Outcomes in health-related issues including psychological, educational, Behavioral, environmental, and social are intended to sustain positive change by digital interferences. These changes may be delivered using any digital device like a phone or computer, and make them gainful for the provider. Complex and large-scale datasets that contain usage data can be yielded by testing a digital intervention. This data provides invaluable detail about how the users interact with these interventions and notify their knowledge of engagement, if they are analyzed properly. This paper recommends an innovative framework for the process of analyzing usage associated with a digital intervention . PhD Assistance is an Academic The Best Dissertation Writing Service & Consulting Support Company established in 2001. specialiWeze in providing PhD Assignments, PhD Dissertation Writing Help , Statistical Analyses, and Programming Services to students in the USA, UK, Canada, UAE, Australia, New Zealand, Singapore and many more. Website Visit: https://bit.ly/3dANXUD Contact Us: UK NO: +44-1143520021 India No: +91-8754446690 Email: info@phdassistance.com

1. Copyright © 2020 PhdAssistance. All rights reserved 1
Selection of Articles Using Data Analytics for Behavioral Dissertation
Research
Dr. Nancy Agens, Head,
Technical Operations, Phdassistance
info@phdassistance.com
In Brief
Data analytics has been considered widely
as a breakthrough in Research and
Technological development in various
fields. Despite the data analytics being
launched by an increasing number of
industries, there is still limited knowledge
of how these fields interpret the power of
such technologies into industry value. This
blog shows that to realize the performance
gains and to leverage data analytics,
researchers must develop capabilities of
data analytics. Most studies work under the
concept that there is limited heterogeneity
in the way industries develop their
capabilities in data analytics and regardless
of the framework, the related resources are
of similar importance. The main idea that
data analytics develops is by examining
huge volumes of unstructured data from
many resources, and that actionable
insight can be created that industries can
use to transform their business and gain an
edge over their competition (Mikalef et al.,
2019).
Keywords: Data analytics, behavioral
research, data mining, data analysis.
I. INTRODUCTION
Outcomes in health-related issues
including psychological, educational,
Behavioral, environmental, and social are
intended to sustain positive change by
digital interferences. These changes may be
delivered using any digital device like a
phone or computer, and make them gainful
for the provider. Complex and large-scale
datasets that contain usage data can be
yielded by testing a digital intervention. This
data provides invaluable detail about how
the users interact with these interventions
and notify their knowledge of engagement,
if they are analyzed properly. This paper
recommends an innovative framework for
the process of analyzing usage associated
with a digital intervention by the following
methods: (1) drawing potential measures of
usage together with identifying which are
significant for the intervention, (2)
generating specific research questions that
act as a testable hypothesis, and (3)
sustaining preparation of data and selecting
data analysis methods (Miller et al., 2019).
Data Analytics methods can be
categorized into the following types as
depicted in the figure.
● Descriptive Methods: Descriptive
analytics method is used mainly to
utilize existing data sets to unveil the
properties of data.
● Predictive Analytics: Historical data is
mainly utilized predictive analytics
method to anticipate the development of
data i.e., future developments in data.
● Prescriptive Analytics: The result of
both descriptive and predictive analytics
methods is used in this method to make
the right decisions to get desired
outcomes i.e., ways to achieve the
desired goal (Dai et al., 2019).

2. Copyright © 2020 PhdAssistance. All rights reserved 2
Fig 1 Data Analytics Methods
Fig 2 Research framework
The challenge of deriving business values
from data analytics that has recognized by
conceptual and empirical researches is not
solely a technical one, but also an
organizational one. Organizations face five
main challenges in becoming data-driven
that revolve around data, processes,
technology, organization, and people. The
capabilities of data analytics are responsible
for the conversion of data that is collected
by the organizations into business value by
influencing it into an actionable approach.
This is the main basis behind this point of
view. This importance of factors that relate
to the processes, technology, people, data,
and organization are highlighted in this
framework (Mikalef et al., 2019).

