Southern New Hampshire University Analysis of Research Designs In this assignment, you will use the scholarly articles to analyze the research designs to i
Southern New Hampshire University Analysis of Research Designs In this assignment, you will use the scholarly articles to analyze the research designs to identify their advantages and disadvantages.
Alper, M., Durose, M. R., & Markman, J. (2018). 2018 Update On Prisoner Recidivism: A 9-Year Follow-Up Period (2005-2014) Retrieved from https://www.bjs.gov/content/pub/pdf/18upr9yfup0514…
Explanation of the advantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the advantages.
Explanation of the disadvantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the disadvantages.
2.) Langan, P. A., PhD., & Levin, D. J., PhD. (2002). Recidivism of prisoners released in 1994: Retrieved from https://www.bjs.gov/content/pub/pdf/rpr94.pdf
Explanation of the advantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the advantages.
Explanation of the disadvantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the disadvantages.
3.) Jones, A. E. (2013). Critical success factors for reducing recidivism
Explanation of the advantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the advantages.
Explanation of the disadvantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the disadvantages.
4.) Oliver, B. E. (2011). Recidivism: A multi-level explanation (Order No. 3460066).
Explanation of the advantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the advantages.
Explanation of the disadvantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the disadvantages.
5.) Visher, C. A., Winterfield, L., & Coggeshall, M. B. (2005). Ex-offender employment programs and recidivism: A meta-analysis. Journal of Experimental Criminology
Explanation of the advantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the advantages.
Explanation of the disadvantages of the research methodology uses (qualitative or quantitative) to making informed decisions about how to address the project issue. Be sure to name the research method used in the article. Provides specific examples from the research to support the disadvantages.
Note, the project issue is Recidivism-Which gender has the highest recidivism rate Recidivism: A Multi-Level Explanation
By
Brian E. Oliver
Bachelor of Liberal Studies, University of Iowa, 2000
M.A., Criminology and Criminal Justice, University of Missouri St. Louis, 2007
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A DISSERTATION
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Submitted to the Graduate School of the
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UNIVERSITY OF MISSOURI ST. LOUIS
In partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
in
Criminology and Criminal Justice
May, 2011
Advisory Committee
Richard Rosenfeld, Ph.D.
Chairperson
Beth Huebner, Ph.D.
Andres Rengifo, Ph.D.
Joan Petersilia, Ph.D.
UMI Number: 3460066
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent on the quality of the copy submitted.
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In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMI 3460066
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Copyright 2011 by ProQuest LLC.
All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC.
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106 – 1346
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Recidivism: A Multi-Level Explanation
Dissertation Abstract
Brian E. Oliver
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Numerous studies have shown that several characteristics of offenders are
related to their likelihood of recidivism after release from prison. Nearly all of
these studies, however, have focused on offenders from just one state. Few studies
have examined recidivism rates controlling for the characteristics of offenders
from multiple states, and virtually none have examined recidivism rates
controlling for characteristics of offenders from multiple states during different
periods of time. Additionally, few studies have explored different types of
recidivism across multiple jurisdictions to determine whether the same individual
level factors explain variations in rearrest, reconviction, reimprisonment, and
parole violations.
To address these shortcomings, this dissertation applied logistic regression
models to data from the publicly available Prisoners Released in 1994 dataset to
investigate the extent to which nine individual level factors explain variation in
recidivism rates within three years of release from prison across 15 states. The
nine factors are: 1) gender, 2) age at first arrest, 3) race, 4) age at release, 5)
number of prior arrests, 6) type of current offense, 7) time served, 8) admission
type and 9) release type. Eight forms of recidivism were examined: 1) rearrest for
any offense, 2) rearrest for a new violent offense, 3) rearrest for a new property
offense, 4) rearrest for a new drug offense, 5) rearrest for a new public order
offense, 6) reconviction probability if rearrested, 7) reimprisonment probability if
reconvicted, and 8) parole violations. The dissertation investigated differences in
the effects of the individual level factors on each form of recidivism.
To investigate the effects of criminal justice policies and practices on state
differences in recidivism rates, multilevel models were estimated that include
three contextual variables, in addition to the nine individual factors. The statelevel contextual variables are: 1) drug arrests per 100,000 residents, 2) police
officers per 1,000 residents and 3) the arrest-offense ratio. In a final analysis,
regression analyses were conducted to determine the extent to which the nine
individual factors explain the increase in the three-year rearrest rates among
persons released from prison in 1983 and 1994.
The findings reveal that differences in individual level characteristics help
to explain the variation across states for some, but not all, forms of recidivism.
