: =+× +× +× +× + × + i
In which,
Dependent variable:
+ termgpa: Dependent variable: the grade point average (GPA) for the fall term of
2010, given on a scale from 0 to 4.0.

7


Independent variables:
+ prigpa: the grade point average (GPA) that students has cumulated in all previous
semester, given on a scale from 0 to 4.0
+ atndrte: Independent variables: the percentage of classes student attended, unit: %.
+ hwrte: the percentage of homework student turned in, unit: %.
+ ACT: American College test score, an entrance exam used by most colleges and
universities to make admissions decisions, unit: grade out of 36
+ D: dummy variable: gender
If the student is male, then D = 1, if the student is female, then D= 0

+ i: random error term
The coefficient
+
+

0:

population termgpa intercept
1, 2, 3, 4, 5:

population slope coefficient of independent variables: prigpa, atndrte, hwrte, ACT, gender respectively

2. Interpretation of the data
We used the data of 680 second-year students in Denison university during the fall
term of 2010.
a. Descriptive statistics
For describing the statistics, we use “sum” command. “Sum” command shows us the
number of observations, mean, standard deviation, max and min of the variables.

8


Variable

Obs

Mean

Std. Dev.

Min

Max

termgpa

680

2.601

0.736586

0

4

prigpa

680

2.586775

0.5447141

0.857

3.93

atndrte

680

81.70956

17.04699

6.25

100

hwrte

674

87.90801

19.26926

12.5

100

ACT

680

22.51029

3.490768

13

32

gender

680

Table 2.1 :Summary of number of observations, mean, standard deviation, max, min of
model’s variables
The standard deviation is a statistic that measures the dispersion of a dataset relative to
its mean and is calculated as the square root of the variance. If the data points are
further from the mean, there is a higher deviation within the data set; thus, the more
spread out the data, the higher the standard deviation. We can see from the table that
variables with the unit of percentile is the most fluctuate one. The spread-out of termGP and prigpa score is quite high. As gender is the dummy variable, we just describe
them in the number of observations in terms of male and female.
b. Correlation analysis
To define whether our model suffers from multicollinearity, we examine the
relationship between dependent and independent variables by identify the correlation
between variables using the “corr” command:
“Corr termgpa prigpa atndrte hwrte ACT frosh soph”.

9


termgpa

prigpa

atndrte

hwrte

ACT

gender

termgpa 1
prigpa

0.6543

1

atndrte

0.5384

0.1497

1

hwrte

0.5049

0.3073

0.6260

1

ACT

0.2733

0.3682

-0.1336

-0.0884

1

gender

0.0464

0.0339

0.1143

0.0675

-0.0431

1

Table 2.2: Correlation between variables of the model
As we can see, the matrix describe relationship between the variables:

•

The correlations which are larger than 0.4 are considered to be strong,
showing that those two variables have tight relationship and strongly affect
each other. From the table above, we can conclude that GPA and Prigpa
Score (0.65), Homework Turned In and Attendance Rate (0.63), GPA and
Attendance Rate (0.54), GPA and Homework Turned In (0.50) strongly
affect each other in a positive way. If the former increases, then the latter
will also increase.

•

The correlations which are smaller than 0.4 are considered to be weak,
showing that those two variables have loose relationship and poorly or
merely do not affect each other: Prigpa Score & Homework Turned In (0.3)
for example.

•

The correlations which are positive express two variables moving in tandem
(ex: GPA & ACT Score), while negative correlations show inverse or opposite
movement of the two variables (ex: ACT Score and Attendance Rate).

All of the above correlations work well with our theory in the beginning of the report.
As expected, the GPA score is affected by 6 other variables at various levels from
10


weak to strong, negative to positive.
No correlations should be larger than 0.8 in order to not create multicollinearity in

the model. Since there is no correlation which is larger than 0.8 then the model does
not suffer from Multicollinearity.
III.

Estimated results of regression model on term-GPA

1. Regression analysis
To find out the sample regression model, we run the “reg” command and get the
following result from STATA:
Source

Model
Residual

Total

SS

df

201.536572
152.212083

5
668

MS

40.3073144
.2278624

674
176.89

Prob > F
R-squared

0.0000
0.5697

Adj R-squared

0.5665

Root MSE

0.47735

353.748655

673

Coef.

