Decision Making in Criminal Justice Revisited: Toward a General
Theory of Criminal Justice
A dissertation submitted to the
Graduate School
of the University of Cincinnati
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
in the School of Criminal Justice
of the College of Education, Criminal Justice, and Human Services
by
Daniel J Lytle
B.A. Marquette University, 2005
M.S. University of Cincinnati, 2007
Committee Members
Lawrence F. Travis, III, Ph.D. (Chair)
James Frank, Ph.D.
Paula Smith, Ph.D.
William King, Ph.D.
ABSTRACT
The study of correlates of decision-making has been an important and integral part of
criminal justice research. While this important research has been studied largely since the
conception of criminal justice research, the attempts to take stock and determine what the body
of research collectively “knows” has been limited. These limitations have included both the
method of synthesis and also the focus of the synthesis. Traditionally, criminal justice scholars
have taken stock of correlate research by using narrative literature review techniques. While this
research does make some contribution to the field, it is flawed because of the inherent problems
in all narrative reviews, namely, double counting studies, and the subjective nature of narrative
reviews. The flaw has been that reviews have focused on a few important variables, race and
gender, and not on a more comprehensive view.
This dissertation seeks to expand upon the current state of correlates of decision-making
research by conducting a meta-analysis to examine decision making across the system,
specifically, arrest, sentencing, and parole revocation. Results indicated that several variables
are important at multiple stages of the criminal justice system. These predictors are both legal
and extra-legal variables, legal predictors of decision-making included seriousness of the
offense, and the offender’s prior criminal record, while extra-legal predictors of decision-making
included race, gender, and ethnicity. These findings were not mitigated by moderating factors,
but instead persisted across moderator categories. In addition to system-wide correlates, there
were several factors, which were unique to a specific decision point, these included, at arrest,
suspect demeanor, and, at sentencing, mode of conviction.
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ACKNOWLEDGEMENTS
There are many people that I would like to thank that made this process possible. First, I
would like to thank my dissertation chair and mentor Dr. Lawrence F. Travis, III. You have
mentored me since I walked through the doors at the University of Cincinnati and I am eternally
grateful for your guidance, mentorship, and the occasional figurative swift kick in the pants.
You have helped to mold me into a scholar. Second, I would like to thank Dr. James Frank;
thank you for your guidance on this project and your mentorship as I progressed through the
doctoral program at the University of Cincinnati. Third, I would like to thank Dr. Paula Smith
for your guidance and assistance with the technical aspects of conducting this meta-analysis.
Fourth, I would like to thank Dr. William King for serving as my outside reader on this project.
Thank you all for your service, helpful comments, and assistance in this dissertation process.
I would also like to thank the entire criminal justice faculty at the University of
Cincinnati. I would especially like to thank Dr. Robin Engel for first exposing me to criminal
justice theory and for writing the piece that served as an inspiration for this project. There are
two other professors that I have had over the course of my academic career that deserve special
mention, Dr. Carol Archbold and Dr. Amy Stichman. Thank you to you both for setting me on
the path of graduate school at the University of Cincinnati.
Finally, I would like to thank my friends and family. First and foremost, I would like to
thank my parents Joseph and Juanita Lytle. Without your encouragement and support this
process would never have been possible. Second, there are several friends that deserve special
mention. I would like to thank Cody Stoddard, Ryan Randa, Rich Lemke and Jackee Rohrbach.
I would also like to thank Jim McCafferty for assisting with my reliability coding, and Kevan
Galyean and Cheryl Jonson for answering those random questions.
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TABLE OF CONTENTS
ABSTRACT
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ACKNOWLEDGEMENTS
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LIST OF TABLES
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LIST OF FIGURES
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CHAPTER 1: PROBLEM STATMENT
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What is Criminal Justice?
Criminal Justice Dependent Variable
Criminal Justice Theory
Duality of criminal justice
Meta-analysis, criminal justice and criminal justice theory
Criminal Justice Knowledge Synthesis
Chapter Summary
A Look Ahead: The Plan for this Dissertation
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CHAPTER 2: LITERATURE REVIEW
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Situational Theories
Gottfredson and Gottfredson
Focal concerns theory
Actor Theories
Workgroup/Organizational Theories
Courtroom workgroup
Police workgroup
Community Theories
Black
Duffee
Wilson
Conflict Theory
Summary
Research Synthesis in Criminal Justice
Narrative Reviews
Arrest
Summary
Sentencing
Parole revocation
Meta-Analysis
Gender
Race
Victim characteristics
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Chapter Summary
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CHAPTER 3: META-ANALYSIS: BENEFITS AND CRITIQUES
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Traditional Narrative Literature Reviews
Vote-counting
Meta-Analysis as an Alternative
Advantages of Meta-Analysis
Effect size
Methodological adjustments
Replication
Dynamic
Critiques of Meta-Analysis
File drawer problem
Apples-and-oranges problem
Independence of effect sizes
Chapter Summary
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CHAPTER 4: METHODS
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Research Questions
Sample of Studies
Inclusion Criteria
Selection Criteria
Dependent Variable
Independent Variables
System-wide variables
Arrest
Sentencing
Parole revocation
Moderating Variables
Study characteristics
Methodological quality
Reliability of Coding
Analytic Strategy
Effect size measure
Heterogeneity of measures
Fail-safe N statistic
Conclusions
Limitations
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CHAPTER 5: RESULTS
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Publication Characteristics
Arrest
Sample characteristics
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Effect sizes
Fail-safe N analysis
Moderating variable analysis
Sentencing
Sample characteristics
Effect sizes
Fail-safe N analysis
Moderating variable analysis
Parole Revocation
Sample characteristics
Effect sizes
Fail-safe N analysis
Moderating variable analysis
Chapter Summary
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CHAPTER 6: DISCUSSION
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Summary of Findings
Missing variables
Criminal Justice Theory Organization
Toward a General Theory of Criminal Justice
Future Research
Limitations
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REFERENCES
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APPENDIX A
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Arrest Coding Guide
Sentencing Coding Guide
Parole Revocation Coding Guide
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LIST OF TABLES
Table 5.1: Publication Characteristics
Table 5.2: Arrest Sample Characteristics
Table 5.3: Mean Effect Sizes Arrest
Table 5.4: Suspect Race Moderator Analysis
Table 5.5: Suspect Gender Moderator Analysis
Table 5.6: Suspect Ethnicity Moderator Analysis
Table 5.7: Suspect Intoxication Moderator Analysis
Table 5.8: Suspect Weapon Use Moderator Analysis
Table 5.9: Suspect Demeanor Moderator Analysis
Table 5.10: Sentencing Sample Characteristics
Table 5.11: Mean Effect Sizes Sentencing
Table 5.12: Defendant Race Moderator Analysis
Table 5.13: Defendant Gender Moderator Analysis
Table 5.14: Defendant Ethnicity Moderator Analysis
Table 5.15: Prior Record Moderator Analysis
Table 5.16: Attorney Type Moderator Analysis
Table 5.17: Mode of Conviction Moderator Analysis
Table 5.18: Parole Revocation Sample Characteristics
Table 5.19: Mean Effect Sizes Parole Revocation
Table 5.20: Offender Race Moderator Analysis
Table 5.21: Risk Moderator Analysis
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LIST OF FIGURES
Figure 2.1: Diagram of Gottfredson and Gottfredson’s Theory of Criminal Justice
Figure 2.2: Diagram of Focal Concerns Theory
Figure 2.3: Diagram of Eisenstein and Jacob’s Courtroom Workgroup Theory
Figure 2.4: Diagram of Klinger’s Ecological Theory of Police Behavior
Figure 2.5: Diagram of Black’s Theory of the Behavior of Law
Figure 2.6: Diagram of Duffee’s Theory of Criminal Justice
Figure 2.7: Diagram of Wilson’s Theory of Police Behavior
Figure 2.8: Diagram of Scheingold’s Theory of Policy Creation
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CHAPTER 1
PROBLEM STATEMENT
According to Marenin and Worrall (1998), “criminal justice is an academic discipline in
practice but not yet in theory” (p. 465). The quote by Marenin and Worrall sums up the major
problem with the field of criminal justice science research. There is a perception that criminal
justice science as a field of study is atheoretical. However, in order for any scientific field to
become a full-fledged discipline, it must create theory. The field of criminal justice is
developing toward a strong scientific discipline. In order to achieve this status and be seen as a
scientific discipline in its own right, criminal justice must continue the trend of recent
scholarship that has put a direct emphasis on examining this critical issue (see Bernard and
Engel, 2001; Crank and Bowman, 2008 and Duffee and Maguire, 2007). The goal of this
dissertation is to continue this recent trend of criminal justice theory development by performing
a comprehensive review of correlates of decision-making. Through this analysis I will seek to
determine the most appropriate way to categorize and organize the field of criminal justice
theory.
What is Criminal Justice?
The concept of criminal justice is complex. Packer (1968) argues that there are two ideal
types of criminal justice systems, the due process and crime control models. The due process
model is described as an obstacle course, where blockades are constantly put up to prevent any
innocent individual from being processed through the system and punished. Conversely, the
crime control model is described as an assembly line. Justice must be meted out with
consistency to have the impact on criminal conduct. Duffee and Allan (2007) attempt to clarify
the study of criminal justice by defining criminal justice science as “the study of governmental
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social control premised on punishment or blameworthiness” (p. 8). In contrast, Duffee and Allan
(2007) define criminology as “the study of crime and criminal behavior” (p. 8). Using these
definitions, a study that examines the factors that influence arrest would be a study of criminal
justice and a study that looks at the effective crime reduction of a policing intervention would be
a study of criminology. The critical difference between the two fields is that criminal justice
science seeks to understand the process of the criminal justice system and criminology is
concerned with the effectiveness of criminal justice interventions. Thus, criminology is the
study of crime and criminal justice is the study of discretion. Discretion is the ability to choose,
and within the context of the criminal justice process the decision of interest is whether to
process an individual through the criminal justice system (Davis, 1969).
Since discretion is the ability to make a choice, it is necessary to consider the definition
of a decision. According to Gottfredson and Gottfredson (1989), decisions have 3 components:
goals, alternatives, and information. By goals, they mean that a decision must have some
objective. For example, the decision to arrest may have a goal of taking a suspect away from the
person he or she is victimizing. The second component to a decision is alternatives, which
means that there must be multiple options. If there was only one option in a “decision,” one
could not make a choice. Instead, that person would be mandated to do some act. According to
the authors, the last component of a decision is information. Information is the data that are used
to make the best possible decision. The best decision is one in which there is little “uncertainty
about the consequences of the decision” (Gottfredson and Gottfredson, 1989, p. 3).
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Criminal Justice Dependent Variable
Criminological theories center around one dependent variable, crime. While crime may
be operationalized in a variety of ways, criminological research centers on the study of crime and
delinquency. The logical organization of criminological theory, since it has one dependent
variable, is to organize theories around primary predictive factors. Since criminal justice is a
related discipline it would appear to be appropriate to categorize and organize criminal justice
theory in a similar manner.
However, scholars do not agree that organizing criminal justice theory by key
independent variables is possible for criminal justice theory. The disagreement revolves around
the number of dependent variables that criminal justice studies. Due to the relatively broad
definition above, criminal justice covers a greater number of discrete topics than criminology.
Bernard and Engel (2001) subsequently argue that since there are so many dependent variables,
criminal justice theories should be categorized around each these dependent variables.
This viewpoint creates a problem for criminal justice research. That problem is
confusion on the number of dependent variables. Since criminal justice is the study of the
process by which formal social control is executed, criminal justice research is chiefly concerned
with discretionary application of that social control. Contrary to Bernard and Engel, it is
possible to think of all the discrete variables studied in criminal justice as different ways to
operationalize discretion. For example, Lundman and Kaufman (2003) and Ulmer, Kurlychek
and Kramer (2007) both conducted studies of criminal justice decision making and both used a
conflict theory framework. Lundman and Kaufman are studying traffic citations and Ulmer and
colleagues are studying judicial discretion in sentencing. Both studies are arguing essentially the
same ideas, yet, they operationalize the dependent variable differently. Since they are examining
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the same idea, these studies should be grouped together. Thus, there are two competing
organizational frameworks. One recommends that theories be grouped based on dependent
variable then by primary independent variables. The other argues that there is one dependent
variable, discretion, and as a result theories should be organized around primary independent
variables.
Criminal Justice Theory
According to Kuhn (1996), for a discipline to be scientific, it must have theory. More
importantly, a true scientific discipline requires a paradigm. Kuhn (1996) defines a paradigm as,
1) “an achievement [that] was sufficiently unprecedented to attract an enduring group of
adherents away from competing modes of scientific activity” and 2) “it was sufficiently openended to leave all sorts of problems for the redefined group of practitioners to resolve” (p. 10).
This means that a paradigm frames a discipline and dictates the important questions. When new
information creates a conflict with the current paradigm a process of scientific revolution occurs
(Kuhn, 1996). This state of flux is the current state of criminal justice theory. There are many
different perspectives operating at multiple levels and the field is waiting for a dominant
paradigm to emerge. It may be that criminal justice has created theories but no one theory has
supplanted the others to emerge as the paradigm of the field.
An equally important and related issue to the paradigm of criminal justice is how criminal
justice theory should be organized. However, because of the issue with dependent variables,
there appears to be a divide in how to categorize and organize criminal justice theory. There
have been two previous significant attempts to categorize, and organize the various perspectives
of criminal justice theory. First, Hagan (1989) attempted to categorize criminal justice theory
breaking down criminal justice theories into two perspectives: consensus and conflict. He
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argued that all theories that place the greatest emphasis on legal variables are consensus theories,
and those theories that place the greatest emphasis on extra-legal variables would be conflict
theories. Some scholars rejected this method as too simple.
Second, Bernard and Engel (2001) argued that criminal justice theories should be
organized by the dependent variable and then by key independent variables. They argued for
grouping theories around three broad categories of dependent variables: individual criminal
justice agents, criminal justice organizations, and overall system and its components. Under this
scheme, conflict theory could be split across all three variable types when the theory is applying
the same concepts to explaining the process of criminal justice, just on different dependent
variables. These two perspectives create a fundamental problem, should criminal justice theories
focus only on the system as a whole or should criminal justice theories be decision-specific?
If criminal justice theories are grander theoretical approaches focusing on the system as a
whole, then predictors in criminal justice theory will be consistent across the system as a whole.
Under this grand theory perspective, predictors of decision-making will be the same at each stage
because the same theoretical concepts are impacting decision makers in the same way. For
example, Gottfredson and Gottfredson (1989) argue that three factors: seriousness of the offense,
victim-offender relationship and criminal history predict the actions of decision makers across
the system. From this perspective, these three factors are the primary predictors of criminal
justice actor behavior for all criminal justice actors.
Duffee and Allan (2007) argue that criminal justice theories need not address all parts of
the system to be a theory of criminal justice. Under this perspective, criminal justice theories
may be decision-specific and factors may vary across decision points. For example, Eisenstein
and Jacob (1997) argue that dynamics of the courtroom workgroup are the key predictors of
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court-case decision making. Conversely, Wilson (1968) argues that the local political culture
and the effectiveness of the chief of police in controlling his or her officers are the key to
predicting the type of police department, which in turn influences the behavior of officers.
Under the grand theory perspective, correlates of decision-making will be consistent across the
system and various decision points within each component. If the decision-specific perspective
is correct, then correlates of decision-making will be different across the system and each
decision point.
Duality of criminal justice
Another issue to consider, as part of the conception of criminal justice decision-making
research, is the issue of the duality of criminal justice. Several scholars have commented on the
duality of the American criminal justice system, for example, Packer (see above). More recently,
Gottfredson and Gottfredson addressed this issue of the duality of criminal justice as a function
of seriousness of the offense
Gottfredson and Gottfredson (1989) argue that there are two criminal justice systems, one
for serious offenses and a second for less serious offenses. They noticed that decision-making
research indicated that criminal justice actors exhibited less variation in the exercise of their
discretion in certain situations. Low-variation discretionary situations were characterized by
serious criminal incidents, those committed by offenders with long criminal histories, and/or
those cases where the victim and offender are strangers to each other. In these cases, there was
“very little discretion exhibited at any of the major criminal justice decisions” (Gottfredson and
Gottfredson, 1989, p. 260). It is near certainty that these serious situations will be processed
through the criminal justice system, provided there is sufficient evidence.
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The second criminal justice system is one that involves the opposite type of incidents
from the first system. Cases in this second criminal justice system involve less serious crimes,
those committed by individuals with short or no criminal history, and/or crimes between those
with a closer relational distance. Gottfredson and Gottfredson (1989) argue that this second
system is where extralegal factors become more prominent because of greater amounts of
discretion afforded to the criminal justice actor. For example, in this second criminal justice
system demeanor of the suspect may play a larger role in the decision to make an arrest.
Gottfredson and Gottfredson (1989) assert that this second system is created because 1) “the
desire by some to pursue alternatives to full processing”, 2) “the inconsistency arising from a
lack of explicit guidelines”, and/or 3) “the result from the use of invidious decision criteria” (p.
261). It is in this second system that there is less certainty that a case will be processed fully
through the criminal justice system and greater variation in the exercise of discretion.
It appears that criminal justice decision making may be a function of offense seriousness.
This means that to fully examine the effect of predictors, other than offense seriousness, offense
seriousness must first be controlled. After offense seriousness is controlled, the effect of other
predictors, such as suspect characteristics, criminal justice agent characteristics, organizational
characteristics or community characteristics may explain the remaining variation in decision
making.
Meta-analysis, criminal justice and criminal justice theory
In order to evaluate these organization methods, and this idea of the duality of criminal
justice, it is necessary to take stock of existing research and evaluate the research on correlates of
decision-making. One approach is to use narrative literature reviews. While this approach has
been a good first step, a newer and potentially superior technique for research synthesis is meta-
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analysis. Meta-analysis statistically examines the effect of independent variables on the
dependent variable across studies. According to Hunt (1997), meta-analysis combines the effects
of variables across studies and creates a single measure of strength for that predictor on the
dependent variable. This technique has been used to evaluate criminological theories and other
anti-crime interventions (see Hubbard and Pratt, 2002; Makarios and Pratt, 2012; Pratt, Cullen,
Blevins, Daigle, and Madensen 2006; and Pratt and Cullen, 2000, 2005). This technique can be
applied to evaluate the issue of a grand theory approach, or a decision-specific approach.
Meta-analysis can be used to evaluate the correlates of decision-making across the
system. If a majority of the same correlates are significant predictors across the system, then the
grand theory approach would appear to be the most appropriate organization method. However,
if each decision yields a number of unique correlates, then criminal justice theories should be
organized around specific decisions. Moreover, for the decision-specific model to be the
preferred model, unique factors would also have to play a larger role than system-wide factor.
This requirement demonstrates that there is some quality about each decision that is a stronger
factor than system-wide factors.
