[FULL ACCESS] Analysis of Social Media Influence on Tinder

This assignment involves a statistical analysis of the relationship between social media usage (Instagram, YouTube, and Facebook) and the influence on Tinder or other similar dating apps. The study uses Stata to run a regression model and perform tests for heteroskedasticity. Results show a significant positive correlation between Instagram usage and Tinder, while YouTube and Facebook have less impact. However, there is evidence of non-constant variance in the residuals, suggesting further analysis is needed.

Effect of Screen Time on Human Social Behavior
by
Thesis 1
Supervisor:

Abstract
Nowadays we spend lots of time in technologies like tab, mobile phone. The more we are
getting used to this technology the less time we are spending with family and society. As a
consequence we become isolated by using frequent technical devices.
The intention of this thesis is to determine the use of anti smartphone addiction in cell
phone and reduce the screen time to increase the social connectivity.
With this growing usage of smartphones is really challenging phenomena become very
apparent: People nowadays are shifting their normal lifestyle and turn into addicted to
diverse services that those smart phone or electronic devices offer us. In this thesis we
present some screen loc apps as app detox:
AppDetox means it will help us to cool of our mobile apps usage, and acquire the
digital detox to reduce the time we spend in these devices. We are capable to put our own
rules for our apps to detox from a number of intense usages and stop procrastinating on
social media and other things on internet. We explain here our exploitation of the apps and
present the preliminary findings gained through observation of about 1,00 users of the
application in few universities. We discover that people are rather careful when limiting
their app usage, and that mostly they restrain use of social networking as well messaging
apps
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Table of Contents
Abstract...................................................................................................................................2
Table of Contents....................................................................................................................3
List of Figures.........................................................................................................................5
List of Tables...........................................................................................................................6
Chapter 1
Background.............................................................................................................................7
Chapter 2
Literature Review....................................................................................................................8
2.1Introduction....................................................................................................................8
2.2Social , Corporate and Family life effected by this screen time...................................17
2.3Historical Context.........................................................................................................18
2.4General Information and Statistical Evidence..............................................................19
2.5Theoretical Framework ...............................................................................................20
2.6Thesis Objectives..........................................................................................................21
2.7Related Works..............................................................................................................22
2.8Thesis Problem.............................................................................................................23
2.9Thesis Question............................................................................................................24
2.10 Screen lock app..........................................................................................................24
2.11Screen time by Gender...............................................................................................26
Chapter 3
Methodology.........................................................................................................................28
3.1Test of Variables ..........................................................................................................29
3.1.1Descriptive Summary of Variables........................................................................29
3.2OLS Regression analysis..............................................................................................29
3.3Diagnostic Test of the Models......................................................................................30
3.3.1Multicollinearity Check.........................................................................................30
3.3.2Heteroskedasticity Check......................................................................................30
3.4Monte Carlo Algorithm ...............................................................................................31
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3.5Other details..................................................................................................................32
4Experiments.....................................................................................................................33
3.5.1University...............................................................................................................34
3.5.2Student...................................................................................................................34
3.5.3Data Collection .....................................................................................................34
3.5.4Data Collection Demo for 5 students.....................................................................35
3.5.5Data analysis ???....................................................................................................39
5.Result ????......................................................................................................................39
5.1. Presentation of results ????........................................................................................39
5.2.Limitation ????............................................................................................................39
5.3.Discussion????............................................................................................................39
References.............................................................................................................................41
4

List of Figures
5

List of Tables
6

Chapter 1
Background
This thesis focuses on the issue of people spending their time on tab or mobile or other
electronic devices and the influence of on individuals and society, as well as the connecting
reasons, with the specific attention on the role of screen time. Screen time does not mean only
the amount of time people spend in front of the computer or tab or other electronic device, we
label it is the combined volume of time people are spending on smart phones, computers and
multitasking with different devices.
For example, how much time the young people losing from their social life? How is it shifting
their normal life? How students absorb and how they interconnect with the society? How can
we measure it is excessive?
Rideout, Foehr, and Roberts (2010) from Henry J. Kaiser Family Foundation carried out a
detailed research from 1999 to 2010 with nearly 2,000 issues that parents stated an average of
nearly seven hours of the screen time every day students who are between 13-16 years,
whereas the average 10-18 years old is expected to spend 7-8 hours per day holding their eyes
with screens. Beside the time people spend in different media, they also highlighted in one
segment in this their screen time with multitasking abilities. Their result was considerable in
viewing the rising of social media or internet usage in the social life of youth and the total
screen time they really spend (Rideout et al., 2010).
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Chapter 2
Literature Review
2.1 Introduction
As people succumb to the addiction of social media and spending more screen time, there a
new medium has been introduced, to reduce the screen time the anti smartphone applications -
smartphone apps claim to assist us to resolve smartphone addiction. Though, the apps are
tricky as they spot to a inadequate understanding of smartphone obsession; the simplicity of
these apps appears conflicting to the difficulties of smartphone addiction. Furthermore, the
anti smartphone addiction app entails a problematical paradox; resolve the smartphone
addiction via smartphone.
As a result the thesis involves with the output of using the smartphone addiction apps-
OffTime and Flipd- tried to reveal how these app frame people’s screen time, on the context
of people’s social life. We can’t consider them as neutral as they are the product of human
decision-making. These are underpinned by hypothesis, discourses and norms which are
already circulating in society (Lupton 2014).
1. Identify the use of current technical tools where the members of our society spend every
day time and the influence on the social aspects
According to the view of Hale and Guan (2015) there are various methods and
techniques which are used by most of the people in their free time period. It can be analysed
that most of the people are spend their time on smartphones and TV. In this, large number of
8

individual are spend more time than ever watching videos, browsing social media and swiping
their lives away on their tablets and smartphones. Smartphone is that technology which is
successful among users that businesses and employees have trouble imagining a day without
them. These smartphones are used in providing accurate direction through GPS, take pictures,
play music and keep track of appointments and contacts. It is considered as cellular telephone
with an integrated computer and other applications which is not generally related with
telephones such as regulating and operating system, web browsing and ability to run software
users. They are able to sending emails and taxes as well as keeping calender of events for the
users. It is simply making calls and sending messages (Allen, 2013). A smartphones has
ability to support accessories which includes bluetooth headphones, power charging cables
and extra speakers.
As per the above described report, it can be analysed that smartphones are such
method where large number of people are spend their more time. This will assist in increasing
and enhancing knowledge about respective technique in better manner. In the society and
community, there are number of people who are mainly use smartphones in order to spend
their time. The emergence of communication and computing for mobile consumers devices is
on the evolutionary course which carry out interoperability and leverage the services and
functions. Smartphones have brought out a massive change in the lives of people. An
individual enjoy great comfort with the advancement in science and technology. Smartphones
are popular among people for the applications so they are offer best services to their
customers. People identify it quite easier to communicate and interact with people in different
manner and also access various things with the features that support smartphones (Bian and
Leung, 2015). The main thing which are enjoy by people to do with smartphones is have
entertainment and get connected with people in all over the world. When an individual are
spend their more time on online so this will make them far from society and their family
members. The usage of smartphones are directly affect on person personal and professional
life and it is create negative impact on their life. Advancement in technology gives people
great ease and it will keep them active. Most of the people are capable to spend time with
9

