the effect of background music to college students academics

De La Salle University – Manila

The Effect of Background Music to College Students' Academic Performance

An Empirical Paper presented to

Faculty of the Economics Department

School of Economics

In Partial Requirements of

Econometrics 1 (ECONMET)

Submitted to:

Dr. Cesar C. Rufino

Submitted by:

Kimberlyn T. Tiu

September 6, 2013

Background Music to Academics Performance | K. Tiu

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Executive Summary

The study explained whether there is a significant relationship between

background music to academic performance of college students. The relationship was

studied based from thirty-five (35) respondents in the Philippines taken through a survey

questionnaire. The data analysis was done using the statistical software namely: Stata

12th and Gretl. For the data gathering, the proponent used Google form and Google

spreadsheet. Initially, only two out of four variables were deemed to be significant which

were hours listening to music and pop music genre. However, sex and hours studying

were not significant. But after testing the model, the model was not suffering from any

violations.


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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ...................................................................................... 6

1.1 Background ............................................................................................................ 6

1.2 Statement of Research Problem ............................................................................ 6

1.3 Significance of the Study ........................................................................................ 7

1.4 Scope and Limitation .............................................................................................. 7

CHAPTER 2: REVIEW OF RELATED LITERATURE .................................................... 8

2.1 Music ...................................................................................................................... 8

2.1.1 Type of Music Genre .................................................................................... 8

2.1.1.1Pop .................................................................................................. 8

2.1.1.2 Rock ............................................................................................... 9

2.1.1.3 Classical ....................................................................................... 10

2.1.1.4 R&B .............................................................................................. 10

2.1.1.5 Country ......................................................................................... 10

2.1.1.6 Jazz/Blues .................................................................................... 11

2.1.1.7 Hip-hop ......................................................................................... 11

2.1.1.8 Modern Folk ................................................................................. 12

2.1.1.9 Electronic ..................................................................................... 12

2.1.1.10 Asian .......................................................................................... 12

2.1.1.11 Comedy ...................................................................................... 13

2.1.1.12 Caribbean and Latin American ................................................... 13

2.2 Academic Performance ........................................................................................ 14

2.3 Biological sex (Gender) ........................................................................................ 15

2.4 Listening to Music ................................................................................................ 15

2.5 Studying ............................................................................................................... 15

2.6 Research Gap ...................................................................................................... 17


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CHAPTER 3: OPERATIONAL FRAMEWORK ............................................................. 18

3.1 Variable Used in the Study ................................................................................... 18

3.2 A-priori Expectation .............................................................................................. 20

3.3 Introduction of Hypothesized Econometric Model ................................................ 21

3.4 Statement of Hypothesis Testing ......................................................................... 22

CHAPTER 4: METHODOLOGY ................................................................................... 23

4.1 Research Design.................................................................................................. 23

4.2 Sampling Plan ...................................................................................................... 23

4.3 Method of Data Analysis ...................................................................................... 26

CHAPTER 5: EMPIRICAL RESULTS AND INTEPRETATION ........................................ 29

5.1 Data Collection Summary and Regression .......................................................... 29

5.2 Testing ................................................................................................................. 31

5.2.1 Testing for Multicollinearity ......................................................................... 31

5.2.2 Testing for Heteroskedasticity .................................................................... 33

5.2.3 Testing for Mis-Specification ...................................................................... 37

CHAPTER 6: CONCLUSION AND RECOMMENDATION ........................................... 40

BIBLIOGRAPHY ........................................................................................................... 41


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List of Figures Figure 3.1: Operational Framework ........................................................................... 19

Figure 3.2: Model specification .................................................................................. 22

Figure 4.1: Flowchart ................................................................................................. 23

Figure 4.2: Survey Form ............................................................................................ 24

Figure 5.1: Data Collection Summary ........................................................................ 29

Figure 5.2: Initial Ordinary Least Square (OLS) Regression ...................................... 29

Figure 5.3: Initial Econometric Ordinary Least Square (OLS) Regression Model ...... 30

Figure 5.4: Ballantine Diagram ................................................................................... 31

Figure 5.5: VIF Criteron .............................................................................................. 32