It is likely not possible to fully explore the idea of the duality of criminal justice using
meta-analysis. None of the studies separated their samples based on the seriousness of their
cases. However, the idea may be partially supported if several conditions are met. First,
seriousness of the offense must be paramount. The duality idea proposed by Gottfredson and
Gottfredson hinges on the idea that there is low variation in the exercise of discretion for serious
incidents and higher variation in the exercise of discretion in less serious incidents. This means
that the mean effect sizes for measures of seriousness must be stronger than the mean effect sizes
for other predictors. Second, the mean effect size for seriousness measures must indicate that as
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seriousness increases, the likelihood of processing also increases. There must be a positive
relationship between the dependent variable, arrest, sentencing or parole revocation, and the
independent variable. Third, mean effect sizes of seriousness measures must be significant
across decisions. Gottfredson and Gottfredson (1989) indicated that their finding regarding the
effect of seriousness was consistent at any decision point.
Criminal Justice Knowledge Synthesis
A second issue with criminal justice research is the lack of a comprehensive synthesis of
criminal justice correlates across the system. Over thirty years ago, Gottfredson and Gottfredson
undertook an attempt to synthesize knowledge of correlates of criminal justice decision-making.
No study since has attempted to replicate their work on a similar scale. Instead, research
synthesis studies have focused on one branch of the criminal justice system or on one decision
point in the criminal justice system. Synthesis research has also at times focused too myopically
on one particular variable, e.g., race, rather than take stock of the entire field of criminal justice
decision making. When the results from these individual branch/decision points are combined a
picture begins to emerge. That picture indicates that similar correlates of decision-making have
been examined at multiple stages of criminal justice processing. For example, race, gender, and
age are constantly examined in numerous studies. Yet, often the effects of these variables are
conflicting from study to study. This conflict creates problems of interpretation in research
synthesis.
The relationship of suspect demeanor to arrest is a more specific example of research
conflict. Generally, the effect of suspect demeanor as a predictor of arrest was that disrespectful
suspects were more likely to be arrested than those that deferred to the officer’s authority (see
Smith, Visher, and Davidson, 1984; Worden, 1989; and Worden and Shepard, 1996). However,
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Klinger (1994) argued that suspects that were disrespectful were not more likely to be arrested.
Instead these supposedly disrespectful suspects were actually committing new crimes during the
interaction phase of the officer-suspect encounter. This interaction phase crime was responsible
for the arrest, not demeanor. Klinger (1994) found that demeanor was not a statistically
significant predictor when controlling for interaction-phase crime. Since Klinger’s study,
Mastrofski, Snipes, Parks and Maxwell (2000) found that police were not significantly
influenced by a suspect’s disrespect.
Meta-analysis can provide a solution to the problem of conflicting information. If the
effect size for a particular variable is significant then that variable would be seen as critical to the
understanding of that decision. For example, if measures of suspect demeanor have an overall
significant effect, then Klinger (1994) is incorrect and suspect demeanor plays a role in arrest
decisions. If demeanor does not produce a significant effect size, then Klinger is correct and
demeanor does not influence officer decision making and instead it is some other mechanism.
Meta-analysis has had limited implementation in criminal justice research, but is an analytical
technique that is expanding in popularity. The potential benefits of the technique have had even
less use in criminal justice decision-making research outside of sentencing.
Chapter Summary
There are several problems with the current state of criminal justice research. First, there
is a perception that the discipline of criminal justice is atheoretical. This problem leads to the
discipline of criminal justice being seen as a secondary or applied science. This lack of theory
development leads to disagreement as to appropriate scope of theories within the discipline,
namely, a system-wide approach versus a decision-specific approach. One goal of this
dissertation is to examine this issue and see if correlates of decision-making are similar across
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the system or if they are unique to particular decision points. If correlates are consistent across
the system, a system-wide approach would appear to be more applicable. However, if correlates
vary from decision point to decision point, then decision-specific explanations of criminal justice
behavior would be more appropriate. In other words, the goal is to determine if there is a
possibility to create a general theory criminal justice or should the field continue on its current
trajectory which focuses more on decision-specific theorizing.
Second, there is a lack of updated knowledge as to correlates of criminal justice
processing. There are a number of predictors that may influence decision making across the
system. However, past efforts at synthesis research were simple literature reviews. These
literature reviews led researchers to treat variables with mixed results as non-significant. One
way to combat this problem is through meta-analysis, which allows for synthesis of statistical
information across studies. The potential solution to both of these issues involves examination
of the correlates of criminal justice decision making using meta-analysis to make more definitive
rejections or acceptance of various influences on decision making.
A Look Ahead: The Plan for this Dissertation
The primary goal of this dissertation is to continue what Gottfredson and Gottfredson
began over thirty years ago. They examined the correlates of criminal justice decision-making
across the system. This dissertation will also examine correlates of decision-making across the
system. Specifically I examine correlates of arrest, sentencing, and parole revocation. Each of
these decisions was chosen because 1) each of these decision points are completely within a
particular branch of the criminal justice system and, 2) each decision point represents one of the
main functions of that branch of the criminal justice system. Unlike Gottfredson and
Gottfredson, this dissertation will use meta-analysis to fully examine the effects of various
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predictors of arrest, sentencing, and parole revocation. The purpose of this examination is to
attempt to determine the best way to organize criminal justice theory and to determine what
correlates should be included in any theory of criminal justice.
In the chapters ahead, Chapter 2 presents a review of current criminal justice theories and
current knowledge of correlates of decision-making based, primarily on the narrative literature
reviews. Chapter 3 presents a critique of narrative literature review methods and the benefits of
meta-analysis. Chapter 4 outlines the research questions, sample generation procedures, and
statistical procedures of the analyses of this dissertation. Chapter 5 discusses the results of the
meta-analysis. Finally, Chapter 6 discusses conclusions and provides a general summary of this
dissertation.
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CHAPTER 2
LITERATURE REVIEW
While criminal justice theory is still developing and organizing, there are distinct and
established theoretical perspectives. These theoretical perspectives vary on two key criteria.
First, variation occurs because of the theory’s primary predictive variables. Many scholars have
suggested that certain factors may influence the behavior of criminal justice agents. Each
scholar that has theorized a perspective has stipulated that there are certain key predictors. For
example community theories emphasize certain aspects of the macro-social community. The
theoretical categories are: situational theories, actor theories, workgroup/organizational theories,
community theories, and conflict theories. The second point of variation is the scope of the
theory, total-system versus decision-specific theories. Total-system theories seek to predict the
use of discretion across the criminal justice system, while decision-specific theories are directed
at one particular decision.
First, there is a discussion of each theoretical perspective in criminal justice. Following
each theory explanation, there is a discussion of the current evidence for each theory. Second,
this chapter explores the current state of knowledge regarding each decision point examined in
this dissertation. There is a discussion of arrest, sentencing, and parole revocation. The section
reviewing decision making is divided into two subsections. The first subsection contains
information based on narrative literature reviews. The second subsection contains information
based on meta-analyses.
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Situational Theories
Situational theories are those theories of criminal justice where the main predictive
elements are aspects of the context in which discretion is exercised. Situational characteristics
are often studied in decision-making research and as such it is natural that theories emerge
emphasizing these factors. These theories promote the idea that the criminal justice system does
not operate in a vacuum and aspects of the criminal event can and will influence, at least in part,
the decisions of the criminal justice actors and the system. Two current situational theories are
Gottfredson and Gottfredson’s theory of criminal justice and focal concerns theory.
Gottfredson and Gottfredson
Gottfredson and Gottfredson’s theory states that situational factors about the criminal
event dictate the decisions made by criminal justice actors (see the discussion in Chapter 1).
These situational factors influence the amount of the exercise discretion for a particular decision,
which ultimately leads to the particular processing decision. Through their analysis, they find
that these factors continually inform all decision points in the criminal justice system, from the
decision of the victim to contact the police through the parole release decision. Numerous
empirical assessments since Gottfredson and Gottfredson discussed their theory have confirmed
the impact of seriousness of the offense, prior record, and victim-offender relationship. See the
discussion below on narrative reviews of decision making for more information.
Figure 2.1: Diagram of Gottfredson and Gottfredson’s theory of criminal justice
Prior record
Seriousness of the offense
Discretion
Victim-offender relationship
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Processing decision
Focal concerns theory
Focal concerns theory is a theory of the exercise of discretion. The theory stipulates that
criminal justice decision making is based on three focal concerns (Steffensmeier, Kramer, and
Ulmer, 1998). According to Steffensmeier et al. (1998), these focal concerns are
blameworthiness of the offender, protection of the community, and practical constraints and
consequences. Blameworthiness deals with the culpability of the offender in the criminal act and
the degree of injury. Protection of the community pertains to the potential for future harm from a
particular offender, and an element of deterrence of other offenders. Finally, Steffensmeier and
his colleagues explain that practical constraints and consequences are other miscellaneous
considerations like the offender’s ability to survive the punishment, and organizational issues
such as the availability of resources and space. These focal concerns shape the exercise of a
criminal justice actor’s discretion and determine their response in a particular criminal event.
Figure 2.2: Diagram of focal concerns theory
Blameworthiness
Protection of the community
Sentence
Practical constraints
Empirical assessments of focal concerns theory have produced findings consistent with
the theory. In addition to Steffensmeier’s and his colleague’s initial assessment of focal
concerns theory, many other studies have found results consistent with the theory (Johnson,
2003; Johnson, 2005; Johnson 2006; Kramer and Ulmer, 2002; Steffensmeier and Britt, 2001;
Ulmer and Johnson, 2004; Ulmer, Kurlychek, and Kramer, 2007). These studies have
consistently shown that male individuals, convicted of more serious offenses, with longer
criminal histories are more likely to not only be incarcerated but also receive longer prison
15
sentences. These offenders are considered more blameworthy and able to survive the prison
environment. Additionally, these studies found that those that committed more serious violent
crime were more likely to receive longer prison terms.
Actor Theories
The critical component that sets actor theories of criminal justice apart from other
theories is that actor theories place a greater emphasis on the individual actors in the criminal
justice system. While the individual is part of the situation, situational theories place a greater
emphasis on the totality of circumstances. Actor theories place a greater emphasis on one aspect
of the situation, the individual criminal justice actor. While there is not a specific theory of
criminal justice based on actor characteristics, it is a studied area of decision-making research
and is a plausible explanation of criminal justice behavior. If criminal justice actor
characteristics emerge as a significant correlate of decision-making, then a theory should be
developed focusing on this particular set of predictors.
Workgroup/Organizational Theories
Criminal justice actors act within the context of a situation, but there are also collective
aspects of their working environment and organization that influence their behavior and the
exercise of their discretion. This idea is the central theme of workgroup and organizational
theories. Workgroup theories argue that the collective norms that develop between coworkers
within an organization are the key factors in the exercise of discretion. Two examples of these
types of theories are Eisenstein and Jacob’s courtroom workgroup theory and Klinger’s
ecological theory of police behavior.
16
Courtroom workgroup
Eisenstein and Jacob (1977) postulate that it is the courtroom workgroup that heavily
influences discretionary decisions made in the criminal justice system and the criminal courts
specifically. By workgroup, Eisenstein and Jacob are referring to the main three courtroom
actors, the judge, prosecutor, and defense attorney. In their original conceptualization of their
workgroup theory, they argue that the political environment influences both the sponsoring
organization characteristics and the workgroup characteristics. These sponsoring organization
characteristics and workgroup characteristics then directly influence the outcomes of a case. The
political environment also directly impacts the outcomes of a case, and the sponsoring
organizations also impact the workgroup characteristics.
Each of the above variables includes multiple components. The political environment
incorporates the larger contextual environment. The police are seen as gatekeepers of the
system; if they do not make arrests then the courts have no cases to process. Therefore,
Eisenstein and Jacob (1977) argue that police are part of the political environment and context.
Specifically, they argue that police influence cases via the quality of evidence they collect. The
next component of the political context is the legislature and appellate courts. Each of these
entities creates rules that the workgroup must follow; the legislature via creation of the court
system and establishment of the law and appellate courts by creating procedural rules that the
workgroup must follow. According to Eisenstein and Jacob (1977), the next component is prison
authorities. This entity influences case processing because they regulate sentencing, judges may
only sentence defendants to the available space. The final influential member of the
environment is the media. The authors explain that the sponsoring agencies and the media have
17
a reciprocal relationship. The sponsoring agencies want to be portrayed positively so they use
the media for that end and the media uses the sponsoring agencies for news content.
Workgroups are complex entities. There is more to the workgroup than simply who is a
member. Many factors influence workgroups. Eisenstein and Jacob (1977) explain that the
degree of workgroup stability and the degree of familiarity that members have with one another
influence the workgroup. They argue that workgroups that are stable and familiar with one
another are able to process cases more informally and rely less on the adversarial process and
more on a negotiation style of case disposition. Additionally, each workgroup works toward
certain goals. The authors explain that goals are shaped by two factors. The first is the origin of
the goal. Origin refers to where the goal originates, either external or internal to the workgroup.
For example, a goal that results from pressures from one of the entities of the political
environment is an external goal. The second goal-shaping component is the function of the goal.
The authors explain that a goal may be expressive or symbolic in nature, or a goal may be
instrumental, or focused on improving efficiency. Each combination of these two categories
produces a different goal that in turn influences the workgroup.
The final independent variable in the workgroup theory is the supporting agency
characteristics. The supporting agencies are those agencies that have control over the personnel
of the workgroup. Eisenstein and Jacob (1977) argue that since these agencies have control over
the workgroup personnel, recruitment and assignment procedures impact the workgroup because
of their effects on stability and familiarity. Assignment procedures that constantly change the
workgroup create instability in the workgroup and the workgroup begins to rely more on the
adversarial process versus negotiations. Eisenstein and Jacob (1977) also argue that the amount
of control a sponsoring agency has over it’s members influences the workgroup. For example, if
18
a prosecutor’s office creates a policy that restricts plea-bargaining in felony cases, then
prosecutors must turn toward a more adversarial case processing because their ability to
negotiate has been reduced by the supporting agency
Figure 2.3: Diagram of Eisenstein and Jacob’s courtroom workgroup theory
Sponsoring Organization Characteristics
Informal Norms
Political Environment
Outcomes
Workgroup Characteristics
Police workgroup
Klinger provides another workgroup theory that is aimed at explaining the exercise of
police discretion. Klinger (1997) argues that the amount of deviance in a district leads to an
overall district workload, but also impacts officers’ perceptions of deviance in that area and the
seriousness of the immediate offense. As the district workload also increases, resource
constraints increase along with perceptions of deviance among officers. He argues that an
officer’s understanding of district deviance impacts what crimes they perceive as normal, the
deservedness of victims and levels of police cynicism. As perceptions of deviance increase,
more serious crimes become “normal”, victims become less deserving, and police officers
become more cynical. According to Klinger (1997), resource constraints, police cynicism,
deservedness of victims, and perceptions of normal crime all impact workgroup rules about
discretion. These workgroup rules about police discretion then directly impact the exercise of
discretion. Finally, the seriousness of the immediate offense also directly impacts police action.
19
Figure 2.4: Diagram of Klinger’s ecological theory of police behavior
Level of district deviance
Seriousness of the immediate offense
Normal crimes
Officer’s understanding
of district deviance
Deserving victims
Group rules
Vigor
Police cynicism
District workload
Resource constraints
Recently scholars have begun to test Klinger’s theory. Sobol (2010) attempted a partial
test of Klinger’s theory examining measures of cynicism and district workload on police vigor.
He found that neither of these two variables were significantly associated with vigor in the full
model. Workload was significantly related to vigor at the bivariate level but the relationship was
rendered non-significant in the full model, which lead Sobol (2010) to conclude that the
relationship was spurious. While not a complete test, Philips and Sobol (2011) were able to
support the workload hypothesis of Klinger’s theory. They found that officers that worked in
districts with higher workloads were less likely to view force as unnecessary. None of the above
studies were able to completely test Klinger’s theory. Both studies were unable to tap into
victim deservedness. Another limitation of Philips and Sobol’s (2011) study is that they used
vignettes and were not able to examine police cynicism.
20
Community Theories
Community theories argue that the larger community is the driving factor in criminal
justice decision making. In other words, the collective norms and attitudes of the community
dictate the type of criminal justice organizations and thus, the actions of the individual criminal
justice actors. Three examples of these types of theories are Black’s theory of the behavior of
law, Duffee’s community theory of criminal justice, and Wilson’s community theory of police.
Black (1976) argues that the law, which is defined as “governmental social control” (p.2) and
can be interpreted as the discretionary action of a criminal justice system, is the product of five
macro-social constructs: stratification, morphology, culture, organization, and social-control.
Duffee (1990) argues that criminal justice action is the result of a type of community. That
community is the product of two characteristics vertical relations and horizontal articulation.
Each of these constructs leads to a particular type of community, which leads to a particular type
of criminal justice organization. These organizations will have differing approaches to criminal
justice. Finally, Wilson (1968) argues that the local political culture of the community is critical
in determining the type of police agency in the community. The critical component in all three
of these theories is the role of the community in shaping the type of criminal justice exercised
within that community.
Black
Black argues that five predictor variables: stratification, morphology, culture,
organization, and social control, influence the behavior of law. According to Black (1976),
stratification is a measure of the wealth of a society, and law varies directly with stratification.
Black (1976) also states that stratification is “the vertical distance between the people of a social
setting” (p. 13). In other words, stratification refers to a measure of social status. Morphology is
21
a person’s relational distance to another individual; it is a person’s level of interconnectedness.
He explains that if stratification is the vertical distance of society, then morphology is the
horizontal distance of society. This concept is similar to collective efficacy. According to
Sampson, Raudenbush and Earls (1997), collective efficacy is a community’s level of
integration. When communities have a greater degree of interconnection they are able to police
themselves and have less reliance on formal social control. Black (1976) explains that the
relationship between law and morphology is curvilinear.
Black’s final three variables are culture, organization, social control. According to Black
(1976), “culture is the symbolic aspect of social life, including expressions of what is true, good,
and beautiful” (p. 61). Culture incorporates a society’s level of education, religion, and
technology. Black (1976) states that societies with more culture will have more law. The fourth
variable is organization. Organization is a society’s ability to use collective action. He argues
that societies with greater amounts of organization will have greater amounts of law. The final
variable is social control. Black (1976) explains that social control is society’s informal social
control. Communities that have greater amounts of social control will have less law.
Figure 2.5: Diagram of Black’s theory of the behavior of law
Stratification
Morphology
Culture
Law
Organization
Social Control
Empirical assessments of Black’s theory have been partially supportive. Gottfredson and
Hindelang (1979) and Braithwaite and Biles (1980) tested the theory and found that some of
22
Black’s constructs did not hold up empirically. Gottfredson and Hindelang (1979) found support
for organization; they found that businesses were more likely to report their victimization to the
police. There was also support for culture, measured as education, but the effect of culture was
small. The finding for the culture construct was not as large as Black indicated it should have
been. Braithwaite and Biles (1980) also found that there was limited evidence in support of
Black’s theory.