family and friends when they finish their works from home. It is an essential people in take
into account so it is positively aspect of smartphones and help in improving their lifestyle.
2. Recognise the causes for getting attracted to the screen time
On the basis of opinion of Maras (2015) Smartphones are analysed as great invention
which allows to have access the global communication, direction and maps. There are various
causes for getting attached with screen time which are reduces their memory power. It can be
analysed that an individual who are used smartphones while driving so this will be dangerous
for their life. They are create bad impact ion their life which is one of the important thing.
Most of the couples are sitting at the same table but does not have any time to speak with each
other. Families sit in a separate rooms where all the people are use different devices for spend
their time. It is directly effect on human people relationship where they does not maintain
relation with their peers, colleagues and other family members (Campbell and Choudhury,
2012). Many employees or staff members never leave the office as all their emails which are
important for their business operations are conducted in better manner. They never take time
off to unwind, vacation and even spend time with their family and friends. There are various
other causes by use of smartphones that are describe as under:
 Increasing Stress level – It can be analysed that when a person are use cell phone al
the day so this will create negative impact on their life. An individual who are spend
most of the time with their mobile phones so the are lead towards more prone to stress
and fatigue. Along with this, this can lead towards psychological disorders in some
cases.
 Sleep loss – It is the major cause which are develop due to more usage of cell phones.
Most of the people keep their mobile phones or smartphones nearby, at the time of
sleeping to respond to texts and calls with another person. They feel controlled to
remain achievable around the timepiece. It can lead towards sleep disruption and
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interruption and this will became irritable when they are sleep deprived (Coskun,
2013).
 Discourage socials gathering – Social gathering is becoming a rare occurrences in
present society and community. As the mobile communication has developed and
formulated largely with extreme reliability, people tend to finish their social
communications over phone. In addition to this, smartphones includes various apps
which are used by most of the people such as Facebook, twitter and Instagram through
which communication are developed in easy manner. There are various people who
are spend their time by sitting at home without caring about social gathering (Derks
and Bakker, 2014). As a result, people became unsocial and less communicate which
is not good for a society and community.
3. Drawbacks of the heavy screen time in all areas of life specially on social life
According to the view of Bucksch (2016) prevalence of smartphones are considered as
a greater method and technique in daily life which have been taken a turn from its original
purpose of staying connected through texting and calling to wider and more extensive
functionally such as capability to play games, find more friends and family members, connect
to the internet and so more. There are various areas in which screen time are heavily impact
on person's personal life such as health, social gathering and so more. There are various
drawbacks which are heavily impact on screen time on social life which are described as
under:
 Cost – It can be analysed that smartphones are not cheap and lower in price so there
are large number of users who does not purchase this due to lack of money.
Smartphones do not come cheap and with the producers constantly coming up with
new updated and upgraded technology, people, especially with the young generation
so they want to keep up and always have their latest model even if additional
functionality proves insufficient to warrant a new purchasing (Elhai, 2016). This will
mainly affect on human behaviour and its attitude which are fluctuate as per their
needs or demand.
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 Social networking – The prevalence of social networking may not be purely done
through smartphones, but this means it is definitely most commonly used method. It
can be analysed that social networking is slowly talking the place and location of
actual social communication and interaction. With the advanced use of social
networking sites, an individual can face various issues which directly affect on their
health and make them unable to maintain their mind.
 Destroy personal life – It is one of the major drawback which are develop while using
smartphones so this will create bad impact on person social life. When a person are
used more social media so this will create negatively impact on their social life
because they are not go outside which reduces their social gathering (Franko and
Tirrell, 2012).
 Internet addiction disorder – In a society, there are various users who are taking
internet around with them all the time in a small compact device so this can fit in their
pouch. Sometime it can demonstrate that it could be harmful to the users as there have
been many reported cases of internet addiction to date. There are large large number of
people who are addicted with internet which are directly affect on person's health. This
will create various impact on an individual mind such as stress, anxiety and many
more. With the more use of internet, this will create negative impact on person mind
and health.
 Dangers of distraction – There are enormous number of applications, web pages,
sports scores, TV and movies which are shows at the press of button on smart phone
(Haug, 2015). As a result, smart phones can be distracting. The temptation is to check
the phone during a conversation which can alienate colleagues and friends. It can lead
towards longer wait times when an individual purchase goods and services.
4. Features and behaviour of addiction control apps
On the basis of opinion of Jeremy Goldman (2019) it has been stated that overuse of
the mobile phone is proposed form of psychological dependence on the cell phones which are
related to the other formers of the digital media overuse for an instance internet addiction
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disorder or social media addiction. Some of the mobile users exhibit the problematic
behaviours which are concerned to the substance use of disorders. These kind of behaviour
can consists preoccupation with the mobile communication, time spent, excessive money on
mobile phones in physically or socially situations for an instance driving an automobile. On
the other hand, cell phones are being improved through expanding upon the functionalities
that turn in enhance likelihood of addiction and overuse. Addiction of cell phone is not listed
yet in Diagnostic and the Statistical Manual of Mental Disorders (Joo and Sang, 2013).
Overuse of the smart phone or cell phone can be result in number of various physical issues
that may be cause the permanent damage or difficult to be treat consisting digital eye strain,
enhanced illness, neck issues, male infertility etc. These all develop the negative impact on
health of people. In order to control the phone addiction, there are several applications
available on iOS and Android stores which aids in track the usage of mobile. For an example-
In iOS 12 Apple added a unique function that is known as “Single Time” that permit the users
to see how much the time they have to spent on phone. The addiction control applications
work generally through doing the two main things such as enhancing awareness through
sending the user usage summaries and notifying user when he or she has exceeded some of
the user- defined the time- limit for each apps category. Under this, there are some features
and behaviour of addiction control apps:
App Detox- It helps in calm down usage of mobile application and also take digital
detox. In this, user is able to be set own rules for applications to detox from more heavy usage
and also stop the procrastinating as well as phubbing. On the other hand, AppDetox will
remind to take the proper break and also stop heavy app usage. The main feature of AppDetox
is to calm down usage of mobile app and take digital detox.
Offtime- Thus addiction control app aids users to unplug through blocking the
distracting applications for an instance games and Facebook and filtering the
communications. It consists information on how much actually use the smart phone. The user
can select the tailored mode such as Family, work or Me Time to assure that user can have
access to things which required (Lanaj, Johnson and Barnes, 2014).
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BreakFree- It incorporates usage the tracking features which found in several same
applications but it differ in that it can break down information in easy- to- be understand
“addiction score”. This application makes it great choice for those people which like to be set
goals as well as challenges themselves. This application is effective to reduce the addiction of
phone.
Moment- This application tracks device to allows and usage to set the daily limits. It is
helpful in notifies if the user exceed them. On the other hand, user can use setting that can
force off phone through flooding screen with an annoying alerts when try to be extend screen
time. This type of application can used for the families with an option to track the family
device use from own phone.
Quality Time- It provides in- depth and unique analysis of smartphone activities
through tracking the total usage, individual apps and screen unlocks with daily, hourly and
weekly summary reporting options. This app gives ability to be curb habits through using an
actionable features allowing to be set own time restrictions such as 'scheduled breaks', 'take a
break' etc. these features can helpful in manage as well as control usage when required
(Lemola, 2015). On the other hand, quality time profiles give better options to users to block
the notifications as well as reject the phone calls with auto reply text messages.
5. Investigate the potential of these apps
According to the view of Kwon (2016) addiction control applications work generally
through doing the two main things such as enhancing awareness through sending the user
usage summaries and notifying user when he or she has exceeded some of the user- defined
the time- limit for each apps category. According to opinion of … it has been stated that
addiction control apps are helpful in minimise usage of mobile and smart phones. Under this,
there are some potential of control addiction applications mention below:
App Detox- This application able users with providing an option to lock the apps, limit
heavy usage and stop procrastination. The main potential of this application is that if user
break the rules then it will remind to take proper break.
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Offtime- In this app, user can create own profile where it can block notifications as
well as calls. The user can set the VIP contacts from which user can be continue to receive the
calls. On the other hand, main potential of this app is that user can restrict internet activity, get
alerts and usage to overall phone when it start to overuse device. This app let user focus and
also find the digital balance in hyper connected world (LiKamWa, 2013). Award winning lets
to monitor smartphone usage in the real time and also take proper dedicated time-outs from
digital. Analytics and initiative app make it easier to determine habits and also take the proper
action to change them in an effective manner. On the other hand, this app limits the social
media on smart phone that enable to control the usage of smart phone through tracking it in
the real-time as well as scheduling the time-outs that aids users break through.
BreakFree – It is that device or app which is established through internet need of sales
and marketing organisation. It is combined with usage of tracking mobile features and
applications in many similar apps but it is different when it is separate whole data and
information into easy parts. This is helpful in unlock the phone screen and comprehensively
logs their usage for the day. It is that system which develop great choice for such when they
are likely to set challenges and goals themselves.
Moment – It is that app which are designed to give extra push in order o spend more
time in real world with actual people (McTavish, 2012). This will assist in track moment and
allow to set regular limits. It is mainly used for reducing addiction among young and adults
who are mostly used this app in current time period. This is mainly used by families with the
option of tracking family device usage in the phone. Along with this, it is helpful in
identifying and finding actual location of an individual.
Quality Time – It is that app which offer an engaging and user friendly interface and it
will help them in tracking and organising app and usage of phone. They are provide real time
reports for better management of techniques and devices. It is useful in analysing in depth
usage of application and such device usage by managing and monitoring whole usage, total
screen unlocks, individual app usage and so more. With the using of tools and methods, it can
assist in set alert when a person use mobile and device for more than an hours at continuous
basis. This is more beneficial in order to manage smartphone usage to secure and save their
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valuable time period. This will allow an individual to track app, share top app features,
customise and exclude tracking by apps and so more.
6. Role of the family in dropping the risk of screen time using these apps
It is clearly shown that technology is very effective device which help them in dealing
with communicate and interact with domestic as well as international people. As per the view
of Lee (2016) Technology can be empowering for adults for all ages with the techniques in
order to assist in learn fun and engaging ways, express their innovative and creative mind.
This is required for family members is to perform major functions and actions which assist
them in dropping the risk of using such apps. They are using various app which aid them in
interacting and communicating with different level of people in various regions of countries.
Along with this, they need to analyse and examine various people habits which are required to
reduce in better manner related to smartphone usage. The role of family members is to
eliminate risk and factors which assist are directly affect on human behaviour. In current time
period, families are mainly concentrate on their children’s so they are play an essential role in
reducing risk which occurs due to more use of devices and smartphones. Addiction related
apps are considered as low cost, easy to use and it is private device which help in recovery
procedure. There are various apps which are designed and developed by using health
behaviour change and power of social assistance (Oulasvirta, Rattenbury, 2012). Generally,
such apps includes motivational notifications, sobriety trackers and quick access to learning
resources and searcher meetings. Such apps are useful and effective at the time of recovery
and they does not replace attention of meetings and consulting.
Along with this, it is used for maintaining and managing all apps which are useful in
controlling children’s behaviour and attitude. Most of the adults are use smartphone through
which there are issues faced by them related to stress, depression, anxiety and so more. This is
required for family members is to control and focus on their kids who are mainly use devices
in better manner. With such apps, they are seeing their child at every time every day and
concentrate on their activities which are performed by them. It is useful and helpful in
maintaining their regular actions that are essential for reach with set goals. It may seen that
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counter-intuitive to use an app to unplug the mobile. There are various apps which are used by
families in order to safe and secure their children’s behaviour and attitude towards other. This
will assist in maintaining and managing all required human behaviour towards using of
different techniques and methods in better manner.
2.2 Social , Corporate and Family life effected by this screen time
A recent study from Florida State University (FSU) which states that Smartphone’s
notification can harm our attentiveness as well as reducing the productivity even if its for a
short duration it plays a negative role in our concentration to work or study by derailing our
mind from the focus to work.That causes a fatal result in a few definite circumstances, for
example driving; even a simple notification can be the reason of a fatal accident and which
led to death.
Most of the employees in the multinational companies have social media accounts. Many of
them are getting accustomed to check their accounts multiple times during their office time.
People spending their on social network means it is the time that is not being spent on the
work tasks. Moreover it is the time not being spent communicating with co-workers and
missing a chance to develop quality work relations.
People spend hours after hours per day on TV, mobile. It plays a negative role in their mood
when they spend this long period on screen. In fact, we’re more likely to develop poor mental
health, including the signs of frustration and anxiety.
Since last decade, many researches show that the harmful influence of social media on affairs
and conjugal relations. Constantly these researches describe that people who has regular
access on social media; they are more expected to involvement of the contradiction with their
partners.
These study also reveals that there is a strong relation between using the social media and a
declining the value of a marriage life. As a result, concentrating on our social network
17