Figure 5.6: Graphical Illustrations of the Differences between Homoscedasticity and Heteroskedasticity (Imdadullah, 2013) ....................................................................... 33

Figure 5.7: Breusch-Pagan-Godfrey (BPG) Test for heteroskedasticity ..................... 34

Figure 5.8: White’s Test ............................................................................................. 35

Figure 5.9: Koenker and Basset Test ......................................................................... 36

Figure 5.10: Ramsey’s RESET Test (squares only) ................................................... 37

Figure 5.11: Testing for Normality .............................................................................. 38

Figure 5.12: Summary of the Testing ......................................................................... 39

List of Tables Table 2.1: Grading system of De La Salle University-Manila ..................................... 14

Table 4.1: Data Collection .......................................................................................... 26

Table 4.2: Assumption of CLRM ................................................................................ 27

Table 4.3: Type of Violation ....................................................................................... 28


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CHAPTER 1: INTRODUCTION

1.1 Background

Everyone has music. It can be different from one person to another (Titon, 2009).

Music had been part of people's lives from generations to generations. It affected the

body unconsciously and consciously. People, when listening to music, may have a

series of action that can be a combination of tapping, nodding, swaying, clapping,

stamping and many more (Daunow, 1977). It thought to be as a form of entertainment,

however, it can be used to affects behaviour (Milliman, 1982). In addition, music can be

used as a form of therapy for a mental illness such as anxiety. It can increase

productivity and minimize boredom (Peretti & Swenson, 1974). Meyer (as cited in

Peretti & Swenson, 1974) said that music could affect emotions, while Lidz (as cited in

Peretti & Swenson, 1974) can help relieve the person of their own tensions and

frustrations.

The study focused on the effect of background music to college students'

academic performance whether it is positive or negative in the Philippine setting.

1.2 Statement of the Research Problem

The study attempts to identify whether background music has an effect to the

academic performance of the selected random college students. In this study, the

dependent variable is the academic performance of the students, while the independent

variables are sex (gender), length of time studying per week, length of time listening to

music per week and pop music genre as dummy variable. The result will be shown later.


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1.3 Significance of the Study

The study intends to show whether students' performance in terms of cGPA are

affected by independent variable such as the degree of level in listening to music,

studying time. Lastly, biological sex (gender) and music genre (pop) were used as

dummy variables.

1.4 Scope and Limitation

The dataset obtained for study was taken from thirty-five (35) random college

students who were based in the Philippines. The data collection was completed through

survey, while the data was analysed using Stata 12th and Gretl. The nature is cross-

sectional which the data are observations from subjects with or without the difference in

time (Gujarati & Porter, 2009).

The result of the study cannot be used to generalize, however it is true for those

who have taken the survey.


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CHAPTER 2: REVIEW OF RELATED LITERATURE

In the review of related literature, there were studies about music and academic

performance. In addition, there were information about biological sex (gender), listening

to music and studying.

2.1 Music

Music is something that is produce by man that can be form into a work of art or

complement the activities (Titon, 2009). Music was defined as a form of entertainment

that lessens boredom (Milliman, 1982). Music can be generalized as type of genre

such as Pop, Rock, Classical, R&B, Country, Jazz/Blues, Hip-Hop, Modern Folk,

Electronic, Asian, Comedy, Caribbean and Latin American music.

2.1.1 Type of Music Genre

2.1.1.1 Pop Music

Pop music is different from popular music. Popular music is the same as music

which is for the urban middle class that include different music from classical to heavy

metal. However, pop music had come from the roll n' roll evolution (Lamb, 2013). Pop

music appealed to teenagers that focus on rhythms and romance (thefreedictionary,

2013). The lyric for pop song contained verses that are repeatative that has usually a

duration of 2.50 minutes to 5.50 minutes. Pop music were influenced by other genres in

many ways for the past years (Lamb, 2013).


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2.1.1.2 Rock

During the 1960s, rock music progressed into three type of metal as follows:

country rock, progress rock, and heavy metal (Bennett, 2001).

According to Straw (as cited from Bennett, 2001), “heavy metal represented a

return to the more gritty aesthetic of rock ‘n’ roll while at the same time ‘retaining

from psychedelia an emphasis on technological effect and instrumental

virtuosity’.”

