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The impact of error on offender risk classification

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TitleInfo
Title
The impact of error on offender risk classification
Name (type = personal)
NamePart (type = family)
Ho
NamePart (type = given)
Aaron
DisplayForm
Aaron Ho
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Clear
NamePart (type = given)
Todd
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Todd Clear
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Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Apel
NamePart (type = given)
Robert
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Robert Apel
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Miller
NamePart (type = given)
Joel
DisplayForm
Joel Miller
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Baird
NamePart (type = given)
Christopher
DisplayForm
Christopher Baird
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - Newark
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-05
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In criminal justice, offender risk classification seeks to divide individuals into different groups, normally so that varying levels of program treatment, custody, or supervision can be effectively and optimally allocated. The goal of effectively separating offenders based on prearranged criteria, however, is often thwarted by error problems, resulting in the misclassification of individuals. How the initial error problems eventually translate into final misclassification is not completely understood. Thus, the dissertation attempts to model the effects of error on the tolerance of offender risk classification instruments. Specifically, different properties and characteristics of classification devices are analyzed to understand their impact on the transfer of error from initial to final classification phases. Suitable risk data and instruments that would facilitate the testing of all proposed research questions and hypotheses in the current study are not readily available. This is because, in order to explore the different facets of the proposed inquiries, specific situations are requisite- and these particular situations may be easier tailored into a fabricated data than to be found in the real world. Thus, relying on both conceptual data and actual risk data, random and systematic error are simulated and injected into each risk instrument to gain insight onto how unreliability and invalidity statistically impact classification. The risk data are engineered using Monte Carlo Simulation: construction methods making use of random draws from an error distribution and multiple replications over a set of known parameters. This methodology is particularly relevant in situations where the only analytical findings involve asymptotic, large-sample results. Monte Carlo Simulations enables the construction of multiple datasets in a “laboratory setting” that would simulate data in the real world. This allows evaluations concerning the impact of different risk properties on the transfer of error to be made. For the current study, two main questions are asked: 1) what is the impact of error in risk data on overall classification outcomes; and 2) how does such error impact validity. The study found that risk tools generally have a low tolerance for error. The injection of 10 percent error into risk assessment information produced 25 to 40 percent error in classification outcomes. However, the injection of random error only minimally reduces classification validity by causing the subgroup recidivism/base rates for each category to mildly shrink towards the mean. Different risk tools and factors play a critical role in determining an instrument’s sensitivity to error. Specific risk properties such as dichotomous risk items, having fewer risk categories, risk items with lower weights, and having more risk items reduce the sensitivity of error in risk tools. A risk tool’s tolerance for error is, thereby, controlled by a confluence of factors. This dissertation facilitates a better understanding of the interplay between error in risk information and error in classification outcomes. The findings improve knowledge of the sensitivity of error in offender risk classification instruments. Furthermore, it explains how the sensitivity of error is aggravated or mitigated by the inclusion of different common risk device properties.
Subject (authority = RUETD)
Topic
Criminal Justice
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4777
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
iii, 201 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Aaron K.T. Ho
Subject (authority = ETD-LCSH)
Topic
Criminals--Identification
Subject (authority = ETD-LCSH)
Topic
Risk assessment
Subject (authority = ETD-LCSH)
Topic
Criminal justice, Administration of
Subject (authority = ETD-LCSH)
Topic
Monte Carlo method
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10002600001.ETD.000068670
RelatedItem (type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3P849H1
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Ho
GivenName
Aaron
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-04-30 14:49:49
AssociatedEntity
Name
Aaron Ho
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
Type
License
Name
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
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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ContentModel
ETD
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windows xp
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