The findings related to rearrest for a new violent offense, reconviction probability,
and parole violations were not conclusive. Results from the multilevel models
indicate that the contextual factor of police officers per 1,000 has a significant
impact on property rearrests and a marginal impact on drug rearrests and
reconviction probability. The analysis of rearrests during two separate time
periods revealed that changes in contextual factors, as opposed to individual level
characteristics, were responsible for the increase in rearrest rates which occurred
between 1983 and 1994.
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This study provides evidence that both individual level and contextual
factors play a role in recidivism and need to be taken into consideration in
implementing policy and designing programming. Two conclusions consistent
with the findings are that treatment services need to be based on offender need
and risk level and that states should consider reinstating discretionary parole. It
would be beneficial for future research to examine the effect of additional
individual and contextual variables on recidivism rates, particularly if a multistate dataset, similar to the one used in this study, becomes available in which
county of release is specified.
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ACKNOWLEDGEMENTS
The pages of this dissertation reflect the end of one journey and the beginning of
another. They represent the culmination of six and a half years of change, a period
filled with highs and lows, frustration and joy, and love and pain.
My deepest gratitude is to my mentor and adviser, Dr. Richard Rosenfeld, who,
during the fall of 2004, saw potential in me while I was taking Proseminar with
him and encouraged me to pursue a Masters and then a Doctorate. He has been
my ally, advocate, mentor and friend during my tenure at UMSL, and helped me
overcome some very unique, yet very daunting, obstacles.
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I also wish to thank Dr. David Curry, Dr. Callie Rennison, Dr. Janet Lauitsen and
Dr. Stephen Richards for the encouragement, support and opportunities they have
given me during my time as a graduate student at UMSL. Their words of
encouragement and their help with editing and data analysis along with their
rebukes and correction when I acted too important helped keep me grounded and
somewhat sane.
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I definitely could not have made it to where I am without the love and support of
my family. To my mother Carol, to my sister Christy and to my brother Geoff, I
owe so much and am so pleased that you were able to witness the change and the
growth which have occurred during my time in graduate school.
Beyond my family and criminal justice faculty, I would also like to give thanks to
people and families from Dorsett Village Church in Maryland Heights, Missouri
who have prayed for me, counseled me, talked to me, studied the bible with me,
played volleyball with me, corrected me and loved me unconditionally ever since
I set foot in the church. To Pastor Mark Cline, his wife Janet and the rest of their
family; to Brad Garlich, his wife Sella and the rest of their family; to Kevin
Royal, his wife Tina and the rest of their family; to Stewart Johnson, his wife
Christine and the rest of their family; to Alice Lee and the rest of her family; to
Delpha Slaughter and the rest of her family; as well as to the rest of my brothers
and sisters in Christ, too numerous to mention individually, I wish to say thank
you.
Last but not least, I wish to give thanks, honor and glory to God, the Father, His
Son Jesus Christ and to the Holy Spirit. Without You, none of this would have
been possible.
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TABLE OF CONTENTS
Page
1
1
6
7
8
9
1. Introduction
1.1: Introduction
1.2: Research Question 1
1.3: Research Question 2
1.4: Research Question 3
1.5: Chapter Overview
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17
23
3. Exploring the Impact of Individual Level Covariates
3.1: Introduction
3.2: Gender
3.3: Age at First Arrest
3.4: Race
3.5: Age at Release
3.6: Number of Prior Arrests
3.7: Current Offense Type
3.8: Time Served
3.9: Type of Admission
3.10: Type of Release
3.11: Relationships among the Individual Predictors
3.12: Discussion
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26
28
31
33
35
37
39
42
45
46
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4. Exploring the Impact of Individual Level Characteristics on
Type of Arrest
4.1: Introduction
4.2: Exploring the Differences in Rearrest Probabilities
across States
4.3: Exploring the Differences in Rearrest Probabilities
across States by Type of Rearrest
4.4: Exploring the Differences in Violent Rearrest
Probabilities across States
4.4.1: Reasons Why the Individual Covariates May
Not Explain Variation in Violent Rearrest
Rates across States
4.5: Exploring the Differences in Property Rearrest
Probabilities across States
4.6: Exploring the Differences in Drug Rearrest
Probabilities across States
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2. Data, Measures and Analytic Strategy
2.1: Introduction
2.2: Data
2.3: Measures
2.4: Analytic Strategy
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65
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4.7: Exploring the Differences in Public Order Rearrest
Probabilities across States
4.7.1: Explaining the Wide Degree of
Variation in Public Order Rearrests Across
States
4.8: Discussion
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5. Exploring the Impact of Individual Level Characteristics on
Reconviction, Reconfinement and Parole Violations
5.1: Introduction
5.2: Exploring the Differences in Reconviction
Probabilities across States
5.2.1: Reasons Why the Individual Covariates May
Not Explain Variation in Reconviction
Probabilities across States
5.2.2: Individual Level Factors Related to
Increased Probability of Reconviction
5.3: Exploring the Differences in Reimprisonment
Probabilities across States
5.3.1: Individual Level Factors Related to
Increased Probability of Reconviction
5.3.2: Discussing an Unexpected Non-Finding
5.4: Exploring the Differences in Parole Violation
Rearrests and Reimprisonment on Parole
Revocations across States
5.4.1: Offenders Rearrested for Parole Violations
or Reimprisoned for Technical Violations
5.4.2: Offenders Reimprisoned for Technical
Violations or Parole Violation Convictions
5.4.3: Individual Level Factors Related to
Increased Probability of Parole Violations
5.4.4: Exploring the Different Findings using
Different Approaches
5.5: Goodness of Fit of the Models
5.6: Discussion
6. Do Contextual Variables Explain Recidivism Rates Across
States?
