Issa Population and Sampling The constructs of population and sampling are integral to all research undertakings. Populations refer to a complete set of elements, whether persons or objects, that possess some common characteristics defined by the sampling criteria established by the researcher(Han,2019). The number of elements in the populations reflects the size of the population. Populations can fall into either target populations or accessible populations. The targeted population for this research study will consist investment leaders from capital investment companies in the northeast of U.S , who have a least 5 years experiences  in the industry and implanted strategies to increase investment return, and investment performance.   The population is a collection of individuals from which researchers develop their sample research study(Han, 2019). Researchers must ensure the targeted population to be accessible; selecting an inaccessible population could potentially affect the scholar's ability to collect data(Binde & Romild, 2019). Aligning the population with the research question allows researchers to collect data from strategic investment decision making leaders from capital investment industry in northeast region of the U.S ,who met the criteria from the research study. Hence, providing enough data is a precursor to credible analysis and reporting to determine an adequate sample size that has a direct relation with data saturation. However, the quantity is not the determining factor when it comes to data saturation; the quality of the data must align with the research study(Binde & Romild, 2019).  Sampling refers to the process of selecting a sample from a population of interest so that a researcher can fairly generalize the results gained from these participants to the population from which the researcher obtained the sample(Wei et al,2019). There are different types of sampling techniques that researchers can utilize during their investigations, which broadly fall into probability and non-probability sampling(Wei et al ,2019). Some of the most notable probability sampling techniques include simple random sampling, stratified sampling, clustered sampling, and systematic sampling (Han, 2019). Thu, on the contrary, non-probability sampling techniques include convenience sampling, purposive sampling, quota sampling, and snowball sampling. The main difference between a sample and a population has to do with the manner in which the researcher assigns observations to the data set(Binde & Romild, 2019). A population includes all the elements from a data set, whereas a sample consists of one or more observations derived from the population. The constructs of population and sampling can indeed find direct application in my doctoral research study. I will use purposive sample to  compute the correct sample size from the population that can make the findings easy to generalize to the wider population. In selecting samples during my research endea ...

1. Issa
Population and Sampling
The constructs of population and sampling are integral to all
research undertakings. Populations refer to a complete set of
elements, whether persons or objects, that possess some
common characteristics defined by the sampling criteria
established by the researcher(Han,2019). The number of
elements in the populations reflects the size of the population.
Populations can fall into either target populations or accessible
populations. The targeted population for this research study will
consist investment leaders from capital investment companies in
the northeast of U.S , who have a least 5 years experiences in
the industry and implanted strategies to increase investment
return, and investment performance.
The population is a collection of individuals from which
researchers develop their sample research study(Han, 2019).
Researchers must ensure the targeted population to be
accessible; selecting an inaccessible population could
potentially affect the scholar's ability to collect data(Binde &
Romild, 2019). Aligning the population with the research
question allows researchers to collect data from strategic
investment decision making leaders from capital investment
industry in northeast region of the U.S ,who met the criteria
from the research study. Hence, providing enough data is a
precursor to credible analysis and reporting to determine an
adequate sample size that has a direct relation with data
saturation. However, the quantity is not the determining factor
when it comes to data saturation; the quality of the data must
align with the research study(Binde & Romild, 2019).
Sampling refers to the process of selecting a sample from a
population of interest so that a researcher can fairly generalize
the results gained from these participants to the population from
which the researcher obtained the sample(Wei et al,2019). There
are different types of sampling techniques that researchers can

2. utilize during their investigations, which broadly fall into
probability and non-probability sampling(Wei et al ,2019).
Some of the most notable probability sampling techniques
include simple random sampling, stratified sampling, clustered
sampling, and systematic sampling (Han, 2019).
Thu, on the contrary, non-probability sampling techniques
include convenience sampling, purposive sampling, quota
sampling, and snowball sampling. The main difference between
a sample and a population has to do with the manner in which
the researcher assigns observations to the data set(Binde &
Romild, 2019). A population includes all the elements from a
data set, whereas a sample consists of one or more observations
derived from the population. The constructs of population and
sampling can indeed find direct application in my doctoral
research study. I will use purposive sample to compute the
correct sample size from the population that can make the
findings easy to generalize to the wider population.
In selecting samples during my research endeavors, I will
ensure that I take into account different attributes, including the
demographic profile of the participants, and experiences to
enhance representativeness.
Moreover, to determine the desired representative sample size
,the G*Power3.1.2 power analysis will be used to determine
the appropriate sample size for this research study. The use
of component of G*Power3.1.2 analysis is considered to be an
excellent power analysis software tools to analyze sample size
of doctoral research study(Faul, Erdfelder , Buchner, & Lang,
2009).
References
Binde, P., & Romild, U. (2019). Self-reported negative
influence of gambling advertising in a Swedish population-
based sample. Journal of gambling studies, 35(2), 709-
724.doi:10.1007/s10899-018-9791-x
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009).

