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. SELECTION OF ARTICLES
USING DATA ANALYTICS
FOR BEHAVIORAL
DISSERTATION RESEARCH
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. In Brief
Introduction
Methods of Data Analytics
Development of the Framework
Future Scopes
Conclusion
Outline
TODAY'S DISCUSSION

3. 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.
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.
In Brief

4. Outcomes in health-related issues including psychological, educational, Behavioral,
environmental, and social are intended to sustain positive change by digital interferences.
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.
Contd..
Introduction

5. Fig 1. Data Analytics Methods

6. 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.
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.
Contd..
Methods of Data Analytics

7. Fig 2 Research Framework

8. 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.
Contd..
Development of the Framework

9. 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.
Stage 1: Familiarization with Data Identification of Variables
Contd..

10. Fig 3. Intervention
Development-Prior to
Data Collection
Contd..

11. Fig 3.1 Post Hoc Analysis-
after Data Collection

12. 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.
Stage 2: Selecting Usage Measures and Generating
Research Questions for Engagement:

13. 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 and the analysis plan for efficacy.
They also consider more specific issues of selecting a suitable type of analysis and
analytical software, and management of data like manipulation and data cleaning.
Stage 3: Preparation for Analysis:

14. 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, 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.
Future Scopes

15. 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.
Pre-processing steps like integration, conversion, sorting, reduction, and knowledge
presentation are involved in data mining.
Conclusion

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