More recent examinations of Black’s theory have found similar results to previous
examinations. Kuo, Longmire, Cuvelier, and Chang (2010) found that measures of culture and
organization significantly impacted prosecutorial decision making in Taiwan in the directions
predicted by Black. Clay-Warner and McMahon-Howard (2009) found that one measure of
stratification, household income, significantly predicted rape reporting. Additionally, they
found that as relational distance increased odds of reporting increased. There were no significant
results for the other constructs: organization, culture, or social control.
Duffee
Duffee (1980) argues that two factors, a community’s vertical relations and a
community’s horizontal articulation, shape the community’s criminal justice system. Vertical
relations refer to a community’s reliance on other non-local organizations. Communities that
have a high level of vertical relations are reliant on private or the state and Federal Government
to provide goods and services. Duffee (1980) describes horizontal articulation in similar terms to
Black’s concept of morphology; it is the degree of cohesiveness of the community.
Communities with strong horizontal articulation “interface and cooperate with each other”
(Duffee, 1980, p. 153). Populations that live in communities with high levels of horizontal
articulation are homogeneous and share similar norms and values.
23
Duffee (1980) describes each of the four types of communities based on the level amount
of vertical relations and horizontal articulation. The first community, disorganized, has both low
vertical relations and low horizontal articulation. Norms in these communities are not well
defined. Duffee (1980) explains that “there will be no group boundaries against which to define
deviant action, and on the other hand there will be no processes by which groups inculcate
mutual standards in individuals” (p. 155). Attempts at formal control have little legitimacy.
When criminal justice action does occur it is limited to major issues and crises. Criminal justice
agents are seen as outsiders and any criminal justice action is seen as coercive.
The second type of community, solidary, has low vertical relations and high horizontal
articulation. Duffee (1980) argues that the norms of this community are so well established that
there is little need for formal sanctions. He states that enforcement is often based on peer
pressure. This community is self-contained and is able to control actions informally rather than
formally. Formal sanctions that take place will need to conform to local goals and norms.
The third type of community has high vertical relations but low horizontal articulation.
This community is described as a fragmented community (Duffee, 1980). According to Duffee,
(1980) this community uses organizations that focus on special individual clients “without regard
for the community” (p. 157). This community type has unclear norms. They also have a high
reliance on external entities. The combination of these two situations creates an environment
where multiple organizations have very specific functions. These specifically focused
organizations often have turf wars. The focus of these agencies tends to be client centered. In
these communities criminal justice is concerned with “reduction of future crime[…]rather than a
concern for maintaining an existing normative pattern” (Duffee, 1980, p. 157).
24
The final community has high vertical relations and high horizontal articulation.
According to Duffee (1980), this community is interdependent; it wants to rely on outsiders to
provide services, but at the same time it is interconnected to be self-sufficient. This community
is somewhat odd and not likely to exist. Like the solidary community type they can police
themselves because they have good integration amongst members, but yet they rely on an outside
authority due to the high vertical relations. Duffee explains that “administration of formal
organizations involved in social control would seem to be a difficult activity here” (Duffee,
1980, p. 157). There is a reliance on complex outside funded entities, but community members
will likely be dissatisfied with the services provided when services are not directed toward the
community as a whole.
Figure 2.6: Diagram of Duffee’s theory of criminal justice
Vertical relations
Community type
Execution of criminal justice
Horizontal articulation
While not explicitly testing Duffee’s theory, Liederbach and Travis (2008) found that
police agency style, a proxy for criminal justice system type, was dependent upon community
size. Specifically, they found that larger communities tended toward legalistic type department.
Smaller communities tended toward order maintenance policing. Legalistic departments tend
toward the law enforcement function and rely on the formal sanction to deal with problems
(Wilson, 1968). As the community grows larger it cannot maintain that high level of horizontal
articulation and begins to rely on outsiders for criminal justice services. They are simply too big
to maintain close interpersonal relationships within the community as a whole. Smaller
25
communities can move away from formal law enforcement function because they may be more
likely to have higher levels of horizontal articulation and thus, can resolve matters informally.
Wilson
Criminal justice systems and specifically policing systems in a community may be
influenced by the local political culture of the community. Wilson (1968) argues that local
political culture plays an important role in the creation of a certain type of police department.
Political culture has little direct impact on the actual decision making of police officers, but it
does impact organizational and policy issues, and choosing a chief. Because the chief plays an
important role in controlling officers’ behavior via policy, the community gains control over
officers through the local political culture. Wilson (1968) states that city managers and mayors
were concerned about illegal business and those political leaders placed pressure on the chief to
intervene, and the chief emphasized illegal business issues to his officers.
The local political culture and chief led to the creation of a certain type of police
department. According to Wilson (1968), there are three types of departments: legalistic,
watchmen, and service. Each one of the departments emphasizes a different function of police.
Legalistic departments focus on exercise of formal authority. They emphasize the law
enforcement function. Service departments emphasize the service function. Officers in these
departments tend to resolve problems informally. Finally, watchmen departments emphasize the
order maintenance function. These officers use a combination of formal and informal sanctions
to execute the order maintenance function. The type of department then influences how officers
interact with citizens.
Wilson (1968) explains that there are four types of discretionary situations and responses
are based on whether or not the police have frequent or infrequent contact with citizens and
26
whether or not the situation is formal or informal. Liederbach and Travis (2008), argue that
frequent, formal intervention is common in legalistic departments. These police departments
stress law enforcement and these officers issue many citations and make more arrests. Service
departments also intervene regularly but the solutions and issues tend to be less formal. The
emphasis on informality is consistent with officers in service agencies. Finally, officers in
watchmen agencies do not intervene often with citizens, and when interventions do occur they
tend to be a mix of formal and informal. Thus, officers in these agencies emphasize the order
maintenance function.
Figure 2.7: Diagram of Wilson’s theory of police behavior
Community
Local political culture
Dept. type
Primary police
function
Chief
Wilson’s theory has been assessed, at least in part, several times. The results have been
mixed. Langworthy (1985) assessed the relationship between arrest rates and government type,
i.e., what Wilson identified as a good government run by a city manager as opposed to a more
traditional mayoral system of government. He found that it was indeed the case that arrest rates
for marginal crimes varied based on the government type. While not a perfect test of Wilson’s
theory, Langworthy’s test did partially support Wilson’s conception of police behavior. Crank
(1990) also found support for the government-arrest hypothesis. He found that rural
municipalities were significantly more likely to have higher arrest rates for trespassing,
disorderly conduct, motor vehicle theft, and cannabis control. In urban environments, police in
areas that were run by city managers were significantly more likely to make arrests for motor
27
vehicle theft, but not for the other crime categories. Additionally, Smith (1984) found that
correlates of arrest varied depending on the police style of the department. He concluded that
any explanation of legal force would be incomplete without a discussion of police organizational
characteristics.
While some scholars have found limited support for Wilson’s theory, other scholars have
not found the same results when testing Wilson’s theory. Several studies have failed to find a
relationship between political culture and police organization (Hassell, Zhao, and Maguire, 2003;
Liederbach and Travis, 2008; Zhao and Hassell, 2005; and Zhao, Ren, and Lovrich, 2010). Zhao
and Hassell (2005) went on to claim that this consistent lack of a finding of a correlation between
political structure and police style was evidence of falsification of Wilson’s theory. However,
Liederbach and Travis (2008) were not as quick to signal the end of Wilson’s theory. Instead,
they argued that no assessment had fully replicated Wilson’s original test and that each test,
including theirs, was subject to issues of model misspecification. The primary model
misspecification is that no study to date had adequately examined the correct combination of
communities. Often communities were fairly constant in assessments of Wilson’s theory.
In sum, three scholars created a theory that places an emphasis on the impact of the
community. Black outlines the social dynamics of communities that lead to the amount of
formal criminal justice processing. Duffee argued that qualities of a community, vertical
relations and horizontal articulation, led to the creation of a certain type of criminal justice
system, which dictated how offenders are processed. Finally, Wilson stated that local political
culture has an impact on the type of police department and the chief; who sets policy that dictates
how officers handle situations.
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Conflict Theory
The final theoretical perspective is the conflict perspective. Conflict theory argues that
macro-social group dynamics influence the behavior of criminal justice actors. According to
Hagan (1989), conflict theory argues that extra-legal factors like race or gender will play a larger
role in criminal justice decision making. Scheingold’s (1984) conception of conflict theory
argues that increases in victimization, both direct and vicarious victimization via media, increase
fear, which leads to increased punitive decision making. Under Scheingold’s (1984) model
extra-legal factors play a large role in vicarious victimization; vicarious victimization occurs due
to increased media attention, which is the result of increased levels of crime. Additionally,
Scheingold (1984) argues that punitive policy responses create yet more increased media
attention on crime. Thus, culturally driven fear creates a picture of a typical offender which
leads to punitive punishments and punitive decision making toward that group.
Figure 2.8: Diagram of Scheingold’s theory of policy creation
Increased crime  victimization
non-punitive response
non-punitive policy
FEAR
Increased media attention
vicarious victimization
punitive response
punitive policy
More recently, conflict theory has emerged as a form of the minority group threat
hypothesis/perspective. Under this perspective, as certain minority groups increase in size they
become more threatening and laws are created to keep that minority population in check (Kane,
2002). Kane (2002) argues that as threatening groups increase in size police are deployed to
those areas and use stricter enforcement practices. While he specifically applies the minority
29
group threat hypothesis to police decision making, the same idea can be applied to the other
branches of the criminal justice system.
According to Hagan (1989), conflict theory is examined when scholars look at extra-legal
explanations of criminal justice outcomes. Many scholars have examined the impact of these
extra-legal variables such as age, race, ethnicity and gender on criminal justice decision making.
Empirical assessments of these relationships have been mixed (see below).
Summary
There are a variety of criminal justice theory perspectives. These theories are broken
down into 5 classifications based on the primary predictive variables. These five theory
categories are: situational, actor, organizational/workgroup, community and conflict
perspectives. Gottfredson and Gottfredson and Steffensmeier and colleagues created situational
theories that stress the importance of the context in which the criminal event occurs in order to
explain why individuals are processed through the system. Eisenstein and Jacob and Klinger
both created theories that emphasize the role of the workgroup on actor decision making. Black,
Duffee, and Wilson all created theories that emphasize the role of community as a predictive
factor of the criminal justice process. Black emphasized the dynamics of societal interaction,
and Duffee emphasized the two elements of the community, vertical relations and horizontal
articulation. Wilson argued that local political culture influence police departments and through
departments the actions of officers. Lastly, several conflict perspectives have emerged each
emphasizing the role of societal conflict on criminal justice decision making.
Some perspectives apply a system-wide approach. For example, Gottfredson and
Gottfredson, Duffee, and Black all created theories to explain the process of criminal justice
generally. Other scholars created theories that focus on a particular branch of the criminal justice
30
system, for example, Steffensmeier and colleagues, Eisenstein and Jacob, Klinger, and Wilson.
Some of the decision-specific theories can be applied to other components of the criminal justice
system. Focal concerns theory was created as a way to explain judicial discretion. However, the
concepts of blameworthiness, protection of the community, and other practical concerns are
applicable to other criminal justice decision points and decision makers. For example, an officer
making an arrest could very easily be deciding who to arrest based on all three of the factors
discussed in focal concerns theory. Additionally, when some decision-specific theories are
considered together the key components appear to be applied at multiple stages of the criminal
justice system. For example, both workgroup theories are decision-specific but examining the
theories in concert shows how the concept of a workgroup can apply across the system. The
workgroup may change slightly, but the concept is consistent across the system.
Research Synthesis in Criminal Justice
Now that each of the theoretical perspectives has been discussed, it is important to take
stock of the current empirical knowledge on criminal justice decision making. The research
synthesis takes two primary forms, narrative literature reviews and meta-analysis.1 Criminal
justice has traditionally used narrative reviews to assess correlates of decision-making (see
Gottfredson and Gottfredson, 1989; National Research Council, 2004, 2008; Riksheim and
Chermak, 1993; Sherman, 1980; and Spohn, 2000). With the exception of Gottfredson and
Gottfredson, each of the aforementioned reviews focuses on one decision point and at times on
the effects of one particular variable (see Spohn, 2000).2 The use of meta-analysis has been
much more limited. Meta-analysis has been primarily used to examine correlates of sentencing,
but recently meta-analysis was used to examine the effect of suspect race on arrest (see Daly and
1
The merits and difficulties of each form of synthesis are discussed in detail in the next chapter.
Since Gottfredson and Gottfredson (1989) has been discussed in detail above there is no need to include another
discussion of their work.
2
31
Bordt, 1995; Kochel et al., 2011; Mitchell, 2005; Pratt, 1998; and Whatley, 1996). Each of these
meta-analyses, discussed in detail below, focuses both on only one decision point and on one
particular relationship.
Correlates of decision-making have been a long-standing research interest of criminal
justice science for some time. The vast majority of synthesis literature has been decisionspecific; emphasis has been given to one particular decision point. The discussion that follows
will first discuss narrative literature reviews. Finally, this section concludes with a discussion of
the current meta-analytic studies on criminal justice decision making.
Narrative Reviews
Arrest
Several studies have summarized the correlates of arrest. The first study to
systematically evaluate the correlates of arrest was Sherman (1980). Sherman’s study was
followed by Riksheim and Chermak (1993). Finally, the National Research Council (2004)
reviewed the correlates of arrest as part of a larger examination of all aspects of policing
research. Each of these studies is a narrative review and each will be reviewed in detail below.
Sherman
Sherman’s (1980) study was the first narrative review of the correlates of police officer
decision making.
Sherman (1980) separates explanations of officer behavior into five
classifications: individual, situations, organizational, community and legal. With regard to
individual explanations, Sherman (1980) found that less experienced officers, male officers,
black officers, better educated officers, officers with greater job satisfaction, and officers that had
negative views of black individuals were more likely to make arrests. Situational factors that
increased the odds of arrest included: when officers proactively enter into a police-citizen
32
encounter, when more officers are present at the police-citizen encounter, when suspects are
disrespectful, when the victim does not know the offender, when suspects are younger, when
suspects are male, and when suspects are members of the lower class. Additionally, when the
complainant prefers arrest, and when the complainant is respectful, an officer is more likely to
make an arrest. He also notes that legal variables play a significant role in predicting arrest.
Offense seriousness, and the amount of evidence increase the odds of arrest.
There was a large amount of research on individual and situational effects on arrest.
Sherman (1980) states that there has been little examination of the organizational effects on
arrest behavior. Finally, Community variables have also been neglected in terms of examination
regarding their impact on arrest behavior.
Riksheim and Chermak
Riksheim and Chermak (1993) separate arrest predictors into four categories: individuallevel, situational-level, organizational-level, and community-level. They depart from Sherman
slightly by collapsing legal-based explanations into situational-level variables. They organize
their discussion of correlates as pre- and post-1980.
The increased amount of research changed the impact of individual factors. Riksheim
and Chermak (1993) found that an officer’s education level had a mixed impact. They found
that some research showed that an individual officer’s education had no impact on arrest, but
some departments with better-educated officers had fewer arrests. They cited other research,
which reported that the impact of education was conditional on situational factors. Officer
experience as a predictor of arrest also became more muddled. Some studies found evidence that
was consistent with Sherman, and others found that there was no effect of officer experience on
arrest. Post-1980 research appears to contradict earlier findings with regard to the effect of
33
officer race, gender and attitudes. Riksheim and Chermak (1993) found that these variables had
no effect on arrest in studies conducted post-1980. However, this finding may be a
methodological artifact due to the majority of pre-1980 studies focusing on bivariate
relationships and post-1980 there was an increase in the use of multivariate statistics.
Situational characteristics are likely the most commonly studied correlate of officer
decision making and there is little consistency in findings from before 1980 to those findings
from studies after 1980. Riksheim and Chermak (1993) found that the research was consistent
with respect to the effect of lower class individuals; they were more likely to be arrested. They
found that results were mixed on the impact of victim initiated police-citizen encounters in terms
of arrest. Some studies found police were less likely to arrest when a victim calls the police in
domestic disturbance calls; other studies found no effect. Research conducted in the 1980s
indicates many mixed findings. Gender, race, and age have had mixed findings. A largely equal
number of studies found that males were more likely to be arrested while other studies found no
effect. Race was one of the most commonly studied variables in arrest research, yet the effect of
race was inconclusive. A large number of studies found no effect of race and only a handful
found that minorities were more likely to be arrested, and one study found that whites were more
likely to be arrested. According to Riksheim and Chermak (1993), age has generally been a nonsignificant predictor of arrest. But, some studies have found that age played a role with certain
groups such as females, and adult misdemeanants.
Other situational variables have shown mixed effects. The overwhelming majority of
research indicates that complainant preference influences arrest, but this factor had no effect in
some situations such as department type. Overall, research cited by Riksheim and Chermak
(1993) demonstrates that disrespectful suspects are more likely to be arrested. Other research
34
has indicated that suspect demeanor has no effect in certain situations. Victim and offender
relationship has also produced mixed findings. In many studies cited by Riksheim and Chermak
(1993) when the victim and offender do not know each other the suspect is more likely to be
arrested. Studies of police-citizen encounters involving alleged domestic violence found that
victim-offender relationship did not have a significant effect. This finding may be the result of
changing societal attitudes or department policies. The location of the incident has shown to
have mixed results as well. Some research indicated that there was no difference between
incidents that were in public areas and private areas. Other research showed that males were
arrested more often in public locations than females. Police were more likely to arrest for more
serious incidents. This effect held even after creating more detailed scales than felony vs.
misdemeanor. However, some research cited by Riksheim and Chermak (1993) found that the
presence of an injury did not significantly increase the likelihood of arrest. A new variable,
examining the influence of police supervisors, had mixed results. For some department types
this increased arrest, while for others it decreased arrest odds or had no effect.
Few organizational-level variables have shown an effect on arrest behavior. The other
issue with organizational-level variables is that they have not been extensively studied and some
of the measures are crude at best. Research on department deployment strategies has found
mixed results. According to research cited by Riksheim and Chermak (1993), one study found
no effect of deployment strategy on arrest. Another study found that deploying officers to
smaller assignment areas was associated with higher odds of arrest in areas with high violent
crime. Also, target specific deployments, and more aggressive police tactics appear to increase
arrest rates. The number and length of shift durations do not appear to influence arrest
productivity. Research on department type has produced mixed results; some research has found
35
that certain department types increase arrest. Other studies have found no effect of department
type on arrest. Finally, the effect of department size is also unclear; an equal number of studies
have found that officers in larger departments were less likely to arrest as studies that have found
no effect of department size on arrest.
Like organizational-level variables, research on community-level variables has been
limited. There has been some study of community-level economic variables; research cited by
Riksheim and Chermak (1993) indicates that police have higher arrest rates in poorer
communities. Many different measures of poverty appear to indicate the same findings. City
manager style government appears to increase arrests rates for specific crimes, which include:
property crime, DWI, trespassing, disorderly conduct, and motor vehicle crimes. Research cited
by Riksheim and Chermak (1993) was inconclusive on percent non-white. Some research found
that this variable was correlated with increased arrest rates. While other research showed that
when this variable was measured as percent Hispanic the results varied based on the location of
the police department, urban versus rural. Finally, Riksheim and Chermak (1993) cited
contradictory research regarding the effect of neighborhood crime rates and victimization rates
on arrest.