friendship can rapidly ruin our social life as well as real life communication and relationship
with our partner.
It rationally pursues that the social media is becoming a villain in recent divorce actions. In
recent times, lawyers have noticed a big number of divorce actions are being processed
because of social network usage.
Husband or wife often gets attracted to other from the social network. According
to Huffington Post, a study of around 2,000 married people in the UK pointed out that a
spouse’s dubious social media actions played the main role in one out of seven divorce
actions.
2.3 Historical Context
There are lots of discoveries earlier to the personal computer, however the focus of the
researchers is at the individual social and emotional influence of the technology and therefore
will focal point is on the creations as they initiated to be commonplace in our personal life as
well as at the home. This consists of the use of the smartphones, tablets, desktop computers
and laptop computers.
Rising interest in possessing the computers in the home started in the early1970s and 1980s
that took about more competition and cheaper prices (Computer History Museum, 2013a).
Before
The decade of 70’s computers was accessible to those who could assemble them themselves
from kits (Computer History Museum, 2013a). After a small phase of time, personal
computers started to reach to those without the expertise to put them together. The three
influential computers of that period were the Apple II, Commodore PET, and the Tandy
Radio Shack’s TRS-80 which started to reach to the mass markets (Computer History
Museum, 2013a).
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Time Magazine selects the computer as Machine of the Year rather than Man of the Year In
1982 . At that period Time publisher John A. Meyer marked, “Numerous human contenders
might have represented 1982, but not represents the past year more splendidly, or will be
viewed by history as more important, than a machine: the computer” (Computer History
Museum, 2006).
The plan to have a computer on the go was not a fresh concept either. Actually desktop,
laptop or smartphones, began at roughly the same time as the personal computer (Computer
History Museum, 2013b). The first laptop computer created by IBM, the PC Convertible, was
prepared in 1986 and weighed twelve pounds (Computer Hope, 2013a). Personal computers
have come into the daily lives of those who had the ability to afford them since then.
Then internet usage started growing to the mass level people. It was initially developed in the
late 60’s and the first message was delivered in 1969 (Computer Hope, 2013b). By 1993 the
Internet had the biggest expansion to date and computers were able to talk to each other
through TCP/IP networks and the World Wide Web grew (Computer Hope, 2013b).
Then cell phones made their first appearance in the late 70’s and early 80’s. In 2002 Nokia
cell phones integrated built-in cameras. Blackberry released their first phone with access to
the Internet, email,and texting (CBC Radio-Canada, 2013) In 2003. In 2007 the iPhone
released, revolutionary shift in technology with touch screen capacity and the streamlined
combination of three devices in one, mobile music, Internet access and wireless
communication via cellular service (CBC Radio-Canada, 2013). Basically the discovery of the
Internet conveyed humans in the global market that is rising ever smaller as the year’s
development. All these creations made the transformations now we can see in the exercise of
digital media.
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2.4 General Information and Statistical Evidence
Rosen described the enthusiastic technology users are presenting the symptoms of
psychological chaos. Obsessive-compulsive disorder, Anxiety, attention seeking disorder and
depression are the frequent displayed issues. The character that youths try to make is one of
online presence as well as physical illustration. “Our online self is a discovery that, for most
people, is a recurrent estimation of the tendency to present themselves to the world” (Rosen et
al.,2012, p. 34). This personality and the thought of all the time being “plugged in” create a
state of anxiety for heavy use media users.
Time Incorporated at Innerscope Research made a biometric investigation In May of 2013 to
recognize the social media and internet use in different age groups. During 300 hours digital
migrant, those who have the access to the technology and digital natives, those who is
growing up with the use of the technology, wore biometric belts and also wore glasses with
video capture. Their outcome showed that the digital natives change their concentration
between media platform each and every minute (Frank,Martin, Marci, Rule & Williams,
2013, p. 3). Digital natives transform their concentration at the primary symptom of boredom.
This regular transformation consequences in little attention that limits their emotional
response (Frank et al., 2013, p. 4). “This study robustly recommend a switch in the time
spent, prototypes of visual concentration and emotional cost of modern media use that is
rewiring the brains of a generation of people like never before,” Marci (as cited in Marketing
Profs, 2013). Small and Vorgan state that, “Under this kind of stress, our brains instinctively
signal the adrenal gland to secrete cortical and adrenaline. In the short run, these stress
hormones boost energy levels and augment memory, but over time they actually impair
cognition, lead to depression, stress and other mental disorder.
2.5 Theoretical Framework
As people succumb to the addiction of social media and spending more screen time, there a
new medium has been introduced, to reduce the screen time the anti smartphone applications -
20