The origin of heavy metal were still debated among writers that one argued that

one US band in the 1960s created the form compared to another author who said that

Britain started heavy metal sound. By mid-1980s, there was an expansion of heavy

metal to pop music which can be called ‘soft metal’ due to the popularity of Bon Jovi,

that the band diversified the classic heavy metal into a combination of guitar-based

sound with keyboard and string sound of pop music. It featured romantic love and

upbeat sound which enticed the female audience. Yet again, it was short lived and

changed into more hardcore “Extreme Metal” that glorified sex, drugs, and rock n roll.

More people, who turned into extreme metal fans, were encouraged to be more

rebellious. They were at risk of having the state of loneliness, or depression, which

people were likely to become the worshipper of Satan (Bennett, 2001).


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2.1.1.3 Classical

According to Cooley (2009), classical music is a model that has support of

institutions like the governments and social organizations. It often based on the "written

sources such as music notation and written treatises about the music". Frequently heard

in churches, festivals, celebrations, and more, classical music considered to be renown

around the world.

2.1.1.4 R&B

R&B or rhythms and blues music, formerly called "race music" had musical

rhythms from other genre such as jazz, gospel and blues. Originally, for black people, it

used to focus on disco dance rhythms. After 1950s, it evolved into a type of upbeat

sound rather than the original that used the classical instruments such as violin (Nero,

2013; Urban Dictionary, 2013). Notably for its sexual suggestive lyrics and dancing,

famous artists like Rihanna, Bruno Mars, Chris Brown and Beyonce are good example

of this (Artist Direct, 2013)

2.1.1.5 Country

Country music contained a mixture of various musical forms such as old English

ballads, heart songs, guitar tunes, breakdowns and novelty songs. In addition to that, it

also included country blues and religious songs. It had a distinctive sound which gave

out a special feeling, the closeness to reality. The influences of the country music were

represented by the contemporary sounds of the violins, brass, and more (Rublowsky,

1967).


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2.1.1.6 Jazz/Blues

Jazz music is from African-American origin that has characteristics of rhythms

and improvisation that uses idioms which exist in numbers of styles like Blues. Blues is

one of the genres under Jazz (thefreedictionary, 2013). Not only that, it was considered

to have the combination of all melodies and rhythms by the people of the New World.

The examples of the artists were as follows: Louis "Satchmo" Armstrong, the Supremes,

and Bert Keyes (Rublowsky, 1967).

Jazz started before the 1850s when some of its characteristics were parts of

previous musical traditions (All About Jazz, 2013). It was considered to be a secular

music which evolved around emotional and spiritial demand of the people. It

represented the Negro people's style. Jazz's ways had been influencing the people on

all aspects of popular music today and beyond (Rublowsky, 1967).

2.1.1.7 Hip-Hop

During the 1970s in New York City, a group of people were fighting because of

the tension from urban renewal programs and recession. The person namely Afrika

Bambaataa made a group that helped people to channel their anger into music and

dance rather than physical brawls. Rap music (or hip-hop music) that can be narrative

vocal which was spoken in rhythm continuously had become a way to communicate in

their place. Across the globe and in time, hip-hop music was incorporated into their local

culture that had become the means of cultural expression (Bennett, 2001).


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2.1.1.8 Modern Folk

Modern Folk examples were as follows: Bella Hardy's Songs Lost & Stolen,

considered to be beautiful and simple, and Christy Moore's Folk Tale, helping people to

address issues of the day with his voice (Matthews, 2012).

2.1.1.9 Electronic

In the past, electronic was defined to be acoustic in nature that live performers

pluck the strings and more. But in the 1940s, Pierre Schaeffer created first ever studio

for electronic music which helped him process pre-recorded sounds. Now, electronic

music meant having synthetic sounds, manipulated by the electronics, not anymore

acoustic (Types of music, 2013). Top dance/club play songs as of September 1, 2013 to

September 7, 2013 were from electronic music. Examples of these songs were Avicii's

"Wake Me Up!", and Enrique Iglesias's "Turn The Night Up" (Billboard, 2013).