National Research Council
The National Research Council (2004) evaluated the majority of the currently available
information regarding correlates of police behavior. They argued that police action is most
heavily influenced by legal variables, particularly seriousness of the offense, evidence of
wrongdoing, and willingness to file a complaint. The committee also stated that there is
consistent evidence to indicate that high crime areas or disadvantaged areas will increase the
odds of arrest. Finally, the committee found that there is some evidence that police agencies are
36
influenced by court rulings but the committee also argues that there is evidence that agencies try
and circumvent rulings as well. These three factors were the only influences on police behavior
that showed consistent effects across studies using the narrative review approach. For all other
explanations of police behavior there was either mixed findings or not enough research to draw a
conclusion with confidence.
Summary
Taken together these narrative reviews begin to create a picture that emphasizes
situational-level factors such as suspect demeanor and legal variables. What is consistent across
these literature reviews is inconsistency. At some points in time scholars were willing to assert
with more conviction that certain factors are significant and as time progresses confusion appears
to increase. In this case, more data seems to be cumbersome for traditional narrative reviews.
Another issue with these narrative reviews is that they are reporting on multiple studies that use
the same data over and over. This fact may be contributing to the confusion rather than helping
to eliminate it. Simply because even within the same data set, findings appear to disagree with
each other, for example, research cited by Riksheim and Chermak (1993) showed that some
research from the Police Services Study found that race was a significant factor in arrest
behavior while other research did not find an effect for race.
Sentencing
Spohn (2000) reviewed the correlates of sentencing, but her review focuses on how race
influences sentencing. All other correlates are always put in the context of the effect of race on
sentencing. Meta-analytic reviews of sentencing have discussed other issues with sentencing.
They are discussed after the narrative literature review section.
37
Spohn (2000) reviews sentencing literature to examine the impact of race and ethnicity
on sentencing. Of the studies reviewed, Spohn (2000) found that 43.2 percent of state-level
studies found an effect for black vs. white defendants and 27.6 percent of state-level studies
found an effect for Hispanic vs. white defendants. Additionally, she found that 55.5 percent of
studies found a significant effect of race on the in/out decision and 23.1 percent of studies found
an effect of race on the length of sentencing decision. Finally, 41.7 percent of state-level studies
found an impact for ethnicity on the in/out decision and one study found an effect of ethnicity on
the length of the sentence. It is also important to note that many studies that did not find race
and/or ethnicity had main effects did find interaction effects of race and/or ethnicity combining
with other factors.
Spohn (2000) also reviewed the impact of race and ethnicity on federal sentencing
studies. Research cited by Spohn (2000) showed that 68.2 percent of studies of federal-level
sentencing data found an effect of race. Also, 47.6 percent of studies of federal-level sentencing
data found an effect of ethnicity. Three of seven studies that examined the in/out decision found
significant effects for race and three of seven studies found a significant effect for ethnicity.
Finally, six of nine studies on race found significant effect of race on the length of sentence and
two of eight studies on ethnicity found a significant effect of ethnicity on the length of sentence.
According to Spohn (2000), based on the above picture, race appears to play a role in sentencing.
Parole revocation
The status of review research on parole revocation is very limited. Summary research on
parole often barely mentions the topic of parole revocation and instead focuses more on racial
disparities and parole. For example, Latessa and Smith (2007), Travis and Lawrence (2002) and
the National Research Council’s (2008) discussions of parole fail to explain the correlates of
38
parole revocation. Caplan (2007) reviewed literature surrounding parole but focused entirely on
release and not revocation. Instead each of these research endeavors has focused on other issues
of parole such as release or the elimination of parole as a release mechanism. Additionally,
Gottfredson and Gottfredson (1989) offer limited information on the correlates of revocation.
Research cited by Gottfredson and Gottfredson (1989) states that prison performance predicts
parole success or failure. This measure is similar to the idea of criminal history and is consistent
with one of Gottfredson and Gottfredson’s (1989) three main correlates of criminal justice
decision making.
In sum, narrative literature reviews have shown limited consistencies across the system.
For example, Gottfredson and Gottfredson (1989) found victim-offender relationship,
seriousness of the offense and criminal history were the primary explanatory factors across the
system. Aside from the influence of these variables, narrative literature reviews have been
inconsistent across the system. At the arrest decision, literature reviews are rather exhaustive.
Yet, at the sentencing decision, narrative reviews have focused almost exclusively on the effects
of race and have virtually ignored the effects of other theoretically relevant variables. Finally,
the status of synthesis of research on parole revocation is very limited to the point of nonexistence. Reviews of literature on parole generally have focused on general issues with parole
or the release decision not the revocation decision.
Meta-Analysis
Meta-analysis has had some use in criminal justice decision-making research. Metaanalysis has primarily been used to examine the correlates of sentencing. Daly and Bordt,
(1995) examine the effects of gender on sentencing. Mitchell (2005) and Pratt (1998) both
examine the effects of race on sentencing. Finally, Whatley (1996) examines the effects of
39
victim blameworthiness. More recently, Kochel, Wilson, and Mastrofski (2011) analyzed the
effects of race on the decision to arrest.
Gender
It had long been argued that gender played a role in sentencing decisions. Daly and
Bordt (1995) examined the effects of gender on sentencing meta-analytically. They found that
even after controlling for legally relevant variables, such as prior record, gender remained a
significant correlate of decision-making. The vast majority of studies that showed gender effects
showed effects that favored women. Additionally, gender effects were a more common result
than race effects. Finally, Daly and Bordt (1995) found that gender effects were more likely to
be found when the study had a high proportion of non-white defendants. The authors do
conclude that gender effects may be contextual depending on the context of the criminal justice
decision.
Race
When examining the effect sizes of race, prior record and seriousness of the offense, Pratt
(1998) found that only measures of offense seriousness had a significant impact on measures of
sentence length. Given the consistent finding across studies that prior record plays a large role,
Pratt’s findings are somewhat surprising. Pratt argues that prior record may not have been a
significant correlate due to a measurement issue. He states that the measures of prior record
were often interval measures which implies a linear relationship and the relationship between
prior record and sentence length may be non-linear, hence no relationship. Pratt (1998) argues
that his non-significant race finding may also be due to measurement issues. There are two
majors ways that race was operationalized across studies: white/black, and white/nonwhite. Pratt
also created a third, other category, which acted as a catch all for any other race
40
conceptualizations. Pratt (1998) found that there was a significant difference between the
operationalization of race and that variables impact on sentence length. Specifically, he found
that white/non-white operationalization had the greatest likelihood of finding a significant effect
of race on sentence length. Thus, it may be possible that measures of race were masking a real
effect (Pratt, 1998).
Unlike Pratt’s meta-analysis, Mitchell’s (2005) meta-analytic examination of sentencing
correlates found that race was a significant factor in sentencing. Mitchell (2005) found a small
but statistically significant effect of race even after controlling for sentence severity.3 African
Americans were punished more severely than white Americans. Additionally, he found that the
magnitude of the effect size varied depending on several measurement issues. For example,
larger effect sizes were found with less precise measures of criminal history. Race appeared to
have a stronger influence on sentencing when worse measures of criminal history were used in a
particular study. Even after controlling for other variables such as use of a weapon, trial type,
type of defense counsel, and socio-economic, a small but statistically significant race effect
persisted (Mitchell, 2005).
Kochel et al. (2011) examined the effect of race on officer decision to arrest. They found
that effect sizes indicated that race was a significant predictor of officer arrest decisions. They
found that African Americans were significantly more likely to be arrested. This effect persisted
even after controlling for a variety of relevant moderating variables. These control variables
included whether the study examined domestic violence, other legally relevant variables such as
seriousness of the offense, victim request for arrest, presence of a witness, quality of evidence,
use of alcohol or drugs, and prior record. In addition, they controlled for extra-legal variables
3
Mitchell (2005) analyzed studies that used multiple dependent variables unlike Pratt (1998), who examined only
studies that used sentence length as a metric outcome.
41
such as demeanor of the suspect and they examined whether or not the effect of race on arrest
changed over time. In all of these instances the effect of race remained.
Victim characteristics
Another consideration in sentencing is victim responsibility. To examine this issue,
Whatley (1996) meta-analyzed studies investigating the effects of victim characteristics on
victim blameworthiness. He found that victims were more likely to be blamed when: they wore
revealing clothing, had questionable character, and had a close relationship with their attacker.
He did not support the hypothesis that unattractive victims would be more likely to be blamed.
In sum, meta-analysis has been used to examine issues with sentencing and, very
recently, arrest. Through the use of meta-analysis, scholars were able to make sense of the
mixed effects of several variables and make definitive conclusions regarding those variables.
Daly and Bordt (19955) found that there was a gender effect on sentencing. While Pratt’s (1998)
study did not find significant race effects on sentencing, Mitchell’s (2005) meta-analysis did find
a small but significant effect of race on sentencing decisions. Finally, Whatley (1996) found that
victim characteristics played a role in victim blameworthiness.
Chapter Summary
Meta-analysis is a new technique that could be used to garner new information regarding
the effects of predictors of criminal justice decision making on various decisions. The majority
of synthesis research has taken the form of narrative literature reviews. With the exception of a
few relationships, the results of these reviews have often ended with the call for more research to
assess conflicting information. Meta-analysis has had limited use in the area of sentencing but
has not been used extensively in any other area of criminal justice decision making. Metaanalysis may be able to shed new light on correlates of decision-making across the system and
42
assess the validity of criminal justice theory. If correlates of decision-making are consistent
across the criminal justice system a grand theory approach may be more appropriate. However,
if there are few consistent correlates across decisions and the strongest relationships are
predictors that are unique to a particular decision, then a decision-specific approach may be most
appropriate.
Given the above review, several factors emerge for the entire system under various
domains or groups of variables. Legal variables appear to consistently influence decision
making across the system. These variables include measures of seriousness of the offense, prior
record and victim-offender relationship. When crimes were considered more serious, suspects
had longer records and the victim and offender did not know one another the system was more
punitive. Suspect characteristics, such as race, ethnicity, age and gender, have also been studied
repeatedly but have had mixed results. Due to the conflicted nature of these results, they would
be ideal to include in a meta-analysis. Other variables that have produced conflicted results
include criminal justice actor characteristics, such as actor race, age/experience, gender,
ethnicity, education level4, and cynicism. The aforementioned variables all tend to focus on the
incident level of case processing. However, research has produced mixed results with more
macro level constructs. These variables that have produced inconsistent results at the macro
level include: neighborhood poverty levels, family structure, and racial heterogeneity. These
variables are system-wide and are often measured at each stage of the criminal justice system.
There are a number of factors that are unique to a particular decision for a variety of
reasons.5 At the arrest decision a number of factors have been examined that are unique to the
arrest decision. The first unique set of factors is organizational characteristics. Measures that
4
While, judges have a similar education level, this does vary for police officers and probation officers.
Some factors are considered unique simply because they are not measured at multiple stages of the criminal justice
system. For example, demeanor may be applicable at multiple stages but is only measured at the arrest stage.
5
43
are organizational characteristics are measures of organizational hierarchy and police agency
size. The arrest decision point also officers several unique situational factors that are not
necessarily found in other decision points. These factors include measures of poor citizen
demeanor, citizen intoxication, citizen non-compliance,6 interaction-phase criminal conduct and
lay perceptions of mental disorder.7 While some of these factors may not seem unique to the
arrest decision, for example suspect demeanor, organizational hierarchy, or police agency size,
they are not measured in studies of other decision points. It is possible that court defendants or
parolees may have a poor demeanor or parole officers may operate within certain organizational
hierarchies or within agencies of varying size, however, these variables are not measured in
studies of sentencing or parole revocation, and thus, these variables included as unique.
Like arrest, sentencing offers a number of unique factors due to the nature of the
sentencing decision. Like arrest, some “unique” factors are unique because they are not
measured at the other stages. These unique characteristics are broken down into court
characteristics and case characteristics. Items included under court characteristics include
measures such as: jurisdiction of the court, the geographic trial location, and the court size.
Jurisdiction examines Federal versus state, county, or local jurisdiction. Geographic trial
location focuses on regional differences in case processing. Finally, court size refers to the
amount of cases processed through a particular court. All three are applicable at any decision
point, but are not measured at other decision points.8 Items included under case characteristics
6
Some research has separated citizen compliance from demeanor. It may be possible that these measures will be
correlated (see Novak et al. 2002). If that is the case, a measure of compliance will be treated as a measure for
demeanor.
7
Engel and Silver (2001) established that officer perceptions of mental illness were not clinical definitions of mental
illness but may influence officer decision making in arrest situations. Lay perceptions of mental illness are
considered unique to arrest because at the sentencing and parole revocation stage the actor will likely have an
official report and would not need to rely on lay opinions.
8
Court size would need to be reoperationalized to the volume of police citizen encounters or case loads of parolees
revocated to be applied to arrest or parole revocation decisions.
44
include measures such as: mode of conviction, education level of the defendant, employment
status and type of counsel. Finally, parole revocation also presents a number of unique
measures. These factors are divided into offender characteristics and parole characteristics.
Items included under offender characteristics include measures such as: education level and
employment status.9 The unique parole characteristic is program completion.
9
Education level and employment status are considered unique factors for sentencing and parole revocation because
these considerations are not measured at the arrest decision so they are not system-wide and thus are unique to these
two particular decision points.
45
CHAPTER 3
META-ANALYSIS: BENEFITS AND CRITIQUES
There have been many attempts to examine correlates of criminal justice decision
making. The purpose of this dissertation is to examine these correlates in the context of the
criminal justice system as a whole and determine if theoretical perspectives should focus on the
entire system or specific decisions. While there have been many examinations of criminal
justice decision making there have been limited attempts to synthesize the research and what has
been done has had several problems. First, other than Gottfredson and Gottfredson, all attempts
at research synthesis have been specific to a particular decision. This creates a myopic approach
that prevents scholars from seeing the larger picture, i.e. that factors that influence decision
making may be consistent across the system. The second limitation, as seen in the previous
chapter, is that the majority of these reviews have been narrative, or vote counting-based
reviews, which in and of themselves are problematic.
In the pages that follow, issues of synthesis literature are discussed. First, there is a
discussion of the problems of narrative literature and vote-counting reviews. Second there is a
discussion of the benefits of meta-analysis. Third, there is a discussion the problems with metaanalysis and the solutions to these problems.
Scholars have always strived to make sense, and take stock of their fields of research.
Cullen (2011) issued a call for scholars in the fields of criminology and criminal justice to
advance the field by taking stock of research and creating a better understanding of these fields
through research synthesis. Many issues in research, such as sampling and measurement
methods, create inconsistencies in findings and these inconsistencies lead to a state of confusion
or “scientific chaos” (Hunt, 1997, p. 1). In an attempt to make order from the chaos, scholars
46
would conduct a massive narrative literature review or a vote-count review in order to make
sense of inconsistent findings (Hunter and Schmidt, 1990). However, these traditional methods
of research synthesis are flawed.
Traditional Narrative Literature Reviews
Traditionally, research on criminal justice decision making was reviewed via the
narrative literature review format. These reviews permeate research on arrest and sentencing
(see National Research Council, 2004; Riksheim and Chermak, 1993; Sherman, 1980; Spohn,
2000). Under these narrative reviews a scholar gathers all available research on a particular
topic, evaluates the research, and makes a determination as to the importance of certain
relationships. While these types of reviews provide valuable insights, the method is a flawed and
antiquated approach to synthesis research.
The first problem is the subjective nature of narrative reviews (Pratt, 2002; Wolf, 1986).
Since the review is based solely on the researcher’s evaluation of the literature, any conclusions
will potentially be biased based on the researcher’s examination of the literature. Essentially,
two scholars could read the same inconsistent evidence and come to opposite conclusions. This
qualitative judgment has no standardized process and this can possibly bias the evaluations.
A second and related issue is there is no set process to conduct a narrative literature
review (Jonson, 2010). This means that there is no procedure for finding, collecting and
evaluating the studies for a narrative review. As a result, bias may be introduced as a result of a
scholar consciously or unconsciously omitting studies from the review. This omitted study bias
creates a subjective review, which may not necessarily be able to be replicated. For example, the
National Research Council’s discussion of correlates of arrest fails to mention any articles using
47
data from the Cincinnati Police Observation study. The omission of these studies is a blatant
example of omitted study bias in narrative literature reviews.
A third issue of narrative reviews is that a large number of studies makes the technique
cumbersome and potentially decreases the accuracy of the findings (Hunter and Schmidt, 1990;
Jonson, 2010). When the literature for a particular topic becomes too voluminous, a researcher
may fail to include studies. The researcher may choose to use a sub-sample of studies in order to
make the review more manageable (Jonson, 2010). There are several problems with this
approach. First, the narrative review is no longer a complete review. Second, omitting studies
creates a selection bias and possibly leads to inaccurate results because critical studies may have
been missed or omitted entirely.
Vote-counting
Another type of traditional research synthesis is the vote-count approach. Under the
vote-count approach, a researcher tallies the results of studies (Hunt, 1997; Hunter and Schmidt,
1990). A researcher divides the findings of studies into three groups: positive relationships,
negative relationships and no relationships. Whichever group is the largest becomes the
interpretation of the relationship for the review. Unlike narrative reviews, vote-counting does
have some objective means of evaluating relationships. However, like narrative reviews, votecounting has the same potential for selection bias because of omitted studies. A second and
equally large problem is that vote-counts treat all studies equally (Jonson, 2010). A study with a
sample size of 40 is weighted the same as a study with a sample size of 1,000. Since sampling
error decrease with larger samples, more weight should be placed on the study with a larger
sample size since there is more confidence in those results (Hanushek and Jackson, 1977).
Another related problem is that vote-counting does not account for varying magnitudes of
48
findings (Hunt, 1997; Jonson, 2010). For example, suppose a study found a weak positive effect
and another found a strong negative effect for the same relationship. Under the vote-counting
scheme each study carries the same weight. Essentially the two findings would cancel each other
out rather than the stronger negative effect taking precedence. Thus, while vote-counting is a
slightly more objective approach than narrative literature reviews, it still suffers from flaws and
there is a need for a better way to synthesize research.
Meta-Analysis as an Alternative
Meta-analysis is an analytical technique that can overcome the inherent problems in
narrative and vote-counting reviews. Meta-analysis is a technique designed to make sense of and
evaluate a large amount of information. Meta-analysis is still a relatively young technique; it
was developed in the 1970s (Hunt, 1997). According to Glass (1976), meta-analysis is a
statistical method for the purpose of integrating and evaluating the findings of multiple studies.
