MG4002 Data Analytics: Examining Internet, Education & Politics

This report investigates the complex relationship between internet use, education, and political engagement, utilizing data from the ESS Round 8 questionnaire. The study aims to determine how education influences internet usage, which in turn impacts political participation. A conceptual framework is established, hypothesizing relationships between these variables, and tested using descriptive statistics, correlation analysis, and multiple regression analysis. The results indicate a weak positive relationship between internet use and both political engagement and education level. While the regression model is statistically significant, it explains only a small portion of the variation in internet use, suggesting other factors play a significant role. The report concludes by discussing these findings in the context of existing literature, highlighting the need for further research to fully understand these dynamics. Desklib provides access to this and other solved assignments to aid student learning.

Data Analytics, MG4002
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20 December 2018

1. Introduction
The connection between the Internet, education and politics is both complex and layered. In
the past few years, internet has taken the world with a storm and it continues to do so. The
Internet can be utilized in empowering dissidents or even in tracking and suppressing them.
It tends to be utilized to the advantage of disenfranchised society or to confirm existing
dynamics of power. It tends to be utilized to fortify or to disintegrate open talk. In the early
1990s, the Internet comprised of email, static website pages, email, access to dial-up phone
and announcement board networks. It was still to a great extent a rising phenomenon, and
was frequently alluded to with the analogy of a "data superhighway." In the early 2000s, the
Internet saw the development of remote and versatile access in numerous modern
propelled nations, alongside the spread of broadband access, client created content and the
representation of "Web 2.0" and the "social Web." It is in this decade that saw the spread of
sites like Yelp.com—social locales, YouTube.com and Wikipedia.org whose esteem was
gotten from the commitments of a gigantic, wilful client base. The current Internet of 2010s
is in the development process still, however seems to highlight critical development in
portable access through cell phones, which thus takes into account overlaying on the web
information over customarily disconnected fields of movement. Political issues, in the
interim, can be seen at the neighbourhood, national, cross-national, and worldwide
dimensions, and can likewise be seen through an institutional or a conduct focal point. The
role of the Internet in political issues is altogether different for the normal London resident,
Seoul, or that resident in Los Angeles. The Internet's job in worldwide tact and statecraft is a
different issue by and large. The effect of the Internet on political issues, at that point,
depends vitally on which Internet and which political issues one is looking to research. This
study will focus on the relationship that exists between Internet, politics and education. The
question this study will try to answer is how education influences the use of internet which
then influences politics. The structure of this paper is as follows; Section one which one
provides the introduction of the study, section two is the conceptual framework that describe the
variables in the study and discusses the literature review, section three provides methodology of the
study, section four provides the data analysis while section five provides discussion and conclusion
of the study.

2. Conceptual framework
When one thinks about that both participation in politics and the Internet to be
multidimensional ideas, it ends up obvious that technological innovations can prompt
a blend of conceivably countervailing consequences for investment. Internet is a
nonexclusive interchanges stage that bolsters an assortment of uses, which
conceivably rival conventional communicate media, for example, TV, point-to-point
innovations, for example, communication, amass correspondences, for example,
videoconferencing, and data accumulation advances, for example, phone surveying
(Weare, 2002). In the meantime democratic participation envelops an assortment of
kinds of interceding associations (for instance, interest groups and political parties),
participatory acts for instance, casting a ballot, campaigning, reaching out to the
different authorities), and focuses to be affected (e.g., political agents and
organization authorities at both the national and local levels) (Stanley, 2002).
Subsequently, standardizing assessment of innovation based participation requires a
more extensive research program to explore various causal connections and a
cautious adjusting of the negative and positive impacts of innovation in varying
settings (Dahlberg, 2001).
Moon (2002), for instance, propose three key reasons people don't take an interest:
an absence of inspiration, an absence of ability to do as such, or an absence of
chances. Observational investigations of these unmistakable segments of
cooperation choices offer an increasingly changed picture of technological (Internet)
impacts. Inspiration. The educational and open effects of the Internet seem most
drastically averse to influence singular inspirations given that the reliable and solid
discoveries of the focal job that financial status plays in clarifying cooperation
(Walters & Aydelotte, 2000; Bimber, 2001; Goldschmidt, 2001). Utilizing chronicled
patterns and national decision examine information, Bimber (2001) contends against
a reasonable on-screen character model of data 6 procurement in which more
prominent data accessibility inspires more hunt and utilization of that data for political
purposes. Interestingly, he recommends that the data accessibility could be having
progressively subjective impacts on mental impression of political data which may
have longer run influences on inspirations.
This study seeks to establish the relationship that exists between the internet use
(dependent variable) and the political engagement as well as education level. The

