Mobile Security Measures Report

This report investigates the extent to which people utilize mobile security measures. It employs a quantitative research design, surveying six participants via a questionnaire and analyzing the data using SPSS, including regression analysis. The study examines correlations between mobile phone usage (weekly and monthly hours online) and various security measures such as registering the phone serial number, using screen locks, physically marking the phone, and installing antivirus software. The results indicate a weak correlation between hours spent online and the adoption of most security measures, suggesting a significant portion of users do not prioritize mobile security despite extensive mobile phone and internet usage. The report concludes by discussing the implications of these findings and the need for improved security awareness among mobile users.

TOPIC: To investigate the extent to which
people make use of mobile security measures

Table of Contents
INTRODUCTION...........................................................................................................................4
METHODS..................................................................................................................................4
Participants..................................................................................................................................4
Design..........................................................................................................................................4
Material........................................................................................................................................5
Procedure.....................................................................................................................................6
RESULTS........................................................................................................................................7
DISCUSSION..................................................................................................................................9
REFERENCES..............................................................................................................................11
TABLES........................................................................................................................................13

ABSTRACT
The use of social networking sites has become the most popular and recent trend which is
used by almost all people in world. This is an online platform which is used for making relation
with people and as per their common interests and backgrounds. There are various sites which
provide these types of facilities in which most popular ones are those which render the services
of instant messaging. All these sites share some common features that is mainly focussed on user
friendly interface. This makes all sites like Facebook, Twitter, Instagram etc. popular and best
way to communicate among people residing at different parts of world. With all such facilities
which is enjoyed by using such social networking sites, there are various issues as well which is
related with the safety of persons using these services. Most of the people are not much aware of
these safety aspect because of which cases related to leak of personal information and pother
cyber crimes have increased. The present report is also based on such aspect to study the
awareness of people for safety measures. For this purpose, SPSS technique has been applied in
the research under which regression tool is also used.

INTRODUCTION
The social networking site is a digital platform that is most commonly used for the purpose
of sharing messages and making relations with different people. The relations are made on the
basis of common interests and hobbies or for knowing different persons of various parts of
world. The facilities delivered by such sites have made it very easier to share messages instantly
along with sharing of various photographs and memories between people. With the invention of
these sites, the interests of people have taken a new shape. The organizations who use to provide
such facilities continuously try to make it more interesting and user friendly to increase their
usage. With these advantages, there are some drawbacks of these sites as well. With enhanced
usage of social networking sites, the risks related to privacy issues have also been developed.
The cyber crimes have increased a lot where many people have filed cases of hacking their
personal information’s by hackers and its misuse. Many times, these cases reach to severe level
also due to which many people go under depression or commit suicides. Therefore, it becomes
very necessary for users of these sites to be aware of such activities so that various risks related
to their private information can be restricted.
METHODS
Methodology is one of the vital elements of every research study as use of perfect and
suitable methods and technique enable investigator to conclude the investigation effectively
(Maher, Markey and Ebert-May, 2013).
Participants
In order to obtain required data, sample of 6 people on the street has been surveyed
through questionnaire. All the participants have been selected randomly without any bias from
the street. All the persons who were above the age of 18 have been invited to give their responses
by filling-up the questionnaire. The reason behind selecting a sample of total 6 people is to
minimize complexities in gathering data from every resident. Moreover, generating data from the
representative sample also consumed less time period and helps to examine the entire universe
(Hartas, 2015).

Design
Research design specifies and standardise that how a particular investigation will be carry
out thoroughly by the investigator. Referring the current study, design of study incorporated by
the researcher is analytical. As in these, the findings and outcome of the mobile phone security
questionnaire result have been evaluated and examined by applying statistical tools and
technique through SPSS. Data collection is a very important and crucial facet of design, as
authenticate, reliable and prominent information gathered by the investigators helps to find out
an appropriate solution of the issue or vice-versa. In order to gather required quantum of data,
there are two alternatives available to researchers to either generate information by applying
primary method or secondary or a combination of both (Punch, 2013). Former is used to acquire
fresh or unique information that not already obtained by any other researcher scholar previously.
Most importantly, it is incorporated to tailor certain specific requirement of the users through
conducting observation, field-survey, questionnaire survey or interviews. Unlike primary
method, secondary data collection process aims at utilizing already available data sources either
published or non-published (Schmidt and Hunter, 2014). In order to assure validity, it is
necessary for the investigator to take some precautions while using secondary data so as to avoid
the use of time-outdated data.
Here, in the present investigation, scholar has applied primary technique to generate
reliable and specific information that will be helpful for fulfilling the aims and objectives of the
study. In such regards, a survey has been conducted by the investigator of selected participants to
obtain sufficient quantum of data (Davis, Evershed and Mills, 2013). It will be proves greatly
beneficial for the researcher to tailor and meet-out their specific information need.
The present study followed a quantitative research, as in which, scholar applied a famous
statistical software, Statistical Package for Social Science (SPSS) to evaluate and examine the
findings and results of numerical data (Creswell, 2013). In such regards, regression and
descriptive statistical methods have been performed to reach the effective and accurate solution
of the problem.
Material
Researcher has designed a mobile phone security questionnaire as an instrument of
research. In total, three set of questionnaire have been developed to study the extent to which