3. Statistical power analyses using G*Power 3.1: Tests for
correlation and regression analyses. Behavior Research
Methods, 41, 1149-1160.doi:10.3758/BRM.41.4.1149
Han, Q. (2019). A Literature Review on the Study of Income
Distribution, Education and Education Return Rate-Based on
Family Background and Gender Differences. Journal of
Accounting, Business and Finance Research, 5(2), 43-
50.doi:10.20448/2002.52.43.50
Wei, P., Song, J., Bi, S., Broggi, M., Beer, M., Lu, Z., & Yue,
Z. (2019). Non-intrusive stochastic analysis with parameterized
imprecise probability models: I. Performance
estimation. Mechanical Systems and Signal Processing, 124,
349-368.doi:10.1016/j.ymssp.2019.01.058
Joey
Bruns, A., & Moon, B. (2019). One Day in the Life of a
National Twittersphere. NORDICOM
Review, 40, 11–30. https://doi-org /10.2478/nor-2019-
0011
The authors explore the Twitter social platform. This
article addresses the different types of imbalances such as

4. loudest voices and hashtag activities etc. by exploring in-depth
the day-to-day patterns of activity within the Australian
Twittersphere for a day. Bruns and Moon (2018) use a very
different methodology. The authors explore a shift in
perspective through focus groups, medial diaries, and other
forms of self-reporting. The used data set that for the analysis
is tracking infrastructure for Social Media Analysis (TrISMA).
This infrastructure uses the public activities of all the national
Twittersphere for sports and politics.
Hambrick, M. E. (2017). Sport communication research: A
social network analysis. Sports
Management Review, 20(2), 170-183.
doi:10.1016/j.smr.2016.08.002
Hambrick (2017) researches the way sport
communication research has grown through social network
analysis (SNA). The author uses a methodological approach to
show different research collaborations and help understand
many areas for growth. The author focuses on the research field
within social networks for sample and data collection.
Hambrick (2017) uses three different approaches to increase
validity. Hambrick (2017) uses studies from other researchers,
snowball sampling from popular scholars, and combines
citations.
Harker, J. L., & Saffer, A. J. (2018). Mapping a subfield’s
sociology of science: A 25-year
network and bibliometric analysis of the knowledge
construction of sports crisis communication. Journal of Sports
& Social Issues, 42(5), 369–392. https://doi-
org.ezp.waldenulibrary.org/10.1177/0193723518790011
The author of this research wants to evaluate the
development of different authors work to find other authors,
journals, and theories involved in the subfield’s knowledge
construction process. Harker and Saffer (2018) use the network
analysis and bibliometric method to analyze 25 years of
scholarship in over 20 journals to reveal significant areas of
focus in sports crisis communication. The author also focuses

5. on applied and critical cultural learning.
Running head: BIG DATA APPROACH
6
BIG DATA APPROACH
Big Data Approach
Student's name
Professor's name
Institution
Course Title
Date:
As a result of the enormous amount of data required for
analysis, big data approach has become a necessity for health-
oriented organizations. This approach enables the companies to
perform analysis in a systematic way from data repositories.
The structure of storing data in a dataset is also an important
aspect. The approach offers better statistical power through the
tools it provides to organizations (Chen, Mao, & Liu, 2014).
The approach seeks to solve challenges about handling large
amounts of data. The challenges range from data analysis,
storage, capture, visualization as well as information privacy.
This paper will, therefore, seek to discuss the big data approach,
the origin of big data, methods of storing the data as well as the