2.1.1.10 Asian

Asian music is diverse from different cultures from parts of Asia such as India,

Indonesia, Philippines, China, Japan, Korea and more. In India when they were

colonized by the British rule to date, valuing art (including music) is their tradition. With

the support from their government, Indian musician have many connection to improve

their skills and talents. There is even an academy namely Sangeet Natak Academy that

gives out prestigious awards to those deserving it. Indian music has inspired people

such as Ravi Shankar and George Harrison of the Beatles, one of his student. Some of

Indian-based songs from Beatles' album were "Within You, Without You" and “My sweet

Lord" (Reck, 2009).


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Those have the influence of the raga (the melodic system) that has its own

personality. It can be associated with emotions, colors, seasons, animals, and more

(Reck, 2009). Another type of Asian music is Korean pop music, or commonly known as

K-pop. It is very popular among teenagers, Asian or non-Asian. It has started shortly

after the hit of pop music in Korea. K-pop can be identified easily by entertainers with

their trendy outfits and songs with electrifying tones (Tesol, 2013).

2.1.1.11 Comedy

The comedy genre was closely associated with amusing actions that partake in

plays, parody videos, comedy rock, and more (thefreedictionary, 2013).

2.1.1.12 Caribbean and Latin American

From the Latin American countries such as Cuba, Latin music was born. It

contained sounds that were from the African religious ceremonies. It is commonly

known for its rhythm. It has elements and styles from Salsa, Tango and Brazilian music

(Quintana, n.d.).


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2.2 Academic Performance

The academic performance was based on the grading system of De La Salle

University which can be seen below.

Table 2.1: Grading system of De La Salle University - Manila

Grade Point Grade range Description

4.0 97 to 100 Excellent

3.5 93 to 96 Superior

3.0 89 to 92 Very Good

2.5 85 to 88 Good

2.0 80 to 84 Satisfactory

1.5 75 to 79 Fair

1.0 70 to 74 Passed

0.0 Below 70 Failed

Audit - Audit

6.5 - Withdrawn

7.0 - Passed (For Pass/Fail Courses)

8.0 - Failed (For Pass/Fail Courses)

9.9 - Deferred (For Thesis and Practicum)

For the cumulative GPA, it referred to the weighted average of the grades for all

academic courses taken during his/her stay at the University (De La Salle University -

Manila , 2012).


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2.3 Biological sex (Gender)

Biological sex, or simply, sex and gender were similar yet different. Sex pertained

to the biological aspect of the human body whether they were categorize as either male

or female or intersex (that meant the person was combined with the features that

separate male from female and vice versa). However, for gender, it was said that it

refers to the behaviour and feelings of the person associated to their biological sex

(American Psychological Association, 2011).

2.4 Listening to Music

Listening to Music while doing something like studying helped people to relax,

reduce blood pressure, remedy for headaches and migraines, improve intelligence and

concentration, and lastly good coordination (Eastday.com, 2010)

2.5 Studying

Studying methods can be different one person to another. But, according to Duff,

Boyle, Dunleavy, & Ferguson (2002), there were three approaches (deep processing

approach, surface approach, and strategic approach. If a person is using the deep

processing approach, that person related it to their personal experiences in life critically.

However, surface approach used more on memorization, while, strategic approach rely

more in organized and study skills to achieve the highest grade possible. Two of them,

deep processing and strategic, were more likely to help boost up academic performance,

but not surface approach.


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In related to studying, the environment including educational institutions might

have an effect on their performance. According to Rufino (2006), the institutions for

higher education (College & University Level) differ from public and private. One evident

fact was that more people enroll in private schools compared to public. When more

people are together in a class, they might be pressured to study or not depending on

their company. For Gneezy and Rustichini (as cited in Bellemare, Lepage, & Shearer,

2010), they said that male, not female, were more affected by peer pressure negatively

which their working producivity deteriorate. In addition to that, teenagers who were more

influenced by peer pressure were low achievers that they overestimate their skills, yet

underestimated others. It is the opposite for high achievers (Sung, Chang, Chang, & Yu,

2010).

Studying skills depends on their field. People who were engaged in physical

education or sport teaching may be different from people engaged in classroom

teaching. Yet, both get distracted while in class and do listen to music while studying.

