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Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 1
MIXED SIGNALS AN ANALYSIS OF THE MOBILE
PHONE SIGNAL COVERAGE IN
SAUGHALL
Jack Hughes
Victoria Byrne
September 2016
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 2
Acknowledgements
We are very grateful to a number of individuals without whose help this report would have not been
possible.
We would especially like to thank Michelle Collins for her innovative and sensible advice that helped
tremendously with our data collection methods. We would also like to thank Jessie O’Malley for her
support at the Saughall Farmers Market, as well as the many Facebook group admins who kindly
agreed to let us share the online survey link on their pages. Lastly, we are very grateful to the Saughall
and Shotwick Park Parish Council, and particularly Councillor Kathy Ford, Councillor Howard Jennings,
and Clerk Shirley Hudspeth, for keeping us up to date with any developments in this area and being so
cooperative with our report.
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 3
Contents
Preface ................................................................................................................................. 4
Summary ............................................................................................................................. 5
SECTION I ............................................................................................................................ 6 1.1 Introduction........................................................................................................................... 6 1.2 The Questions ....................................................................................................................... 7 1.3 Area and Subjects of Analysis .............................................................................................. 8 1.4 Asking the Questions ............................................................................................................ 9 1.5 Data Collection Methods .................................................................................................... 12
SECTION II ......................................................................................................................... 15 2.1 Foreword to Results ............................................................................................................ 15 2.2 General Findings ................................................................................................................. 15 2.3 The Hypotheses .................................................................................................................. 16 2.5 Closing Remarks ................................................................................................................. 26
Limitations ......................................................................................................................... 27
Clarifications ...................................................................................................................... 28
Appendix ........................................................................................................................... 30
Bibliography ...................................................................................................................... 36
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Victoria Byrne September 2016 4
Preface
This report is the product of a collaborative effort and seeks to examine the experiences of mobile
phone users in the village of Saughall.
Mixed Signals as the title of this paper is more than just a clever application of the usual saying, it
underpins the paradox that surrounds this issue. On the one hand we have the undertones of the
dissatisfied community crying out for change, while on the other we have those content with the
existing situation stating that things have changed. Whatever the reality, the mobile signal coverage
debate has been an enduring controversy within the village of Saughall. It has been a topic ripe with
deliberation and discussion at all levels, nonetheless, marginal headway has been made.
The purpose of this report is to shed some much needed light on the matter with the aim of arriving
at a progressive consensus. This will be done using statistical methodology backed up with tangible
evidence that will help to evaluate some of the opposing arguments that surround this issue. The way
in which this report will carry out this task is through two research questions:1) What is the signal
coverage like for the Village? and 2) How does the signal coverage affect certain age groups in the
Village? The data we obtain from this will help us to formulate some appropriate and coherent
recommendations, which may be used at the discretion of those with the power to implement them.
The work in this paper is aided by my background as a resident of Saughall and a student studying
social sciences at the University of Manchester. The motivation behind the research resides within a
mixture of personal interest in the topic and a frustration with the status quo.
Jack Hughes
September 2016
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Victoria Byrne September 2016 5
Summary
Saughall is an extremely desirable location with great transport links, a bustling nearby city,
and a supportive community network.
Two presumptive hypotheses will be made about the signal coverage in Saughall:
[HYP1] - Any individual using a mobile phone on any network in Saughall will suffer from poor
network coverage.
[HYP2] - Poor network coverage will impact a younger aged demographic group more severely
than an older aged demographic group.
We will ask an 8 question survey to 346 residents and non-residents in the Saughall area. This
is an appropriate sample size based on our statistical parameters and the approximated
population of the area.
We will obtain respondents through online and offline surveying methods. The online method
will involve an e-survey while the offline method will involve door-to-door surveying and
static surveying (surveying passers by).
From the 346 respondents that were asked, the most common response was ‘Very poor’ with
223 answers (64%), followed by ‘Poor’ with 79 (23%). This totals to 302/346 respondents
(87.3%) who said they had poor network coverage.
The worst network provider in our survey was Three with a 95% chance of the user
experiencing ‘Poor’ or ‘Very poor’ network coverage, shortly followed by O2 with a 91.7%
chance.
There is minimal evidence to suggest that poor network coverage always impact younger age
groups more severely.
In total, 82% of respondents said that their signal coverage was either ‘Problematic’ or ‘Very
problematic’ to their daily lives.
Overall, a staggering 98% of respondents answered ‘Yes’ when asked whether something
should be done about Saughall’s network coverage.
We recommend a number of measures to tackle these issues that are based around a
desirability framework. The First Best Solutions involve the installation of a mast and
discounted phone tariffs, the Second Best Solutions involve distributing booster technology
and more information for consumers, and the Third Best Solution involves greater recognition
of the problem.
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SECTION I
1.1 Introduction The invention of the mobile phone is unquestionably one of our greatest achievements. Since Martin
Cooper’s grand unveil of the first handheld mobile telephonic device in 1973, mobile technology has
been revised, adapted and improved to make communication in each and every one of our lives 1
more accessible.
The advancements made in mobile telecommunications in the past decade have increased
exponentially with recent innovations in Smart Phone’s and 5G technology which will ripple into the
future. Over the next decade, there is little doubt that mobile phones will continue to become more
and more capable of carrying out the daily tasks that we would have otherwise carried out.
Nevertheless, if we trim back the embellishment, mobile phones are still unrivalled as convenient
methods of communicating with others around the world. They have and will continue to tear down
the boundaries of distance, help us to build stronger relationships, and bring us all closer on this
single planet.
The grand picture is however a topic for another day, and for us to continue would lead us astray and
beyond the scope of this paper. The purpose of this report is to assess the functionality of mobile
devices on a much smaller scale involving much fewer people, but do not let these last two points
trick you into underestimating the significance of this topic.
We will be focusing on the village of Saughall in this report. Saughall is located in the county of
Cheshire in the North West of England, it occupies a sizable piece of land and is home to over 3,000
residents and several small businesses. Its geographical placement is extremely well situated with
access to the M56 (for Manchester), the M53 (for Liverpool) and the M6 (for Birmingham) a matter of
minutes away. The nearest large settlement, Chester (a city of around 120,000), is a bustling,
expanding and historically rich city filled with heritage and things to do 2. The Roman Walls, Chester
Racecourse and Chester Cathedral alone bring hundreds of thousands of visitors from across the world
every year 3. Unity is at the heart of the Village with the Parish Council working diligently to ensure a
safe and thriving community. Saughall is without question an exceptionally desirable place to settle
down. Yet for all its merits, one persistent issue still plagues the area and its residents – poor mobile
network coverage.
This topic has the Village at an impasse and without the tools to form a coherent solution to the
problem, Saughall will remain locked into this stalemate. Depending on the results of our field
research, we hope our work can be used as a vehicle to crash through this stalemate and make some
headway on this issue. We will achieve this by employing the voices of those who have experienced
the signal coverage in the area, and it is now time for us to tell you how we are going to do this.
We plan to assess the coverage in the area through a dual analysis method: the first level (our primary
analysis) will look at how individuals experience their signal coverage and how certain networks fare
against others, while the second level (our secondary analysis) looks at how the coverage impacts
certain age groups (how their mobile signal coverage affects user’s daily lives). In order to obtain the
1 We now have more digital gadgets on this planet than we do people, see http://www.independent.co.uk/life-style/gadgets-
and-tech/news/there-are-officially-more-mobile-devices-than-people-in-the-world-9780518.html 2 Chester has been nationally recognised last year in the UK Hot Housing Index as the best place to live in the UK, see
http://www.chesterchronicle.co.uk/news/chester-cheshire-news/chester-named-top-place-live-10103325 3 See the following link for detail, http://www.chesterchronicle.co.uk/news/chester-cheshire-news/tourism-boost-cheshire-
record-visitor-9753702
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data required to assess these objectives, we will carry out a comprehensive survey of the area that
when coupled with some data analysis we will be able to look into the general experiences of signal
coverage in the area.
We think that it would be beneficial to the reader to consider this report in two sections: Section I
covers introductory material, our hypotheses/presuppositions and how we plan to carry out our
research, Section II focuses on the results of our research and the recommendations we make based
on them.