public incorporates a range of precautions regards to mobile-phone usage. The focus of the
selected investigation material is to ask respondents about the type of mobile phone usage and
their adopted security measures to prevent crime (Monette, Sullivan and DeJong, 2013). In total,
three questionnaires have been prepared by the researcher differentiated from each other only on
the basis of time such as last week, typical week and typical month.
Procedure
While collecting data, every participant has been asked to fill only one of the three
drafted questionnaires and randomly assigned to one of the three groups. Every questions
prepared in the questionnaire has been answered by two people so as to generate responses
quickly (Douzenis and Seretis, 2013). While surveying respondents, scholar assured that at one
time, only one respondents will be surveyed in order to eliminate confusion or confound in the
experiment (Yilmaz, 2013). After gathering data, SPSS technique has been applied to generate
the needed outcome so as to find out the right solution of the selected issue (Belk, 2013).

RESULTS
Quantitative analysis
Hypothesis 1
Ho: There is no significant difference between mean values of mobile phone usage in terms of
weeks and mobile security measures.
H1: There is a significant difference between mean values of mobile phone usage in terms of
weeks and mobile security measures.
Hypothesis 2
Ho: There is no significant difference between mean values of monthly mobile phone usage and
security measures.
H1: There is a significant difference between mean values of monthly mobile phone usage and
security measures.
By doing descriptive statistics, it has been assessed that mean value of recorded serial
number and hours online is 1.80 & 22.23. Further, extent to which mean amount will deviate in
the future by .404 & 17.11. Along with this, it has been identified that people who were spent
more time on online makes use of recorded phone serial number. Moreover, level of significance
between both the variables is 0.1 which shows that alternative hypothesis is true. In addition to
this, mean value of the option of screen code or pattern is 1.09 & 0.29. Correlation-ship which
takes place between the two variables is .13. By considering this, it can be stated that moderate
level of relationship takes place between lock pattern option and hours which are spent by the
customers online. R square is 0.18 which presents that minute changes will take place in the
usage of the option of lock pattern. Level of significance which takes place between the usage of
screen code lock or pattern and online spending hours is 0.04. It lies within the value of 0.05
which shows that null hypothesis is rejected.
Along with this, output of SPSS presents that people who spent 22 hours on internet in a
typical week had not registered their phone on asset register. Outcome of descriptive statistics
present that people whose weekly spending hours are 22 prefers to track or lock mobile phones

with the aim to prevent the threat aspects. Significance level of such aspect is also near to the
standard criteria. Outcome of SPSS entails that customers with average spending hours of 22
does not make use of the security measure such as physically marked phone. Thus, by following
such aspects it can be said that there is a significant difference takes place in the weekly usage of
mobile phones and employment of security measures (Seuring, 2013). All these aspects shows
that there are several people who spend more online weekly hours do not make use of mobile
security measures.
From quantitative investigation, it has been assessed that people who spent 22 monthly
hours do not make use of serial numbers to protect the mobile phone. Level of significance
which is related to such aspect is .14. By doing investigation, it also has been identified that
people who spend more time online makes use of screen lock or pattern to protect their mobile
phones. On the other side, as similar to weekly users, people who make use of internet monthly
basis lays high level of emphasis on using lock system. The rationale behind this, it prevents
other to make use of mobile and access to private details (Backonja and et.al., 2013). By taking
into account all the above presented aspects it can be said that there is a significant difference
takes place in the mean value of monthly mobile usage and usage of security measures. In
survey, most the respondents have presented their views in similar direction. Thus, usage rate of
security measure is very slow which in turn negatively affects the information to the large extent.
The reason behind this, now hacking related activities are increased with the very high pace. In
this situation, by making use of suitable measures people can keep secure the information shared
by them (Gibson and Morgan, 2013). Thus, outcome of SPSS shows that alternative hypothesis
is accepted which presents that now there is less number of people who make use mobile security
measures.