Std. Err.

prigpa

.5570013

.0423926

13.14

0.0000 .4737626

.6402401

atndrte

.0101971

.001563

6.52

0.0000 .0071281

.0132661

hwrte

.0092812

.001228

7.56

0.0000 .0068701

.0116923

ACT

.0358269

.0060531

5.92

0.0000 .0239415

.0477122

gender

-.005208

.0370319

-0.14

0.888

.067505

_cons

-1.285815

.1664332

-7.73

0.0000 -1.61261

termgpa

.525629503

Number of obs
F (6, 668)

t

P>t

[95% Conf.Interval]

-.0779209

Table 3.1: The testing regression result from STATA
From the above tables, we can derive such information:
11

-.9590199


• The number of observations is 674
• The coefficient of determination is 0.5697, which means changes in

students' GPA are 56.97% explained by the independent variables in
the model.
As we can see, there is a variable with the P-value higher than 5% level of
significance, the “gender”. As it is dummy variable, we will go further with an
independent group t-test, to check whether there is a difference between the male
student’s term-GPA and the female student’s term-GPA. We compare the mean termGPA score between the group of male students and the group of female students. The
test assumes that variances for the two group are the same.
Group

Obs

Mean

Std. Err Std. Dev [95% Conf. Interval]

female
male

341
339

2.563912
2.638307

.0421781
.0375081

.7788688 2.480949
.6905966 2.564528

2.646875
2.712085

combined 680

2.601

.0282468

.736586 2.545538

2.656462

diff

-.0743948 .0564632

-.1852585

Diff = mean(female) – mean(male)

t = -1.3176

Ho: diff = 0
Ha: diff < 0
Pr (T < t) = 0.094

.036469

degrees of freedom = 678

Ha: diff != 0

Ha: diff > 0

Pr (
Pr (T > t) = 0.9060
Table 3.2: The t-test result of gender from STATA
| |>| |)=0.1881

Because the two-tailed p-value is 0.1881, which is greater than 0.05. Then, we accept
statistical difference between the means in term-GPA between males and females.

0

and conclude that there is no

Therefore, we considered to omit the dummy variable, which is gender, from our model.
12


Our new model is: PRM:
=+× +× +× +× + i

2. Sample regression model
To find out the sample regression model, we run the “reg” command in order to get the
beta value of each variable.
After running “reg termgpa prigpa atndrte hwrte ACT”, we have the following table:
Source

SS

Model
201.532066
Residual 152.21659

df

4
669

MS

Number of obs 674
F(4, 669)
221.44

50.3830164
.227528535

Prob > F
R-squared

0.0000
0.5697

Adj R-squared 0.5671
Total

353.748658

673

termgpa

Coef.

prigpa
atndrte

.5570142
.0101788

.0423614
.0015564

13.15
6.54

0.00000 .4738369
0.00000 .0071227

.6401915
.0132349

hwrte

.0092821

.001227

7.56

0.00000 .0068728

.0116914

ACT

.0358472

.0060469

5.93

0.00000 .023974

.0477204

_cons

-1.287477

.1658916

-7.76

0.00000 -1.613208

-.961746

Std. Err.

.525629503
t

Root MSE
P>t

.477

[95% Conf. Interval]

Table 3.3: The regression result after removing dummy variables from STATA
Our sample regression model is:
SRM:

=− .+ .×+ .×+ .×+ .×+

13


3. Testing for possible problem with the model
a. Multicollinearity
In order to test whether the model has multicollinearity or not, we use another way
which is VIF (Variance inflation factor) Test as follows: running "vif" command. Here
under is the test result from stata.
Variable

VIF

1/VIF

atndrte
hwrte

1.95
1.65

0.512628
0.604743

prigpa

1.57

0.636988

ACT

1.31

0.762362

Mean VIF

1.62
Table 3.4: The VIF test result from STATA

We can see from the table that all VIF value of the variables are smaller than 10.
Therefore, the model does not suffer from Multicollinearity.

b. Heteroskedasticity
In order to test this kind of violation, we use the “hettest” command, which represents
for the Breusch – Pagan test. Here under is the test result we get from STATA.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of termgpa
chi2(1)

= 51.91

Prob > chi2 = 0.0000

Table 3.5: The Breusch - Pagan result from STATA
The "Prob > chi2 = 0.000" is smaller than α /2, which is 1.96. We then reject Ho and came to conclusion that the model is suffering from heteroskedasticity.

14


c. Misspecification
For this kind of violation, we used the Ramsey reset test. To run this test in STATA, we
use the “ovtest” command. Here under is the test result we get from STATA.
Ramsey RESET test using powers of the fitted values of termgpa
Ho: model has no omitted variables
F(3, 666) =
Prob > F =

1.84
0.1391

Table 3.6: The Ramsey reset test result from STATA

Since the p-value of the test: Prob > F = 0.1391 greater than the 5% level of
significance, we do not reject Ho. Thus, the model suffers from no misspecification.