Also, students from physical education and sports teaching procrastinate more than

student from classroom teaching (Pepe, 2012). Not only that, DiVesta and Gray (as

cited in Hassanbeigi, et al., 2011) alleged that students who have good studying skills

such as taking down notes and reviewing it benefit more than getting the notes from

other people. Reasoned out by Cogmen & Saracaloglu (as cited in Hassanbeigi, et al.,

2011), students must "use reading comprehension strategies while reading academic

materials for an effective learning." Doing this would help anyone to have a higher

cGPA and be more motivated and happier. With all of those factors, the students would

decide on their studying skills to attain their target cGPA (academic performance).


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2.6 Research Gap

There were numerous studies about music in general, the music genre, and

studying around the world. However, there seem to be no study of music to academic

performance in the Philippines setting. The study aims to determine whether there is a

relationship between music and academic performance.


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CHAPTER 3: OPERATIONAL FRAMEWORK

3.1 Variables Used in the Study

These are the variables used in the study to check the relationship between

background music to academic performance of the students which can be seen below:

Variable Label Measurement Definition

Cumulative Grade Point Average

(cGPA)

cGPA

Highest is 4.0, while the lowest is

1.0.

Measurement of academic performance in his or her

stay at the University

Biological Sex

(Gender)

SEX

1 if Male

0 if Female

Biological Aspect of the

human body, not the behaviour of the person

pertaining to the biological form of the person

Music in a Week

MWk

Time - Unit of Hours

Duration of the person listening to music in a week

Studying in a Week

SWk

Time - Unit of Hours

Duration of college students' studying time in a

week

Pop Music Genre

Dgmpop

1 if pop music

0 if otherwise

(such as rock, classical, blues, and many more)

Person that listens to pop music


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All of the variables were separate into groups of independent and one dependent

variable that is cGPA which can be seen below (Figure 3.1).

Figure 3.1: Operational Framework

Independent Variables

Independent Variables, such as sex, listening to music per week, studying per

week, and pop music genre, are the factors that can stand on its own. It was used as

the controlled variables.

Dependent Variable

One dependent variable, cumulative Grade Point Average (cGPA), relied on its

relationship to the independent variables regarding it is positive or negative.

Dummy Variables

Dummy variables capture quantitative variables, seasonality and interaction. For

the case of the study, sex and pop music genre are the dummy variables

College Students

Independent

Variables

Sex

Listening to Music per

week

Studying per week

Pop music genre

Dependent

Variable

Cumulative Grade

Point Average

(cGPA)


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3.2 Apriori Expectation

The A-priori Expectation showed the independent variables, its description and

sign expectation which can be seen below.

INDEPENDENT

Variables

Variable Description Sign Expectation

SEX

According to American

Psychological Association

(2011), biological sex has a

effect on the mindset on the

person. For Sung et al (2010),

men and women who are

hardworking and humble were

more likely to achieve their

goals while underestimating it

lead to the downfall.

Positive (+) Relationship

MuWK

According to Pepe (2012),

students listen to music while

studying. For the researcher, It

may help boosting up their

academic performance.



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StAct

Hours studying may have a

significant effect on the grade

since the person is more

prepared rather than cramming.


Dgmpop

According to Milliman (1982),

slow tempo music, while

comparing to fast tempo music,

have higher gross sales

increase. It may be the same

for people listening to pop

music genre while studying.

Negative (-) Relationship


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3.3 Introduction of Hypothesized Econometric Model

Figure 3.2: Model specification

Model For Background Music Affecting The Academic Performance

cGPA = β0 + β1 SEX + β2 MuWk+ β3 StWk - β4 Dgmpop + u

With the signs expectation, the model was grouped together by variables which

can be seen at Figure 3.2: Model Specification. The β (beta's) represented the

coefficient for the independent variables. The u is known to be the random stochastic

variable or disturbance term, which represented the factors that were not taken account

in the model but affect the dependent variable. The cGPA equation (seen at Figure 3.2)

was categorized as classical linear regression model (Gujarati & Porter, 2009).

3.4 Statement of Hypothesis Testing

Null Hypothesis (Ho): There is no significant relationship between background music

and academic performance of college students.

Alternative Hypothesis (Ha): There is a significant relationship between background

music and academic performance of college students.