6. format of the database that will be used.
Use of semi-custom applications.
Use of semi-custom applications will form a basis for the
handling big data. This technique will employ machine learning
and artificial intelligence. Use of artificial and machine
learning will significantly add value to the organization by
providing platforms for handling big data in an efficient manner
(Li, Li, Wang, Zhu, & Li, 2019). This technique will help in
shaping the data analytics mindset at the Health-cop company.
Customized applications will help the company convert model-
based recommendations of treatment into actual insights that
can be used in treatment of diabetes.
The rationale for using semi-customized applications
According to the prevailing circumstances at Health-cop
company, a semi-customized application would suit the
organization in a better way. Semi customized applications take
relatively short development time. Therefore, it takes a very
short time to deploy these applications. When a semi-
customized application is well constructed, they offer stability
by offering great reliability levels as well as more resilience
(Eapen, & Peterson, 2015). Semi-custom applications are more
flexible offering great service through an extended lifetime,
adaptability as well as their scalability. Lastly, semi-customized
applications offer better quality. Their package components
have robust performance levels. Moreover, they offer high-
quality standards due to their applicability in many
environments.
Source of Big Data
According to statistics by the world health organization,
the prevalence of diabetes disease is about 9% in the unites
states of America. Considering these statistics, this number of
people is large. Going further to consider the daily data required
to be fetched each day in monitoring disease in each patient, the
data collected each day is enormous. The cloud platform will
offer daily data collection from patients through the use of
artificial intelligence in collaboration of sensor-based networks

7. (Aazam, et al, 2014). The internet of things will provide support
for the collection of data through miniaturized sensors. These
miniaturized sensors will then be controlled through artificial
intelligence. Since the cloud platform uses the software as a
service technique. Each patient in the Health-cop database will
have their portals that they can access services from any
environment. Machine learning techniques will help in
identifying patients that require urgent help. Considering all
these actions that are performed on the cloud platform, big data
will be generated as a result.
Storage of Data
From the proposed architectures of data storage done
before, data storage will be handled through cloud storage
facilities. The company aims to implement a cloud data
repository. The cloud platform will provide one to many
replications. One to many replications will provide data
reliability as a failure of one storage node will not affect the
operations in the company. It will also help in consolidating
data from all remote locations, therefore, enabling an analysis
of data at a central point (Jiang, et al, 2014). Storage will
depend on high-speed transmissions of data from the patient's
local location to the cloud storage. This will enable continuous
synchronization of data in the database and therefore enabling
data in the database to be up to date. This will enhance its
reliability and therefore giving a clear reflection of analytics.
Storage in the database will also be supported by high-speed
data acceleration. Cloud storage will enable the semi-
customized data-intensive health support application to collect
data from the sensor sources and pass it over to the cloud
(Sookhak, 2015). Data obtained will be stored by using data
segmentation methods. Several segments that will range
according to the type of diabetes disease on is suffering from
will be enhanced. This will enable easier querying and
analyzing data from the database.
Database Formats
Modern technologies have come up with formats that enable

8. easier storage of biodata. Among the formats, is the Next
Generation Sequencing. Health cop company intends to use this
database format due to its suitability to storing biodata
(Banerjee, & Sheth, 2017). Additionally, the database format is
of an advantage as it will help in providing useful data mining
techniques as well as machine learning techniques that will help
in inputting data into specific data types and formats. The main
agenda towards choosing this format is to enable Health-cop
company store and analyze the data more efficiently
Conclusion
Considering the factors in play at the Health-cop company,
semi-custom applications will help the company achieve its
objectives in handling big data. The Next-generation sequencing
database format will enable the company to store biodata more
efficiently.
References
Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014,
January). Cloud of Things: Integrating the Internet of Things
and cloud computing and the issues involved. In Proceedings of
2014 11th International Bhurban Conference on Applied
Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th-
18th January 2014 (pp. 414-419). IEEE.
Banerjee, T., & Sheth, A. (2017). Iot quality control for data
and application needs. IEEE Intelligent Systems, 32(2), 68-73.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey.
Mobile networks and applications, 19(2), 171-209.
Eapen, Z. J., & Peterson, E. D. (2015). Can mobile health
applications facilitate meaningful behaviour change?: time for
answers. Jama, 314(12), 1236-1237.
Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014).
An IoT-oriented data storage framework in the cloud computing
platform. IEEE Transactions on Industrial Informatics, 10(2),
1443-1451.

9. Li, Y., Li, G., Wang, T., Zhu, Y., & Li, X. (2019).
Semicustomized Design Framework of Container
Accommodation for Migrant Construction Workers. Journal of
Construction Engineering and Management, 145(4), 04019014.
Sookhak, M. (2015). Dynamic remote data auditing for securing
big data storage in cloud computing (Doctoral dissertation,
University of Malaya).
Cloud Computing
Cloud Computing
Institution Affiliation
Student Name
Date:
Health-cop company is a start -up company will offer data
analytics services to various companies. The company helps
heath facilities through proper service delivery to their clients
through support of data analysis. The company aims at
improving its services through the adoption of innovative
technologies. In a review, the company aims at implementing a