As mentioned, this section will contain some presuppositions that will help with later comparisons and
structure. Before our field research is conducted we are therefore presupposing two hypotheses:
[HYP1] - Any individual using a mobile phone on any network in Saughall will suffer from poor network
coverage.
[HYP2] - Poor network coverage will impact a younger aged demographic group more severely than an
older aged demographic group.
The consequences and recommendations of this report will be shaped by the results of our field
research. This means that the validity of these hypotheses will be tested against and compared with
the data we collect.
With the introductory material out of the way it is now appropriate to move onto the questions that
we will be asking.
1.2 The Questions The questions that will be asked in our survey are designed around our two hypotheses (HYP1 and
HYP2). These hypotheses produce two questions: the first question is whether Saughall really does
have poor network coverage and whether that coverage is exclusive to certain providers? The second
question is how does the network coverage impact certain age groups?
As discussed, we intend to answer these questions through a survey designed to provide us with
adequate data to make the necessary inferences. The survey we have opted for is a short, 8 question
survey (a copy of which can be seen in the Appendix, Figure 1).
We will now go through each of the questions briefly (Q) and the reasons (R) that we have chosen to
ask them:
Q1) (Q) What is your road name and number? (R) This question will help us to determine
how widespread poor signal coverage may be. We also think that recording road names
(and numbers) enables us to obtain our data more fairly by covering each road as equal
as possible. Lastly, if we need to return to a certain road for more data, we can avoid the
houses who have given us their numbers.
Q2) (Q) How would you rate the signal coverage for your mobile handset when using it
within the Saughall area? (R) The answers to this question will help us to determine our
first level of analysis. It will also enable respondents to share their experience of the
mobile phone signal coverage.
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Q3) (Q) How consistent is the signal coverage for your mobile device within the Saughall
area? (R) Similarly to Q2, this will help to answer our first level of analysis.
An important note must be made about this question in particular (Q3). We actually chose
not to use the data from this question in our analysis in Section II. The reasoning for this
was that we noticed its incompatibility with Q2 too late and we were unable to account for
poor wording of the question. This incompatibility can be seen when answering ‘Mixed’ for
Q2, which practically voids the consistency question. Furthermore, two of the answers in Q3
(‘Mixed’ and ‘Inconsistent’) are virtually synonymous making the question confusing for the
respondent. Lastly, we felt this question was unnecessary; Q2 covers the consistency element
as users undoubtedly will already be factoring in signal consistency when deciding how to
rate their coverage.
Q4) (Q) Who is the provider of your mobile device signal? (R) This will allow us to
determine whether poor signal coverage is limited to certain providers. It will prove
helpful for when we come to making recommendations as we may be able to advise
residents (current and potential) which networks to avoid.
Q5) (Q) What is your age range? (R) The answers to this question are important in unison
with the following question to help answer our secondary level of analysis.
Q6) (Q) How would you rate the impact of the mobile signal service on your day-to-day
life? (R) Coupled with an age range from Q5, we can determine how the network
coverage may impact certain age groups from the responses to this question.
Q7) (Q) Do you have a landline? (R) The answers to this question helps us to develop on our
secondary level of analysis.
Q8) (Q) Broadly speaking, do you think something should be done about the mobile
phone signal coverage in the Saughall area? (R) This last question concludes the
survey. It does not specify the means by which change could be achieved, but users
answering ‘Yes’ clearly would demand something to be done. We feel a direct question
like this is necessary when it comes to formulating a consensus.
We will now move onto the area we wish to assess and the individuals we plan to survey.
1.3 Area and Subjects of Analysis
Now we have determined what questions we are going to ask and the reasoning for them, it is
appropriate to be more specific about who we are going to ask. It should already be clear to the
reader at this point that this report focuses explicitly on the village of Saughall; it therefore does not
concern the surrounding areas such as Mollington, Shotwick Park, Etc., (but this is not to say that the
findings may not be common amongst residents from these other areas). We would certainly like to
assess a larger area, however it is more realistic to constrain our field research to a single designated
place. There are many good reasons for doing this, such as a lack of resources, but more importantly
we feel focusing on a single place means we can be more thorough in our analysis.
To ensure that we are clear about the area we are assessing we have included a mapped diagram
which can be found in the Appendix, Figure 2. In the diagram our area of analysis is contained within
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the drawn boundary, meaning any external locations are not to be included in our survey. It is now
important for us to elaborate on the subjects of analysis, which is a more complicated issue.
We could reserve our investigation to just permanent residents of Saughall, however we believe the
true longevity of residence does not matter greatly when considering what we are assessing. It is true
that a long-term, permanent resident and mobile phone user would have more experience of the
signal coverage in the area, but we are more concerned about the general experience of any users in
the area, be it visitors, long-term or new residents. While we anticipate that the majority of responses
will come from permanent residents, we are expecting several to not be, but this does not impact our
analysis by any significant degree, if anything, a view from an externally located individual is valuable
for the user would have experience of both places and be able to make a sound judgment.
1.4 Asking the Questions Up to this point we have discussed some of the key preliminary elements for this report, and it is now
time for us to be more explicit about the amount of individuals we will be surveying. This part of the
report does contain some of our statistical methodology but we shall clarify any necessary subject
material, so this should not seem daunting to the reader.
As mentioned previously, in consideration of time and resource constraints, we have chosen (as many
statistical agencies do) to survey a representative sample of the population. The key word in that last
sentence is representative; we are looking to obtain a sample that tells us certain things about the
population as a whole. An analogy may be helpful for visualisation: imagine a sandwich, you would
like to know what the whole sandwich tastes like without eating it all, so you take a small bite with the
hope that you will taste all of the contents in the single section. How much that bite resembles the
whole sandwich is a measure of representativeness. In the same way that you would not choose to eat
a section with nothing in, we would not take a survey from a single network provider on a single road
and generalise about all providers and all roads, for example.
There are four things that we must determine in order to calculate our sample size, and now would be
a good time to briefly familiarise ourselves with them:
1) Confidence level: A confidence level is a number which tells us how many times we think our
survey results would fall within a parameter (range) of the total population (if they were
surveyed) if we conducted the survey over and over again. For example, a 95% confidence
level tells us that if we completed that survey of the population 100 times, in 95 of those cases
our survey data would fall within a parameter of the population (the parameter is made more
clear in the next point).
2) Margin of error (confidence interval): This is the parameter that was discussed in the previous
point; it is a range given as a plus or minus number (i.e. +/- 10%, +/- 5%, +/- 1%, etc.). The
choice of number depends on how accurate we want our sample to be (or how much
inaccuracy we would allow) as no sample will perfectly represent the population. The more
accurate we want to be, the smaller the margin of error and the less the answer deviates from
the population. The smaller the margin of error, the larger the sample size has to be to ensure
accuracy (however a balance must be struck between a sensible sample size and the accuracy
of data, as a general rule most statistical agencies and outlets will use a margin of error of
5%). When this is combined with (1) the confidence level, we get a spread, e.g. we would
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expect our sample results to be +/- 5% (margin of error) within the population 95%
(confidence level) of the time 4.
3) Response distribution: this is used to predict the variability of answers in the survey. In one of
the questions if we obtained results of 99% ‘Yes’ and 1% ‘No’ then the error is minimal.
However, if the chances of either answer were 51% and 49% then the chance of our error
increases. As we don’t know what the answers will be as of yet, we must use the safest value
of 50%.
4) Population size: the total amount of individuals that are within the area that we are measuring
and related to the subject we are assessing. For example, if we were doing a report about dog
walkers in the North West, our population would be the total number of dog walkers in the
North West 5.
For (1) and (2) we will be using a margin of error of 5% and a confidence level of 95%. These are
industry standards and they mean that our survey data will fall between +/- 5% of the population in
95% of the cases. For (3) we will be using a response distribution of 50%, so all that is left is for us to
calculate our population size as accurately as possible.