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CHAPTER 4: METHODOLOGY

4.1 Research Design

The research design is descriptive, the type of study which needs to collected

data without manipulation. It can be also called as co-relational or observational studies.

It can be used to demonstrate relationship between the independent and dependent

variables. For this study, it is cross-sectional in nature, which means relationships of the

variables (e.g. group of people) with or without the sense of time (U.S. Department of

Health and Human Service - Office of Research Integrity, 2013).

4.2 Sampling Plan

For the collection of data, survey form was used. The responses were taken from

a group of thirty-five (35) college students. The process of the data collection and

analysis can be seen in Figure 4.1: Flowchart.

Figure 4.1: Flowchart

Present Analysis of

Data Results

End

Answer

questionnaire

survey

Get respondents

Start

Process Data


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The survey form contained five (5) questions about biological sex (gender),

music and academic performance that can be seen below (Figure 4.2).

Figure 4.2: Survey form


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4.3 Method of Data Analysis

The data collected from thirty-five (35) respondents was gathered using Google

form and Google spreadsheet. The results can be seen below.

Table 4.1: Data Collection

SEX MuWk StWk Dgmpop cGPA SEX MuWk StWk Dgmpop cGPA

1 6 2 1 3.5 0 6 6 1 3.5

0 6 4 1 3.5 1 6 4 1 3

0 6 6 0 3 1 6 4 1 3

0 6 4 1 3 0 6 6 1 3

1 6 6 0 4 0 4 2 0 3.5

1 6 6 1 3.5 0 5 4 1 3

1 3 4 1 3.5 0 3 6 1 2.5

0 3 4 1 3 1 6 6 1 2.5

0 6 6 1 3 1 6 4 1 2.5

1 6 2 0 3.5 1 6 4 1 3

1 1 4 1 2 1 5 4 1 3

0 1 4 1 3 0 6 6 1 2.5

0 6 4 1 3.5 0 6 6 1 3

1 6 4 1 3.5 0 6 2 1 3

0 6 4 1 3 0 4 6 0 3

0 6 2 1 3.5 0 5 4 1 2.5

1 6 4 1 3 1 6 4 1 2.5

1 6 4 1 3.5

The statistical software to be used are Stata 12th and Gretl. The model must

undergo testing whether the model has explanatory power or not. First, before all of

these to happen, the analysis to be done is regression analysis method (Gujarati &

Porter, 2009).


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CLRM is the basic framework of regression analysis. The model would be check

whether it satisfies the assumptions of the Classical Linear Regression Model or CLRM.

According to Gujarati & Porter (2009), CLRM was thought to be the cornerstone of

econometric theory that was represented by seven (7) basic assumptions.

Table 4.2: Assumption of CLRM

Assumption Number Assumption Name Description

1

Linear Regression Model

Linear in parameter whether the model shows linear or not linear in the variables

2

Fixed X Values or X Values Independent of the Error Term

X variable(s) and the error term are independent, that can seen below: cov (Xi , uj )=0

3

Zero Mean Value of Disturbance ui

Value of the Disturbance is zero regardless it being expected or nonstochastic.

4

Homoscedasticity or Constant Variance of ui

Variance of the error and disturbance are similar to each other in the absence of X value. var(ui) = E[ui - E (ui | Xj )

2

var = σ2

5

No Autocorrelation between the Disturbances

Correlation between any X variables is zero.

6

The Number of Observations n Must Be Greater than the Number of Parameters to Be Estimated.

Reason


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7

The Nature of X Variables

Values in the sample should not be the same. The model must have a positive variance. In addition to that, there must be no outliers in the X variables.

Based on Gujarati & Porter's (2009) Basic Econometrics Chapter 3

To process the data, Ordinary Least Square (OLS) estimation was used.

Through the estimation, it must show However, if the one of the assumption is not

satisfied, the violation depending on the assumption that was violated (see table 4.3)

are present.

Table 4.3: Type of Violation

Assumption Number

Type of Violation Assumption Number

Type of Violation

1

Nonlinearity in

parameters

6

Sample observations less than the number

of regressors

2

Stochastic regressor(s)

7

Insufficient variability

in regressors

3

Nonzero mean of ui

8

Multicollinearity

4

Heteroscedasticity

9

Specification bias

5

Autocorrelated disturbances

10

Nonnormality of

disturbances

To check, the model would undergo testing of Multicollinearity, testing for mis-

specification and testing of Heteroskedasticity, but not testing for autocorrelation. The

data is cross-sectional which autocorrelation is not present.