idea here is that education level and political engagement influences the internet
use. The conceptual model is given in figure 1 below.
The following hypothesis were to be tested in this study.
Hypothesis 1:
Null hypothesis (H0): There is no significant relationship between political
engagement and internet use.
Null hypothesis (H0): There is significant relationship between political engagement
and internet use.
Hypothesis 2:
Null hypothesis (H0): There is no significant relationship between education level and
internet use.
Null hypothesis (H0): There is significant relationship between education level and
internet use.
Hypothesis 3:
Null hypothesis (H0): There is no significant relationship between education level and
political engagement.
Null hypothesis (H0): There is significant relationship between education level and
political engagement.
Political
engagement Internet use
Education
Level
Figure 1: Conceptual framework

3. Methodology
Data was collected from ESS Round 8 questionnaire. The data has 44387 observations.
Different statistical methodologies will be employed to analyse the hypothesis of the study
presented above. Both descriptive and inferential statistics will be used to present the
results. Some of the descriptive statistics that will be presented include the measures of
central tendency (mean, mode, median), measures of spread (range and standard deviation)
and measures of normality and shape (skewness and kurtosis). For inferential analysis, both
correlation test and multiple regression analysis was performed to test for the relationship
between the variables.
The following table gives the description of the variables used in the model.
Table 1: Description of the variables
Variable name Variable type Dependent or Independent?
Internet use Numerical Dependent variable
Political engagement Numerical Independent variable
Education level Numerical Independent variable
We sought to investigate the following regression model
y=β0 + β1 x1 + β2 x2 +ε
Where, y=Internet Use, x1=Political engagement x2=Educationlevel and ε =error term.
β0=Intercept coefficient (constant coefficient)
β1=coefficient for the political engagement ( x1 )
β2=coefficient for the education level(x2 )
4. Data Analysis/ Results
Statistical assumptions
The following statistical assumptions were made before the analysis was performed.
Independence of variables; this assumption is that the variables under study are
independent of each other.
Normality; that the data follows a normal distribution
Randomness; that collection of the data from the subjects was done at random.

Descriptive analysis
The table below presents the descriptive statistics. On average the score for political
engagement was found to be 85.43 while the average internet use was found to be 3.86
with a median score of 5.
Table 2: Descriptive statistics
Politics engagement Internet
use
Highest level of
education
N Valid 43863 44338 44170
Missing 524 49 217
Mean 85.43 3.86 392.06
Median 60.00 5.00 322.00
Mode 60 5 213
Std. Deviation 136.799 1.591 191.512
Variance 18713.934 2.530 36676.873
Skewness 4.965 -.949 .433
Std. Error of Skewness .012 .012 .012
Kurtosis 29.288 -.814 -.840
Std. Error of Kurtosis .023 .023 .023
Range 1428 4 800
Minimum 0 1 0
Maximum 1428 5 800
Correlation analysis
The correlation analysis showed that a very positive relationship exists between internet use
and engagement in politics (r = 0.022). There was also a week positive relationship between
highest education level and internet use (r = 0.100). Surprisingly, there was a weak negative
relationship between highest education level and engagement in politics (r = -0.030).
Table 3: Correlation analysis
Politics
engagement
Internet
use
Highest level of
education
Politics engagement
Pearson Correlation 1 .022** -.030**
Sig. (2-tailed) .000 .000
N 43863 29913 43651
Internet use Pearson Correlation .022** 1 .100**
Sig. (2-tailed) .000 .000