Determining the population size of an area is no easy task; we feel the most sensible way to go about
this is to use data from the Office for National Statistics (ONS) (the largest producer of statistical data
in the UK) for the most recent population size for the village. This is given in the 2011 Census and is
calculated to be 3,009 (Office for National Statistics , 2011). As we are looking to obtain the most
accurate results possible, it would be unfair for us to assume this population has remained static since
that date. We will therefore be using this population size as a basis for our adjusted population.
With an absence of more recent data we feel the most rational way to progress is to assume that the
population of Saughall has risen in the past 5 years. The first step we shall take is to increase the 2011
Census base data in line with the average population increase in England (where the village is located)
over the same period (2011-2016). Using population estimates (Office for National Statistics , 2016), a
simple calculation will tell us that in the last 5 years, from 2011 (Census year – 53,107,200) to 2015 (last
year – 54,786,300), that the population in England has increased by around 3.2%. Taking this result, we
can apply it to our Census data giving us a new approximate resident population of 3,106 people.
The next logical step would be for us to look at who may have migrated into the area in the last five
years. New residents living in the area will almost likely be housed, meaning it is most appropriate to
consider any recent housing developments in that five-year period. Our research has brought up three
in particular:
28 6 new properties built by Morris Homes in Willow Hey (opposite Rakeway) on the old site of
The Riding’s infant-school that was closed down in 2009.
18 7 new properties were completed last year on Thomas Wedge Road (just off Lodge Lane).
4 If you are still unsure about confidence levels and margins of error, here are some helpful links that explain it in a very
simplistic way (and probably much better than we do):
http://www.statisticshowto.com/confidence-level/
http://www.dummies.com/education/math/statistics/how-to-interpret-the-margin-of-error-in-statistics/
https://www.youtube.com/watch?v=NH40E65TWqg 5 Because of the difficulties involved in determining such a figure, populations are often approximated. 6 Taken from the Morris Homes website stating a new development of three and four bedroom detached and semi-detached
properties, see http://morrishomes.co.uk/news/2013/october/morris-homes-opens-new-saughall-development/ 7 This quantity was counted by us but confirmation can be found in the Saughall and Shotwick Park Parish Council Chairman’s
Annual Report (2014/2015) (pg.4) http://www.saughall.gov.uk/wp-content/uploads/2015/03/Chairmans-Report-2015.pdf
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3 8 new properties were completed in 2015 on Hermitage Road.
(High Grove, a large housing development large built by Antler Homes on the site of the old
railway station (Seahill Road), was constructed in late 2008 9 and so we shall assume that new
residents were included in the 2011 ONS survey and will make no attempt to inflate the
population in relation to this development)
These three developments are all contained in the last five years meaning that we must account for a
further 49 households in our population estimate. To determine how many individuals this translates
into we can once again refer to the 2011 Census for the area; the data tells us that there were 1,265
(Office for National Statistics , 2011) households in the area. Taking this figure and adding our 49 new
households we get an updated figure of 1,314. Using this figure again we can divide our adjusted
population size of 3,106 by the new households figure to find that there is approximately 2.4 residents
per household. Multiplying the household average by the 49 new homes we get a figure of
approximately 118 new residents. We feel this is the fairest way to calculate the new population of this
households without physically questioning each of them. Adding this figure to our previously adjusted
population estimate we get a new population of 3,224 which will undoubtedly be closer to the true
population.
We recognise that this result could still be an underestimation of the true population and so we will
take one final measure to ensure our population is fairer. We will add a 5% margin on top of the
adjusted population based on current statistics. The results of which could pose two questionable
scenarios, both of which are still positive as we explain:
1) What if the 5% margin still meant that we fall short of our true population? This is still a
possibility, however, it will put us closer to the true population so it is a sensible calculation.
2) What if the 5% margin puts us over the true population? Again this is a possibility, however it is
a much more desirable situation to overestimate than to underestimate. Because our soon to
be calculated sample size is linked to the population (they rise together), overestimating the
population would only lead to a larger necessary sample size. In any case a larger sample size
than is required will only lead to more accurate results.
In light of these apprehensions adding this 5% margin should account for any new households we did
miss or any errors in our calculations. A 5% margin equates to 162 new residents (rounding up), which
when added to our adjusted Census population gives us a final population estimate of 3,386.
Now that we have our population size, we can use the aforementioned calculations to work out an
appropriately representative sample size. With a population of 3,386, a margin of error (confidence
interval) of 5%, a confidence level of 95% and a response distribution of 50% we calculate that an
appropriate sample size would have to be 346 individual respondents.
All that is left to discuss about this sample size is how we will distribute it across the area as fairly as
possible. We have created a list of roads from our designated area, which can be seen in the Appendix,
Figure 3. The list shows that there are a total of 49 roads in the area 10 which we will use to guide the
8 This quantity was physically counted by us 9 The most accurate log of the development dated http://www.easier.com/39697-the-perfect-gift-a-new-home-at-high-grove-
in-saughall.html 10 This list was constructed using another list as a basis which can be found here:
http://www.geographic.org/streetview/uk/Cheshire_West_and_Chester/saughall.html). We adjusted this list by working through
and making sure that all roads were located in the area before adding any new roads that have been created such as Thomas
Wedge Road and Willow Hey.
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distribution of our survey data. Dividing 346 (sample size) by 49 (number of roads) we get around 7
respondents per road. We will use this as a guide to determine the amount of respondents to take
from each road. However, we can envisage a number of issues with this method: certain roads that do
not have 7 respondents, where we find it impossible to obtain this many, or where we obtain more
respondents than is necessary. In these cases, we will have no choice but to proceed with obtaining as
many respondents as necessary. We will use the 7 per road guide to ensure that through our door-to-
door methods we will not ask more respondents from a road that we have already obtained 7
responses for, furthermore we will actively seek to obtain more respondents for roads which we have
not obtained 7 from. In most cases, this guide will help to safeguard against over-representation and
underrepresentation of certain roads. In our other methods of surveying (offline and online, which we
will soon speak about), we will be unable to guarantee a 7 per road quota. Overall however, we will be
seeking to obtain respondents from each road on the list as we feel this does adequately cover the
village entirely.
We shall now move on to how we intend to obtain these results.
1.5 Data Collection Methods There are two main avenues by which we plan to obtain our data: offline and online surveying.
Offline surveying can be split further into two areas: door-to-door and static surveying. Door-to-
door surveying will involve us making our way round the entirety of the village seeking to obtain
a set amount of residents from each road. Surveys will be done face-to-face with the answers
given by the individual/s in the household/s and the data recorded by one of us on the survey
sheets. The second offline method, static surveying, involves us being present in a certain
location for a period of time during which individuals will be asked by us if they would like to
complete the survey, or they may approach us to do so. There are two particular locations where
this will be carried out: 1) the Saughall Farmers Market (a regular gathering in the Vernon Institute
(the Village Hall) of buyers and local sellers of meat and produce), and 2) the Saughall All Saints
school pick-up. Both these locations and time periods have two beneficial things in common:
they have ways in which we can announce our intentions beforehand, and they are both
occasions when many individuals will be present in one place. Both of these things help us to
spread awareness about this issue as well as ensuring that we can obtain a lot of respondents.
The second avenue of data collection is online and will be done through an e-survey on
Kwiksurveys. Kwiksurveys is a powerful surveying tool that offers real-time data analysis and
presentations and is used by many large organisations such as the BBC and NHS 11. The online
survey will mirror that used in the offline collection except it will not record road numbers (to
allow this feature we would have to pay a premium which is beyond our budget). The survey can
be accessed via an online link which will be posted in several groups on Facebook. These groups
will be selected to ensure that we are not appealing to the wrong audience, and will often be
placed in groups closed to non-Saughall residents. The answers to the e-survey will relate to a
single respondent, but it will be made clear that if households wish to express multiple views that
this can be done through multiple surveys.
It would be important now to discuss how we will make these methods random. Randomness is
important for making the sample representative and to be fair to the overall results. Randomising
a sample is not a simple endeavour, but we feel that by their very nature, our survey methods are
inherently random. Online, the e-survey is random as we have no idea who will see the link and
who will complete the survey. Similarly, the offline static surveying will also be random as we
11 Logos of these organisations appear on the Kwiksurveys website, see https://kwiksurveys.com/
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have no way of knowing who will attend the event. In terms of door-to-door surveying, we will be
using a random number generator to randomise houses on each road. The number generator will
be completely random and we will use the generated number to determine which houses to
knock on, i.e. a number 5 means we will call at every fifth house on the road.