0

.2

.4

.6

.8 1

d. Disturbance’s distribution

-2

-1

0
Residuals

1

2

Graphic 3.1: Histogram of the residuals of the model
From the histogram above we can see that our residuals are closely to the normal
distribution. For large number of observations (n>30), these residuals are accepted.
15

4. Fixing the model

Number of obs 674
F (4, 669)

210.94

Prob > F

0.0000

R-squared

0.5697

Root MSE

.477

From analysis above, our model just suffers from the heteroskedasticity. In order to fix
the model, we use the Robust method with “reg termgpa prigpa atndrte hwrte ACT,
robust” command in stata.
Linear regression

termgpa

Coef.

Std. Err. t

P>t

[95% Conf. Interval]

prigpa
atndrte

.5570142
.0101788

.0471609 11.81
.0018694 5.44

0.00000.464413
0.00000.0065081

.6496155
.0138494

hwrte

.0092821

.0014467 6.42

0.00000.0064416

.0121227

ACT

.0358472

.0061215 5.86

0.00000.0238276

.
0478668

_cons

-1.287477 .1714774 -7.51

0.00000-1.624175

.9507782

Table 3.7: The fixed regression model result from STATA
All the P-value is smaller than 5% level of significance, implies that all variables are


statistically significant.
As, we have used Robust method to solve the heteroskedasticity, the new model
automatically does not suffer this kind of violation.
5. Hypothesis testing
From the regression result, we can see that the Prob(F-Statistic) = 0.0000 <5% level of
significance. Therefore, with the 5% level of significance, we can reject the null

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hypothesis Ho: Students’ GPA in a term in the university is not related to students’
cumulative GPA, number of classes students attend, homework turned in by students,
and ACT score.
Therefore, we came to conclusion that Students’ GPA in a term in the university is
related to students’ cumulative GPA, number of classes students attend, homework
turned in by students, and ACT score in the 2010 fall semester in Denison university.
6. Analyzing the prigpa model
a. The overall model fit
The number of observations used in regression analysis is 674.
The F-value with 4 variables and 669 degree of freedom is 210.94. The p-value
associated with this F-value is very small, which is 0.0000. This indicates that the
independent variables reliably predict the dependent variable. In other words, variables
group of cumulative GPA, number of classes students attend, homework turned in by
students, and ACT score reliably predict the term-GPA.
The coefficient of determination (R-squared) is 0.5697 = 56.97%, which means
changes in students' GPA are 56.97% explained by the 4 independent variables in the
model and the rest 43.03% may be explained by the other factors not mentioned in this
regression model.
b. The estimated coefficient
It was assumed that cumulative GPA and term GPA has the positive relationship with the term GPA. A student who
already has the high cumulative GPA indicates that he/she knows the way to study and get high scores at university.
As a result, in the next semester, he/she has the tendency to keep the study momentum and get the higher score. The
coefficient = 0.5570142 indicates that Student GPA of the term is
β

expected to increase by 0.5570142 point when the Cumulative GPA of Students
increases by one point, ceteris paribus.

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It is believed that the relationship between dependent variable and the attendant rate is
positive. A student who regularly attend classes will have a better understanding of the
lecture and able to grasp the main points that needed to pay attention to in the lesson. As
a result, their revision for the final test will be close to the learning content and they can
get a higher score than those who do not attend the class. In addition, students'
attendance is also scored, accounting for 10% of the total score in 10-point
scale . The coe ficie nt value

β

of 0.0101788 indicates that Student GPA of the term

is expected to increase by 0.0101788 percent when the Attendant Rate of Students
increases by one percent, ceteris paribus.
It was assumed that the students' academic performance is positively related to the homework turned in by students.
Doing homework helps students to better understand the contents of the lecture and get more familiar with the
question format that may appear in the final test. Therefore, they can get a higher score than those who do not
practice exercises at home. The coefficient = 0.0092821 indicates that Student
β

GPA of the term is expected to increase by 0.0092821 percent when the Homework
Turned In By Students Rate increases by one percent, ceteris paribus.
It was expected that there is a positive relationship between the students' GPA in the semester and ACT score. ACT
is a test used to assess the mastery of college readiness standards. Student with the high ACT score implies that they
accomplished necessary skills for studying higher education. As a result, they have tendency to get the higher score
in final exam, as well as GPA. The coefficient of 0.0358472 indicates that
β

Student GPA of the term is expected to increase by 0.0358472 point when the ACT
Score of Students increases by one point, ceteris paribus.
To sum up, as mentioned in the Literature review part, there are various researcher
found that cognitive factors such as ACT Score and Cumulative GPA were a
significant predictors of Students’ GPA (Pascarella et al, 1981; Bean & Bradley,
1986; White & Sedlacek, 1986; Boyer & Sedlacek, 1988; Tracey & Sedlacek, 1989;
McLaughlin, 2006), there for, our results of 0.5570142 for Cumulative GPA and
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0.0358472 for ACT Score were significantly consistent with others research.
Since cognitive variables alone cannot adequately predict the academic success of, our
group combines others Non-cognitive factors such as Attendant Rate and Homework
Turned in by Students. The results of 0.0101788 and 0.0092821 represent for
Attendance Rate and Homework Turned in by Students alone were lower than
cognitive factors, also consistent with others related published.
IV.