smartphone apps claim to assist us to resolve smartphone addiction. Though, the apps are
tricky as they spot to a inadequate understanding of smartphone obsession; the simplicity of
these apps appears conflicting to the difficulties of smartphone addiction. Furthermore, the
anti smartphone addiction app entails a problematical paradox; resolve the smartphone
addiction via smartphone.
As a result the thesis involves with the output of using the smartphone addiction apps-
OffTime and Flipd- tried to reveal how these app frame people’s screen time, on the context
of people’s social life. We can’t consider them as neutral as they are the product of human
decision-making. These are underpinned by hypothesis, discourses and norms which are
already circulating in society (Lupton 2014).
In the year of 1980 Howard Gardner explained the theory of several intelligences, like every
person is build up of numerous nature in his character like intelligence, musical mind, logical-
mathematical, linguistic, social and intrapersonal (Gardner, 1983). So every human being has
a distinctive makeup and some prosper in diverse learning atmospheres than the others.
Albert Bandura (1991) set a social cognitive theory that people action and reaction is being
build up on those observed and experiential in others, which means what people views
another doing reflects in his own activities and social life. The theory is vital when talking
about the transformation in people social life.
How can social behavior be influenced by the quantity of screen time people consume every
day? How the conjugal life being hampered by screen time in social network? How can we
get rid of it using the screen lock app?
2.6 Thesis Objectives
Key objectives of the Thesis:
1. To identify the use of current technical tools where the members of our society spend
every day time and the influence on the social aspects.
21