We have now considered all the necessary steps preceding our survey, so it is now time for us to
conclude Section I and to progress into Section II to look at our results.
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SECTION II
2.1 Foreword to Results
We have now completed our survey and are pleased to say that we have met our target of 346
respondents.
We began our data collection on time and as intended operating through both online and offline
avenues. Both survey methods were very well received and respondents were keen to express their
views; we thank each and every one of you who completed the online or offline survey. It was a
wonderful experience meeting so many interesting characters with countless fascinating stories about
their experiences.
The online survey was posted in Facebook groups that related to the area 12 and many of which were
closed subject to admins approval. Admins were asked beforehand if we were able to do this and all
of them were very cooperative.
All of the findings from both the online and offline surveying were recording and analysed using the
same program, Microsoft Excel 13. Kwiksurveys does offer some analysis tools but we felt this was
unsuitable as it was only able to analyse the online responses.
2.2 General Findings We will begin our analysis with a general breakdown of the findings:
These 346 respondents come from all 49 roads in the Saughall area as well as from three
external locations, Blacon, Mollington and Connah’s Quay 14.
The most common signal coverage rating was ‘Very poor’ with a total of 223 responses (64%).
Data was collected from over 12 different network providers, but 5 of these providers (O2, EE,
Vodafone, Tesco Mobile and Three) accounted for 91% of the results.
The worst network provider was Three with a 95% chance of user’s experiencing ‘Poor’ or ‘Very
poor’ signal coverage. This was shortly followed by O2 with 91.7% (with a 72.2% chance of this
being ‘Very poor’)
12 You can see these posts and the pages they were posted on here:
- https://www.facebook.com/groups/532494326910779/permalink/611520845674793/?comment_id=6115845156
68426&ref=notif¬if_t=group_comment¬if_id=1472842203946480
- https://www.facebook.com/groups/833759223336087/permalink/1248293068549365/?comment_id=124871797
1840208&reply_comment_id=1249432545102084&ref=notif¬if_t=group_comment¬if_id=147291856769
6298
- https://www.facebook.com/groups/833759223336087/permalink/1248293068549365/?comment_id=124871797
1840208&reply_comment_id=1249432545102084&ref=notif¬if_t=group_comment¬if_id=147291856769
6298
- https://www.facebook.com/groups/16402642209/permalink/10154471895422210/?comment_id=101544746344
97210&ref=notif¬if_t=group_comment¬if_id=1473363047497412
- https://www.facebook.com/groups/1445222725797084/permalink/1672300409755980/?comment_id=16723551
43083840&ref=notif¬if_t=group_comment¬if_id=1473616090669235
13 If anybody would like a copy of this data we would be happy to share it with you, please email [email protected]
under the subject ‘Data Request’ 14 1 respondent was recorded from each of these external locations with the exception of Mollington which had 2.
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 16
We obtained respondents from all of our questioned age brackets, but the most common age
range was our mid age bracket of 25-45, shortly followed by our mid-old bracket 46-65, both
of which account for just short of 75% of responses.
In total, 82% of respondents said that their signal coverage was either ‘Problematic’ or ‘Very
problematic’ to their daily lives.
Overall, a staggering 98% of respondents answered ‘Yes’ when asked whether something
should be done about Saughall’s network coverage.
Now we have considered these general points, we can begin to look at the data more intricately and
in relation to our two hypotheses.
2.3 The Hypotheses
We will now look at the data in relation to our initial presumptions to compare and contrast. This will
help to structure the section as well as demonstrating our findings.
Hypotheses One (HYP1)
Any individual using a mobile phone on any network in Saughall will suffer from poor
network coverage. For this hypothesis we must look more closely at the answers obtained in Q2 and Q4 in the survey. As
mentioned we will no longer be assessing the data from Q3 despite its relevance to HYP1.
We shall first examine the
results from Q2 and see
how the respondents
rated their mobile signal
coverage.
Out of the 346
respondents that were
asked, 223 (64%) said
that their mobile signal
coverage was ‘Very poor’
(making this the most
common answer), and a
further 79 (23%) said that
their mobile signal
coverage was ‘Poor’. In
total this equates to a
staggering 302/346
respondents (87.3%) who
said they had poor signal
coverage. These results
are modelled in Figure 4
to the right with the
different answers
4%8%
23%
1%64%
Signal Coverage Rating
Good
Mixed
Poor
Very Good
Very Poor
Figure 4
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 17
represented by different shades of blue. A quick glance will tell us how large the share of results went
to the ‘Very poor’ and ‘Poor’ responses.
Only 14 (4%) respondents rated their signal coverage ‘Good’, while just 3 respondents (<1%) rated
their signal coverage ‘Very good’. The remaining 27 respondents (8%) said their signal coverage was
‘Mixed’ (sometimes good and sometimes poor). These last figures are dwarfed by those rating the
signal coverage poor which demonstrate that only an extremely small minority can be considered
reasonably content with their signal coverage.
The way in which our sample was calculated (see 1.4) means we can make some statistical inferences
about the total population based on this data.
We are therefore 95% certain that these results will fall within +/- 5% (margin of error) of the true
population values. This means that the percentage that said their connection was ‘Very poor’ will
range between 59% (64% - 5%), and 69% (64+ 5%) in 95% of the cases. Similarly, the percentage of
individuals who would rate their coverage ‘Poor’ will range between 18% (23% - 5%) and 28% (23% +
5%). From these calculations we can determine that 95% of the time we will obtain a minimum ‘Poor’
signal coverage response rate of 77% (minimum values added), and a maximum ‘Poor’ signal coverage
response rate of 97% (maximum values added). These minimum and maximum values present a
hugely negative perception of the signal coverage in Saughall. In consideration of HYP1, we cannot
guarantee that a device user in Saughall will always have poor network coverage, but we can say
based on these results that the overwhelming majority will.
We will now move on to the second part of the hypothesis and look more specifically into how certain
networks fare.
Due to the limited scope of this report, we will confine our analysis to the top 5 mobile providers
which accounted for 91% of the respondents: O2, EE, Vodaphone, Three and Tesco Mobile. Our
hypotheses states that poor network coverage will be experienced on any network provider. In this
instance then it does not help to generalise the networks but to be more specific about how they are
individually. We can obtain this data by looking at conditional probabilities. A conditional probability is
the probability of one event occurring (A) given another event has occurred (B). For example, suppose
we wanted to look at the probability of a device user having ‘Good’ signal coverage given that they are
on O2, we can do this by using a conditional probability which tells us the chances of having ‘Good’
signal (event A), given that they are on O2 (event B). The mathematical notation for calculating
conditional probabilities is as follows:
𝑃(𝐴|𝐵) = 𝑃(𝐴 ∩ 𝐵)
𝑃(𝐵)
Although this may seem a little daunting, once explained and broken down it is actually very simple.
The left hand side of the equation (P(A|B)) is our conditional probability formula and can be read as,
“The probability (P) of event (A) occurring, given (|) event (B) has already occurred.” For example, the
probability of rolling a 3 on a dice, given that we have already landed heads on a coin.
Where P = Probability
A = Event A
B = Event B
∩ = Intersection/and
| = Conditional on
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 18
As we can see, the = sign tells us that this is given by the right hand side of the equation, in other
words the fraction on the right hand side equals our conditional probability. The top half of the
fraction (P(A∩B)) can be read as, “The probability (P) of event (A) occurring and (∩) event (B) occurring.”
This is what’s known as a joint probability (the probability of two events occurring at the same time).
For example, the probability of rolling a 3 on a dice and landing a heads on a flipped coin.
This is not to be confused with our conditional probability which assumes that one event has or will
occur and then looks at the probability of the other occurring (A given B), while a joint probability
simply looks at the probability of them both happening (A and B) at the same time.
Lastly, we have the bottom half of the fraction on the right hand side. This is a marginal probability
and is the most straightforward of all of these probabilities. It is simply the probability of one event
occurring. For example, the probability of rolling a 3 on a dice.
