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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).

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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).

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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

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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/

[3] Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. Big

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

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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.,

Jayaraman, P. P., & Perera, C. The role of big data

analytics in industrial Internet of Things. Future

Generation Computer Systems, 99, (2019), pp. 247–

259.

https://www.sciencedirect.com/science/article/pii/S0

167739X18313645