Jonson (2010) comments that “[meta-analysis involves a] five-step process: 1) formulating the
problem, 2) collecting the data, 3) evaluating the data, 4) synthesizing the data, and 5) presenting
the findings” (p. 73). This technique has become increasingly more common in the fields of
criminology and criminal justice (Pratt, 2010; Wells, 2009). Wells (2009) found that the
majority of meta-analyses in criminology and criminal justice have been conducted since the
1990s. Furthermore, Jonson (2010) states that meta-analysis is a common technique of the
broader social science research community. However, not all scholars have been advocates of
meta-analysis. Eysenck (1976) referred to the technique as an “exercise in mega-silliness” (p.
517). With this disagreement in mind it is important to discuss the strengths and weaknesses of
the technique.
49
Advantages of Meta-Analysis
Meta-analysis has several advantages over traditional synthesis techniques. According to
Pratt (2001) there are four advantages of meta-analysis; first, meta-analysis can derive exact
measures of strength for a particular relationship called an effect size. Second, meta-analysis can
use multivariate analysis to examine if relationships change under different methodological
conditions. Third, other scholars may replicate meta-analyses. Fourth, meta-analysis is
dynamic, as opposed to static, and when new studies are conducted they can easily be added to
existing meta-analyses and the results may be updated. Additionally, Jonson (2010) stated that
meta-analysis is a better approach for policy analysis and consequently meta-analysis can assess
the implications of policy much better than traditional literature reviews.
Effect size
First, meta-analysis provides an exact measure of strength of the relationship between
two variables. The effect size measure is superior to narrative literature reviews because
traditional reviews can at best vote count (Pratt, 2001). Vote counting may be biased by multiple
studies using the same single data set. The effect size measure gets around this problem by
creating an effect size per data set. The effect size provides a precise measure of strength and
direction for the relationship between the two variables (Hunt, 1997; Lipsey and Wilson, 2001;
Pratt, 2001). Additionally, Jonson (2010), states that effect sizes account for variation in sample
sizes. Thus the studies with larger, more representative samples are granted more weight than
smaller samples. Under vote-counting each sample is given the same weight regardless of
sample size. The effect size essentially creates a better understanding of the true relationship
between the predictors and the outcome variable (Hunt, 1997; Hunter and Schmidt, 1990;
Jonson, 2010).
50
Methodological adjustments
Second, meta-analysis can adjust magnitude measures to account for varying research
methodologies. According to Pratt (2001), Meta-analysis allows for the separation of studies
into groups based on methodological differences and calculates effect sizes for each group.
Meta-analysis allows the researcher to examine if certain pertinent methodological factors
influence the relationship of interest. These methodological considerations include:
demographic information (age, race and gender), study characteristics (longitudinal versus cross
sectional, study location, or year published), and measurement of the outcome variable (Durlak
and Lipsey, 1991; Jonson, 2010; Lipsey and Wilson, 2001). For example, Pratt (1998) found
that the effect of race on arrest was contingent on how race was measured (see discussion of
Pratt, 1998 in Chapter 2).
Replication
Third, according to Pratt (2001), “coding decisions in a meta-analysis are ‘public’” (p.
31). Coding procedures are described in detail with the study and coding sheets are often
published with the study (Jonson, 2010). Because coding decisions are public, the results may be
replicated and research integrity is preserved. In addition to a public coding process, the
eligibility criteria for the meta-analysis are also included and thus another aspect of the metaanalysis may be replicated (Jonson, 2010). The transparent nature of meta-analysis allows for
any other scholar to replicate the findings and thus ensure research integrity. This transparency
is not found with traditional literature reviews where inclusion criteria are not provided.
Consequently, the sample in a traditional literature review is potentially subject to selection bias.
51
Dynamic
Finally, meta-analysis is a dynamic syntheses method. As such, when other studies are
written they can be added to the meta-analysis and the information can continually be
reevaluated (Pratt, 2001). Weisburd, Telep, Hinkle and Eck (2010) recently reviewed the status
of problem oriented policing as part of the Campbell Collaborative’s systematic review process.
Their review specifically mentioned that as new studies emerged they would be added to the
review and the meta-analysis on the effectiveness of problem oriented policing would
continually be updated. This example demonstrates the dynamic nature of meta-analysis. As
new studies emerge on a particular topic, in this case problem oriented policing, new information
can be added to the meta-analysis and new effect sizes may be calculated with relative ease.
Thus, meta-analysis allows for the most up-to-date and accurate reviews and synthesis of
information possible.
Critiques of Meta-Analysis
Meta-analysis is not a solution to research synthesis that is free from flaws. However,
scholars have created ways to account for these potential problems and adjust the analyses.
There are three problems for this meta-analysis: 1) the file drawer problem, 2) the apples-andoranges problem, and 3) the issue of independence of studies. The following section will discuss
each problem and potential solutions.
File drawer problem
According to Rosenthal (1991), the process of publishing research biases meta-analysis.
There is an emphasis on publishing findings that support research questions and thus, academic
journals may not want to publish null findings. These null studies are therefore sitting in the
52
bottom of a scholar’s file drawer. Consequently, meta-analyses may be biased in the direction of
finding an effect. If these unpublished studies included, effect size calculations may be different.
While this is a valid criticism, according to Jonson (2010) this problem is equally valid
for narrative literature reviews and vote-counting studies. Pratt (2010) contends that this
problem may not be as serious of an issue because of the nature of tenure and promotion
amongst scholars in academe. The “publish or perish” phenomenon does not create an incentive
for research to sit in a file drawer. A scholar will not continue to be employed if he/she sits on
null findings and does not make an attempt to get them published. Additionally, given the
research surrounding certain variables in criminal justice research, for example, suspect
demeanor, null findings may question the validity of past research and are certainly worth
publishing.
With these considerations in mind, it appears that the file drawer problem may not be as
large of a problem as originally perceived. There are two approaches to examine this problem.
The first is to try and retrieve unpublished studies (Lipsey and Wilson, 2001). These
unpublished reports may take the form of government reports, dissertations, conference
presentations, and directly contacting well-known scholars in the field for any additional studies.
By using these methods, there is potentially less bias in meta-analytic calculations. The second
method is to statistically examine for bias using the fail-safe N statistic (Rosenthal, 1991). This
statistic calculates how many null findings would be necessary to render the effect size
statistically zero (Lipsey and Wilson, 2001). Higher values on a fail-safe N statistic indicate that
a large number of studies would be necessary to nullify the effect size. As a result, metaanalyses with higher fail-safe N values can be interpreted as more likely to be valid because it
would take an unlikely number of null findings to invalidate the findings.
53
Apples-and-oranges problem
The second problem with meta-analysis is the apples-and-oranges problem. This
problem is related to varying measures of independent and dependent variables (Lipsey and
Wilson, 2001; Pratt, 2001, 2002). If analyzed studies are not measuring constructs in the same
manner then it may be inappropriate to meta-analyze them together. Meta-analysis assumes that
each study is measuring the same variables in the same way (Hunter and Schmidt, 1990). If this
is not the case then the measures are not necessarily tapping the same idea. Consequently, the
effect size is meaningless (Hunter and Schmidt, 1990). For example, Mitchell (2005) used
multiple measures of sentence severity that included: in/out decisions, sentence length, ordinal
scales of severity, discretionary release (reversed coded so that positive values indicated more
punitive punishments) and discretionary punitiveness. Critics would argue that meta-analyzing
these measures would be inappropriate because each is different. However, proponents would
argue that on all of these scales increasing values indicated more punitive sentencing. Thus,
while individual measures of punishment are slightly different, the overall indication of each
measure was in the same direction and each measure is tapping into the same construct.
A related issue to the apples-and-oranges problem is the quality of the research being
analyzed. Critics have argued that more rigorous studies, for example longitudinal and classical
experiment studies, should not be meta analyzed with less rigorous studies such as quasiexperimental and non-experimental designs (Logan and Gaes, 1993). Varying quality of
methodological rigor means that studies will vary in internal and external validity and this can
bias the effect size. Critics argue that these meta-analysis attempts statistically improve poor
research (Jonson, 2010). This idea is commonly referred to as “garbage in, garbage out,” which
means that poor data will produce poor statistics. There are two solutions to this issue. The first
54
is to include only studies that measure all variables the same way and/or have greater
methodological rigor. However, this solution would severely limit the sample. The alternative
solution is to statistically control this issue. By statistically controlling the issue, methodological
rigor may be a factor influencing the relationships between independent variables and the
dependent variable.
Independence of Effect Sizes
A third problem for this particular meta-analysis is independence of effect sizes.
According to Pratt (2000), current studies in criminal justice and criminology use multiple
models that can each produce multiple effect sizes. This is especially true in arrest research
given that a large amount of knowledge of arrest is produced via large systematic social
observations like the Project on Policing Neighborhoods, the Cincinnati Study of Police
Behavior and the Police Services Study. The lack of independence creates errors in estimates,
which decrease the variance and increase the likelihood that an effect size will be significantly
different from zero (Pratt, 2000). In other words, the likelihood of making a type II error
increases dramatically. In order to deal with this issue, scholars suggest using a system of
selection criteria to select the best possible representation. This process has been used in
previous meta-analyses of criminal justice decision making (see Kochel et al., 2011, and
Mitchell, 2005). While some scholars have suggested the use of independence adjustment or
hierarchical linear modeling to deal with these issues, Lipsey and Wilson (2001) suggest that if
possible using one representative per data set is the preferred method.
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Chapter Summary
Meta-analysis has been used for a variety of research questions. Most recently, metaanalysis has been used to evaluate criminological theory. Some of the meta-analytically
evaluated theories include: Gottfredson and Hirschi’s general theory of crime, social learning
theory, deterrence theory, and bio-social predictors of crime (Paternoster, 1987; Pratt and Cullen,
2000; Pratt, Cullen, Blevins, et al., 2002; Pratt, Cullen, Blevins, et al. 2006; Pratt, Cullen, Sellers
et al., 2010; Pratt, McGloin and Fearn, 2006; Walters, 1992). Each of these evaluations used
meta-analysis to determine the overall worth of the theory and the efficacy of each of the
components of the theory. For example, Pratt and Cullen (2000) found that low self-control had
an effect size over .20 and stated that self-control is “one of the strongest known correlates of
crime” (p. 952). Meta-analysis serves to create an alternative to evaluate theories other than
purely narrative literature reviews.
Meta-analysis can also be used to evaluate the relative strength of known predictors of a
relationship. For example, Pratt and Cullen (2005) used meta-analysis to evaluate a number of
macro-level predictors of crime, such as unemployment, racial heterogeneity, regional effects,
and residential mobility among others. Pratt and Cullen (2005) examined the effects of 23
different variable types on crime. This same principle can be applied to the determinants of
criminal justice decision making.
Meta-analysis provides a method for synthesizing a large number of studies. It is
superior to traditional narrative literature reviews and vote-counting reviews in a number of
ways. First, meta-analysis allows for precise measures of strength of relationships via the effect
size. This statistic indicates whether the relationship between an independent variable exists and
the strength of that relationship. Second, meta-analysis allows for methodological adjustment.
56
Under traditional research synthesis, each study was given the same weight regardless of the
quality of the study. Meta-analysis can correct for this and adjust the effect size based on
methodological considerations. Third, meta-analysis can be replicated. Coding decisions are
public because coding protocols are often included with the presentation of results. This allows
another scholar to replicate the process or modify the process used in the original meta-analysis.
For example, Mitchell replicated Pratt’s meta-analysis of the sentencing literature. The two
scholars came to different conclusions (see Chapter 2). Finally, meta-analysis is dynamic. With
meta-analysis, when more studies are generated new effect sizes for those studies can be
calculated and a new average effect size can be calculated and the relationship can be
reevaluated. Mitchell was able to add additional studies that had been published between his
study and Pratt’s. With narrative literature reviews an entirely new narrative literature review
would have to be conducted and that process would be long and involved. Meta-analysis offers a
faster and more efficient alternative for updating a particular body of knowledge.
While there are many benefits to meta-analysis there are several problems, but scholars
have worked to correct these known issues. The first problem is commonly known as the “filedrawer” problem. This problem involves the bias of results in favor of a relationship because of
the nature of the publication process. This problem is corrected in two ways, first the metaanalyst should do his/her best to find as many unpublished studies as possible, and second they
should calculate a fail-safe N statistic (see Chapter 4). The second problem is the “apples-andoranges” problem. This problem refers to a bias that is created when a meta-analyst is not
comparing similar constructs. Because of the nature of research not every study is identical. As
a result, there is a possibility that the measures being meta-analyzed may not be the same for a
variety of reasons both on a measurement level and a study design level (i.e. longitudinal versus
57
cross sectional etc.). To combat this problem a meta-analyst can either remove studies of low
methodological rigor or they can include moderator variables and treat methodological rigor as a
factor in the relationship between the independent and dependent variables. The final problem is
the potential lack of independence of effect sizes. This issue is the result of massive data
gathering projects. When large amounts of money are used to generate data, scholars often do
not want to publish just one paper from the data, but instead publish multiple papers from one
data set. This creates a problem for meta-analysis because it requires that each effect size be
generated from an independent source. To combat this problem Kochel et al. (2011) and
Mitchell (2005) suggest that selection criteria be created to select the best possible representation
of a particular data set (see chapter 4 for the specific selection criteria). With these benefits and
problems in mind, the next chapter outlines the statistical procedures for this dissertation.
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CHAPTER 4
METHODS
With the benefits, and potential problems, of meta-analysis in mind, this chapter outlines
the statistical procedures used in this dissertation. First, there is a discussion of the research
questions. Second, there is an examination of the sampling procedures and a discussion of the
ways to acquire unpublished studies as an attempt to counter the file-drawer problem (see
Chapter 3 for a discussion of the file-drawer problem). Third, there is a discussion of the
dependent, independent and moderating variables that will be used in the statistical analysis.
Fourth, analytical procedures are discussed. Fifth there is a discussion of how the analysis will
indicate which correlates are significant, how it was determined whether criminal justice
theoretical perspectives should be system-wide or decision-specific. This chapter concludes with
a brief discussion of the limitations of this study.
Research Questions
This dissertation is examining the current status of criminal justice theory via an
examination of correlates of criminal justice decision-making. In an effort to examine this idea,
this dissertation proposed to examine several decision points in the criminal justice system in
order to determine the answers to the following questions:
1.
What factors influence decision making at arrest, sentencing, and parole revocation?
2.
Given these factors, are they consistent across the system as a whole?
3.
Given the presence or lack of consistency, should theories of criminal justice focus on the
system as a whole or a particular branch/decision point?
These questions are designed to assess the correlates at each stage and then the entire system. As
a result of this process it is possible to determine what factors influence each decision point and
59
if those are the same influential factors across the system or if factors vary based on the decision
of interest.
Sample of Studies
The first step in the process of meta-analysis is to collect all relevant studies. Several
search strategies were employed to gather all the relevant studies. First, the bibliographies of the
current synthesis studies were scoured to gather all possible studies. However, for any particular
decision, the most recent narrative review is between six and ten years old. Consequently, other
search strategies were utilized.
The second search strategy was to search various criminal justice and social science
research databases. Separate keyword searches were performed on the following databases:
Criminal Justice Abstracts, Criminal Justice Periodical Index, Dissertation Abstracts Online,
ERIC, National Criminal Justice Reference Service, PsycINFO, Social Sciences Index,
Sociological Abstracts, and SocINDEX. These are the most common databases searched in
criminology and criminal justice meta-analyses. The search terms for these searches were kept
intentionally broad due to the generalist nature of the research questions discussed. For arrest,
the search terms used “police” “arrest NOT cardiac” “decision or decisions, or ‘decision
making’”. For sentencing, the search terms used were “sentencing” and “decision, decisions or
‘decision making’”. For parole revocation, the search terms used were “revocation” and
“decision, decisions or ‘decision making’. When compared to existing meta-analyses and
narrative reviews these search procedures produced virtually identical study lists.
The third search strategy was to search all relevant peer-reviewed academic journals issue
by issue to look for more recent information that may not yet be in the databases.10 Jonson
10
Due to the thoroughness of the searches themselves, journal search was limited to online-first articles as those
already in print were in the databases and covered by second search strategy.
60
(2010) performed a Google search to find unpublished data and the tactic did glean a few state
and national reports. Thus, the fourth search strategy performed was a Google search to look for
unpublished reports. Because of the broad nature of the searches used Google searches were
impractical. While they were performed, only the first ten pages of results were searched
because search result pages numbered in the thousands and the results went too far off topic to
have any utility past this point. A negligible number of potential studies were actually gleaned
from this method.
In total, the raw number of results from these various methods was over 3000 for arrest,
over 1000 for sentencing and approximately 500 hits for parole revocation (all three estimates
are inclusive of duplicates). After each search was performed, abstracts and titles were inspected
for topic appropriateness. For arrest, 236 articles were determined to have a potential to be
coded, and of those, 62 were deemed codeable and subsequently coded. A total of 54 separate
data sets or studies were found. The reason for the difference is that some articles used the same
data set to produce the results and could not all be included in the meta-analysis due to
dependency issues (see below).
For sentencing, 396 articles were determined to have potential, and of those, 65 were
deemed codeable, and a total of 46 separate data sets or studies were collected. It is important to
note that 20 of the 396 studies were removed because they dealt with juveniles. Since the
juvenile adjudication process is substantively different from adults and are two separate systems,
studies focusing on juveniles were not included in analyses of sentencing and parole revocation.
However juvenile focused studies were included in arrest because the arrest process is not
substantively different for juveniles or adults. Juveniles are arrested by the same officers that
arrest adults. Finally, for parole revocation, a total of 82 studies were thought to have potential,
61
and of those 17 were coded, which yielded 19 separate data sets/studies. Surprisingly, parole
revocation research did not utilize massive data sets to produce multiple studies as often as arrest
or sentencing articles.
Inclusion Criteria
For studies to be included in this meta-analysis they must have met the following criteria.
First, the study must have examined one of the three decisions of interest i.e. arrest, sentencing,
or parole revocation, and the measurement of those decision points must be consistent with the
discussion in the dependent variable section (see below). Second, the study must include at least
one measure per system-wide domain for the independent variable. These independent variable
domains include measures for offense severity11, and suspect/defendant/offender
characteristics.12 Third, the study must provide the necessary information to calculate an effect
size. These inclusion criteria are relatively lax in an effort to include more studies as opposed to
limiting the number of studies. Including multiple studies allows for better examination of
moderating variables such as methodological rigor.
Selection Criteria
Meta-analysis requires every effect size to be independent. If effect sizes are not
independent then the mean effect size may be inflated and produce biased results in favor of
finding an effect (Lipsey and Wilson, 2001). Because of the nature of the studies that were
analyzed, this was not always possible if every possible. Every article did not pertain to one data
set. For example, many sentencing research articles were generated from sentencing data from
11
Domain refers to a grouping of predictors for a particular outcome.
Criminal justice actor characteristics were not included as very few studies include these measures and including
this requirement would needlessly shrink the sample size. Community characteristics were not included here
because they are often aggregate measures and the nature of decision-making research tends to be more micro level
analysis and as a result valid studies may be needlessly eliminated if community characteristic measures were made
mandatory.