With this understood 15 we can look at the equation as a whole which may be read as, “The conditional
probability of event A given event B equals the joint probability of event A and event B divided by the
marginal probability of event B occurring.” To find the answers we are looking for in relation to HYP1,
all we need to do is substitute our data into the equation.
We are now able to look at the best and worst network providers using our probabilities as an analysis
frame. We will look at the conditional probability of having a certain level of signal coverage (‘Good’,
‘Poor’, etc.) given that we are on a certain network provider (O2, EE, etc.).
Conditional Probability Signal Coverage Rating
Network Very Good Good Mixed Poor Very Poor
Total Poor
(Poor and
Very Poor)
Total Good
(Good and
Very Good)
Three 0.0% 5.0% 0.0% 35.0% 60.0% 95.0% 5.0%
O2 1.5% 2.3% 4.5% 19.6% 72.2% 91.7% 3.8%
Tesco Mobile 0.0% 0.0% 10.5% 21.1% 68.4% 89.5% 0.0%
EE 0.0% 5.6% 10.1% 24.7% 59.6% 84.3% 5.6%
Vodaphone 1.9% 3.8% 17.0% 18.9% 58.5% 77.4% 5.7%
These results can be seen in the table below (Figure 5) and were calculated using our survey data. We
followed the sample methodology that was mentioned before to determine all of these results.
Across the rows we have network providers (the 5 that we will be focusing on and that make up 91%
of our survey) and down the columns we have our signal coverage rating. In the boxes is the
conditional probability that you obtain that level of signal coverage (down the column) given that you
are on the provider (across the row).
A brief glance will tell us that the worst network provider is Three, with a total poor probability (the
probability of having ‘Very poor’ or ‘Poor’ network coverage) is 95% (which can be understood as
having a 95% probability of having poor network coverage given that you are on the network Three).
This is shortly followed by O2 which has a lower total poor rating of 91.7% but the highest result for
‘Very poor’ at 72.2%.
15 If these probabilities are still confusing to the reader, here are some helpful explanations:
http://sites.nicholas.duke.edu/statsreview/probability/jmc/
http://www.investinganswers.com/financial-dictionary/ratio-analysis/joint-probability-3379
Figure 5
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 19
Conversely in our list we have the best networks to be on (a term we use lightly). The network with the
highest probability of a good network coverage is Vodaphone at 5.7% (also the most likely to be ‘Very
good’) shortly followed by EE at 5.6%.
These results demonstrate a bleak picture for the 91% 16 of respondents that are on these networks
with very many at an extremely high chance of poor signal coverage, and a very low chance of good
signal coverage.
Conclusion
Our hypothesis (HYP1) stated that any user on any network will suffer poor signal coverage. While
HYP1 does not hold for the small amount of respondents that said their signal coverage was not poor
(13%), or for the 7 networks which had respondents who said they had ‘Very good’, ‘Good’ or ‘Mixed’
network coverage, the data shows a clear network coverage endemic adversely affecting those using
mobile devices in Saughall.
An 87% response percentage for users saying they had ‘Poor’ or ‘Very poor’ network coverage coupled
with the best network having just over a 1 in 4 chance of having ‘Good’, ‘Very good’ or ‘Mixed’ network
coverage is simply not acceptable for active paying users of these devices.
Amongst the 87% of users who experience poor network coverage are business users who rely on the
use of their mobile phones to communicate with clients and customers around the country. There are
also individuals in danger of life-threatening accidents who would need to contact emergency services
at any point. With these results, mobile device users in Saughall have virtually no network they can
turn to for an acceptable and reliable service, and for anyone in the aforementioned circumstances
this is a very dangerous reality indeed.
We feel that intertwined within this data is a cogent and poignant argument for improving the
network coverage in the area.
16 The other 9%, while not included, we can assure do not fare much better.
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 20
Hypotheses Two (HYP2)
Poor network coverage will impact a younger aged demographic group more severely
than an older aged demographic group.
In assessing the validity of this hypotheses we will be focusing on the data obtained from Q5 (age
ranges), Q6 (the daily impact of the signal coverage) and Q7 (whether respondents have access to a
landline). The hypotheses states poor network coverage which means our analysis will also make use
of the answers from Q1 who answered ‘Very poor’ or ‘Poor’.
Across the data, 302/346 (87.3%) said they had ‘Very poor’ or ‘Poor’ network coverage. Looking at this
group (those who rated their network coverage ‘Very poor’ or ‘Poor’), 77.8% (77.8% of the 87.3%/a
percentage of a percentage) said that this was either ‘Very problematic’ or ‘Problematic’ to their daily
lives. In consideration of our hypotheses, we need to look at how this data plays out across different
age groups.
Figure 6
Figure 6 is a helpful representation of how the data played out. Before looking at the data, it is helpful
to explain what each of the axes represents. On the left side we can see progressively increasing
percentages under the heading ‘Severity’. Severity in this case represents the amount of individuals
with ‘Poor’ or ‘Very poor’ signal coverage who said this was ‘Problematic’ or ‘Very problematic’ to their
daily lives. On the horizontal axis we have the age ranges that were included on the survey. To break
this down further, it is helpful to explain some of the data on the diagram. As we can see, the first
input is the age range of <16 with a severity of 100%. This means that all of the <16 range
respondents who had ‘Poor’ or ‘Very poor’ network coverage said this was ‘Problematic’ or ‘Very
problematic’ to their daily lives (i.e. maximum severity).
We will now assess how the data fits on the diagram. There are three lines to explain: the broken line
represents our expected results based on HYP2 which illustrates a steadily declining severity as age
100.0% 89.3%91.0% 91.2%
75.6%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
<16 16-24 25-45 46-65 66+
Seve
rity
Age Range
Severity of Poor Singal Coverage by Age Range
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 21
increases. The solid black line models the data that was recorded in our research. Lastly, the green line
represents the trend of our research data (displayed on the black line).
We will first speak generally about the diagram and then be more specific about the age ranges.
A quick glance will tell us that that our expected results were not as severe as our true results. The true
results do decrease to begin with but levels off and even increases before falling once again. The
trend line (green arrow) does support HYP2 demonstrating a falling trend in severity as age increases,
however the gradient of this line is much shallower than we would have expected from HYP2 (the
broken line).
We shall now look at how the data progressed in relation to the hypotheses. Firstly, both the expected
and true data support HYP2 at our initial youngest age demographic (<16) of which 100% said a poor
network coverage is ‘Problematic’ or ‘Very problematic’ to their day to day lives. As we move onto the
older age range of 16-24, severity falls further to 89.3%, and while this is not as much as our expected
results, it does still support HYP2. We then have an increase to 91% in 25-45 age bracket and a further
increase to 91.2% for the 46-65 age bracket. While these results are not as severe as our <16 age
range, they are worse than the 16-24 range and thus work against HYP2 (which would have expected
further drops in severity, not increases). Following the 46-65 range, we do have a second drop to
75.6% for our 66+ age bracket; this again is in keeping with HYP2 but is by no means close to our
expected results for the age range of around 20%. Our trend line shows that as we move in the 66+
age range, severity does not fall by the amount we expected.
While our general trend line does support HYP2 to some extent, the individual results for the age
ranges are not as consistent as we expected. Our results do show that the <16 range suffered the
most severe impact overall but this is shortly followed by the 46-65 and 25-45 age groups, while older
than the 16-24 group they demonstrate an increase in severity. In terms of an explanation for these
results, there are potentially a lot of business users within these age brackets, who may be heavily
reliant on their device for business use (meaning a poor signal would be very problematic/severe).
One thing that cannot be disputed is the level of severity across all age groups; while the results do
fluctuate, they remain at a high level across the board. This does suggest that a poor network
coverage will more than likely be problematic to the individual irrespective of their age range, and
thus it must be recognised that the severities of poor signal coverage are not restricted to certain
ages.