DISCUSSION
As per detailed analysis of primary and secondary data, it can be said that people are
more attracted towards effective use of mobile network. In addition to this, it can be said that
people are spending half of their day in the usage of mobile. Number of social networking sites is
there which is being used by the members such as facebook, twitter is being accessed. Along
with this, it has also been noticed that there is slightly difference between mean values of mobile
phone usage in terms of weeks and mobile security measures (Marshall and et.al., 2013). In
addition to this, it can be said that people are using mobile phone but they does not focus much
on security measures. It indicates the difference between overall outcome. Analysis of
information also reflects that there is no significant difference between mean values of monthly
mobile phone usage and security measures.
In addition to this, it can also be said that phone serial number is one of security measure
which helps in effective development of organization. Recorded security measures are also being
taken into account for measuring the use of mobile. Number of people is using mobile and
internet but at the same time the ratio of usage of recorded phone serial number is not in much
use. It impacts the overall outcome in diverse manner (Flick, 2015). Along with this, male and
female both are using mobile firms but there is slight difference between the ratio. Male are
using mobile phone more as compared to female. It is significant to have improvement in
security measures so that goals and objectives can be accomplished effectively. Moreover,
overall outcome in respect to security mobile need to be understand effectively.

REFERENCES
Books and Journals
Backonja, M. M. and et.al., 2013. Value of quantitative sensory testing in neurological and pain
disorders: NeuPSIG consensus. Pain. 154(9). pp.1807-1819.
Belk, R. W., 2013. Qualitative versus quantitative research in marketing. Revista de Negócios.
18(1). pp.5-9.
Creswell, J. W., 2013. Research design: Qualitative, quantitative, and mixed methods
approaches. Sage publications.
Davis, G. R., Evershed, A. N. and Mills, D., 2013. Quantitative high contrast X-ray
microtomography for dental research. Journal of dentistry. 41(5). pp.475-482.
Douzenis, A. and Seretis, D., 2013. Descriptive and predictive validity of somatic attributions in
patients with somatoform disorders: A systematic review of quantitative research. Journal of
psychosomatic research. 75(3). pp.199-210.
Flick, U., 2015. Introducing research methodology: A beginner's guide to doing a research
project. Sage.
Gibson, K. and Morgan, M., 2013. Narrative research on child sexual abuse: Addressing
perennial problems in quantitative research. Qualitative Research in Psychology. 10(3).
pp.298-317.
Hartas, D., 2015. Educational research and inquiry: Qualitative and quantitative approaches.
Bloomsbury Publishing.
Maher, J. M., Markey, J. C. and Ebert-May, D., 2013. The other half of the story: effect size
analysis in quantitative research. CBE-Life Sciences Education. 12(3). pp.345-351.

Marshall, B. and et.al., 2013. Does sample size matter in qualitative research?: A review of
qualitative interviews in IS research. Journal of Computer Information Systems. 54(1). pp.11-
22.
Monette, D. R., Sullivan, T. J. and DeJong, C. R., 2013. Applied social research: A tool for the
human services. Cengage Learning.
Punch, K. F., 2013. Introduction to social research: Quantitative and qualitative approaches.
Sage.
Schmidt, F. L. and Hunter, J. E., 2014. Methods of meta-analysis: Correcting error and bias in
research findings. Sage publications.
Seuring, S., 2013. A review of modeling approaches for sustainable supply chain management.
Decision support systems. 54(4). pp.1513-1520.
Yilmaz, K., 2013. Comparison of Quantitative and Qualitative Research Traditions:
epistemological, theoretical, and methodological differences. European Journal of Education.
48(2). pp.311-325.