10. cloud platform to act as data heaven that will support for data
storage as well as predictive data analysis. The company intends
to use the software as a service approach in the cloud computing
environment (Tsai, Bai, & Huang, 2014). Fetching of data will
be performed through the support of the internet of things. This
paper will, therefore, seek to identify how the company intends
to use the cloud platform in its operations.
Goals and Objectives
The company aims to be a leading provider of predictive
data analytics services across the united states. In its goal to
become a diverse company in its service providence, the
company aims at earning profits from its wide range of services
it offers. The main aim is to provide organizations with data
handling support. In this view, the company aims at providing
real-time streams of data analytics. The software as a service
platform will enable the company to have accessibility from
many places in the nation (Khoshafian, 2016). In its mandate,
the organization aims to provide the safest and the most
dependable data facilities that the clients can have confidence
in. The company aims at providing scheduled as well as random
reports on the various predictive data analysis dockets it is
tasked with. The company provides for this intending to provide
a robust structure that will enable its client organization to
perform health information analysis that will enable them to
attain a competitive advantage in privately owned hospitals.
Content Security Policy
Considering the fact that Health-cop company intends to
use a cloud platform in data storage and handling as well as
offering support for data analytics, there will be a need to have
a policy that will ensure the security of data. The expected
security is enabled through the website that will be used to
access data from the cloud platform. The content security
policy will help in detection as well as prevention of certain
types of attacks that target cloud platforms (Patil, & Frederik,
2016). A content security policy will be capable of efficiently
handling forms of attacks such as cross-site scripting, browser

11. hijacking, form jacking as well as ad injecting. The company
plans to have a regularly updated inventory of the first- and
third-party domains, lists of whitelisted domains and a method
of alerting violations of the content security policy. The
company also aims to have a regular update of the policy in
order to ensure that it meets data security standards.
Organizational Structure
Health-cop Company is headed by a chief executive officer
who is in charge of coordinating different departments in the
organization. In his mandate, the chief executive officer is in
charge of fostering a good relationship between the company
and the target client companies. Through his influence, he
makes approval of innovative technology such as the current
impending cloud computing platform. Under the chief
executive officer, lies a business manager and a functional
manager. The business manager ensures that the company is
strategically positioned to perform business (Goetsch, & Davis,
2014). The functional manager coordinates activities that lay
down the structure of the business. He is in charge of
coordinating information technology issues. The three top
bosses are mandated to sit in board meetings that discuss the
reports of the business. There are other supervisors who are I
charge of other smaller departments in the company.
Target Market
The company target all the health facilities across in the state.
The intention of targeting these companies is that they are in a
position to purchase the data storage and analytics plans the
company offers. The company will provide the predictive data
analysis services to companies that are in need to perform
digitized and more efficient market analysis (Liu, 2014). The
idea is to enable these companies to identify market niches as
well as to attain competitive advantages. The company will
target these companies through specialized plans that will
enable favourable conditions that are economically viable.
Market Niche
With the many chronic diseases in the country, health

12. organizations are increasingly having the need to predict the
prevalence of these diseases. Health-cop services will provide
the much-needed reports to health facilities. These will help
health facilities draw plans on how to curb as well as prevent
diseases.
Budget Estimation
With the infrastructure that comes with the cloud
computing platform, it is will be necessary to have a special
room where architectural equipment will be placed. The cost of
building a physically secure room will be incurred. There will
also be a cost incurred in buying a domain that will be used to
access cloud resources. A server will also be procured in order
to support the large network of organizations that will be linked
to the company’s cloud platform. Installing the technology will
also require an investment in financial resources in acquiring
skilled personnel to install and maintain the cloud platform. In
addition, training of human resource personnel in the company
will incur some cost.
Conclusion
A review of current marketing trends in many
organizations indicates that data handling an analysis are key
components of every organization. Every organization strives to
ensure that they can grasp market requirements that gives them
a niche in the market. For these reasons, Health-cop Company’s
cloud computing project fits the market requirements of data
handling and analysis.
References
Goetsch, D. L., & Davis, S. B. (2014). Quality management for
organizational excellence. Upper Saddle River, NJ: Pearson.
Khoshafian, S. (2016). Service oriented enterprises. Auerbach
Publications.
Liu, Y. (2014). Big data and predictive business analytics. The
Journal of Business Forecasting, 33(4), 40.
Patil, K., & Frederik, B. (2016). A Measurement Study of the
Content Security Policy on Real-World Applications. IJ