12
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Pennsylvania and the years of data often overlap. The same data cannot be continually used as it
would bias the results. As a result, some criteria are necessary to select an article that best
represents that particular data set. This dissertation used criteria modified from Mitchell (2005)
and Kochel et al. (2011). The criteria for selecting the ideal representation of a particular data
set were: 1) codeability- the particular article that provides the most information for coding
purposes was included, 2) number of control variables- the article that has the greatest number of
control variables was included, and 3) sample size- the article with the largest sample size was
included because that represents the largest number of cases and larger samples are generally
seen as more representative. These selection criteria are consistent with current meta-analyses of
criminal justice decision making.
Dependent Variable
The overall dependent variable in this study is criminal justice decision processing. The
definition in Chapter 1 established that criminal justice examines “governmental social control
premised on punishment or blameworthiness” (Duffee and Allan, 2007, p. 8). As such, criminal
justice is the study of processing. More simply, criminal justice is the study of discretion. It
seeks to examine what causes people to be pushed further through the system and others are
released or not pursued through the system. Operationalization of this concept varies depending
upon the decision point of interest. Because of the nature of this study, each decision point
presents a different operationalization of the dependent variable. For example, a study of
sentencing would not operationalize criminal justice processing in the same manner as a study of
arrest. They are looking at different steps in the criminal justice process. However, many
studies measure process outcomes similarly in that they measure the dependent variable
dichotomously as yes or no.
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The arrest decision, which is defined as a police officer taking a person into custody in
order to be processed further into the criminal justice system, measures processing as a yes/no
dichotomy. In this dissertation, studies used for the arrest analysis, measured the arrest
dichotomously. The decision to only use studies that measure arrest dichotomously is to
eliminate studies that examine use of force or a continuum of law enforcement sanctions as they
are outside the scope of the arrest decision, and could lead to a problem with heterogeneity (see
below for a discussion of heterogeneity).
The sentencing decision point is more complex than the arrest decision point from an
operationalization perspective. Sentencing is defined here as a judge’s determination of the
appropriate punishment for an individual. Many studies of sentencing focus on the incarceration
decision as an in/out dichotomy. That is, studies focus on the decision to incarcerate or not. Yet,
these same studies often also collect information on the length of the incarceration term.
Mitchell (2005) used multiple sentencing measures, this strategy was not used in this
dissertation. This decision was made to avoid both dependency and the apples-and-oranges
problem. By limiting the sample in this way, the issue of dependency is avoided. There is no
overlap between metric sentence length measures and dichotomous in/out scales that apply to the
same set of defendants. This limitation still allows for a sufficiently large sample, but avoids any
chance that the dependent variables are not compatible with one another. Additionally, limiting
to the in/out decision required fewer artificial transformation procedures of effect sizes.
Like sentencing, parole revocation is more complex, in terms of measurement, than
arrest. For this study, parole revocation is defined as a parole officer's or parole board’s pursuit
of re-incarceration of an individual serving the remainder of their sentence under parole
supervision. This decision point has been measured dichotomously using the yes/no for a
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measure of parole revocation. Since the previous two decisions used a dichotomous dependent
variable measure, it is appropriate that the same standard is applied to the parole revocation
decision. The reasons for using only a revoke/no revoke are the same as in sentencing, to avoid
dependency issues, and the apples-and-oranges problem.
Independent Variables
With the research questions in mind, there should be some variables that are common to
each branch of the system, for example, race of the criminal justice actor and other variables that
are decision-specific, for example attorney type. The following discussion will explain what
variables should function system-wide and which variables are unique to a particular decision
point.
System-wide variables
Several factors have consistently been predicted to explain decision making across the
system. Each of these factors constitutes a separate domain of independent variables in this
meta-analysis. The four principle domains cover offense seriousness, criminal justice actor
characteristics, suspect/defendant characteristics, and community characteristics. Items under
the seriousness domain include measures of seriousness of the offense (e.g. felony versus
misdemeanor), and prior record. Items included under the criminal justice actor domain would
include measures of race, ethnicity, gender, cynicism, professionalism/education,13 and
experience of the criminal justice actor. Items included under the suspect/defendant
characteristic domain would include race, ethnicity, age, gender, and social class. Finally, items
included under the community conditions domain include poverty, family structure, community
racial composition and other socio-economic predictors.
13
Education and professionalism are combined because they are generally measured interchangeably see Smith
(1984).
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Arrest
While the above domains do apply to the arrest decision, there are several domains that
appear to be uniquely applied to the arrest decision. These two arrest specific domains are
organizational characteristics, and other situation characteristics. Items included under the
organizational characteristics domain include measures of organizational hierarchy and police
agency size. Items included under other situational characteristics include measures of suspect
demeanor, intoxication, suspect non-compliance, interaction-phase crime, and lay perceptions of
mental illness. It should be noted that items included under these domains are “unique” because
they are only used in arrest research but are applicable to other decision points. Prior arrest
research has examined these factors, but studies of sentencing or parole revocation have not
included these measures. As a result, they are “unique” to arrest for analytical purposes.
Sentencing
As with arrest, the sentencing decision adds other domains that are unique to the
sentencing decision. These new domains include court characteristics, and case characteristics.
Items under the court characteristics domain include measures of jurisdiction of the trial (e.g.
federal versus local court), Trial location (e.g. region of the United States in which the trial took
place, or a particular region of the state examined for the study), and court size (e.g. the number
of judges in a particular jurisdiction). Items under the case characteristics domain include
measures of mode of conviction (e.g. plea versus guilt at trial) and type of counsel (e.g. as public
versus private counsel). While case characteristics are unique to the sentencing decision, the
court characteristics are similar theoretically speaking to organizational characteristics described
in the previous section. Like arrest, these court characteristics are unique because they are not
measured at other stages.
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Parole revocation
The parole revocation decision presents another set of variables that are unique to the
particular decision. These new factors are divided into two domains: offender characteristics,
and parole conditions. Factors included under the offender characteristics domain include:
education level of the offender (e.g. ordinal scale or a series of dummy variables), employment
status (e.g. ordinal scale or as a series of dummy variables). The new factor under the parole
condition domain is program completion. Again, like the previous two decision-specific domain
discussions, offender characteristics can be applied to other domains, but they are not measured
in studies of the other decision points.
Moderating Variables
Moderating variables are similar to control variables but in this case they are variables
that pertain to the studies themselves and not necessarily the theories within the study. This is
one of the advantages of meta-analysis because it allows for considerations of items such as
whether the study utilized a particular data collection method, or if the data were collected in a
particular time period. Each variable is discussed below.
Study characteristics:
The present meta-analysis coded data on several study characteristics. First, the
publication year and decade were recorded. Additionally, the decade and year of data collection
were also recorded. The type of report (e.g. book, book chapter, journal article, or conference
paper), and the location of the study (what region of the United States: Northeast, South,
Midwest, Southwest, and Pacific/West) were recorded. A variable was created for whether or
not the study was multi-jurisdictional/national.
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Methodological quality:
The second domain of moderating variables was related to the methodological quality.
Since this meta-analysis did not want to eliminate all studies of poor quality, the alternative
solution is to examine the impact of methodological quality on outcome variables.
Methodological quality was assessed via the type of data collected. The duration of the study
was recorded. In order to account for the possibility of seasonality bias, a dummy variable was
created to examine whether or not data collection was continuous or had periodic breaks. For
example, the Cincinnati Police Observation Study was conducted year round as opposed to the
Project on Policing Neighborhoods that collected data over two consecutive summers.
Interaction effects are an important consideration in decision-making research. For
example, many studies have found that it is not necessarily young offenders, racial/ethnic
minorities or males that alone are significantly more likely to be incarcerated, but rather it is the
interaction of all three so that young minority males are most likely to be severely punished
(Spohn, 2000). Wooldredge (1998) argues that studies of sentencing need to improve
measurement and include measures of interaction effects. Therefore, a dummy variable was
created to indicate whether or not the effect size was generated from a study that measured
interaction effects.
Pratt (1998) found that the manner in which race was measured had an impact on the
effect of race. A moderating variable that notes the way in which race is measured was recorded
(coded as a three-category variable, where 1 = Black/White, 2 = White/non-White, and 3 = some
other racial classification scheme). Because race and ethnicity are closely related and at times
used interchangeably, a moderating variable noting the measurement of ethnicity was also
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created (coded as a three-category variable, where 1 = Hispanic/non-Hispanic, 2 =
Black/White/Hispanic, and 3 = some other ethnicity classification scheme).
Arrest research has two additional moderating variables that apply to the arrest analysis.
First, systematic social observation is a well-respected form of data collection and studies not
using this data collection method may not be as methodologically sound. However, not all
police research has been conducted via systematic social observation. A dummy variable was
created to measure whether or not the effect size was generated from data recorded via
systematic social observation. This variable applies only to the arrest assessment.
Second, an issue that has been raised in research on arrest is the operationalization of
disrespect or demeanor. According to Klinger (1994), suspect demeanor was not operationalized
properly and as a result what was actually interaction-phase crime that should have accounted for
the arrest was measured as suspect disrespect. Due to this critique, another moderating variable
was created to account for this operationalization issue. This variable was coded as 0 when there
was no interaction-phase crime measure and 1 when an interaction-phase crime measure was
present.
Reliability of Coding
It is important to produce coded results that are reliable. One of the benefits of metaanalysis is that it can be reproduced because meta-analysts are proponents of transparency and
replication. This dissertation evaluated coding reliability using the Yeaton-Wortman method.
Yeaton and Wortman (1993) suggest that agreement is equal to the total number of agreements
divided by the total number of comparisons. Thus, if reliability is checked and the two coders
agree 50 times over 100 total points of comparison then the amount of agreement would be equal
to 0.50. Values of 1.00 indicate total agreement and values of 0.00 indicate a complete lack of
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agreement. The principle coder selected thirteen studies at random and had those studies coded
by a reliability coder. After the thirteen studies were double coded, Yeaton and Wortman’s
method was applied. The level of agreement between the reliability coder and the principle
coder was .92. This value is well above any threshold where reliability is questioned.
Analytic Strategy
In order to address the research questions listed above, a series of analyses were
conducted. First, a series of univariate statistics were calculated. These statistics are designed to
generate a general description of the sample. Second, effect size measures were calculated
between the independent variables of interest and the appropriate dependent variables. Next, a
mean effect size was calculated for each relationship. Third, heterogeneity of effect sizes and the
fail-safe N were evaluated in order to address common criticisms of meta-analysis, namely the
apples-and-oranges problem and the file-drawer problem. Each of the techniques is described in
detail below.
Effect size measure
Consistent with the current meta-analysis literature (see for example: Berlin, and Colditz,
1990; Brind, Chinchilli, Severs, et al., 1996; Cosgrove, Sakoulas, Perencevich, et al., 2003;
Fleiss 1993; Fleiss and Berlin, 2009; Gaugler, Duval, Anderson, et al., 2007; Geddes, and
Lawrie, 1995; Greenland, 1987; Greenland, 1993; Haddock, Rindskopf, and Shadish, 1998;
Kochel et al., 2011; Lasky-Su, Faraone, Glatt, et al., 2005; Lipsey and Wilson, 2001; Lӧsel and
Schmucker, 2005; Mitchell, 2005; Tenback, van Harten, van Os, 2009), the measure of effect
size estimate used here was a logged odds ratio. This effect size estimate was chosen for several
reasons. First, logged odds ratios were the most common effect size available for all three
groups of studies. Second, logged odds ratios are symmetrical around zero making them easily
70
interpreted regarding significance and directionality (Hanushek and Jackson, 1977). Third,
formulas exist for transforming other test statistics (such as t, F, and χ2) into a logged odds ratio
value (Lipsey and Wilson, 2001). Additionally, Hedges and Olkin (1985) provide conversion
formulas for probit coefficients and variances. Fourth, logged odds ratios can be used even with
the presence of covariates and “inferences about the [odds ratios] may be drawn in the usual
ways” (Fleiss and Berlin, 2009, p. 245). Sixth, logged odds ratios can be easily transformed into
their more interpretable odds ratio counterpart by simply taking the natural anti-log of the logged
odds ratio, giving them easy interpretation. The natural anti-log, also known as exponent B, is
easily interpreted as an increase in the dependent variable given an increase in the independent
variable.
After each effect size was calculated, it was then weighted by the inverse of the variance.
This step was taken to ensure that studies with larger samples are given more influence. Effect
sizes generated from larger samples are given more weight because larger samples are assumed
to be more representative of the population and thus, more accurate. The weighting procedure
follows those outlined by Lipsey and Wilson (2001) where weights are equal to the product of
the effect size, in this case the natural log of the odds ratio, and the inverse of the variance. The
inverse of the variance is equal to:
wLOR = ______abcd______
ab(c+d) + cd(a+b)
where wLOR = the inverse variance weight for the effect size
a = when both variables have a value of zero
b = when the independent variable has a value of zero and the dependent variable
has a value of one
c = when the independent variable has a value of one and the dependent variable
has a value of zero
d = when both variables have a value of zero
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Once each effect size has been weighted using the inverse variance method, a mean effect
size was calculated. The mean effect size is equal to the sum of the weighted effect sizes divided
by the sum of the inverse of the variance for each weighted effect size (Lipsey and Wilson,
2001). In order to determine that the mean effect size was significantly different from zero, a
95% confidence interval was calculated. If the confidence interval contains zero, then that
particular mean effect size was considered non-significant. If zero was not contained within the
confidence interval, then the mean effect size was considered significant at the .05 level and that
relationship was included in the grand theory versus decision-specific discussion.14
Independence of effect size measures
There is a large chance that effect sizes drawn may not be independent of one another.
As a result, the effect sizes generated from these data sets may not be independent. Inclusion of
dependent effect sizes will bias the mean effect size. This issue was combated by selecting only
one measure per variable per data set. This method is consistent with current criminal justice
decision-making meta-analyses.
Heterogeneity of measures
A criticism of meta-analysis is that meta-analysts combine measures that are too
dissimilar to be meta-analyzed properly. If measures generating effect sizes are not similar or
tapping into the same constructs, then the effect sizes estimates are effectively meaningless.
However, meta-analysts are aware of this issue and they created Q statistics to combat this
specific problem (Pratt, 2001). The Q-statistic uses a chi-square distribution. According to
Lipsey and Wilson (2001) the formula used to assess heterogeneity of effect size estimates is as
follows:
14
All effect size calculations, Q, and moderating variable analyses were conducted using STATA macros available
on David Wilson’s website http://mason.gmu.edu/~dwilsonb/ma.html
72
Qes = ∑(wi(ESi – ES+) 2)
where wi is the inverse variance weight for effect size i.
ESi is the individual effect size
and ES+ is the weighted mean effect size.
Once Qes is calculated for each effect size, all values are summed. The summed value is
designated Qobt. Values of Qobt were compared to critical values of the chi-square distribution
with k-1 degrees of freedom where k is the number of effect size estimates. Values found to be
significant were considered heterogeneous. According to Pratt (2001), values found to be
significant may potentially be too dissimilar to be combined. However, Lipsey and Wilson
(2001) suggest that significant Q values simply represent data gathered from varying
populations. Effect sizes generated from heterogeneous populations can be adjusted and
reanalyzed once outliers are removed (Lipsey and Wilson, 2001). When Q values were
significant, several steps were taken. First, Bonta et al. (1998) suggest that any value greater
than three standard deviations from the mean should be considered an outlier and removed.
Second, estimates that are discontinuous from the rest of the distribution were also removed
(Durlak and Lipsey, 1991). Once the outliers were removed, a new mean effect size, weighted
mean effect size, and confidence interval were calculated. Both results, one with outliers and
one without outliers, are presented. Additionally, random effects modeling was used for effect
size analyses. Random effects modeling generates larger confidence intervals than fixed effects
modeling, and is a more conservative approach (Lipsey and Wilson, 2001).15
15
For mean effect sizes both the fixed and random effects results are presented in the next chapter.
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Fail-safe N statistic
As discussed above, a critique of meta-analysis is that studies with null findings are less
likely to be published and rather will sit in a scholars file drawer collecting dust. Given the
current environment of scholars at universities, it seems unlikely that someone would hold a
paper in a file drawer. Moreover, null findings that question the validity of previous research are
equally worthy of publication. Nonetheless, meta-analysts have developed statistics to combat
this file-drawer problem critique, the fail-safe N statistic. This statistic estimates the number of
studies with alternative findings necessary to render the current effect size to the point of
insignificance (Wolf, 1986). The larger the value of the fail-safe N statistic, the more studies
with alternative findings that would be necessary to find a non-significant effect size (Hunter and
Schmidt, 1990; Lipsey and Wilson, 2001; Pratt, 2001). According to Lipsey and Wilson (2001)
the fail-safe N formula is as follows:
k0 = k[(ESk/ESc)-1]
where: k = number of studies used to calculate the mean effect size
ESk = the mean effect size
ESc = the alternative effect size value of non-significance
The values used for ESc varied depending on the particular calculation. The value used was the
minimum value that would incorporate the null value, zero, within a 95% confidence interval.
Conclusions
The analyses discussed above were designed to assess the average strength of correlates
of decision-making at three distinct decision points across the criminal justice system. The
ultimate goal of this assessment is to examine the support for various criminal justice theories
and to work toward the plausibility of a general theory of criminal justice. In order to achieve
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these goals, each relationship was assessed via the mean effect size process. Correlates that
generate significant effect sizes were retained for each particular decision point. Those that did
not generate a significant mean effect size were eliminated from consideration toward a general
theory of criminal justice.
After determining which correlates of decision-making were significant, an assessment
was made to examine the existence of a grand theory approach versus decision-specific approach
to theories of criminal justice. When mean effect sizes for various predictors are significant at
multiple decision points and have sufficiently large fail-safe N values, then a grand theory of
criminal justice is the recommended pathway. It is not necessary that correlates function in the
same manner at each decision point. It is conceivable that the same variable may act differently
depending on the decision point. For example, it is possible that officers are more likely to push
women through the formal criminal justice process and judges are more likely to be lenient at
sentencing because their action removes women from their familial responsibilities for a longer
period of time than an arrest. In this case, the same variable, gender, is functioning differently at
two different decision points. Yet the end result is that gender matters at both decision points.
The alternative approach to criminal justice theory organization is a series of decisionspecific theories. These theories would emphasize decision-specific variables or each decision
point would emphasize different predictor variables. If decision-specific variables produce
larger effect sizes or each decision point has a unique set of significant effect sizes, with
sufficiently large fail-safe N values, then decision-specific theories will be most appropriate for
theories of criminal justice.
75
Limitations
There have been some concerns raised about meta-analysis. These concerns about metaanalysis are three main problems: 1) the publication bias, 2) the incompatibility problem and 3)
the lack of independence of effect sizes. Each of these problems has been discussed in detail
above and solutions were proposed.
Another issue is that the data are limited to mostly published materials and as a result this
study will be biased by the same biases in those other studies when assessing the correlates of
decision-making. This problem is commonly known as “garbage in, garbage out”. A flaw with
this meta-analysis, and any meta-analysis, is that the effect sizes are only as good as the data
used to generate them. There is no cure for this issue other than going through the moderating
variable analysis. Missing variables are another related issue, and they happen for a multitude of
reasons. Since this study is a meta-analysis there is even less control over missing variables.