N 29913 30113 29975
Highest level of
education
Pearson Correlation -.030** .100** 1
Sig. (2-tailed) .000 .000
N 43651 29975 44170
**. Correlation is significant at the 0.01 level (2-tailed).
Regression analysis
In this section, we present the regression analysis results. Table 4 gives the model summary
where we can see that the value of R-Squared is 0.011; this implies that only 1.1% of the
variation in the dependent variable (internet use) is explained by the two independent
variables in the model (education level and engagement in politics score). This further shows
that a huge proportion (99%) in the variation in the dependent variable (internet use) is
explained by other factors outside the model.
Table 4: Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .103a .011 .011 170.802
a. Predictors: (Constant), Highest level of education, News about
politics and current affairs, watching, reading or listening
Table 5 gives the ANOVA table where we can see that the F-statistics is 159.33 with a p-
value of 0.000 (a value less than 5% level of significance), we therefore reject the null
hypothesis and conclude that the model is significant and fit in predicting the internet use
based on the two independent variables.
Table 5: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 9296290.609 2 4648145.304 159.328 .000b
Residual 868578262.120 29773 29173.354
Total 877874552.729 29775
a. Dependent Variable: Internet use, how much time on typical day, in minutes
b. Predictors: (Constant), Highest level of education, News about politics and current affairs,
watching, reading or listening

Table 6 gives the regression coefficients where we can see that all the two independent
variables are significant in the model (p < 0.000).
Table 6: Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 155.596 2.555 60.890 .000
Political engagement .028 .008 .022 3.779 .000
Highest level of education .092 .005 .101 17.452 .000
a. Dependent Variable: Internet use, how much time on typical day, in minutes
The coefficient political engagement is 0.028; this suggests that a unit increase in the
political engagement score would result to an increase in the internet use by 0.028.
Similarly, a unit decrease in the political engagement score would result to a decrease in the
internet use by 0.028.
The coefficient education level is 0.092; this suggests that a unit increase in the education
level would result to an increase in the internet use by 0.092. Similarly, a unit decrease in
the education level would result to a decrease in the internet use by 0.092.
Lastly, the intercept coefficient is given as 155.596; this means that holding all other factors
constant we would expect the internet use score to stand at 155.596.
Based on the above results, we would estimate the regression model as follows;
y=155.596+ 0.028 x1 +0.092 x2
Where, y=Internet Use, x1=Political engagement x2=Educationlevel and ε =error term.
5. Discussion and Conclusion
This study sought to investigate the relationship that exists between Internet, politics
and education. The question this study sought to answer is how education influences
the use of internet which then influences politics. Previous studies had shown that
significant positive relationships exists between internet use and political engagements as
well as education level. The more learned people tend to use internet more often and for
political engagements. Other studies however should negative relationship between the
variables while others did not find any significant relationship. In light with this, this study

aimed to verify what other authors have found out. We established that positive
relationship exists between internet use and political engagements. We also established a
positive relationship between internet use and education level. However, even though there
were positive relationships established, the relationships were very weak. In fact the
proportion of variation in the dependent variable explained by the two variables was only
1.1% implying that a huge proportion in the variation in the dependent variable (internet
use) is explained by other factors outside the model.
References

Bimber, B. (2001). Information and Political Engagement in America: The Search for Effects
of Information Technology at the Individual Level. Political Science Quarterly, 54(1),
53-67.
Dahlberg, L. (2001). The internet and democratic discourse. Information, Communication,
and Society, 4(1), 615-633.
Goldschmidt, K. (2001). Email Overload in Congress: Managing a Communications Crisis.
Congress Online Project, 1-15.
Moon, M. J. (2002). The evolution of E-government among municipalities: Rhetoric or
reality? Public Administration Review, 62(4), 424.
Stanley, J. W. (2002). Participation, Democratic Deliberation, and the Internet: Lessons from
a National Forum on Commercial Vehicle Safety. School of Policy, Planning and
Development.
Walters, L. C., & Aydelotte, J. (2000). Putting more public in policy analysis. Public
Administration Review, 60, 349-359.
Weare, C. (2002). The Internet and Democracy: The Causal Links Between Technology and
Politics. International Journal of Public Administration, 25(5), 659-692.