2. To recognize the causes for getting attracted to the screen time ( why people getting
obsessed here to spend their time rather than their social time )
3. To disclose the drawbacks of the heavy screen time in all areas of life specially on social
life
4. Understand the features and behaviors of addiction control apps.
5. Investigate the potential of these apps
6. To be concern with the role of the family in dropping the risk of screen time using these
apps.
2.7 Related Works
There are lots of researches which were involved to study the negative effect of misusing
information technology on society in different countries in this world. We can mention the
following studies which are quite relevant to my work.
1. Lailah (2000) went through a study that was intended to clarify the use of social media in
the information technology that has bad influence in the family bonding, as well as
focusing on the bad output of the act of the social media and information technology in
the society. Consequently, this leads to deteriorate the moral value of family
communication. This study suggested nurturing the youth as well as the adult according to
an ethical system sustaining own identity.
2. Khulaifi (2002) wanted to discover the pros and cons of internet. This outcome
demonstrated that majority contributors (91 %) are addicted to internet and social media;
they focus mainly the uses of the network to interact, exchange views and information
with other people, looking for information for their study or work as well as
entertainment. The participants in the study showing the neg characteristics of using the
22

internet as serving into the cultural attack, as well as reason of social, moral, and health
troubles by frequently uses.
3. Bryant (2006) conducted a research aimed to clarify the connection between the exercise
of texting and the structure of the social network for the teenagers. 40 contributors
participated in this sample of study ranged between the age range (11-13 years).This
research consequences that there is very little intersection connecting friendship in the life
of an individual and friendship through the interactional tools of technology. So, there is
very small number of contacts and online friends to talk to them on line and instant
messages has demonstrated that it can’t be an alternate for a basis of social bonding to
people those who are isolated.
4. Al Aga (2009) also conducted a study which was designed to discover the affect of new
technology, the models of using internet, discovery of the usage which don’t play a
positive role and identify the exercising of cell phone and Wi-Fi between the study
samples on young misbehavior in the Gulf Cooperation Council Countries. The study
outcome demonstrated that there are vital diversities in the model of exercising the
internet technology and cell phone between young offenders and non-offenders.
Furthermore, young offenders use the Internet and cell phones in negative perform more
than the others. Though, there is dissimilarity between young offenders and non-offenders
in the key study axes according to the prime variable.
2.8 Thesis Problem
It is necessary to analyze these smart phone addiction apps, because they are problematic in
different ways. Firstly, the question of solving the screen time issue is addressed by the app
developers instead of society, which is problematic because these apps are created out of
commercial interests and - as the analysis will show- are developed by people who only have
knowledge about app design.1 Hence, it is expected that these apps do not foster a profound
23

understanding of screen time addiction which makes them unsuitable for treating screen time
addiction.
Second, screen time is really complex and so the simplicity of smartphone addiction apps
seems contradictory to the complexity of the addiction itself. Such apps thrive in our current
technology: a society in which the culture seeks out its authorization in technology, finds its
approval in technology, and takes its orders from technology (Morozov 2014, p.323).
According to professor in gambling studies Mark Griffiths, smartphone addiction is rare: “Just
because something is exceptionally vital in your life, and you carry it everywhere, and when
you forget it, you feel like your left arm’s missing, that does not mean that you’re addicted” .
1 This might be because addiction has not yet been fully recognized as a pathological problem
in western countries. For example, the government in The Netherlands does not intervene
with smartphones users, while other addictions such as smoking have led to several
regulations, including smoking zones. In South Korea however, the problem of smartphone
addiction is taken more seriously; the government developed a monitoring app called “Smart
Sheriff” that smartphone users under the age of nineteen have to install so that their web
activity is monitored and their parents can control their smartphone usage.
For more information, visit: http://www.bbc.com/news/technology-33091990.
2.9 Thesis Question
The key enquiry for the thesis is: What are the social and emotional costs of the screen time in
technology and how can we get rid of this using the apps?
We have considered the technology and the screen time associate to the use of computers,
tablets and all other digital tools which are using for personal, professional as well as
educational purposes.
The key question that will be at the focus of this thesis is: How can the apps shape screen
time? Regarding this issue we can create the following sub-questions:
1- How to minimise the screen time by using the method of smartphone app?
24

2- Compare the human behaviour before and after using this smartphone app method and
how can be improved?
2.10 Screen lock app
Anti smart phone addiction assists the people to calm down their cell phone app practice, and
get a digital detox. People are able to set their own rules for their apps or social network to
detoxify from the deep usage and prevent procrastination ans obsession in the app through
lock their apps with this screen lock.
Every time people violate one of your own rules, Screen lock will remind the user to take a
break and stop deep app usage. You can also keep track of these violations in a log. Some
people are using App Detox for parental control of their screen time
25

Figure 1 Offtime screenlock app
Figure 2 : Flipd Screenlock app
2.11 Screen time by Gender
On average, boys watched screens for twice as long as girls during the Thursday after school
period. Mean screen time for girls was 29m 16s, and 59m 57s for boys. The maximum time
girls spent watching screens was 2hr 53m, while for boys the maximum was 3hr 25m. Figure
21 compares the distribution of mean screen time for boys and girls. It shows that the lower
quartile is similar for boys and girls, however the mid quartiles stretch across a far greater
range in boys. The median of mean screen time is substantially greater for boys than for girls,
and mean screen time in the upper quartile is greater in boys.
26

Figure 3: Distribution of mean screen time by gender from PEW Research Centre
27

Chapter 3
Methodology
This section explains the methods using in the thesis to resolve the character and amount of
people’s screen time and resolve it by the anti smartphone addiction app. This anti
smartphone addiction app offtime and flipd were used for data collection as a part of the
thesis
This thesis utilized recent technologies, anti smartphone addiction app which will not allow
the user to access the social network or other app after certain time – to reach a more precise
and purpose representation of people’s atmosphere. This dataset is exclusive and offers
precious approaching to other features of people’s social lives that are of social psychology
interest.
This section also describes, the sampling and data collection methods for the anti smartphone
addiction app (offtime and flipd) project are primary summarized. The thesis question is
precise to anti smartphone addiction app and its consequence after its implementation. An
explanation of this scoping thesis that supported the progress of the annotation calendar used
in the thesis follows. Grounds for selecting the Friday after University class period are
recognized as there is less pressure of class on Friday.
The annotation protocol, as well as definitions of a variety of settings, screen types and screen
actions and policy for blocked images, screens that are noticeable but not actively being
engaged with, and images that are partly blocked in the images, are talked about. Lastly, this
method of statistically analyze the data output from the annotations is explained.
28