3. Copyright © 2020 PhdAssistance. All rights reserved 3
II. DEVELOPMENT OF THE
FRAMEWORK
The framework has three stages:
1. Familiarization with datasets,
2. Selecting significant measures of usage
and generation of research questions,
and
3. Preparation for analysis.
Each stage is presented in a checklist
format, which is prompted by generic
questions for the Researcher to consider
from the perspective of their own specific
involvement. Depending upon whether the
framework is applied after data collection or
applied in advance, the use of the three
stages will be iterative. The examination of
the relationships between measures of usage
and user data, behavior, theoretical
variables, and health-related outcomes are
mainly focused on this framework.
III. STAGE 1: FAMILIARIZATION WITH
DATA—IDENTIFICATION OF
VARIABLES:
Large datasets that contain
information in different formats are created
by the evaluation of the digital intervention.
Before the analysis of the usage of data has
been conducted, it is mandatory to collect all
relevant data across the datasets and figure
out new variables. This Framework
comprises a set of generic questions that will
provide a comprehensive understanding of
the process, structure, and also content of
the intervention related to data capture and
contents of the datasheets. This makes the
process even simpler. This framework can
be used in advance during the development
to identify the important data that is crucial
in software development or else alternative
research works.
Fig 3 Intervention development-prior to data
collection
Fig 3 Post hoc analysis- after data collection

4. Copyright © 2020 PhdAssistance. All rights reserved 3
IV. STAGE 2: SELECTING USAGE
MEASURES AND GENERATING
RESEARCH QUESTIONS FOR
ENGAGEMENT
The aim of this stage is to sustain the
generation of a specific set of research
questions to handle the testing hypothesis.
To reveal the increasing complexity of
comprehensive usage analysis, this stage has
been divided into three sections: the first
section helps to define specific measures of
usage i.e., descriptive statistics, while
second and third sections facilitate the
generation of research questions i.e.,
bivariate and multivariate analysis.
V. STAGE 3: PREPARATION FOR
ANALYSIS:
The process of the selection of
appropriate types of analysis is done in the
third and final stage. This stage also
facilitates the identification of analytical
software, as well as the preparation of data
that is significant in the translation of the
research questions into an analysis plan.
Researchers follow generic questions as a
guide to consider broad issues, such as
available resources like timeframe, the
analysis plan for efficacy, and additional
researcher support. They also consider more
specific issues of selecting a suitable type of
analysis and analytical software, and
management of data like manipulation,
amalgamation, and data cleaning (Miller et
al., 2019).
VI. FUTURE RESEARCH
Data analytics will evidently help
projects in the process of value creation.
Data analytics processes will help to
maximize the efficiency of operation, reduce
the cost of software development, ensure
massively personalized production, and
restructure the management of the supply
chain. Emerging technologies like
blockchain and fog computing play a major
role in Data Analytics for the Internet of
Things. New standards for interoperability
among the data analytics platform must be
devised by conducting future research and
also to provide the capability for the end-to-
end reliable application process (ur Rehman
et al., 2019).
VII. CONCLUSION
The latest techniques in Artificial
Intelligence (AI) have gained attention to a
greater extent in many applications because
of their ability to mine information. The
most powerful tool in AI is considered to be
data mining for the collection of a large set
of data. Data mining also helps to translate
these data into useful information.
Knowledge discovery and data mining are
used in many fields of biological data
analysis, telecommunication, and financial
data, etc., Pre-processing steps like
integration, conversion, sorting, reduction,
and knowledge presentation are involved in
data mining (El-Hasnony et al., 2020).
REFERENCES
[1] Dai, H.-N., Wong, R. C.-W., Wang, H., Zheng, Z., &
Vasilakos, A. V. Big data analytics for large-scale
wireless networks: Challenges and opportunities.
ACM Computing Surveys (CSUR), 52 5, (2019), pp.
1–36. https://dl.acm.org/doi/abs/10.1145/3337065
[2] El-Hasnony, I. M., Barakat, S. I., Elhoseny, M., &
Mostafa, R. R. Improved Feature Selection Model
for Big Data Analytics. IEEE Access, 8, (2020), pp.
66989–67004.
https://ieeexplore.ieee.org/abstract/document/905871
5/
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data analytics and firm performance: Findings from
a mixed-method approach. Journal of Business
Research, 98, (2019), pp. 261–276.
https://www.sciencedirect.com/science/article/pii/S0
14829631930061X
[4] Miller, S., Ainsworth, B., Yardley, L., Milton, A., Weal,
M., Smith, P., & Morrison, L. A framework for
Analyzing and Measuring Usage and Engagement
Data (AMUsED) in digital interventions. Journal of

5. Copyright © 2020 PhdAssistance. All rights reserved 3
Medical Internet Research, 21 2, (2019), pp. e10966.
https://www.jmir.org/2019/2/e10966
[5] ur Rehman, M. H., Yaqoob, I., Salah, K., Imran, M.,
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https://www.sciencedirect.com/science/article/pii/S0
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