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CHAPTER 5: EMPIRICAL RESULTS AND INTEPRETATION

5.1 Data Collection Summary and Regression

With all of the variables put to the model, it was determined to be linear-linear

type of model. There were a total of thirty-five (35) respondents.

Figure 5.1 Data Collection Summary

As seen in Figure 5.1, the average mean of sex was 0.4571429 which meant that

there were more female than male respondents. People, having 3.071429 cGPA,

normally listen to pop music for 5 hours and more and study for 4 hours and more. With

the standard deviation ranged from 0.422 to 1.42, the proponent can say that


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Figure 5.2: Initial Ordinary Least Square (OLS) Regression

The OLS or Ordinary Least Square, by Carl Fredrich Gauss, was used widely in

regression analysis. It is the procedure that was used to estimate the CLRM. OLS must

be BLUE or Best Linear Unbiased Estimator (Gujarati & Porter, 2009).

According to Gujarati & Porter (2009), "Higher the R2, better the explanatory

power of the model." It shows the proportion of the dependent variable by the

independent (explanatory) variables. With the overall coefficient of determination or R2

as 0.2537, the model has explanatory power and fit.

Figure 5.3: Initial Econometric Ordinary Least Square (OLS) Regression Model

cGPA = 3.21004 - 0.00698632 SEX + 0.0975183 MuWk - 0.0714411 StWk - 0.394132 Dgmpop

For the initial Econometric OLS regression model and based on the Apriori table,

sex and pop music genre really have negative relationship with cGPA. Listening to

Music per week is positive. Contrary to the table, it assumed that the person who study

more has worse cGPA compared to the person who study less but better cGPA.


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5.2 Testing

To know if the model do not suffer from the violations of the assumption, the

model undergo testing.

5.2.1 Testing for Multicollinearity

The testing for Multicollinearity check the model if it violated the critical

assumption of non-multicollinearity. The model must show no high correlation among

the X’s variables. If violated, it faced a grave consequence that the model is misleading.

There are four types of multicollinearity based on Ballantine Diagram.

Figure 5.4: Ballantine Diagram

No Multicollinearity can never ever happen in real life since it is made in heaven.

It meant that X2 is independent of X3

Perfect Multicollinearity is an academic type that is synonymous with dummy

variable trap.

Dangerous (Imperfect) Multicollinearity is the worst kind of multicollinearity that

X2 and X3 are highly correlated.

Perfect Multicollinearity

Dangerous

Multicollinearity

No Multicollinearity Made in Heaven

Tolerable Multicollinearity

y

X2 X3

y X2 X3

y X2 X3

y

X2 X3


32

Tolerable Multicollinearity is the type of multicollinearity that the people can

accept. The reason for its acceptance was that the X2 and X3 are lightly

correlated.

To check if the model suffers multicollinearity, the proponent used Variance-

Inflating Factor (VIF) criterion, rather than Auxiliary F test or L. Klein Rule. The VIF

criterion rules that if the variable has value more than 10, then it has dangerous

multicollinearity. If there is dangerous multicollinearity, the person must remove the

variable that has the highest VIF. It is the variable that has many relationships in the

model. In order to correct the model, there are various measures such as usage of

advanced tools (e.g. Ridge Regression and Principal Components Analysis), adding

observation or replate sample, and make variable transformation.

For the study, all of the variables have the value below 10 (see Figure 5.5) so the

model has tolerable multicollinearity. Since it is not dangerous, there is no need to drop

the Highest VIF variable. No need to do anything, since it is tolerable.

Figure 5.5: VIF Criteron


33

5.2.2 Testing for Heteroskedasticity

Heteroskedasticity is the violation of the CLRM assumption for homoscedasticity.

This type of violation is often found in cross-sectional (CS) dataset. A graphical

illustration by Mr. Muhammad Imdadullah (2013) shows the difference of homo and

hetero graphically below at Figure 5.7.