TABLES
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT serialnumber
/METHOD=ENTER hoursonline.
Regression
Notes
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Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
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hoursonline.
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Descriptive Statistics
Mean Std. Deviation N
serialnumber 1.80 .404 431
hoursonline 22.23 17.113 431
Correlations
serialnumber hoursonline
Pearson Correlation serialnumber 1.000 -.071
hoursonline -.071 1.000
Sig. (1-tailed) serialnumber . .072
hoursonline .072 .
N serialnumber 431 431
hoursonline 431 431

Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: serialnumber
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .071a .005 .003 .403 .005 2.150 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .349 1 .349 2.150 .143b
Residual 69.683 429 .162
Total 70.032 430
a. Dependent Variable: serialnumber
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.833 .032 57.548 .000
hoursonline -.002 .001 -.071 -1.466 .143 -.071 -.07
a. Dependent Variable: serialnumber
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT screenlockcodeorpattern
/METHOD=ENTER hoursonline.
Regression
Notes
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Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
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POUT(.10)
/NOORIGIN
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screenlockcodeorpattern
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hoursonline.
Resources Processor Time 00:00:00.06
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[DataSet0]
Descriptive Statistics
Mean Std. Deviation N
screenlockcodeorpattern 1.09 .291 450
hoursonline 22.20 17.010 450
Correlations
screenlockcodeor
pattern
hoursonline
Pearson Correlation screenlockcodeorpattern 1.000 -.134
hoursonline -.134 1.000
Sig. (1-tailed) screenlockcodeorpattern . .002
hoursonline .002 .
N screenlockcodeorpattern 450 450
hoursonline 450 450
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: screenlockcodeorpattern
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Std. Error of the Change Statistics

Square Estimate R Square Change F Change df1 df2
1 .134a .018 .016 .289 .018 8.200 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .684 1 .684 8.200 .004b
Residual 37.396 448 .083
Total 38.080 449
a. Dependent Variable: screenlockcodeorpattern
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.144 .022 51.071 .000
hoursonline -.002 .001 -.134 -2.864 .004 -.134 -.13
a. Dependent Variable: screenlockcodeorpattern
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT registeredphone
/METHOD=ENTER hoursonline.
Regression

Notes
Output Created 08-JAN-2017 16:42:05
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N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
registeredphone
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.03
Elapsed Time 00:00:00.06
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0]
Descriptive Statistics
Mean Std. Deviation N

registeredphone 1.87 .335 450
hoursonline 22.20 17.010 450
Correlations
registeredphone hoursonline
Pearson Correlation registeredphone 1.000 -.027
hoursonline -.027 1.000
Sig. (1-tailed) registeredphone . .283
hoursonline .283 .
N registeredphone 450 450
hoursonline 450 450
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: registeredphone
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .027a .001 -.001 .336 .001 .332 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .037 1 .037 .332 .565b
Residual 50.487 448 .113
Total 50.524 449
a. Dependent Variable: registeredphone
b. Predictors: (Constant), hoursonline

Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.883 .026 72.329 .000
hoursonline -.001 .001 -.027 -.576 .565 -.027 -.02
a. Dependent Variable: registeredphone
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT trackorlockphone
/METHOD=ENTER hoursonline.
Regression
Notes
Output Created 08-JAN-2017 16:42:21
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N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
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Cases Used
Statistics are based on cases
with no missing values for any
variable used.

Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
trackorlockphone
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.05
Memory Required 1716 bytes
Additional Memory Required
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[DataSet0]
Descriptive Statistics
Mean Std. Deviation N
trackorlockphone 1.39 .489 450
hoursonline 22.20 17.010 450
Correlations
trackorlockphone hoursonline
Pearson Correlation trackorlockphone 1.000 -.087
hoursonline -.087 1.000
Sig. (1-tailed) trackorlockphone . .032
hoursonline .032 .
N trackorlockphone 450 450
hoursonline 450 450

Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: trackorlockphone
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .087a .008 .005 .488 .008 3.451 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .821 1 .821 3.451 .064b
Residual 106.559 448 .238
Total 107.380 449
a. Dependent Variable: trackorlockphone
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.449 .038 38.314 .000
hoursonline -.003 .001 -.087 -1.858 .064 -.087 -.08
a. Dependent Variable: trackorlockphone
REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT physicalluymarked
/METHOD=ENTER hoursonline.
Regression
Notes
Output Created 08-JAN-2017 16:42:33
Comments
Input
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Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
physicalluymarked
/METHOD=ENTER
hoursonline.