We only examined 3 Universities and 100 students. There is an area bias in our thesis
population as no periphery areas were included in our thesis. So there were few limitations in
the thesis.
3.1 Test of Variables
3.1.1 Descriptive Summary of Variables
Mean, standard deviation, minimum and maximum value of the variables have been
calculated. Ratio of the variable Facebook to others variables such as Youtube, Instagram and
Tinder or others app have been estimated. Zero matrix of the variables have also been
assessed to see the relation among themselves.
3.2 OLS Regression analysis
Multiple regression analysis of multiple independent variables have been used in this study.
The mathematical formation of multiple linear regression is
Y = a + bX1 + cX2 + dX3 + ϵ
Where:
Y – Dependent variable
X1, X2, X3 – Independent variables
a – Intercept
b, c, d – Slopes
ϵ – Residual (Error)
29

In this study, four models have been fitted such as
i) The variable, Facebook has been taken as dependent variable and others variables
such as Youtube, Instagram and Tinder or others dating app have been assumed as
independent variables.
ii) The variable, Youtube has been taken as dependent variable and others variables
such as, Facebook, Instagram and Tinder or others dating app have been assumed
as independent variables.
iii) The variable, Instagram has been taken as dependent variable and others variables
such as Facebook, Youtube and Tinder or others dating app have been assumed as
independent variables.
iv) The variable, Tinder or others dating app has been taken as dependent variable and
others variables such as Facebook, Youtube and Instagram have been assumed as
independent variables.
3.3 Diagnostic Test of the Models
3.3.1 Multicollinearity Check
Multicollinearity can easily be detected by using the value of Variance Inflation Factor (VIF).
VIF may be calculated as , where R2 is the R-squared value for that x's regression on the other
x variables.
3.3.2 Heteroskedasticity Check
Breusch-Pagan / Cook-Weisberg test has been used to check heteroskedasticity.
30

It is noted that the statistical software, STATA 13.0 has been used for different types of
analysis.
3.4 Monte Carlo Algorithm
For the methodology we can go through the Monte Carlo algorithm which is an algorithm
based on probability, it is not the precise technique though, its process requires a low
computation power.
Monte Carlo provides solutions based on entropy and possibility of distribution. It consists of
four principal phases: it firstly defines a solution space, then it assigns random values to its
inputs, after that it evaluates the solutions produced and finally it aggregates the results. The
Monte Carlo approach is employed to solve the scheduling problem considering tasks as
inputs; tasks aim to obtain a resource for their execution. Applications that apply the Monte
Carlo algorithm to scheduling problems
In decision problem issue, this algorithm is normally can classify as either false-biased
or true-biased. **The false-biased Monte Carlo algorithm is all the time right when it
proceeds false; a true-biased algorithm is all the time right when it proceeds true. While the
describes algorithm with one-sided errors, others might have no biased;
For the methodology we can go through the Monte Carlo which is an algorithm based on
uncertainty, it is not the precise technique though, its process requires a low computation
power. For the decision problem issues, this algorithm is normally can classify as either false-
biased or true-biased. The false-biased Monte Carlo algorithm is all the time right when it
proceeds false; a true-biased algorithm is all the time right when it proceeds true. While it
describes algorithm with one-sided errors, others might have no biased; these are supposed to
have two-sided errors. The answer they provide (either true or false) will be incorrect, or
correct, with some bounded probability.
Therefore, in the case of prime number then the result is always correct, and if the number is
not prime and composite then the answer is correct with probability at least 1−(1−½)k = 1−2−k.
31

Basically Monte Carlo methods and algorithms are used in physical replication
and computational stats grounded on getting random sample.
** Wikipedia
3.5 Other details
Looking at the literary works of prior researchers, firstly, the data collected included
secondary data as well as primary data. For instance, the study related to “An Actor-Based
Model of Social Network Influence on Adolescent Body Size, Screen Time, and Playing
Sports” used secondary data collected from the first and second waves of the National
Longitudinal Study of Adolescent Health. Wherein, 16 schools were invited to participate in
the study out of which only 2 schools included a enough student power to facilitate school-
stratified analyses and thus, only two schools were taken into consideration. The data
underwent an analysis using Descriptive Statistics, Network Objective Function and BMI
average similarity score among others. In the context of given research study, the Screen time
was taken as a summation of total hours expended watching television and playing video
games among others. Furthermore, the screen-time was also segregated into 10-hour
categories that ranged between 0 to 9. These results derived that social influence played a
critical role when it came to Screen-time for students attending one of the schools.
In another study, Smart-phone use and smart-phone addiction in middle school
students in Korea has been analysed in regards to its Prevalence, social networking service,
and game use. The methodology adopted for the same included random sampling of data
collected from 1824 middle school students of mainly 17 cities such as Busan, Seoul, Daegu
and Daejeon among others. Under which, Questionnaires were used along with face-to-face
interviews so as to investigate the extent of addiction to Smart-phones prevalent in South
Korea by the Korean Information Society Agency. Apart from this, a 4-point Likert Scale was
also employed to ascertain degree of life, relationship satisfaction and future career plan in the
32

context of Smart-phone Addiction. After the accumulation of relevant data, the data analysis
included execution of various statistical tests such as Chi-Square, t-Test and Multiple
Regression analysis. The results segregated 30.9% of the respondents at risk whereas 69.1%
were considered as normal users of smart-phone. It was also asserted that there was no
significant relationship between gender, family income or parents' education when it came to
smart-phone addiction. Also, this condition was directly attributed to the shyness and
loneliness one feels in their social environment.
This is reaffirmed in another study that inferred relationships among smart-phone
addiction, stress, academic performance, and satisfaction with life among students aged
between 18 to 25 years wherein 293 students filled out the survey and 249 respondents were
considered to be valid. The sample obtained included a cross-sectional study that was mainly
based on stratified random sampling method. Again, a 6-point Likert Scale with 1 being
“Strongly Disagree” and “Strongly Agree” coded as 6. Employing SAS-SV, a Cronbach's
Alpha coefficient was taken into consideration. This helped in the ascertainment of reliability.
A score of 0.91 showed that the data collected was best to the study undertaken for
exploration purposes. The results showed that out of 249 respondents, 44.6% were at a
slightly higher risk of addiction to smart-phone. Whereas 49.1% if the students were at lower
risk. Pearson-Correlation Coefficient indicated no relationship between the life satisfaction
and addiction to smart-phone (zero-order correlation). However, one could indirectly relate
the two variables on the basis of perceived stress and academic performance to a certain
extent. Also, addiction to mobile devices and academic performance were inversely
proportional. Hence, an increase in addiction would be reflected as a similar decline in the
overall Academic Performance (or GPA) of the student. On the other hand, a positive
relationship was ascertained between addiction to phones and stress.
By analysing the methodologies adopted and results derived by other scholars, one can
get an overall idea of what was the initiation point for such researches and how these studies
were concluded. These facilitate the analysis of relevant investigation in light of various
factors such as sleep loss, personal life, Social Networking and Internet addiction disorder that
are critical for the successful addressing of given thesis questions.
33

