November 21, 2019

Introduction: that urban design plays a role in moulding


This report will talk about how secondary data analysis
can be helpful in conducting a research about studying the negative effects
urban life has on the mental health.

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A healthy population means a healthy, thriving, and
resilient urban city, and it is with time becoming clear that urban design
plays a role in moulding this. This realisation has led to various
health-themed planning and design guidelines for innovation, and yet amidst the
need to integrate health promotion into urban design projects, one key question
is often missing: what about mental health?

Cities offer economic, cultural, and educational
opportunities that most rural areas might not be able to. Urban areas in the
future are expected to house about two-thirds of the world’s population by 2050
and some urban areas, mostly in the Asian countries, are predicted to
experience unparalleled development, thus bringing forth the relationship
between urban environments and mental health more rapidly.

Cities continue to grow with unprecedented challenges and
uncertainties, making mental health of the city dwellers a more concerning
factor to consider, while charting planning policies and designing urban
expansions and development.

There have been studies pointing out about how living in
an urban area undesirably affects mental health. It is equally beneficial and harmful
to be living in a city when considering mental health. One of the factors
affecting is the sensory overload due to the many things bombarded on to a
human in a single particular moment. Some scholars have argued that the high
stimulation of the human brain to capture and process all the fast information
of the surrounding environment is challenging for the mental health in the long

There is also a substantial amount of study done internationally
by psychologists and neuroscientists proposing that urban living, particularly unthoughtfully
designed environments, are one of the reasons to the negative implications on
mental health of the city dwellers. For example, insufficient, overcrowded, unhygienic
housing has been proven to reduce the human coping capacity, while the lack of a
basic amenity as simple as a playground can influence children’s wellbeing. Cities
are becoming more crowded by the day and the rising proportion of people living
alone is considerably leading to higher levels of loneliness, creating a future
risk for deterioration of mental health. (Rapoport, 2016) It has thus, also
been studied that in comparison to non-urban areas, cities around the world
have a rising occurrence of acute mental illnesses, and problems such as stress
and isolation. (Adli, 2011)

A recent study showed that urban dwellers have a 20 per
cent higher risk of developing anxiety disorders, and a 40 per cent higher risk
of developing mood disorders. Longitudinal studies on patients with
schizophrenia indicate that it is urban living and upbringing per se, rather than
other epidemiological variables, which increase the risk for mental disorders.
(Adli, 2011)

It has been marked that living in an urban environment could
be a concerning factor for psychiatric diseases such as depression or
schizophrenia. The high exposure to stress and specifically social stress seems
to play a crucial role in increasing the risk of mental disorders. Highly
populated cities lead to people facing higher social evaluation and social
stress, outweighing other urban stressors like pollution or noise.

The major purpose of the conceptual framework is to point
out the necessity of understanding the negative effects of rapid unthoughtful
urban expansions and thus the importance of making sensitive design amendments
and planning policies to avert such an unhealthy development. It is also to
understand the different dimensions of the negative consequences that city life
can give rise to, other than just the benefits it has to offer.

The major objectives to achieve a reasonable result of my
study are outlined below:

To explore the various design factors
affecting mental health

To explore the non-design factors affecting
mental health

To identify the impact of population growth
on urbanism and deterioration of mental health

To identify the cognitive responses to the
physical environment

To explore how different police making bodies
like the Government, Non-government organisations can help cater to the

To understand if urban design can really help
make a difference

To make policy recommendations



To initiate a dissertation as outlined in the conceptual
framework it is necessary to understand the methodological inferences and what
kind of a research method can be useful for analysing the data to come to any
kind of conclusion for the research question in hand. Secondary data analysis proves
to be a helpful research method to overcome the restrains of the limiting
factors like time and cost. My research would be based on urban development and
its effects on mental health globally but I would try and focus more on the Asian
countries like India and China and thus it would be necessary to analyse the
official data collected by various government institutions. Another factor
would be to compare the eastern countries with the western countries in terms
of the population growth and rapid development effecting mental disorders. This
will help gain more insight and clarity for the policy recommendations to be
made at the end of the research.