Figure 5.6: Graphical Illustrations of the Differences between Homoscedasticity

and Heteroskedasticity (Imdadullah, 2013)


34

When the model has heteroskedasticity, then it face a graver consequences

compared to multicollinearity. Everything is wrong from the OLS to the standard errors.

It would be the breakdown of inference. To check if the model is diagnosed with

heteroscedasticity, there are two approaches: graphical approach and formal statistical

test. In the graphical approach, homo graph shows scattered sample, while the hetero

graph display a pattern. The formal statistical tests used for the study were the Breusch-

Pagan-Godfrey (BPG) via Stata 12th, Koenker and Basset Test and White’s Test via

Gretl. There are other tests like Park’s Test and Goldfeld-Quandt (GQ) Procedure. The

procedure for the BPG was first done by running the model and getting the average

likelihood estimator. To pass the BPG which meant to have no heteroskedasticity, the

null and alternative hypothesis was proposed. The null shows the homoscedasticity of

the model and the alternative as heteroskedasticity.

Figure 5.7: Breusch-Pagan-Godfrey (BPG) Test for heteroskedasticity

With the p-value of 0.3104 in the BPG test, the model satisfied the condition of

homoscedasticity. Using Gretl software, the model was once again tested for

heteroskedasticity.


35

Figure 5.8: White's Test

The White’s Test, compared to other tests, have the advantage of unbiasedness

since it does not assume any functional forms of heteroskedasticity. It evaluated it on

the auxiliary regression with squared dependent variable from the independent

variables (Laura, 2012). The White’s test was given a nickname of specification-robust

test because of its approximating many possibilities of the square powers and crosss-

products of the independent variables (Universidad Autonoma de Madrid, 2013). The p-

value overall and per variable shows that it exceed the 0.05 that support the model

having no heteroskedasticity.


36

Figure 5.9: Koenker and Basset Test

The Koenker and Basset Test, by Roger Koenker and Gilbert Bassett, Jr., used

random coefficients which specifically used a functional form for heteroskeadsticity. The

difference between this test and the Breusch-Pagan-Godfrey (BPG) test was that the

residuals were made to be studentized. With the p-value as 0.075100, the model is truly

free of heteroskedasticity.


37

5.2.3 Testing for Mis-Specification

Mis-specification is the violation of the model being bias. It might have problems

such as omitted variable bias, irrelevant variable bias, and incorrect transformation. The

model was checked through the Ramsey’s RESET Test via Gretl to check whether the

omitted or incorrect or both are present in the model.

The statement of the hypothesis for the testing of mis-specification was that the

null hypothesis is correctly specified and alternative is not correctly specified.

Figure 5.10: Ramsey’s RESET Test (squares only)

With all of the variables having the p-value higher than 0.05 (level of significance),

the null hypothesis is true. The model is correctly specified.


38

5.2.4 Normality Testing

Figure 5.11: Testing for Normality

The testing for normality checks whether the terms in the datasets were normally

distributed. Another definition is that the test computes on what degree does the

underlying random variable normal. The null hypothesis indicated that the data set

terms are normal; otherwise, reject it since it shows irregularity in the model.

As shown in Figure 5.12, the p-value is 0.75474. It shows that the model has

been normally distributed.


39

Figure 5.12: Summary of the Testing

Through a series of testing, the model has proved true that it is tolerable,

normally distributed and correctly specified. No violation has occurred. The model has

shown to have no bias. The OLS regression has the Best Linear Unbiased Estimator.

The initial model is now the final model for OLS.


40

Chapter 6: Conclusion and Recommendation

In the result and discussion, two variables (listening to music per week and pop

music genre) have shown to have significant effect on the student’s academic

performance. The biological sex whether the student is a male or a female do not really

matter when it relate itself to their academic performance.

Also, the number of hours studying is insignificant. According to Thompson (2013)

that shows the amount of time studying do not affect the performance but the strategies.

How the person copes up with the studies like reading the whole book or notes twice or

thrice or answering the homework can help the person increase their mastery that help

keep up their academic performance.

The proponent suggested the study can be improve by add more variables since

there can be factors that might be beneficiary which the proponent did not cover.

Studies such as one music genre (Rock, Classical and more) to academic performance

can be one example. The sampling can be focused in one educational institution.


41

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