13. Network Security, 18(2), 383-392.
Tsai, W., Bai, X., & Huang, Y. (2014). Software-as-a-service
(SaaS): perspectives and challenges. Science China Information
Sciences, 57(5), 1-15.
Running head: PROJECT PROPOSAL 1
PROJECT PROPOSAL 4
PROJECT PROPOSAL
Institution Affiliation
Student Name
Data
Start-up Proposal: HEALTH-COP COMPANY
Predicting When and Where Lifestyle & Dietetic Related Health
Issues Are Most Likely to Occur.
Introduction
Health-cop company is a data mining company that predicts
health trends and possible illnesses that could be witnessed in
the near future. The company will mainly focus on data mining
and analytics to establish links between diet composition and
health issues in society, (Larose, 2015). The data to be used in

14. the predictive analytics will mainly be obtained from hospital
databases, nutrition and dietetics websites, health journals as
well as information shared through social media platforms.
Health-cop company intends to predict such issues before they
can become tough to manage.
Goals & Objectives
The main goal is to become a leader in health predictive
analytics in the health sector, improve the level of preparedness
for various health issues, and earn a profit from running the
business. Health-cop’s main objective is to identify certain
lifestyle and dietetic related illnesses that are most likely to be
experienced within a certain region in the near future. The
company will analyze purchases from food stores and groceries
and also analyze the various meals ordered for from various
food joints. The company also aims at providing consolidated
reports on diet composition of various people from various
regions based on data obtained from websites and social media
platforms.
Organizational Structure
The company will be headed by a chief executive officer who
will be in charge of overseeing all operations. A seven-member
board of directors will be selected among data analytics
professionals to undertake the duties of policy formulation and
implementation. Health-cop will have a data mining division,
analytics division, IT department, as well as a human resource
and customer relations departments; each headed by a
departmental manager. An independent division to deal with
business modeling and statistical database creation will receive
data from the analytics division. This division will create
various projections that will be used to make predictions about
specific illnesses.
Target Market
The company targets to sell its information to health
departments at various levels of governments. The company will
also provide its analysis to various hospitals for an agreed fee.
Health-cop will also sell its findings to private health care

15. institutions especially nutritionists and pharmaceutical
organizations. The existing competitors in the market offer
predictive analytics for chronic diseases unrelated to dietetics,
(Sepah, et.al., 2015). Health-cop will majorly focus on lifestyle
and dietetics related illnesses that are easily preventable thus
the company will be unique in the market. The major illnesses
that the company will analyze and report on are diabetics,
obesity, and osteoporosis.
Budgetary Estimation
The start-up will require planning and preparation finances to
facilitate sufficient research before launching the company.
Costs will also be incurred to secure strategically positioned
premises for the company. Acquisition of digital equipment
such as computers and network cables as well as the installation
of internet services will require sufficient funding, (Shah, et.al.,
2018). Other operational expenses that are expected include
salaries and wages for the company’s staff and marketing of the
company and its services in the market.
Conclusion
In recent years, lifestyle-related illnesses have become an issue
for many people in the world, (Peirson, et.al., 2015). The main
factors that contribute to the increased incidence of such
illnesses are changes in lifestyle and dietary behavior. The
reported cases of diabetes, obesity, and osteoporosis have
significantly shot up in recent times. This can all be attributed
to the changes in diet behavior. A preventive analytical
algorithm would be most suitable to manage these illnesses. A
computer algorithm programmed to analyze what is being
consumed in various regions and link the food substance to a
certain lifestyle-related disease would be very important,
(Razzak, et.al., 2019). This would facilitate early detection and
application of preventive measures.

16. References
Larose, D. T. (2015). Data mining and predictive analytics.
John Wiley & Sons.
Peirson, L., Fitzpatrick-Lewis, D., Morrison, K., Ciliska, D.,
Kenny, M., Ali, M. U., & Raina, P. (2015). Prevention of
overweight and obesity in children and youth: a systematic
review and meta-analysis. CMAJ open, 3(1), E23.
Razzak, M. I., Imran, M., & Xu, G. (2019). Big data analytics
for preventive medicine. Neural Computing and Applications, 1-
35.
Sepah, S. C., Jiang, L., & Peters, A. L. (2015). Long-term
outcomes of a Web-based diabetes prevention program: 2-year
results of a single-arm longitudinal study. Journal of medical
Internet research, 17(4), e92.
Shah, N. D., Sternberg, E. W., & Kent, D. M. (2018). Big data
and predictive analytics: recalibrating
expectations. Jama, 320(1), 27-28.