One last thing to look at before concluding this section is the answers we obtained from Q7 on
whether respondents had access to a landline. From our survey we found that the mass majority of
respondents had access to a landline (96.2%), while only a handful did not (3.8%). What is interesting
here is the amount of each group who said this was still an issue to their daily lives. We would expect
that those without access to a landline would find poor network coverage an issue, and rightly so with
92.3% (of those without a landline) stating that this is either ‘Problematic’ or ‘Very problematic’. What
we also expect is for those with landlines to be the only group to state that poor network coverage
has ‘Little impact’ or even enhances their daily lives, except only a small fraction of this group did so.
Just 17.6% of those with landlines said that a poor network coverage for their mobile phone ‘Enhances
Daily Life’, or has ‘Little impact’ or ‘No affect whatsoever’. The remaining 82.4% of this group said that a
poor network coverage was still ‘Problematic’ or ‘Very problematic’ despite having access to a landline.
This demonstrates that even when alternative methods of communication are available, a functioning
mobile phone is much more desirable and superior as a method of communication. Our conclusion
from this should be that access to landlines will not in the majority of cases, alleviate the troubles
associated with a poor network coverage for a mobile phone.
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 22
Conclusion
These results support HYP2 generally, but individually they are not as forgiving. The young-mid age
ranges of 25-45 and 46-65 are contrary to our expectations; this is an important observation as it
helps to break down a common misconception that the younger you are the more severe poor
network coverage will be to your daily life. Across the board poor networks coverage does largely
result in problems for device users across the area. We should therefore not restrict our approach to a
certain age group, nor hide behind the excuse that the area is dominated by one. We must recognise
the severe impact this has on all age ranges and to not assume that the elderly does not suffer in a
similar way to younger aged groups. We should also not presume that users with access to landlines
do not mind having poor mobile network coverage, as demonstrated by the many with access to
landline who still state that a poor network coverage is an issue to their daily lives.
2.4 Recommendations
There are many important sections in this report each with their own independent value, however, out
of all of them this is the most significant to our efforts and we hope that it is the most referred to.
We are now about to make some recommendations based on our analysis of the data. These
recommendations are motivated by the results we have obtained and the interpretation/analysis we
have just done. We use the term recommendations as opposed to intentions because we ultimately are
not the body with the capacity to carry them out. Instead, we are confident that these suggestions will
be taken seriously by those who do possess this capacity and we hope that the majority of them will
be acted upon.
We will be organising these recommendations into three categories, First Best Solutions, Second Best
Solutions and Third Best Solutions. They are in order of the impact we predict them to have on the
signal coverage issue in the area, but this is not to be confused with an order of desirability as we
strongly believe that all these solutions are desirable in their own right.
There will of course be additional untouched-points/counter-arguments made about these
recommendations and we thoroughly intend for them to be brought under scrutiny and open
discussion to ensure that all things are considered in a fair and compromising manner.
First Best Solutions
We recommend the installation of a new phone mast within, or nearby, the area
that will boost the signals of all, or certain, mobile networks
Out of all of these recommendations, this will undoubtedly be the most expected, logical and
impactful (and almost certainly the most contentious). Based on our results, it is apparent that the
network coverage issue in Saughall is a serious endemic. Mobile phones require phone masts to
communicate and transmit calls and messages (in the form of electromagnetic fields or EMFs). A new
mast in the area will improve signal coverage for users which will be the most necessary and
important step towards tackling the issue. We do of course understand the many arguments against
the installation of phone masts and we shall take just a few of the popular ones to give our response:
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 23
1) They pose health risks
We would like anyone with this apprehension to consider the following reports that have
conducted large scale, heavily-funded research into this area:
The Mobile Telecommunications and Health Research Programme has released two
reports in 2007 17 and 2014 18 (completed in 2012) which collate a mass collection of
data that finds no apparent association between mobile phone usage and health
risks.
The independent Advisory Group on Non-ionising Radiation (AGNIR) released a report
in 2012 19 that looked into the health effects associated with mobile phones and
electromagnetic fields (EMFs) (the means by which mobile devices communicate). In
short, the conclusions were again that there have been no detectable health risks
associated with exposure to EMFs.
The Million Women Study 20 was conducted in partnership with Cancer Research UK,
the National Health Service (NHS), and researchers at the University of Oxford. This
joint effort has also found no evidence to suggest that mobile phone usage has any
impact on the chances of developing certain types of cancer.
These reports demonstrate a strong consensus within the field of study that mobile
phone usage does not pose any noticeable health risks. It would be wrong to assume
that all publications in this area are in complete agreement but we feel that these
three, thorough and expertly led studies should be taken as strong evidence against
any opposing claims.
2) Phone masts are unsightly
We certainly do agree that there are more visually appealing, man-made structures, however,
we feel this is a weak argument for a number of reasons.
Firstly, the unsightliness of a phone mast in the area is a small price to pay when considering a
cost-benefit analysis. Our data shows that there is a significant and severe daily impact for the
many users in the area that suffer poor network coverage. This impact cannot be said to just
affect casual users, and we must consider those who use their mobile phones for business
purposes and as a device of last resort in times of emergency. However, in all cases, the
unsightliness of a mast cannot be said to override the severity of poor signal coverage for the
majority.
Secondly, the benefit of wireless connectivity between masts and phones means that we may
choose where to locate the structure. We therefore have the ability to minimise its visual
impact by locating it somewhere where it is visually obscured, or ‘disguising’ the mast so as to
blend it in with its natural surroundings 21. This last point should encourage designers and the
Council to be given free rein in determining where the mast will be located and how it will
17 http://www.mthr.org.uk/documents/MTHR_report_2007.pdf 18 http://www.mthr.org.uk/documents/MTHRreport2012.pdf 19https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/333080/RCE-
20_Health_Effects_RF_Electromagnetic_fields.pdf 20 http://www.millionwomenstudy.org/introduction/ 21 Here is an article of some measures that have been taken around the world to improve the visual quality of phone masts:
http://techmash.co.uk/2012/08/20/why-do-some-people-think-mobile-phone-masts-are-ugly/
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 24
look. The product of such a negotiation will undoubtedly help to alleviate the visual impact
and with it any concerns that may have arisen.
3) Installation may be damaging to the environment
We feel strongly about environmental degradation and consciously oppose unnecessary or
mass destruction of it. However, the construction of a phone mast does not pose a significant
threat to the environment nor is it an unnecessary exercise as we will now explain.
The aforementioned reports, as well as The Environmental Health Trust’s rebuttal of an
opposing argument in 2013 22 has identified no environmental impact of mobile devices and
masts. We feel these reports provide a forceful argument on this matter that should be
considered by anyone with environmental concerns. In addition to this, environmentalists
must recognise that improved digital communications mean individuals require less travelling
to connect with others and carry out daily tasks. For example, if a business user in the Village
can now contact their clients more regularly by mobile (assuming the connection has been
improved), then they can avoid unnecessary transportation that would have otherwise
emitted certain pollutants.
In terms of importance, the installation of a phone mast will provide better coverage to the
many in the area who have stated that they cannot obtain a good network signal. It is for this
reason and the 98% (of our surveyed respondents) who stated their agreement that
something needed to be done, that installation cannot be considered an unnecessary
exercise.
4) There is nowhere to put the mast
We feel again that this excuse does not pose a significant threat to mast installation. Saughall
is geographically ideal for placement as it is high above seawater and is surrounded by a lot
of used and unused land. The distinction will be important with unused land much more
promising than the used land, but there will undoubtedly have to be discussion and
negotiation in both cases.
We understand that the central area of the village is built up with very little room, but in an
ideal situation the mast would be placed more towards the north of the village where the land
is much higher leading to better signal coverage.
The exact location of the mast would be a highly debated matter, which we think is a
desirable situation to ensure all concerns are addressed. However, to assume that there is no
place for the mast seems unfair to the location and should not be the deciding factor in
determining whether or not to have one.
We recommend users should be entitled discounted phone tariffs
Secondly, we recommend that users suffering from poor network coverage in the area should receive
discounted tariffs on their mobile contracts. This will of course depend on a number of factors as well
22 http://ehtrust.org/wp-content/uploads/2013/11/FCC.pdf
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 25
as whether or not the previous recommendation has been put into action. We have therefore left this
recommendation deliberately vague, as we feel a more profitable venture would be to negotiate on all
tariffs and all potential discounts.