Omitted variable bias is even more of an issue. To combat this issue, random effects modeling
was used to generate larger confidence intervals and thus, more conservative estimates.
Finally, these analyses cannot examine interaction effects. The present analysis cannot
include interaction effects because those measures are potentially dependent with the noninteractive variables. For example, the inclusion of race_X_gender interaction effect may cause
dependency issues with the separate measures of race and gender. The best this meta-analysis
can do is to assess potential differences in studies that used interaction effects and those that did
not via moderating variable analysis.
76
CHAPTER 5
RESULTS
This dissertation set out to answer three questions. First, what factors influence three
decisions across the criminal justice system? Each of these decisions, arrest, sentencing, and
parole revocation, are assessed and discussed. The second question examined in this dissertation
is, given significant factors, are they consistent across the system or are significant factors unique
to a particular decision point? The third question examined is, should criminal justice theory
take a whole-system or decision-specific approach when formulating theories of criminal justice?
The second and third questions will be discussed in the next chapter.
This chapter will discuss the results from the meta-analysis described in the previous
chapter. The first part of this chapter is a discussion of the general publication characteristics of
each sample. The second part is a discussion of the results of the analysis of each of the
decisions. The discussion of each decision is subdivided into sample characteristics, effect sizes
and moderating variable analysis. It is important to note that effect sizes and confidence
intervals in tables were left as logged odds ratios. This was done for ease of interpretation of the
confidence intervals as logged odds ratios are symmetrical around zero and thus, any confidence
interval containing zero is not significant at 95 percent confidence.
Publication Characteristics
As shown in Table 5.1, the analysis of arrest was based on 54 studies. The majority of
studies were published recently. Approximately 96 percent of studies were published since
1990. However, only 70.3 percent of studies began data collection during that same time frame.
This is likely due to large data collection efforts like POPN, PSS, or the Cincinnati study, which
are still being used to generate publications today. The vast majority of data were generated
77
from studies published in journals. Geographically, studies were scattered. The Midwest region
was where the largest number of studies were conducted, 27.80 percent. The next most common
geographic categories were the South and multi-site studies (18.50 percent of studies each). The
least common study location was the Pacific/Northwest category. Finally, the majority of
authors were university/college professors and the most common discipline was
criminology/criminal justice.
As shown in Table 5.1, the sentencing analysis was based on 46 studies. The majority of
studies were published recently. Specifically, 66.70% of studies were published since 2000.
However, some studies were based on older data. Approximately one quarter of studies were
based on data gathered in the 1960s or 1970s. Sentencing studies tended to be scattered across
the United States. The most common study region was the Midwest, 34.80 percent, followed by
the South, 28.30 percent. Eight studies were based on data collected in multiple states or federal
court. The most common publication type was a journal, with about 70 percent of studies
published in journals. The vast majority, of authors (95.30%) were affiliated with universities or
colleges. Finally, approximately 70 percent of authors worked in the criminology/criminal
justice discipline.
Table 5.1 also presents the general publication information for the parole revocation
analysis. It should be noted that studies of parole revocation were the least consistent. The
parole revocation analysis presented the smallest sample size, with only 19 accepted studies.
Additionally, the parole revocation publication analysis had a fair amount of missing data given
the small sample size. The results indicate that the majority of studies were published recently.
63.10 percent of studies had been published since 2000. The majority of data collection has
taken place since 1990. As with previous decisions, studies are scattered throughout the United
78
States. Parole revocation studies were most commonly conducted on the West Coast, 33.30
percent, followed by the Midwest, 22.20 percent. The most common publication type was an
academic journal, 73.70 percent. Finally, authors were primarily affiliated with universities and
colleges and the most common discipline was criminology/criminal justice or mixed, 36.80
percent each.
79
Table 5.1: Publication
Characteristics
Publication Characteristic
Publication Decade
1970
1980
1990
2000
2010
Sentencing
N
%
Parole
Revocation
N
%
1
1
20
32
0
1.90
1.90
37.00
59.30
0.00
0
6
9
27
3
0.00
13.30
20.00
60.00
6.70
1
3
3
5
7
5.30
15.80
15.80
26.30
36.80
Publication Type
Journal
Dissertation/Thesis
Agency Report
Other
37
11
6
0
68.50
20.40
11.10
0.00
32
12
1
1
69.60
26.10
2.20
2.20
14
2
3
0
73.70
10.50
15.80
0.00
Data Gathered
1960
1970
1980
1990
2000
2
6
8
20
18
3.70
11.10
14.80
37.00
33.30
2
10
4
24
6
4.30
21.70
8.70
52.20
13.00
0
0
3
5
8
0.00
0.00
18.80
31.80
50.00
Geographic Region of the Study
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed/Nationwide
9
10
15
7
3
10
16.70
18.50
27.80
13.00
5.60
18.50
4
13
16
4
1
8
8.70
28.30
34.80
8.70
2.20
17.40
1
3
4
3
6
1
5.60
16.70
22.20
16.70
33.30
5.60
Author Affiliation
University/College
State Agency
Mixed
Other
51
0
1
2
94.40
0.00
1.90
3.70
41
0
2
0
95.30
0.00
4.70
0.00
15
1
3
0
78.90
5.30
15.80
0.00
N
Arrest
%
80
Table 5.1 continued
Publication Characteristic
Author Discipline
Criminology/Criminal Justice
Psychology
Sociology
Social Work
Mixed
Other
34
1
9
1
3
6
N
54
N
Arrest
%
63.00
1.90
16.70
1.90
5.60
11.10
Sentencing
N
%
Parole
Revocation
N
%
29
0
10
0
2
1
7
1
2
1
7
1
46
69.00
0.00
21.30
0.00
4.80
2.40
36.80
5.30
10.50
5.30
36.80
5.30
19
Arrest
Sample characteristics
Table 5.2 describes the sample characteristics for the arrest study analysis. As shown in
Table 5.2, the average sample size was 60,423.61 cases and across all 54 studies, 3,262,875 cases
were studied. The mean number of agencies involved in each study was 4.311 agencies. The
mean study length was 21.67 months. Approximately, 20 percent of studies reported at least one
analysis with interaction effects, a quarter of studies used systematic social observation, and
nearly all studies presented information on control variables and used continuous data collection,
92.60 and 98.10 respectively. Finally, approximately 70 percent of studies collected information
on offense specific arrests.
Table 5.2 also describes what variables were collected in various studies. These variables
are separated into 4 groups: suspect characteristics, officer characteristics, seriousness
characteristics, and other characteristics. For suspects, the majority of studies collected
information on race, gender and age of suspects, 83.30, 72.20, and 66.70 respectively.
Approximately 30 to 25 percent of studies reported information on suspect ethnicity, demeanor,
81
or the presence of interaction phase crime. Finally, few studies collected information on suspect
compliance, or whether the suspect was mentally ill, 16.70 and 5.60 percent, respectively.
Officer characteristics were not commonly recorded. Officer race and experience
measures were most common. Each was recorded in 20.40 percent of studies. Officer gender
was slightly less common 18.50 percent of studies. Officer education was recorded in 16.70
percent studies.
As shown in Table 5.2, the mean number of seriousness indicators was 1.407 per study.
Weapon use and a general seriousness measure, most commonly measured as felony versus
misdemeanor, were recorded in 33.30 and 31.50 percent studies respectively. An intoxication
measure was recorded in 29.60 percent of the studies. Evidence and victim preference for arrest
were recorded in about one quarter of studies, 25.90 and 24.10 percent respectively. Finally,
other characteristics were relatively uncommon. Less than 10 percent of studies recorded
organization measures and slightly more than 10 percent recorded information on community
variables. However, it should be noted that there was very little consistency in these measures
and therefore, they are not included in the effect size analysis.
82
Table 5.2: Arrest Sample Characteristics
Variable
General Study Characteristics
Agencies
Offense Specific (Yes=1)
Control Variables
Systematic Social Observation
Interaction Effects
Length
Continuous Data Collection
Size
Mean/%
S
4.31
68.50
92.60
25.90
18.50
21.67
98.10
60423.61
7.25
Suspect Characteristics
Suspect Race Measure
Suspect Gender Measure
Suspect Age Measure
Suspect Ethnicity Measure
Demeanor Measure
Interaction Phase Crime Measure
Compliance Measure
Mental Illness Measure
83.30
72.20
66.70
27.80
29.60
24.10
16.70
5.60
Officer Characteristics
Officer Race Measure
Officer Gender Measure
Officer Education Measure
Officer Experience Measure
20.40
18.50
16.70
20.40
Seriousness Characteristics
Seriousness Measure
Evidence Measure
Intoxication Measure
Weapon Use Measure
Victim Preference Measure
Total Serious Measures
31.50
25.90
29.60
33.30
24.10
1.41
Other Characteristics
Organization Variables Measured
Community Variables Measured
9.40
11.10
N
54
83
Total
29.19
149133.61
1.27
3262875
Effect sizes
Table 5.3 displays results for the arrest effect size analysis. Suspect race, gender and
ethnicity significantly influenced arrest. Specifically, black suspects were approximately 1.40
times more likely to be arrested than white suspects, male suspects were approximately 1.50
times more likely to be arrested than female suspects and Hispanic suspects were approximately
1.25 times more likely to be arrested than non-Hispanic suspects across studies. Age and mental
illness were not significantly related to arrest. Additionally, all three of the characteristics were
all significant at p < .001. No officer characteristics were significantly related to arrest.
Seriousness measures are all significant and in the expected directions. The strongest
seriousness indicator was strength of evidence. As amounts of evidence increased, arrest
chances were almost 7 times higher. When the victim preferred arrest, the odds of arrest
increased 4.10 times. Felonies were approximately 2.50 times more likely to end in arrest than
misdemeanors. Finally, suspect intoxication and the presence or use of a weapon increased the
odds of arrest by 1.53 and 1.58 times respectively.
Suspect demeanor measures were also influential in arrest outcomes. Demeanor was not
stronger than the effect of interaction phase crime. Demeanor increased the odds of arrest by
almost 200 percent, but when a suspect committed an interaction phase crime the odds of arrest
increased 3.17 times across studies. Additionally, when suspects were considered noncompliant, their odds of arrest increased 1.79 times.
84
Table 5.3: Mean Effect Sizes Arrest
Mean
ES
W Mean
ES
95% CI
Min
95% CI
Max
42 3253123
0.66
0.33
(0.01)
0.221
(0.02)
0.438
(0.03)
Without Outliers***
41 3239557
0.52
0.33
(0.01
0.222
(-0.02)
0.438
(0.03)
Suspect Male***
38 3213071
0.83
0.40
(0.16
0.29
(0.12)
0.52
(0.19)
Without Outliers ***
37 3199505
0.38
0.40
(0.16)
0.29
(0.12)
0.52
(0.19)
Suspect Age
35 2120709
0.06
-0.001
(0.001)
-0.004
(0.001)
0.003
(0.001)
Suspect Ethnicity***
15 2858798
0.26
0.22
(0.02)
0.104
(0.02)
0.343
(0.03)
Suspect Mental Illness
4
-0.51
-0.47
(-0.34)
-1.43
(-0.77)
0.49
(0.09)
Officer Variables
Officer Black
11 1822723
-0.07
-0.035
(0.01)
-0.097
(-0.01)
0.027
(0.02)
Officer Male
10 1787322
0.32
0.095
(-0.020)
-0.069
(-0.05)
0.26
(0.01)
Officer Experience
8
1733917
0.12
0.009
(0.003)
-0.018
(0.002)
0.037
(0.01)
Seriousness Variables
Seriousness***
14
19782
1.91
0.925
(0.34)
0.585
(0.27)
1.265
(0.40)
Variable
k
Suspect Variables
Suspect Black***
N
4364
85
Table 5.3 continued
1728460
Mean
ES
2.63
W Mean
ES
1.93
(0.67)
95% CI
Min
1.38
(0.59)
95% CI
Max
2.49
(0.74)
17
64711
0.60
0.43
(0.38)
0.27
(0.30)
0.59
(0.45)
Weapon Use***
15
73113
0.59
0.46
(0.34)
0.30
(0.25)
0.62
(0.42)
Arrest Preference***
13
12811
1.45
1.41
(1.57)
0.87
(1.36)
1.94
(1.77)
Demeanor Variables
Demeanor***
16
150640
0.03
0.69
(0.02)
0.36
(0.002)
1.02
(0.04)
Without Outliers***
13
150050
0.68
0.64
(0.02)
0.31
(0.001)
0.97
(0.04)
Interaction Phase Crime**
9
43810
1.81
1.15
(0.19)
0.35
(0.01)
1.96
(0.36)
Compliance**
9
6794
0.66
0.58
(0.45)
0.15
(0.31)
1.02
(0.58)
Variable
k
N
Evidence***
12
Intoxication***
(Fixed effects results in parentheses)
* sig p< .05
** sig p<.01
*** sig p< .001
Fail-safe N analysis
The fail-safe N statistic determines the number of studies necessary to render the effect
size to a value of non-significance. Higher values indicate that more studies with null values are
required to nullify the mean effect size. The value chosen for the negligible value varied
depending on the particular mean effect size. A fail-safe N value was calculated for each
86
significant weighted mean effect size value. Overall, there is confidence in these significant
effect sizes. The average fail-safe N value was 36. There was some variation in fail-safe N
values amongst the various significant mean effect sizes. The “suspect black” finding would
require another 86 studies to render the finding presented above to an insignificant value.16 The
effect of suspect gender was also a stable finding; the fail-safe N value for gender was 93.
Ethnicity was the least stable significant suspect characteristic with a fail-safe N value of 15.
While there is some variation amongst these significant suspect characteristics, they are
relatively stable.
All of the seriousness measures are stable as well. The relationship between seriousness
of the offense and arrest would require another 25 null effect sizes, and the evidence effect size
would require an additional 30 null effect sizes to render these findings non-significant.
Additionally, the intoxication effect size’s fail-safe N value is 28, and the weapon use effect size
fail-safe N value is 31. The arrest preference effect size would require an additional 21 effect
sizes to render that finding non-significant. Finally, the fail-safe N values for various demeanor
measures were the least stable. The fail-safe N value for demeanor is 13. The mean effect sizes
for interaction-phase crime and compliance are much less stable than the fail-safe N for
demeanor. The values are 4 and 3 respectively.
Moderating variable analysis
An attempt was made to dig deeper into the effect size analysis above by conducting an
analysis of moderating variables. The moderator variable analysis for arrest is limited. The
primary limitation is lack of variation in moderating variables. Due to the consistency of study
variables there is little variation in the variables found in studies. As a result, effect sizes used in
the moderating variable analysis are largely lumped into one category and very few in the others.
16
Where applicable fail-safe N values are reported with outliers removed.
87
A second limitation is there were few mean effect sizes with enough k values for meaningful
moderator analysis. Using a cutoff of a k value of 15, the variables that could be further
analyzed were: suspect race, suspect gender, suspect ethnicity, suspect intoxication, suspect
weapon use, and suspect demeanor. Due to a technical issue with the STATA software, too
many cases had to be removed in order to analyze suspect age. As a result, that analysis was not
conducted.
Tables 5.4-5.9 below display the results of the moderating variable analysis. Across all
moderator analyses, variables that were significant in the mean effect size analysis remained
significant across moderator categories.17 Table 5.4 displays the full results of the suspect race
moderator analysis. Effect sizes generated by authors that were categorized as “other” discipline
did not report significant race effect sizes. Studies conducted in the South did not report
significant race effect sizes, but all other geographic regions did report significant race effect
sizes. The mean effect size for studies using data gathered in the 1980s did not report a
significant race effect. However, it should be noted that the k value for this finding is small.
Across other moderating variables, race remained significant. There were only a handful of nonsignificant categories and the majority of these were based on a small number of effect sizes, one
to three, which made these results less meaningful.
The findings for the moderator analysis of gender effect sizes are similar to the findings
for race in that, across moderator categories, mean effect sizes remained significant. The full
results are displayed in Table 5.5. Like race, mean effect sizes for studies conducted by those in
the “other” discipline category were not significant. Upon closer examination of when studies
were published, only those published between 2000 and 2009 produced significant mean effect
sizes for gender. However, studies based on data gathered in the 1960s, 1970s, and 1980s did
17
The findings discuss those results that are contrary to the general finding.
88
not produce significant mean effect sizes. Only two studies were based on data from the 1960s
and 1980s, and six studies used data from the 1970s. Finally, the remaining moderator
categories either produced mean effect sizes for gender that remained significant or those that
were not significant were based on a negligible number of studies.
Table 5.6 displays the full results for the ethnicity moderating variable analysis. Relative
to race and gender there are significantly fewer effect sizes making up the suspect ethnicity
moderator analysis. The inclusion of Hispanic status as an empirical variable is still a relatively
modern addition. The majority of studies analyzed here were published between 2000 and 2009
and a few in the 1990s. Nevertheless, there were a sufficient number of effect sizes to conduct a
moderating variable analysis.
The pattern found in these results is similar to the previous moderator analyses for
individual suspect characteristics. Specifically, the majority of mean effect sizes across
categories are significant and those that are not are often limited to one or two effect sizes to
generate the mean effect size. Significant mean effect sizes were found among those that were
conducted by individuals in criminal justice or mixed disciplines. Studies conducted in the
Northeast and the Southwest produced significant mean effect sizes for ethnicity. Hispanics
were significantly more likely to be arrested according to studies conducted between 2000 and
2009. However, it should be noted that the smaller number of studies spread over more
categories creates a small number of effect sizes generating the mean effect size in this finding.
Only state/local reports reported significantly more Hispanics being arrested than non-Hispanics.
Studies that included a measure of age found that Hispanic status increased arrest.
Two seriousness measures had a sufficient number of effect sizes to perform a
meaningful moderator analysis, suspect intoxication and suspect weapon use. Table 5.7 displays
89
the full results for the moderating variable analysis for suspect intoxication. Consistent with
previous moderator analyses, mean effect sizes across moderator categories are generally
significant. Those mean effect sizes that were not significant are those that were created from
one or two effect sizes. One exception is that mean effect sizes generated from studies that did
not measure gender and those that did not measure age did not produce significant mean effect
sizes for suspect intoxication. Table 5.8 displays the full results for suspect weapon use. For this
analysis all mean effect sizes were significant across moderator categories except for a handful
of categories that had one to three effect sizes to create the mean effect size.
The final variable examined for moderator variable analysis was suspect demeanor. The
full results for this analysis are displayed in Table 5.9. The results here are similar to all of the
previous moderator results for the arrest decision. Specifically, mean effect sizes are generally
significant across moderator categories and those that are not significant are often generated
from one to three effect sizes. There were some unique findings. The mean effect size for
studies conducted by sociologists did not find demeanor significant. Studies conducted in the
Northeast and Midwest did not find demeanor significantly predicted arrest. Studies based on
data from the 1980s did not find demeanor significantly predicted arrest. Studies that did not
measure intoxication did not produce a significant mean effect size for demeanor. Finally, both
studies that did and those that did not record a measure of interaction phase crime produced
significant mean effect sizes for demeanor and arrest.