6.1: Introduction
6.2: The Effect of Statewide Drug Arrest Rates on
Recidivism Rates
6.3: The Effect of Statewide Police Per 1,000 Residents
on Recidivism Rates
6.4: The Effect of Statewide Arrest-Offense Ratios
on Recidivism Rates
6.5: Discussion
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8. Contributions and Limitations: Policy Implications and
Suggestions for Future Research
8.1: Introduction
8.2. The Impact of Individual Level Factors on Different
Forms of Recidivism
8.3: Policy Implications
8.3.1: Treatment Services Need to Be Based on
Offender Need and Risk Level
8.3.2: Certain Contextual Variables Affect
Recidivism Rates Above and Beyond
Individual Level Characteristics
8.3.3: States Should Implement Discretionary
Parole
8.3.4: States Should Provide Offense Specific
Treatment for Certain Repeat Offenders
8.4: Study Limitations
8.5. Suggestions for Future Research
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7. Exploring the Impact of Individual Level Characteristics on
Rearrest Rates over Time
7.1: Introduction
7.2: Using Predicted Probability Models to Clarify the
Findings
7.3: Americas War on Drugs
7.4: Increased Numbers of Police Officers
7.5: Changing Police Procedures
7.6: Discussion
References
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List of Figures, Tables and Appendixes
Figure 1
Three Year Rearrest Rates by State
Figure 2
Three Year Violent Rearrest Rates by State
Figure 3
Three Year Property Rearrest Rates by State
Figure 4
Three Year Drug Rearrest Rates by State
Figure 5
Three Year Public Order Rearrest Rates by State
Figure 6
Three Year Reconviction Proportion of Rearrested Offenders by
State
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Three Year Reimprisonment Proportion of Reconvicted Rates by
State
Figure 8
Actual Rearrest Rates (11 State Rearrest Rates Computed from
Bureau of Justice Statistics Datasets)
Figure 9
Predicted Rearrest Rates (Anticipated Rearrest Rates Using the
Predicted Probability Model)
Figure 10
Full Time Law Enforcement Officers Per 1,000 Residents
Table 1
Logistic Regression Models for All Individual Level
Characteristics
Table 2
Correlation Matrix of Individual Level Characteristics
Table 3
Individual Level Characteristics of 1994 Release Cohort by State
of Release
Table 4
Logistic Regression comparing Rearrests for Prisoners Released
from Illinois and Minnesota
Table 5
Logistic Regression comparing Rearrests for Prisoners Released
from Illinois and Minnesota with Individual Level Characteristics
Added to the Model
Table 7
Table 8
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Table 6
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Figure 7
Logistic Regression Results for Three Models for Rearrest
for Any Offense
State by Whether Type of Rearrest is Known
Logistic Regression Results for Three Models for Rearrest for a
New Violent Offense
Table 9
Logistic Regression Results for Three Models for Rearrest for a
New Property Offense
Table 10
Logistic Regression Results for Three Models for Rearrest for a
New Drug Offense
Table 11
Logistic Regression Results for Three Models for Rearrest
for a New Public Order Offense
Table 12
Public Order Rearrests by Specific Charges
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Logistic Regression Results for Three Models of Reconviction for
a New Offense (for Rearrested Offenders)
Table 14
Logistic Regression Results for Three Models for Reimprisonment
for a New Offense (for Reconvicted Offenders)
Table 15
Release Type and Most Serious Reconviction Type
Table 16
Logistic Regression Model for Reimprisonment Probability of
Rearrested Offenders using All Nine Individual Level
Characteristics
Table 17
Offenders Rearrested for Parole Violations by State
Table 18
Offenders Returned to Prison for Parole Violations by State
Table 19
Offenders Rearrested for Parole Violations or Reimprisoned for
Technical Violations by State
Table 20
Logistic Regression Results for Three Models for Rearrests for
Parole Violations and Reimprisonment for Technical Violations
Table 21
Offenders Reimprisoned for Technical Violations or Parole
Violation Convictions by State
Table 23
Table 24
Table 25
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Table 22
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Table 13
Logistic Regression Results for Three Models for Rearrests for
Parole Violations and Reimprisonment for Technical Violations
Contextual Level Variable Averages By State
Multilevel Regressions for Seven Outcomes on Individual Level
Characteristics and State Level Drug Arrests per 100,000
Multilevel Regressions for Seven Outcomes on Individual Level
Characteristics and State Level Police Per 1,000 Residents
Table 26
Multilevel Regressions for Seven Outcomes on Individual Level
Characteristics and Statewide Arrest-Offense Ratios
Table 27
Logistic Regression Results for Rearrest for Any Offense
Comparing Offenders Released in 1983 and 1994
Table 28
Logistic Regression Results for Rearrest for Any Offense
Comparing Offenders Released in 1983 and 1994 with Individual
Level Characteristics Added to the Model
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Logistic Regression Results for Rearrest for Any Offense
Comparing Offenders Released in 1983 and 1994 with Individual
Level Characteristics and State of Release Added to the Model
Appendix A
Matching Procedures for the Merged Data File