Resources
Processor Time 00:00:00.06
Elapsed Time 00:00:00.08
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0]
Descriptive Statistics
Mean Std. Deviation N
physicalluymarked 1.82 .384 425
hoursonline 22.21 17.149 425
Correlations
physicalluymarke
d
hoursonline
Pearson Correlation physicalluymarked 1.000 -.105
hoursonline -.105 1.000
Sig. (1-tailed) physicalluymarked . .016
hoursonline .016 .
N physicalluymarked 425 425
hoursonline 425 425
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: physicalluymarked
b. All requested variables entered.
Model Summary

Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .105a .011 .009 .382 .011 4.671 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .682 1 .682 4.671 .031b
Residual 61.728 423 .146
Total 62.409 424
a. Dependent Variable: physicalluymarked
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.873 .030 61.731 .000
hoursonline -.002 .001 -.105 -2.161 .031 -.105 -.10
a. Dependent Variable: physicalluymarked
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT installedantivirus
/METHOD=ENTER hoursonline.
Regression

Notes
Output Created 08-JAN-2017 16:42:45
Comments
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N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
installedantivirus
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.05
Elapsed Time 00:00:00.05
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0]
Descriptive Statistics

Mean Std. Deviation N
installedantivirus 1.78 .413 431
hoursonline 22.40 17.093 431
Correlations
installedantivirus hoursonline
Pearson Correlation installedantivirus 1.000 .012
hoursonline .012 1.000
Sig. (1-tailed) installedantivirus . .399
hoursonline .399 .
N installedantivirus 431 431
hoursonline 431 431
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: installedantivirus
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .012a .000 -.002 .414 .000 .066 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .011 1 .011 .066 .798b
Residual 73.488 429 .171
Total 73.499 430
a. Dependent Variable: installedantivirus

b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.775 .033 53.978 .000
hoursonline .000 .001 .012 .256 .798 .012 .01
a. Dependent Variable: installedantivirus
SAVE OUTFILE='C:\Users\karen\Documents\A31916-M1data input.sav'
/COMPRESSED.
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT serialnumber
/METHOD=ENTER hoursonline.
Regression
Hypothesis 2
Notes
Output Created 08-JAN-2017 16:43:34
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>

N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT serialnumber
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.05
Elapsed Time 00:00:00.06
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics
Mean Std. Deviation N
serialnumber 1.80 .404 431
hoursonline 22.23 17.113 431
Correlations
serialnumber hoursonline
Pearson Correlation serialnumber 1.000 -.071

hoursonline -.071 1.000
Sig. (1-tailed) serialnumber . .072
hoursonline .072 .
N serialnumber 431 431
hoursonline 431 431
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: serialnumber
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .071a .005 .003 .403 .005 2.150 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .349 1 .349 2.150 .143b
Residual 69.683 429 .162
Total 70.032 430
a. Dependent Variable: serialnumber
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-
order
Partial Part

1 (Constant) 1.833 .032 57.548 .000
hoursonline -.002 .001 -.071 -1.466 .143 -.071 -.071 -.071
a. Dependent Variable: serialnumber
Regression
Notes
Output Created 08-JAN-2017 17:28:58
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
screenlockcodeorpattern
/METHOD=ENTER
hoursonline.
Resources Processor Time 00:00:00.05
Elapsed Time 00:00:00.06
Memory Required 1716 bytes

Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics
Mean Std. Deviation N
screenlockcodeorpattern 1.09 .291 450
hoursonline 22.20 17.010 450
Correlations
screenlockcodeor
pattern
hoursonline
Pearson Correlation screenlockcodeorpattern 1.000 -.134
hoursonline -.134 1.000
Sig. (1-tailed) screenlockcodeorpattern . .002
hoursonline .002 .
N screenlockcodeorpattern 450 450
hoursonline 450 450
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: screenlockcodeorpattern
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2

1 .134a .018 .016 .289 .018 8.200 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .684 1 .684 8.200 .004b
Residual 37.396 448 .083
Total 38.080 449
a. Dependent Variable: screenlockcodeorpattern
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.144 .022 51.071 .000
hoursonline -.002 .001 -.134 -2.864 .004 -.134 -.13
a. Dependent Variable: screenlockcodeorpattern
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT registeredphone
/METHOD=ENTER hoursonline.
Regression
Notes

Output Created 08-JAN-2017 17:29:12
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
registeredphone
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.03
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics

Mean Std. Deviation N
registeredphone 1.87 .335 450
hoursonline 22.20 17.010 450
Correlations
registeredphone hoursonline
Pearson Correlation registeredphone 1.000 -.027
hoursonline -.027 1.000
Sig. (1-tailed) registeredphone . .283
hoursonline .283 .
N registeredphone 450 450
hoursonline 450 450
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: registeredphone
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .027a .001 -.001 .336 .001 .332 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .037 1 .037 .332 .565b
Residual 50.487 448 .113
Total 50.524 449
a. Dependent Variable: registeredphone

b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.883 .026 72.329 .000
hoursonline -.001 .001 -.027 -.576 .565 -.027 -.02
a. Dependent Variable: registeredphone
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT trackorlockphone
/METHOD=ENTER hoursonline.
Regression
Notes
Output Created 08-JAN-2017 17:29:25
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling Definition of Missing User-defined missing values
are treated as missing.