What is not vague is the unacceptable current situation where users are paying for an unusable
service at premiums mirrored by those charged in other areas around the country with excellent signal
coverage. This is inadmissible, unfair and poor customer practices. The village and residents deserve
representation from larger bodies such as Ofcom (the telecommunication regulator), national and
local government, who have the responsibility of acting on behalf of citizens in places around the
country.
We are certain that such bodies can put forward a strong argument if it is positioned correctly. One
way this can be done is by recognising the mechanisms involved in free markets, whereby agreement
from a single provider may be all that is needed to catalyse a broader impact. If one provider agrees
to negotiate and alter terms to ensure Saughall residents are dealt with fairly, individuals would
gravitate towards this network which would stimulate other providers to acquiesce and make similar
concessions; this should eventually ensure a significantly comprehensive benefit for residents and
users in the Village. This report facilitates a powerful argument to support these potential negotiations
and may be drawn upon where necessary.
Second Best Solutions
We recommend boosters be provided for all networks in the area:
Booster technology is used by many networks 23 as a cheap and convenient alternative to mast
installation (or monetary concessions). Boosters are small devices or apps that enable the user to
connect via Wi-Fi (or through other means) which improves signal reception for devices in the
proximity. These are often provided free or at a small cost by the network providers (others are pricier
such as a one off cost of £69 for Vodaphone’s Sure Signal device).
Currently, not all households and users have access to booster technology and we would like to see all
networks with the ability to provide these things to do so. This should be done at small or subsidised
cost for users in the area, but ideally we would like to see this technology given to customers free of
charge. Networks should be asked formally to consider this, and where refused should be required to
give ample reasoning that demonstrates how they come to this decision while still ensuring they are
acting with customer’s best interests at heart.
While boosters do provide a suitable alternative to the aforementioned recommendations they are
many difficulties with them which make them a Second Best rather than First Best Solution. Booster
technology often uses Wi-Fi as a means of transmitting a more powerful signal. This is fine for users
with access to Wi-Fi, but for those who do not or who own old or outdated devices not equipped with
the necessary technology, this is not a suitable solution. Furthermore, other types of boosters only
tend to work if the device is used in the immediate proximity and only for those which are connected
up to the device. This condition seems almost paradoxical to what a mobile device should actually be
capable of: anywhere and anytime communication.
We recommend individuals be informed about the signal coverage in the area
as well as which phone network are best for the area
23 Tu Go (O2), InTouch (Three), Sure Signal (Vodaphone)
Jack Hughes ‘Mixed Signals’
Victoria Byrne September 2016 26
We would also expect those with the capacity to inform those living (or planning to live) in the area
about the severity of this issue as well as which networks are best for users and which they should
avoid entirely. In the case of our data, we would recommend Virgin and Vodaphone (but to also make
users aware that these are the best of a bad bunch and to not assume all users will be guaranteed
good coverage on these networks) and to definitely avoid Three and O2. We feel this must be done if
we are to act within the best interests of residents and users in the area as information provision is a
fairly inexpensive campaign on such a small level.
One last point to make about this recommendation is that it can potentially have a similar impact to
discounted tariffs (a First Best Solution). When individuals are aware which networks are the best, they
will transition towards those providers which catalyses other providers to improve their signal
coverage.
Third Best Solution
We recommend greater recognition that this issue affects all age groups
This is a very simple recommendation to act upon and the reasons for which should be clear to any
who has read this report. Our data has helped to disprove a common assumption that poor signal
coverage only impact certain age groups. We hope the readers of this report will recognise that poor
network coverage impacts all age groups. Ignorance towards this discovery does not help tackle the
issue at play and may in fact lead to an unnecessary regression.
2.5 Closing Remarks
We have now come to the end of this report and it is time for us to conclude with some closing
remarks.
We have covered two main areas of analysis: primarily, the signal coverage in Saughall and
secondarily, how certain network coverages impact different age ranges. The former has
demonstrated that poor network coverage spans across the entire area and to all network providers,
while the latter has shown that such coverage has an adverse effect on individuals from all age ranges.
These discoveries embody the urgency and frustration amongst the community that has been
somewhat neglected. The 98% of respondents who said that something needed to be done about the
signal coverage in Saughall illustrates a clear and overwhelming consensus for change. Based on this
and our other evidence, we have made some recommendations ordered in what we believe will have
the largest impact. Depending on which of these recommendations is enacted, it will have a certain
degree of alleviation. In any case what has to be recognised are the findings in this report; the first
step towards tackling this issue is to understand the problem and who it affects. Left dormant, this
issue will trouble anyone who plans to use a mobile device in the area, which is an undesirable
situation especially in an age where such devices adopt an increasing importance and purpose in our
daily lives.
It will be in the valiant hands of certain community members and councillors that this report will be
most effective; the discussion must be initiated at all levels and as many times as necessary to ensure
something is done. The work within this report and the many voices it speaks for represents an
unparalleled opportunity for action to be taken on this matter, it should not under any circumstances
be missed.
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Lastly we would like to thank anyone who has taken the time to look at our work; it has been a great
pleasure constructing this paper and we hope that pleasure permeates through to the reader.
Limitations
We feel it is important that we mention and consider the limitations we experienced in this report that
did have an impact on our analysis. This section is important to the project but is not part of the main
report.
Time and resources
First and foremost, these two limitations were perhaps the most impactful to this study as they
affected all of its aspects. While this report has been completed thoroughly and with great care, it has
always been subject to time and resource constraints. With more of each we would have been able to
of obtained more responses, and perhaps from a greater area. We would have also had more ability to
analyse and present the data in a more forceful manner.
Absence of up-to-date records
Our report would have benefitted greatly from more up-to-date records on population estimates and
road names. The former would have enabled us to be more intricate about our population
calculations (and thus produce a more accurate sample size) and also of allowed more time for our
study, while the latter would have also saved us a great deal of time and improved our survey
distribution.
Creating a random survey
Secondly, we found it difficult to randomise our collection methods. Randomisation is important to
ensuring a fair and representative sample. As mentioned earlier, our intentions were to use a number
generator on the offline door-to-door surveying while allowing the online surveys (and offline static
survey) to be random by their very nature. Looking back at these measures it was very difficult to
ensure randomness across the board.
The offline door-to-door surveying number generator was not successful as we could not anticipate
whether anyone would be in the property at the time (this often had us going up and down roads
which proved laborious and time consuming). We instead opted for choosing houses which we
believed would have occupants present in the property (those with cars outside, lights on, etc.). While
it may be said that there is a large degree of chance associated with who is and is not in the house, it
was not as suitable as the random number generator. These methods would have to be revised if we
were to conduct the study again.
As for the offline static and online e-survey, we assumed them to be random simply in virtue of not
knowing who might fill them out. However, foresight is much easier than and hindsight and we found
our audiences were isolated to those with Facebook (for the online survey) and who were aware of the
events (for the static survey). This was not as random as we would hope as there was no chance that
anyone in the village without these means could have been aware or filled out the survey. Looking
and for future ventures, we should have provided information about the survey around the village and
Jack Hughes ‘Mixed Signals’
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set up other means by which it could be completed (such as a physical copy left in a certain location,
i.e. the Cooperative village shop).
Clarifications This section, while also not part of the main report, is important for any concerns that the reader may
have had while working through this paper. It takes some of the common questions which may have
arisen and gives our response.
Why was my road not covered?
Our survey area has been clearly defined meaning there are two possibilities if your road has not been
included: 1) your road is not within the survey area or 2) your road is within the survey area. In the
case of (1), your road was not included as we had limited our research to the location within the
boundaries that have been defined, and thus any areas outside would not have been considered in
this survey. In terms of (2) please ensure that your road was not included on our list and that it is
within the area and in Saughall. Assuming those last conditions are met, then we can only apologise if
a mistake has been made on our behalf. This will have no doubt been down to one of the
aforementioned limitations and should not be assumed to be of any significant reasoning.
Why was Q3 not included in the overall report?
Q3 was not included because of two reasons that have already been covered. Firstly, Q3 was
incompatible with Q2 as signal coverage rating is also a questions of signal consistency, i.e. there was
no need for Q2. Secondly, two of the answers provided in Q3 were virtually synonymous (‘Inconsistent’
and ‘Mixed’). We did still obtain the data for this question and have still included mention of it in the
report, but we felt the data it produced was open to many flaws compromising its significance.