Appendix B
Regression Coefficients for State-by-State Comparisons
Appendix C
Multi-Level Regression Tables
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Table 29
1
CHAPTER 1 INTRODUCTION
1.1: Introduction
Much attention has been drawn to the fact that the United States has the
highest incarceration rate in the world. The prison and jail incarceration rate at
yearend 2008 was 754 inmates per 100,000 U.S. residents (Sabol, West and
Cooper, 2009). Although this represented a slight decline over the 2007 rate, it is
nevertheless five to twelve times the average incarceration rate in most European
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countries (Tonry, 2004). Also, given that at least 95 percent of all U.S. prisoners
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will be released at some time (Hughes and Wilson, 2002), an unprecedented
number of people are being released from prison in the United States. In 2008,
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735,454 inmates were released to the community after serving time in prison
(Sabol et al., 2009).
Unfortunately, many of the people who leave prison end up back in prison.
Langan and Levin (2002) found that, within three years of their release, 67.5
percent of state prisoners released in 1994 were rearrested, 46.9 percent were
reconvicted and 51.8 percent were back in prison, serving time for a new prison
sentence or for a technical violation of the conditions of their release. Hughes and
Wilson (2002) similarly found that 42 percent of those released on parole were
returned to prison or jail and another nine percent absconded. What these numbers
indicate is that less than half of the people released from prison are successfully
reintegrated back into society. Meanwhile, the hundreds of thousands of offenders
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on parole or conditional release who return to prison each year end up costing
states billions of dollars (Petersilia, 2001).
In light of the high level of recidivism in the United States, it is not
surprising that much research has been done on why it occurs. The preponderance
of the research over the past 25 years has focused on the effects that individual
level characteristics, such as age, gender and race, have on recidivism. While such
studies can be extremely enlightening in determining policies and programming,
their ability to provide broadly applicable conclusions is limited in that very few
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address variations that exist either from state to state or during different periods of
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time. If offenders released from prison in Delaware have twice the rearrest rate as
offenders released from prison in Michigan, for example, is this because the
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offenders in Delaware have a higher number of traits that are associated with
reoffending, or does it have to do with differing state policies? Similarly, if
national recidivism rates go up over a period of ten or eleven years, is it because
the release cohorts are different or is it because there have been changes in
rehabilitative services offered to offenders released, because police have become
more proactive in their response to crime or because of some other macro-level
change?
Given the high human and monetary costs of recidivism and the complex
interplay between state and federal systems, these are not idle questions. Even
small statistical differences can tip decisions impacting thousands of lives and
millions of dollars. Finding answers to these questions is, moreover, no easy task.
A possible method, comparing recidivism rates across studies, may seem
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appealing, but would, unfortunately, not lead to scientifically valid results. Beck
(2001) pointed out three major problems in this approach. One is that what is
counted as recidivism in one jurisdiction or study may not be counted as
recidivism in a second jurisdiction or study (some states include technical
violators as recidivists while others dont). A second problem is that different
jurisdictions and studies utilized different time frames. A third problem is that
many studies do not include sufficient information to control for variables which
would affect recidivism rates. These three problems make the idea of comparing
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recidivism rates across dissimilar studies an unwise proposition.
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Instead of comparing previous studies, this dissertation draws its material
on the effect of nine individual-level characteristics on recidivism directly from
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two multi-state data sets. Primary of these is the Recidivism of Prisoners Released
in 1994 dataset (United States Department of Justice, 2009a), from which
262,529 of the 302,309 prisoners cases were used. Data on 108,580 prisoners
from the Prisoners Released in 1983 dataset (United States Department of
Justice, 2004) are also brought into discussion of changes over time. From these
two datasets, nine individual-level characteristics were extracted. These nine
individual-level characteristics i…
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