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The application of exploratory data analysis in auditing

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TitleInfo
Title
The application of exploratory data analysis in auditing
Name (type = personal)
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Liu
NamePart (type = given)
Qi
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1985-
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Qi Liu
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author
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Vasarhelyi
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Miklos
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Miklos Vasarhelyi
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Advisory Committee
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chair
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Kogan
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Alex
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Alex Kogan
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Advisory Committee
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internal member
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Alles
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Michael
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Michael Alles
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Advisory Committee
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internal member
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Fisher
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Ingrid
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Ingrid Fisher
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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Graduate School - Newark
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school
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Text
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theses
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2014
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2014-10
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2014
Place
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xx
Language
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eng
Abstract (type = abstract)
Exploratory data analysis (EDA), which originated centuries ago, is a data analysis approach that emphasizes pattern recognition and hypothesis generation from raw data. It is suggested as the first step of any data analysis task for exploring and understanding data, and has been applied in many disciplines such as Geography, Marketing, and Operations Management. However, even though EDA techniques, such as data visualization and data mining, have been used in some procedures in auditing, EDA has not been employed in auditing in a systematical way. This dissertation consists of three essays to investigate the application of EDA in audit research. The study contributes to the auditing literature by identifying the importance of EDA in auditing, proposing a framework to describe how auditors could apply EDA to auditing, and using two cases to demonstrate the benefits that auditors can gain from EDA by following the proposed framework. The first essay identifies the value of EDA in auditing and proposes a conceptual framework to identify EDA’s potential application areas of EDA in various audit stages in both the internal and external audit cycles, describe how auditors can apply various EDA techniques to fulfill different audit purposes, and introduce a recommended process for auditors to implement EDA. In addition, this essay also discusses how EDA can be integrated into a continuous auditing system. The second essay examines the use of EDA in an operational audit. Traditional EDA techniques, such as descriptive statistics, data transformation, and data visualization, are applied in this credit card retention case. Descriptive statistics can reveal the distribution of the data, and data visualization techniques can display the distribution in an effective way so that auditors can easily identify patterns hidden in the data. By integrating these EDA techniques in audit tests, many critical risky issues, such as negative discount, inactive representatives, and short calls, are detected. With the rapid development of modern data analysis, EDA techniques have been greatly enriched. Besides the traditional EDA techniques, some data mining techniques can also be used to fulfill EDA tasks. The third essay investigates the application of two data mining techniques on a Medicare dataset to assess fraud risk. Specifically, this essay utilizes clustering techniques to detect abnormal Medicare claims in terms of claim payment amount and Medicare beneficiaries’ travel distances and hospital stay periods, and applies association analysis to analyze doctors’ diagnoses and performed procedures to identify abnormal combinations. In summary, this dissertation attempts to contribute to auditing literature by identifying the value that EDA can add to auditing, and illustrating some applications to demonstrate how auditors can benefit from EDA.
Subject (authority = RUETD)
Topic
Management
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_5890
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (ix, 174 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Subject (authority = ETD-LCSH)
Topic
Quantitative research
Subject (authority = ETD-LCSH)
Topic
Data mining
Subject (authority = ETD-LCSH)
Topic
Auditing
Note (type = statement of responsibility)
by Qi Liu
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TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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Identifier (type = doi)
doi:10.7282/T3CC129J
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Liu
GivenName
Qi
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-09-24 05:02:08
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Name
Qi Liu
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Copyright holder
Affiliation
Rutgers University. Graduate School - Newark
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Type
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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