How come you only asked 346 people?
We calculated an appropriate sample size for Saughall (see 1.4) based on the population of 3,386
(adjusted from the 2011 Census by the ONS), a confidence interval of 5%, a confidence level of 95%,
and a response distribution of 50%. We could have asked more people, which would have made the
results more accurate but as we reached the target at a point that was far beyond our allocated time
(see Time Frame below), we decided to simply move on to our analysis.
Why did you do this?
As members of the community we feel that there is a problem with the signal coverage in the
Saughall area. We feel we have a duty to other members to ensure their voices are heard on this
matter. Furthermore, we are social science students at the University of Manchester and Manchester
Metropolitan University meaning this type of study is of great interest to us.
Time Frame
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This section again is not part of the main report but is designed to give the reader (who is interested)
an insight into what our project looked like in terms of time and stages.
We split the project up into four stages: Stage [1] was our preparation stage, Stage [2] the field
research, Stage [3] was about statistical analysis, and lastly Stage [4] is about finalising and distributing
the report.
We have included a time frame table in the Appendix, Figure 4, which shows the stages, our objectives
at each stage, the time we allocated for that stage followed by the expected and actual dates of
completion. We have also included a break in the report which is meant to represent a visit we made
to Cambodia during the project and to show that the project had to be put on hold for that duration.
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Appendix
Figure [1] – Survey Form
1. What is your road name and number?
2. How would you rate the signal coverage for your mobile handset when using it within the
Saughall area? (N.B. this does not concern other devices such as a telephone, broadband etc.)
3. How consistent is the signal coverage for your mobile device within the Saughall area? Respondent 1 Respondent
2 Respondent 3 Respondent 4
Consistent Mixed Inconsistent Other (please write)
4. Who is the provider of your mobile device signal?
Respondent 1 Respondent 2
Respondent 3 Respondent 4
O2 EE Vodaphone Tesco Mobile Other (please write)
5. How old are you?
Respondent 1 Respondent 2 Respondent 3 Respondent 4 <16 16-24 25-45 46-65 66+
6. How would you rate the impact of the mobile signal service on your day to day life?
Respondent 1 Respondent 2
Respondent 3 Respondent 4
Respondent 1 Respondent 2
Respondent 3 Respondent 4
Very Poor Poor Mixed Good Very Good
Respondent 1 Respondent 2 Respondent 3
Respondent 4
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7. Do you have a landline?
8. Broadly speaking, do you think something should be done about the mobile phone signal coverage in the Saughall area?
Figure [2] – Survey Area
Very problematic
Problematic Little affect No affect whatsoever Enhances daily life
Respondent 1 Respondent 2 Respondent 3
Respondent 4
Yes
No Other
Respondent 1 Respondent 2 Respondent 3
Respondent 4
Yes
No Other
Jack Hughes ‘Mixed Signals’
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(Google Maps, 2016)
Figure [3] – Road Names
1. Aldersey Close
2. Anvil Close
3. Aspen Grove
4. Chapel Close
5. Church Road
6. Church Road
7. Coalpit Lane
8. Crofters Way
9. Darlington Crescent
10. Eastfield’s Grove
11. Fairholme Close
12. Fernlea Court
13. Fiddlers Lane
14. Fieldway
15. Fox Lea
16. Green Lane
17. Greenway
18. Haymakers Way
19. Hermitage Court
Jack Hughes ‘Mixed Signals’
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20. Humphrey Close
21. Kingston Court
22. Kingswood Avenue
23. Kingswood Lane
24. Larchfields
25. Lodge Lane
26. Long Lane
27. Maplewood Grove
28. Meadowcroft
29. Meadows Lane
30. Newcroft
31. Park Avenue
32. Park Way
33. Parkgate Road
34. Rake Way
35. Rosewood Grove
36. Saughall Hey
37. Seahill Road
38. Smithy Close
39. The Close
40. The Ridings
41. Thomas Wedge Road
42. Thornberry Close
43. Timberfields Road
44. Vernon Close
45. Vernon Institute
46. Whaley Court
47. Willow Hey
48. Worsley Avenue
49. Yew Tree Avenue
Figure [4] – Time Schedule
Stage Objectives Time Allocated Deadline Date of
completion
Project
commences
11/07/2016
Jack Hughes ‘Mixed Signals’
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Stage [1]
Preparation
Establish what we
are analyzing
Design and print
questionnaires
Determine what
and who we are
measuring
Establish a
sample size
Work out how we
will carry our
analysis
Determine a
sample
randomising
method
Two Weeks:
We were quite
generous with time
allocation on this
stage as we felt
preparation and
preliminary research
was vital to the
success of the project.
A clearly defined area,
a suitable
questionnaire and the
methodology behind
our sampling would
mean the difference
between a project
that produced any
statistically significant
results and one that
did not.
24/07/2016 22/07/2016
We felt
comfortable by
this date to say
our preparation
stage was
complete under
our deadline
time.
BREAK FOR
CAMBODIA
Stage [2]
Conducting
field research
Gathering data
using sample size
and sample
methods
Posting the
online survey
through different
channels
Spreading
general
awareness about
the survey
One week:
This is quite an
ambitious time
allocation but with
adequate planning
from Stage [1] and
our determination we
feel we can achieve
this result.
29/08/2016
07/09/16
We ran
considerably
over our
estimated time
as we did not
anticipate how
long data
collection would
take.
Stage [3]
Conducting
statistical
analysis
Record the data.
Conduct analysis
on the data
looking for results
the fit or do not
fit our
hypotheses.
One week:
Recording the data
from our field
research will take
quite some time to
complete. We then
14/09/16 14/09/2016
This section was
completed in the
allocated time
and by the date
Jack Hughes ‘Mixed Signals’
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Make
recommendations
based on this
data
must leave enough
time to engage with
the bulk of our project
and conduct our
statistical analysis.
This will be followed
up by our
recommendations
of expected
completion.
Stage [4]
Finalising
and
distributing
report
Present the data
in reader friendly
ways (charts,
diagrams
distribution,
graphs etc.).
Complete
summary sections
(General and
findings) as well
as our
conclusions.
Distribute report
to relevant bodies
One Week (ongoing
for distribution):
The finalising section
could have been
allocated less time but
we wanted to allow
for any potential
issues that might
come in data analysis.
21/09/2016 26/09/2016
We failed to
meet our
expected target
and began
distributing 5
days after
expectations.
This was down to
some issues that
came up in our
finalising stage.
Once we had
completed the
report we felt
one of the
methods used to
analyse the data
was not as
suitable as an
alternative. This
meant changing
and reorganising
an entire section
which then
changed a lot of
the remaining
report. Despite a
generous time
allocation, we did
not anticipate
coming into an
issue as large as
this.
Project
finishes
26/09/2016
Jack Hughes ‘Mixed Signals’
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Bibliography
Google Maps. (2016). Map of Saughall Area. Retrieved from
https://www.google.co.uk/maps/place/Saughall,+Chester/@53.2276614,-
2.9866055,13z/data=!3m1!4b1!4m5!3m4!1s0x487adc5e2bc4869d:0x961f69099b688b8d!8m2!
3d53.2233329!4d-2.959321 Office for National Statistics . (2011). Saughall - Key Figures for 2011 Census. Retrieved from Office for
National Statistics - Neighbourhood Statistics:
http://www.neighbourhood.statistics.gov.uk/dissemination/LeadKeyFigures.do?a=7&b=11128
209&c=CH1+6BE&d=16&e=62&g=6407207&i=1001x1003x1032x1004&m=0&r=0&s=14680
94879188&enc=1&nsjs=true&nsck=false&nssvg=false&nswid=1206 Office for National Statistics . (2016, June 23). England population mid-year estimate. (E. Shrosbree,
Editor) Retrieved from Office for National Statistics:
Jack Hughes ‘Mixed Signals’
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https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populatio
nestimates/timeseries/enpop/pop
If you have any questions about this report please contact us at [email protected]