Method: Secondary Data Analysis

In terms of finances and time context, methods like
survey research by questionnaire or structured interview or experimentations
can prove to be expensive to conduct. As a student, time and financial resources
are the main limiting factors when it comes to conducting research for the
dissertation. Many organisations, both private and government department
collect a wide range of data presented in a statistical form that can be
utilised by a social scientist. (Bryman, 2012). Generation of new hypothesis or
answer to a critical research question can be analysed by the use of existing
data (Tripathi, 2013). Moreover, technological advancement has created a
scenario where a huge amount of data can be collected, compiled and archived
and is easily accessible to researchers (Johnson, 2014).

Secondary data analysis is the analysis of data without
being involved in the data collection process (Bryman, 2012). These means that
the data was collected for another primary process (Bryman, 2012). This means
that the data was collected for another primary reason but is utilised by
another researcher to find the answer to a different research question.
However, there is a debate around analysis between quantitative and qualitative
data set. For example, quantitative data sets like national census track survey
are considered to be a common and cost effective way of utilising the data for
secondary data analysis (Cherlin 1991; Greit andGraham 1989; Miller 1982). On
the other hand, secondary analysis of qualitative data has been neglected
because of doubts about the quality of the qualitatively generated data (Thorne

of Secondary data analysis:

Secondary data analysis offers varied advantages to
researchers with limited resources.

1. Time and cost effectiveness:

The first advantage of secondary data analysis is it
saves time (Ghauri, 2005). It offers access to quality data for a minimal
expense of resources compared to resources that would have been required to
collect the data themselves (Bryman, 2012) For example, to collect data from
scratch, it will require expenses like salaries, transportation, and so on,
where on the other hand even if the secondary data set needs to be purchased,
the cost would be minimal. There is also time savings because the data is already
collected and updated electronically so the researcher can utilise the valuable
time analysing the data and writing hypothesis around it. (Borslaugh, 2007).
For instance, numerous data sets are available from the data archive available
in the University library for its students to carry out data analysis for
research projects. In addition, web platforms like google scholar always give
an extra advantage of all the information made available by other researchers
to work on. Moreover, the search engines are technologically very advanced that
only the keywords are necessary to find out abundant information for that
specific topic.


2. Good quality data:

The majority of the data sets most frequently used are of
very good quality (Bryman, 2012). To elaborate this claim by Bryman, firstly,
there is a rigorous process involved in the sampling process. Secondly, the
sample data are often national samples governed by the government with lack of
limitations in labour and finance. The geographical coverage and the sample
size if these data are enormous as well which wouldn’t be possible for a
researcher under time and financial constraint. Thirdly, highly experienced
researchers generate majority of the data, and some cases which have huge data
sets are collected by social research organisations which are well equipped
developed structures and control procedures to update and check the quality of
the data all the time. (Bryman, 2012)

3. Feasibility of both longitudinal and cross-cultural

Longitudinal analysis is very expensive and time
consuming in social sciences. Research that requires analysis of any trend with
time, secondary data analysis provides that opportunity in a very reasonable
way (Boslaugh, 2007). For example, ….

These surveys are carried out by a worldwide network of
social scientists since such work isn’t feasible one on one throughout almost
100 countries. (Boslaugh, 2007). To collect such volumes of data through
primary data collection is extremely difficult and often fails to include the diverse
comparison of social …. necessary.