Recommendations

1. For the student
We can see that the cumulative GPA has the biggest effect on Term GPA among 4
factors. As we discussed in part III.6, students should try to study from the very first
semester at university. In contrast to many students’ thought that as the first-year or the
second-year student, they don’t have to pay attention to the study and they can study
hard on the third-year to get the expected GPA, the study in the previous semester at
university will create the momentum for their studying process. Hence, students should
try to get the good GPA every semester.
Addition, Attendance also affects on GPA. Students should fully attend all the class

sessions. If students cannot attend class because of private business, they should ask
for permission from the teachers. These are the grades that students can easily get, so it
is not a good idea to miss the attendance-checking.
Together with Exam scores and Attendance record, the amount of Homework turned in
by students also plays an important part in the students’ overall academic performance.
Therefore, to maintain a good GPA, students are advised to make great effort to finish
all homework and tasks assigned by their lectures. Doing homework and assignment is
not only a way to review the lessons but also to deepen their knowledge by further
beyond books and classes.
ACT test score is also one of the key factors to determine students’ GPA. The ACT is a
curriculum-based achievement test, measuring what you've learned throughout your
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education. Therefore, it is also advised that students should take the test as it reflects
their process of learning. Apart from that, taking the ACT test in advance also is a good
value because it offers a college admissions test, college course placement, and a
career planning component for one modest fee. Furthermore, students can make
themselves visible to colleges and scholarship agencies across the country by taking
the ACT. The higher the score, the higher the likelihood that students would be more
competitive in the eyes of the colleges’ board of selection.
2. For the researchers
According to this study and the regression analysis, only 56,2% of variance in Grade
Point Average determined by the tested variables. Therefore, some other variables
which were not considered in this study would still be contributing to determining the
Grade Point Average of the students. As we all know, many other external factors
could also act as barrier and catalyst to students achieving a good overall academic
performance such as gender, previous academic performance, living place and income
level of family, social environment, time spend for studying and living place during the
university life or some personal preference such as learning ability, leisure activities

and so on. However, we sincerely hope that our research can contribute to the
university administration, policymakers, students as well as students’ families in terms
of identifying the factors that have an impact on the students’ success. And we also
hope that future research in the field can further analyze those other factors so that we
can come up with a more complete model.

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CONCLUSION
In conclusion, аs dеtеrminаnts such аs finаl tеst scorеs, thе numbеr of clаssеs studеnts
аttеnd, homеwork turnеd in by studеnts, аnd ACT scorе cаn hаvе аn impаct on thе
studеnts’ аcаdеmic succеss, it is vеry importаnt to pаy аttеntion to othеr vаriаblеs thаt
аffеct thе studеnt’s Grаdе Point Avеrаgе (GPA). Effеctivе timе аnd еffort spеnt on
studying еnаblеs studеnts to improvе thеir аcаdеmic pеrformаncе аt univеrsity to
аchiеvе highеr Grаdе Point Avеrаgе.
Whilе acadеmic achiеvеmеnt is onе of thе essential factors considеrеd by thе
еmployеr in rеcruiting workеrs, studеnts should makе grеat еffort in thеir study to
obtain a good gradе in ordеr to fulfill thе еmployеr’s dеmand. At thе samе timе, thе
pеrformancе of studеnts in univеrsitiеs should bе a concеrn not only to thе studеnts
thеmsеlvеs, but also to еducators and еducational institutions.
Idеntifying thе fаctors thаt аffеct а good pеrformаncе of studеnt's GPA doеs not only
providе studеnts with informаtion on how to improvе thеir pеrformаncе to obtаin
grеаtеr аcаdеmic succеss but аlso givе suggеstions to еducаtionаl institutions to
prеvеnt low pеrformаncе of studеnt's GPA аs wеll аs to dеsign а succеssful modеl to
аchiеvе good GPA.
Thе findings of this study mаy hаvе аn importаnt contribution to thе univеrsity
аdministrаtion, policymаkеrs, studеnts аs wеll аs studеnts’ fаmiliеs in tеrms of
providing thеm with fаctors thаt hаvе аn impаct on thе studеnts’ succеss.

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