4 Experiments
Sampling for anti screentime addiction app is a two-stage process. Firstly, I focused the young
generation. That’s why I selected few universities in my country those I selected in random
order and then selected few students from every University. Then students were randomly
selected within the participating universities according to a protocol plan by a project
statistician.
Then I focused the guardians of the youth.
I set a rating questions asking them as a survey responder for the comparison of diverse item
which uses a familiar scale (e.g. “Please rate every of the subsequent objects on a rating scale
of 1-5, where 1 is ‘not effective’ and 5 is ‘very effective.’”). This example uses a rating scale
of 0 to 5 to rate the phases of outcome the get after using the app
34

3.5.1 University
A list of universities in Dhaka region in Bangladesh that included few graduate students was
founded from the Ministry of Education. For inclusion in this project, Universities were
required to have an assigned top ranking. For pragmatic reasons, Universities had to be
located in the capital of Bangladesh; this excluded universities outside of Dhaka. Specified
these criterion, 3 Universities were eligible to select. Eligible universities were then arranged
by ranking and ethnicity, and sampled using a probability-proportional-to-size method,
whereby larger Universities were more likely to be selected than smaller Universities. After
sampling, 6 universities were requested to take part in this project, of which 3 approved to
contribute.
3.5.2 Student
Once a university gave consent to participate, researchers obtained the students in that year
according to the Ministry of Education records. For each university, a randomized list was
developed from which the first 20 students were selected. The list was then sent to the
assisting teacher who could validate the Student who met the study’s criterion. Student was
excluded if they don’t get consent from the participant or parent, or those who were taking
data were not capable to bring together data due to disability or circumstance. Student
meeting the exclusion criteria were replaced with the next child on the list. The sample size
calculation indicated that for each selected ethnicity, between three to five students should be
elected from each University. Therefore, the first six students on the list who returned
completed approval forms were received as participants in the study. Over-inviting Student to
participate in the study enlarged the chance of six students consent.
3.5.3 Data Collection
Data collection for Screen time took place over the course of a week for each University. Screen
time researchers first held briefing sessions with the participating Student to discuss the ethical,
35

legal and practical issues of data collection. Participants were asked to use the app for 1 week –
weekdays and weekend as well. The cell phone used for the Screen time project was set to take a
1 week experiment. Participants were also briefed on how to manage the app and use these apps
when they use social network or other uses the cell phone. To decrease the risk of introducing
bias, the Student were advised that the aim of the study was to learn more about their every day
atmosphere from their viewpoint and the significance the environmental characteristics have on
their social behaviour.
3.5.4 Data Collection Demo for 5 students
Screen time before and after using screenlock app (1 week usage)
Table 1 : Student 1 - Before
Device or
app
Day
1
Day
2
Day 3 Day
4
Day
5
Day 6 Day 7 Weekly
use
Facebook 3 2 3 3 2 2 2 17
Youtube 1 3 2 1 3 3 2 15
Instagram 1 1 1 2 1 2 8
Tinder or
others
dating app
2 1 3 2 2 2 2 14
Total screen
time in the
day
7 7 9 6 9 8 8 54
Table 2- Student -After
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 1 2 1 2 2 3 12
Youtube 2 2 1 1 1 1 1 9
36

Instagram 1 2 2 2 2 1 2 12
Tinder or
others dating
app
2 1 2 2 1 2 2 12
Total screen
time in the day
6 6 7 6 6 6 8 45
Table 3 Student 2 Before
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Weekl
y Use
Facebook 2 4 3 3 2 2 1 17
Youtube 1 3 2 1 3 3 2 15
Instagram 2 1 1 3 2 1 2 12
Tinder or
others dating
app
2 1 3 2 2 2 2 14
Total screen
time in the day
7 9 9 9 9 8 7 58
Table 4 Student2 : After
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 1 2 1 2 1 1 9
Youtube 1 1 1 1 1 1 1 7
Instagram 1 2 1 2 1 1 2 10
Tinder or
others dating
app
2 1 2 2 1 2 2 12
Total screen
time in the day
5 5 6 6 5 5 6 38
Table 5 Student 3 :
Before
37

Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 2 2 3 2 2 1 13
Youtube 1 2 2 1 2 2 2 12
Instagram 2 1 1 3 2 1 2 12
Tinder or
others dating
app
2 1 2 2 2 2 2 13
Total screen
time in the day
6 6 7 9 8 7 7 50
Table 6 student 3 After
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 1 0.5 1 0.5 1 1 6
Youtube 1 1 1 1 1 1 1 7
Instagram 1 1 0.5 2 0.5 1 2 8
Tinder or
others dating
app
0 1 2 1 1 1 2 8
Total screen
time in the day
3 4 4 5 3 4 6 29
Student 4
Before
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 4 4 5 4 2 4 5 28
Youtube 0 0 2 1 2 1 1 7
Instagram 2 1 1 3 2 1 2 12
38

Tinder or
others dating
app
4 3 5 6 4 4 3 29
Total screen
time in the day
10 8 13 14 10 10 11 76
After
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 1 1 1 1 1 1 7
Youtube 1 1 1 1 1 1 1 7
Instagram 0.5 1 1 2 0.5 1 2 8
Tinder or
others dating
app
0.5 1 1.5 1 0.5 1 0.5 6
Total screen
time in the day
3 4 4.5 5 3 4 4.5 28
Student 5
Before
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 2 2 6 3 2 4 3 22
Youtube 0 0 2 1 2 1 1 7
Instagram 3 2 2 3 2 1 2 15
Tinder or
others dating
app
4 3 5 6 4 4 3 29
Total screen
time in the day
9 7 15 13 10 10 9 73
Student 5
39

After
Device or app Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 weekly
use
Facebook 1 0.5 1 1 1 0.5 1 6
Youtube 1 0.5 0.5 1 1 1 1 6
Instagram 1 1 1 2 1 1 2 9
Tinder or
others dating
app
1 1 1.5 1 1 1 0.5 7
Total screen
time in the day
4 3 4 5 4 3.5 4.5 28
3.5.5 Data analysis
5. Result ????
5.1. Presentation of results ????
5.2. Limitation ????
5.3. Discussion????
40

research 2 will be based on my research 1....and there is a guidelin
41

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Experimental design
Presentation of Results
Analysis
-----------------------------------------------------------------------------------------------
44