Cases Used
Statistics are based on cases
with no missing values for any
variable used.
Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
trackorlockphone
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.05
Elapsed Time 00:00:00.05
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics
Mean Std. Deviation N
trackorlockphone 1.39 .489 450
hoursonline 22.20 17.010 450
Correlations
trackorlockphone hoursonline
Pearson Correlation trackorlockphone 1.000 -.087
hoursonline -.087 1.000
Sig. (1-tailed) trackorlockphone . .032

hoursonline .032 .
N trackorlockphone 450 450
hoursonline 450 450
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: trackorlockphone
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .087a .008 .005 .488 .008 3.451 1 44
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .821 1 .821 3.451 .064b
Residual 106.559 448 .238
Total 107.380 449
a. Dependent Variable: trackorlockphone
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.449 .038 38.314 .000
hoursonline -.003 .001 -.087 -1.858 .064 -.087 -.08
a. Dependent Variable: trackorlockphone

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT physicalluymarked
/METHOD=ENTER hoursonline.
Regression
Notes
Output Created 08-JAN-2017 17:29:35
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.

Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
physicalluymarked
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.00
Elapsed Time 00:00:00.03
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics
Mean Std. Deviation N
physicalluymarked 1.82 .384 425
hoursonline 22.21 17.149 425
Correlations
physicalluymarke
d
hoursonline
Pearson Correlation physicalluymarked 1.000 -.105
hoursonline -.105 1.000
Sig. (1-tailed) physicalluymarked . .016
hoursonline .016 .
N physicalluymarked 425 425

hoursonline 425 425
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: physicalluymarked
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .105a .011 .009 .382 .011 4.671 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .682 1 .682 4.671 .031b
Residual 61.728 423 .146
Total 62.409 424
a. Dependent Variable: physicalluymarked
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial
1 (Constant) 1.873 .030 61.731 .000
hoursonline -.002 .001 -.105 -2.161 .031 -.105 -.10
a. Dependent Variable: physicalluymarked

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT installedantivirus
/METHOD=ENTER hoursonline.
Regression
Notes
Output Created 08-JAN-2017 17:29:50
Comments
Input
Data C:\Users\karen\Documents\
A31916-M1data input.sav
Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 648
Missing Value Handling
Definition of Missing User-defined missing values
are treated as missing.
Cases Used
Statistics are based on cases
with no missing values for any
variable used.

Syntax
REGRESSION
/DESCRIPTIVES MEAN
STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS
R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
installedantivirus
/METHOD=ENTER
hoursonline.
Resources
Processor Time 00:00:00.05
Elapsed Time 00:00:00.05
Memory Required 1716 bytes
Additional Memory Required
for Residual Plots 0 bytes
[DataSet0] C:\Users\karen\Documents\A31916-M1data input.sav
Descriptive Statistics
Mean Std. Deviation N
installedantivirus 1.78 .413 431
hoursonline 22.40 17.093 431
Correlations
installedantivirus hoursonline
Pearson Correlation installedantivirus 1.000 .012
hoursonline .012 1.000
Sig. (1-tailed) installedantivirus . .399
hoursonline .399 .
N installedantivirus 431 431
hoursonline 431 431

Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 hoursonlineb . Enter
a. Dependent Variable: installedantivirus
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2
1 .012a .000 -.002 .414 .000 .066 1 42
a. Predictors: (Constant), hoursonline
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .011 1 .011 .066 .798b
Residual 73.488 429 .171
Total 73.499 430
a. Dependent Variable: installedantivirus
b. Predictors: (Constant), hoursonline
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Correlations
B Std. Error Beta Zero-
order
Partial Part
1 (Constant) 1.775 .033 53.978 .000
hoursonline .000 .001 .012 .256 .798 .012 .012 .012
a. Dependent Variable: installedantivirus