4. Generating new insights:

New insights can be generated from previous analysis and
may lead to unanticipated new discoveries. For example, a particular set of
data can be analysed in many ways and the secondary data analyst may choose to
take certain variations of interest and consider the impact of it (Bryman,
2012) As new methods and techniques of quantitative data analysis are developed
by the statistics and —- disciplines, researchers become more —- in applying
them in the data sets and come up with new conclusions and perceptions.
(Bryman, 2012)

5. Responsibility of Social researcher:

The research participants give some of their time with
basically no reward for the purpose of the social research. If the data sets
collected are surely used for a single study, that research is under analysed
and does not justify the time given both by the participant and the whole data
collection process. Secondary data analysis provides that window of utilising
the data to the fullest (Bryman, 2012)

6. Consistency of the primary data set:

It is always beneficial as a secondary data analyst to
compare findings from two or more sources that have arrived at the same
conclusion or they did not provide an option for contrast. This gives the
researcher an extra confidence to check fo the consistency of the information
for the most valid conclusion. (Johnson, 2014)

of Secondary Data Analysis:

Compared to the benefits of the secondary data analysis
explained above the limitations might not be as dominant but still warrants
some attention.

1. Lack of familiarity with data:

As researchers do not participate in the initial data
collection process, there will be a range of variables that the researcher will
be unaware of that took place during the data collection (Bryman, 2012). For
instance, the secondary data analyst is unaware of the problems during data
collection such as participants misunderstanding of specific survey questions
or low response rates (Johnson, 2014).

2. Complexity of data:

A data set consisting of large number of large numbers of
variables and respondents might pose complexity to the secondary research
through the management of the information in hand and also to identify which
level of analysis is required (Bryman, 2012).

3. Quality of Data:

Although secondary data analysis provides opportunities
to researchers to verify data of higher quality compared to collecting it
themselves, quality of data should never be taken for granted (Bryman, 2012).
As stated by Saunders (2009) that as the secondary analyst always lacks a
control over data quality an intensive verification must be taken into
consideration before using it.

of Secondary data analysis in research and practice:

The secondary data analysis is widely used as research
method in both research and practice.

A case study of such a research was national workforce
dataset to examine the support people with dementia receive from social care
workforce in England. The secondary analysis was done by taking 457031 unique
workers recorded from the national workforce dataset from England. The
researchers came up with the results that reflected that dementia care
workforce are mainly female and are less qualified and should be provided with
training and skills. (Hussein and Manthorpe, 2011)


As secondary analysis is the use of existing primary
research data to find answers to questions which are different from the
original one there is always pressing issue of what fundamental ethical issues
should it follow (Tripathy, 2013) or are the secondary data analyst free from
any kind of burden of ethical approval for their research (Data Big and Small,
2015). Although the whole process of secondary data analysis involves ethical
considerations regardless of primary data used or not. From the initial design
of the research to the findings of results, it should aim at the ethical codes
of public good, should pose no harm to the public and ensure transparency and
reproducibility (Data Big and Small, 2015). But the value of secondary analysis
will only be noticeable when ethical consents like re-identification of
participants and disclosure of sensitive information are considered. However,
to make secondary data analysis a highly ethical practice the secondary analyst
must meet some key ethical conditions outlined below.

Consent of research participants can be
sensibly assumed

De-identification must be maintained of the data
before releasing it to the researcher

Results of the research analysis must not
allow re-identification of the participants

Utilisation of the data must not bring about
any harm or misery



Secondary data analysis offers methodological benefits and
can contribute to my research through generating new knowledge. The overall
goal of the study is to give policy recommendations regarding changes to be
made for reducing negative impacts on mental health due to rapid urban
development. Regarding how complex and time intensive this study is, secondary
data analysis method gives me that advantage of analysing high quality data
with minimal cost and time and providing me with valuable insight. Yet, to
utilise secondary analysis method effectively, it requires a systematic process
and planning that acknowledges the challenges of using huge official data sets
and address the discrete characteristics of secondary data analysis. Although
the benefits of secondary analysis outweigh the limitations, careful measures
are required to minimise them. In addition, special emphasis should be given to
the ethical implications of secondary analysis as well. Thus, by ensuring a
match between the research question and the existing data as well as following
the proposed procedure explained in conceptual framework and methodology
section above. I will try and minimise the errors and limitations of secondary
analysis and help in providing a justifiable policy recommendation at the end
of the study.



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