. summarize Facebook Youtube Instagram Tinder_or_others
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
Facebook | 35 2.771429 1.190297 1 6
Youtube | 35 1.6 .9139443 0 3
Instagram | 34 1.735294 .7096232 1 3
Tinder_or_~s | 35 2.828571 1.316987 1 6
. ratio (Facebook/Youtube) (Facebook/Instagram) (Facebook/Tinder_or_others)
Ratio estimation Number of obs = 34
_ratio_1: Facebook/Youtube
_ratio_2 research 2 will be based on my research 1....and there is a guidelin:
Facebook/Instagram
_ratio_3: Facebook/Tinder_or_others
--------------------------------------------------------------
| Linearized
| Ratio Std. Err. [95% Conf. Interval]
-------------+------------------------------------------------
_ratio_1 | 1.709091 .2341388 1.232732 2.18545
_ratio_2 | 1.59322 .169481 1.248409 1.938032
_ratio_3 | .9690722 .0717168 .8231633 1.114981
--------------------------------------------------------------
. correlate Facebook Youtube Instagram Tinder_or_others
45

(obs=34)
| Facebook Youtube Instag~m Tinder~s
-------------+------------------------------------
Facebook | 1.0000
Youtube | -0.2467 1.0000
Instagram | -0.0749 -0.3911 1.0000
Tinder_or_~s | 0.5444 -0.4185 0.3431 1.0000
. tabstat Facebook Youtube Instagram Tinder_or_others
stats | Facebook Youtube Instag~m Tinder~s
---------+----------------------------------------
mean | 2.771429 1.6 1.735294 2.828571
--------------------------------------------------
. regress Facebook Youtube Instagram Tinder_or_others
Source | SS df MS Number of obs = 34
-------------+------------------------------ F( 3, 30) = 6.27
Model | 18.5356191 3 6.17853968 Prob > F = 0.0020
Residual | 29.582028 30 .9860676 R-squared = 0.3852
-------------+------------------------------ Adj R-squared = 0.3237
Total | 48.1176471 33 1.45811052 Root MSE = .99301
----------------------------------------------------------------------------------
Facebook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
46

Youtube | -.1600412 .2158 -0.74 0.464 -.6007636 .2806811
Instagram | -.5629981 .2709976 -2.08 0.046 -1.116449 -.0095472
Tinder_or_others | .5514107 .1466568 3.76 0.001 .2518975 .8509239
_cons | 2.427421 .8086571 3.00 0.005 .7759231 4.078919
----------------------------------------------------------------------------------
. estat vif
Variable | VIF 1/VIF
-------------+----------------------
Youtube | 1.32 0.755422
Tinder_or_~s | 1.27 0.786858
Instagram | 1.24 0.807991
-------------+----------------------
Mean VIF | 1.28
. estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of Facebook
chi2(1) = 0.06
Prob > chi2 = 0.8084
. regress Youtube Facebook Instagram Tinder_or_others
Source | SS df MS Number of obs = 34
-------------+------------------------------ F( 3, 30) = 3.48
47

Model | 7.23656615 3 2.41218872 Prob > F = 0.0280
Residual | 20.7928456 30 .693094854 R-squared = 0.2582
-------------+------------------------------ Adj R-squared = 0.1840
Total | 28.0294118 33 .849376114 Root MSE = .83252
----------------------------------------------------------------------------------
Youtube | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
Facebook | -.112491 .1516832 -0.74 0.464 -.4222694 .1972873
Instagram | -.4211504 .2305082 -1.83 0.078 -.8919109 .04961
Tinder_or_others | -.1574362 .1463404 -1.08 0.291 -.4563033 .1414308
_cons | 3.108628 .5249512 5.92 0.000 2.036535 4.180721
----------------------------------------------------------------------------------
. estat vif
Variable | VIF 1/VIF
-------------+----------------------
Tinder_or_~s | 1.80 0.555467
Facebook | 1.60 0.626056
Instagram | 1.27 0.784965
-------------+----------------------
Mean VIF | 1.56
. estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of Youtube
48

chi2(1) = 0.02
Prob > chi2 = 0.8880
. regress Instagram Youtube Facebook Tinder_or_others
Source | SS df MS Number of obs = 34
-------------+------------------------------ F( 3, 30) = 4.16
Model | 4.87948115 3 1.62649372 Prob > F = 0.0141
Residual | 11.7381659 30 .391272197 R-squared = 0.2936
-------------+------------------------------ Adj R-squared = 0.2230
Total | 16.6176471 33 .503565062 Root MSE = .62552
----------------------------------------------------------------------------------
Instagram | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
Youtube | -.2377517 .1301286 -1.83 0.078 -.5035096 .0280063
Facebook | -.223398 .107532 -2.08 0.046 -.4430076 -.0037883
Tinder_or_others | .2247465 .1042708 2.16 0.039 .0117971 .4376959
_cons | 2.096334 .4369528 4.80 0.000 1.203957 2.98871
----------------------------------------------------------------------------------
. estat vif
Variable | VIF 1/VIF
-------------+----------------------
Tinder_or_~s | 1.62 0.617658
Facebook | 1.42 0.703233
Youtube | 1.21 0.824365
49

-------------+----------------------
Mean VIF | 1.42
. estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of Instagram
chi2(1) = 0.14
Prob > chi2 = 0.7097
. regress Tinder_or_others Instagram Youtube Facebook
Source | SS df MS Number of obs = 34
-------------+------------------------------ F( 3, 30) = 8.70
Model | 27.1027908 3 9.03426361 Prob > F = 0.0003
Residual | 31.161915 30 1.0387305 R-squared = 0.4652
-------------+------------------------------ Adj R-squared = 0.4117
Total | 58.2647059 33 1.76559715 Root MSE = 1.0192
------------------------------------------------------------------------------
Tinder_or_~s | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Instagram | .5966461 .2768131 2.16 0.039 .0313183 1.161974
Youtube | -.2359472 .2193181 -1.08 0.291 -.6838546 .2119602
Facebook | .5808599 .1544894 3.76 0.001 .2653505 .8963692
_cons | .5933573 .9402218 0.63 0.533 -1.326832 2.513546
------------------------------------------------------------------------------
50

. estat vif
Variable | VIF 1/VIF
-------------+----------------------
Youtube | 1.30 0.770442
Instagram | 1.23 0.815756
Facebook | 1.11 0.904485
-------------+----------------------
Mean VIF | 1.21
. estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of Tinder_or_others
chi2(1) = 6.66
Prob > chi2 = 0.0099
.
end of do-file
. log close
name: <unnamed>
log: C:\Users\user pc\Desktop\analysis_yr.log
log type: text
closed on: 28 Apr 2019, 21:05:38
-----------------------------------------------------------------------------------------------
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