90
Table 5.4: Suspect Race Moderator Analysis
Moderator Variable
k
Discipline
Criminal Justice/Criminology
26
Psychology
0
Sociology
9
Social Work
0
Mixed
3
Others
6
ES
95% CI Min
95% CI Max
SE
0.34
0.00
0.52
0.00
0.35
0.12
0.22
0.00
0.26
0.00
0.07
-0.04
0.45
0.00
0.77
0.00
0.62
0.28
0.06
0.00
0.13
0.00
0.35
0.08
Publication Decade
1970
1980
1990
2000
1
1
15
28
0.63
0.71
0.34
0.30
0.12
-0.59
0.09
0.19
1.14
2.02
0.59
0.42
0.26
0.66
0.13
0.06
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
6
7
14
7
3
8
0.27
-0.06
0.49
0.43
0.51
0.15
0.05
-0.47
0.27
0.25
0.22
0.00
0.48
0.35
0.71
0.61
0.79
0.29
0.11
0.21
0.11
0.09
0.15
0.07
Data Gathered
1960
1970
1980
1990
2000
2
6
4
16
17
0.41
0.52
0.31
0.43
0.25
0.07
0.09
-0.13
0.21
0.12
0.75
0.96
0.75
0.64
0.39
0.17
0.22
0.23
0.11
0.07
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
30
9
6
0.23
0.33
0.46
0.11
0.15
0.27
0.36
0.50
0.64
0.06
0.09
0.10
91
Table 5.4 continued
Moderator Variable
Agency Number
1
2
3
4
8
9
20
24
28
k
ES
95% CI Min
95% CI Max
SE
28
1
1
1
1
3
1
1
1
0.39
0.21
0.63
0.53
0.31
1.33
0.94
0.45
0.71
0.26
-0.26
0.19
-0.29
-0.31
0.51
0.27
-0.08
-0.57
0.51
0.68
1.08
1.35
0.92
2.14
2.16
0.98
1.99
0.06
0.24
0.23
0.44
0.32
0.42
0.62
0.27
0.65
Offense Specific
No
Yes
16
29
0.45
0.16
0.26
0.16
0.64
0.39
0.10
0.06
Control Variables
No
Yes
4
41
0.52
0.32
0.08
0.21
0.97
0.42
0.23
0.05
SSO Use
No
Yes
32
13
0.29
0.49
0.18
0.24
0.40
0.74
0.06
0.13
Interaction Effect Use
No
Yes
37
8
0.35
0.25
0.23
0.00
0.46
0.51
0.06
0.13
Continuous Data Collection
No
Yes
1
44
0.21
0.34
-0.35
0.22
0.77
0.45
0.29
0.06
Offender Gender Recorded
No
Yes
7
38
0.42
0.32
0.05
0.21
0.79
0.43
0.19
0.06
Offender Age Recorded
No
Yes
11
34
0.26
0.36
0.07
0.23
0.45
0.49
0.10
0.07
92
Table 5.4 continued
Moderator Variable
Offender Ethnicity Recorded
No
Yes
k
ES
95% CI Min
95% CI Max
SE
30
15
0.24
0.42
0.11
0.27
0.36
0.56
0.06
0.07
Officer Race Recorded
No
Yes
34
11
0.38
0.23
0.25
0.58
0.51
0.40
0.07
0.09
Officer Gender Recorded
No
Yes
36
9
0.35
0.30
0.21
0.10
0.49
0.51
0.07
0.11
Seriousness Recorded
No
Yes
30
15
0.32
0.33
0.20
0.15
0.44
0.52
0.06
0.09
Evidence Recorded
No
Yes
31
14
0.27
0.44
0.15
0.26
0.38
0.61
0.06
0.09
Intoxication Recorded
No
Yes
32
13
0.35
0.31
0.21
0.06
0.48
0.56
0.07
0.13
Weapon Use Recorded
No
Yes
35
10
0.36
0.26
0.23
0.01
0.50
0.50
0.07
0.13
Victim Preference Recorded
No
Yes
35
10
0.30
0.43
0.19
0.18
0.42
0.69
0.06
0.13
Demeanor Recorded
No
Yes
30
15
0.27
0.49
0.16
0.27
0.39
0.70
0.06
0.11
93
Table 5.4 Continued
Moderator Variable
Interaction Phase Crime Recorded
No
Yes
Compliance Recorded
No
Yes
k
ES
95% CI Min
95% CI Max
SE
35
10
0.34
0.29
0.22
0.03
0.45
0.55
0.06
0.13
37
8
0.31
0.50
0.20
0.17
0.42
0.83
0.06
0.17
Organization Recorded
No
Yes
41
3
0.35
-0.10
0.28
-0.23
0.43
0.04
0.04
0.07
Community Recorded
No
Yes
41
4
0.33
0.32
0.22
0.02
0.44
0.62
0.06
0.15
94
Table 5.5: Suspect Gender Moderator Analysis
Moderator Variable
k
Discipline
Criminal Justice/Criminology
23
Psychology
0
Sociology
7
Social Work
0
Mixed
3
Others
5
0.42
0.00
0.38
0.00
0.80
0.15
0.28
0.00
0.10
0.00
0.46
-0.08
0.55
0.00
0.66
0.00
1.13
0.37
0.07
0.00
0.14
0.00
0.80
0.15
Publication Decade
1970
1980
1990
2000
1
1
13
23
0.53
0.53
0.22
0.45
-0.09
-0.78
-0.03
0.31
1.15
1.85
0.47
0.59
0.32
0.67
0.13
0.07
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
3
5
13
7
2
8
0.53
0.65
0.29
0.56
0.50
0.24
0.20
0.17
0.04
0.32
0.13
0.04
0.87
1.14
0.55
0.80
0.86
0.44
0.17
0.25
0.13
0.12
0.19
0.10
Data Gathered
1960
1970
1980
1990
2000
2 0.38
6 0.29
2 -0.03
13 0.42
15 0.46
-0.05
-0.17
-0.54
0.19
0.29
0.80
0.75
0.48
0.66
0.64
0.22
0.24
0.26
0.12
0.09
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
26
6
6
0.13
0.32
0.31
0.42
0.80
0.77
0.08
0.12
0.12
95
ES
0.28
0.56
0.54
95% CI Min 95% CI Max
SE
Table 5.5 continued
Moderator Variable
Agency Number
1
2
3
8
9
20
24
28
22 0.56
1 0.23
1 0.53
1 0.93
3 -0.19
1 0.23
1 0.40
1 0.53
0.42
-0.27
0.01
0.33
-0.74
-1.36
-0.24
-0.74
0.71
0.73
1.04
1.52
0.37
1.83
1.04
1.80
0.07
0.25
0.26
0.30
0.28
0.81
0.22
0.65
Offense Specific
No
Yes
14
24
0.36
0.42
0.15
0.28
0.57
0.57
0.11
0.07
Control Variables
No
Yes
3
35
0.50
0.40
-0.02
0.28
1.01
0.52
0.26
0.06
SSO Use
No
Yes
27
11
0.40
0.39
0.28
0.12
0.53
0.67
0.06
0.14
Interaction Effect Use
No
Yes
31
7
0.43
0.29
0.30
0.01
0.57
0.56
0.07
0.14
Continuous Data Collection
No
Yes
1
37
0.23
0.41
-0.36
0.29
0.82
0.53
0.30
0.06
Offender Age Recorded
No
Yes
8
30
0.28
0.45
0.08
0.32
0.48
0.57
0.10
0.07
Offender Ethnicity Recorded
No
Yes
26
12
0.30
0.53
0.15
0.37
0.44
0.70
0.07
0.08
k
96
ES
95% CI Min 95% CI Max
SE
Table 5.5 continued
Moderator Variable
Officer Race Recorded
No
Yes
k
ES
27
11
0.41
0.38
0.27
0.20
0.56
0.56
0.07
0.09
Officer Gender Recorded
No
Yes
30
8
0.41
0.38
0.27
0.17
0.56
0.60
0.07
0.11
Seriousness Recorded
No
Yes
26
12
0.39
0.44
0.25
0.21
0.53
0.66
0.07
0.11
Evidence Recorded
No
Yes
26
12
0.29
0.60
0.16
0.42
0.42
0.79
0.07
0.09
Intoxication Recorded
No
Yes
27
11
0.40
0.41
0.27
0.16
0.54
0.65
0.07
0.12
Weapon Use Recorded
No
Yes
31
7
0.43
0.31
0.30
0.06
0.56
0.56
0.07
0.13
Victim Preference Recorded
No
Yes
31
7
0.38
0.50
0.26
0.21
0.51
0.80
0.06
0.15
Demeanor Recorded
No
Yes
24
14
0.41
0.39
0.27
0.17
0.54
0.61
0.07
0.11
Interaction Phase Crime Recorded
No
Yes
29
9
0.40
0.41
0.28
0.11
0.53
0.71
0.06
0.15
97
95% CI Min 95% CI Max
SE
Table 5.5 continued
Moderator Variable
Compliance Recorded
No
Yes
k
ES
30
8
0.41
0.33
0.29
0.02
0.54
0.64
0.06
0.16
Organization Recorded
No
Yes
34
3
0.43
0.00
0.33
-0.22
0.54
0.22
0.05
0.11
Community Recorded
No
Yes
35
3
0.39
0.54
0.27
0.17
0.51
0.91
0.06
0.19
98
95% CI Min 95% CI Max
SE
Table 5.6: Suspect Ethnicity Moderator Analysis
Moderator Variable
k
Discipline
Criminal Justice/Criminology
8
Psychology
0
Sociology
4
Social Work
0
Mixed
2
Others
1
0.22
0.00
0.18
0.00
0.49
0.25
0.05
0.00
-0.20
0.00
0.10
-0.19
0.40
0.00
0.56
0.00
0.87
0.69
0.09
0.00
0.19
0.00
0.19
0.27
Publication Decade
1990
2000
3
12
0.30
0.25
-0.84
0.12
1.44
0.39
0.58
0.07
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
3
2
2
4
3
1
0.49
-0.10
0.21
0.35
0.21
0.11
0.20
-0.37
-0.01
0.17
-0.01
-0.43
0.79
0.17
0.44
0.54
0.44
0.65
0.15
0.14
0.11
0.05
0.11
0.28
Data Gathered
1980
1990
2000
3
2
10
0.30
0.28
0.25
-0.84
-0.16
0.10
1.43
0.72
0.40
0.58
0.22
0.08
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
4
5
6
0.07
0.17
0.38
-0.15
-0.07
0.22
0.28
0.41
0.53
0.11
0.12
0.08
Agency Number
1
4
12
1
0.25
0.44
0.11
-0.18
0.40
1.05
0.07
0.31
99
ES
95% CI Min 95% CI Max
SE
Table 5.6 continued
Moderator Variable
Offense Specific
No
Yes
k
ES
4
11
0.15
0.26
-0.39
0.12
0.70
0.40
0.28
0.07
SSO Use
No
Yes
13
2
0.25
0.30
0.12
-0.84
0.39
1.44
0.07
0.58
Interaction Effect Use
No
Yes
11
4
0.26
0.23
0.10
-0.09
0.42
0.55
0.08
0.16
Offender Gender Recorded
No
Yes
3
12
0.31
0.24
-0.04
0.09
0.67
0.40
0.18
0.08
Offender Age Recorded
No
Yes
5
10
0.23
0.27
-0.02
0.10
0.48
0.44
0.13
0.09
Officer Race Recorded
No
Yes
10
5
0.22
0.31
0.05
0.03
0.39
0.53
0.09
0.11
Officer Gender Recorded
No
Yes
10
5
0.21
0.31
0.03
0.11
0.38
0.52
0.09
0.10
Seriousness Recorded
No
Yes
12
3
0.24
0.43
0.10
-0.14
0.38
1.00
0.07
0.29
Evidence Recorded
No
Yes
8
7
0.22
0.28
0.00
0.09
0.43
0.47
0.11
0.10
100
95% CI Min 95% CI Max
SE
Table 5.6 continued
Moderator Variable
Weapon Use Recorded
No
Yes
k
ES
14
1
0.24
0.44
0.11
-0.17
0.38
1.05
0.07
0.31
Victim Preference Recorded
No
Yes
14
1
0.24
0.44
0.11
-0.17
0.38
1.05
0.07
0.31
Demeanor Recorded
No
Yes
12
3
0.27
0.15
0.12
-0.26
0.42
0.57
0.08
0.21
Interaction Phase Crime Recorded
No
Yes
13
2
0.26
0.25
0.11
-0.19
0.41
0.68
0.08
0.22
Community Recorded
No
Yes
14
1
0.22
0.57
0.09
0.13
0.35
1.01
0.07
0.23
101
95% CI Min 95% CI Max
SE
Table 5.7: Suspect Intoxication Moderator Analysis
Moderator Variable
k
ES
Discipline
Criminal Justice/Criminology
13
0.41
Psychology
0
0.00
Sociology
2
0.76
Social Work
0
0.00
Mixed
1
0.48
Others
1
0.43
95% CI Min
95% CI Max
SE
0.18
0.00
0.12
0.00
-0.21
-0.16
0.64
0.00
1.40
0.00
1.16
1.01
0.12
0.00
0.33
0.00
0.35
0.30
Publication Decade
1980
1990
2000
1
4
12
0.40
0.68
0.38
-1.06
0.26
0.20
1.86
1.10
0.56
0.74
0.21
0.09
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
1
6
4
2
1
3
-0.26
0.52
0.28
0.57
0.62
0.52
-0.62
0.19
0.07
0.16
0.18
0.34
0.10
0.85
0.49
0.98
1.07
0.70
0.18
0.17
0.11
0.21
0.23
0.09
Data Gathered
1970
1980
1990
2000
3
0
12
2
0.78
0.00
0.48
0.12
0.23
0.00
0.27
-0.27
1.34
0.00
0.70
0.52
0.28
0.00
0.11
0.20
Publication Type
Journal
Graduate Dissertation/Thesis
15
2
0.48
0.12
0.31
-0.24
0.65
0.49
0.08
0.19
102
Table 5.7 continued
Moderator Variable
Agency Number
1
2
3
4
8
9
20
21
24
28
k
ES
95% CI Min
95% CI Max
SE
9
1
0
1
1
0
1
1
1
1
0.38
0.59
0.00
0.62
0.48
0.00
1.44
-0.26
0.74
0.40
0.10
0.17
0.00
0.13
-0.01
0.00
0.08
-0.68
0.24
-1.02
0.58
1.00
0.00
1.12
0.96
0.00
2.80
0.16
1.24
1.82
0.11
0.21
0.00
0.25
0.25
0.00
0.69
0.22
0.25
0.73
Offense Specific
No
Yes
5
12
0.63
0.32
0.36
0.13
0.90
0.51
0.14
0.10
Control Variables
No
Yes
2
15
1.08
0.41
0.13
0.24
2.02
0.57
0.48
0.08
SSO Use
No
Yes
11
6
0.30
0.65
0.11
0.38
0.50
0.91
0.10
0.13
Interaction Effect Use
No
Yes
15
2
0.39
0.74
0.23
0.22
0.56
1.27
0.09
0.27
Continuous Data Collection
No
Yes
1
16
0.59
0.41
0.06
0.24
1.12
0.59
0.27
0.09
Offender Race Recorded
No
Yes
3
17
0.01
0.48
-0.35
0.33
0.37
0.62
0.18
0.08
103
Table 5.7 continued
Moderator Variable
Offender Gender Recorded
No
Yes
k
ES
95% CI Min
95% CI Max
SE
6
11
0.24
0.50
-0.05
0.31
0.53
0.69
0.15
0.09
Offender Age Recorded
No
Yes
6
11
0.25
0.49
-0.05
0.30
0.55
0.67
0.15
0.09
Offender Ethnicity Recorded
No
Yes
16
1
0.41
0.62
0.24
0.03
0.58
1.22
0.09
0.30
Officer Race Recorded
No
Yes
14
3
0.47
0.37
0.26
0.05
0.68
0.68
0.11
0.16
Officer Gender Recorded
No
Yes
12
5
0.61
0.27
0.38
0.07
0.84
0.48
0.12
0.10
Seriousness Recorded
No
Yes
9
8
0.52
0.37
0.25
0.16
0.79
0.59
0.14
0.11
Evidence Recorded
No
Yes
10
7
0.40
0.49
0.16
0.22
0.63
0.76
0.12
0.14
Weapon Use Recorded
No
Yes
8
9
0.27
0.52
0.04
0.33
0.50
0.71
0.12
0.10
Victim Preference Recorded
No
Yes
6
11
0.23
0.58
0.01
0.37
0.46
0.78
0.12
0.11
104
Table 5.7 continued
Moderator Variable
Demeanor Recorded
No
Yes
k
ES
95% CI Min
95% CI Max
SE
9
8
0.33
0.55
0.09
0.24
0.58
0.81
0.13
0.13
Interaction Phase Crime Recorded
No
Yes
12
5
0.32
0.64
0.14
0.36
0.51
0.91
0.09
0.14
Compliance Recorded
No
Yes
13
4
0.45
0.41
0.24
0.10
0.66
0.72
0.11
0.16
Organization Recorded
No
Yes
15
2
0.52
0.12
0.32
-0.28
0.73
0.52
0.10
0.20
Community Recorded
No
Yes
15
2
0.42
0.49
0.23
0.11
0.61
0.87
0.10
0.19
105
Table 5.8: Suspect Weapon Use Moderator Analysis
Moderator Variable
k
ES
Discipline
Criminal Justice/Criminology
11 0.56
Psychology
0 0.00
Sociology
0 0.00
Social Work
1 0.27
Mixed
1 0.34
Others
2 0.35
95% CI Min 95% CI Max
SE
0.34
0.00
0.00
-0.33
-0.99
-0.09
0.78
0.00
0.00
0.88
1.66
0.78
0.11
0.00
0.00
0.31
0.68
0.22
Publication Decade
1990
2000
5
10
0.55
0.44
0.27
0.20
0.83
0.67
0.14
0.12
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
2
5
2
1
0
6
0.41
0.73
0.71
0.34
0.00
0.35
0.07
0.23
0.32
-0.96
0.00
0.11
0.75
1.23
1.10
1.63
0.00
0.59
0.17
0.26
0.20
0.66
0.00
0.12
Data Gathered
1970
1980
1990
2000
1
3
9
2
1.16
0.59
0.42
0.30
0.39
0.24
0.21
-0.04
1.93
0.95
0.63
0.65
0.39
0.18
0.11
0.18
Publication Type
Journal
Graduate Dissertation/Thesis
13
2
0.47
0.44
0.30
-0.04
0.65
0.92
0.09
0.24
Agency Number
1
2
8
9
24
7
1
1
1
2
0.80
0.02
0.34
0.64
0.43
0.46
-0.91
-0.95
0.19
0.10
1.15
0.95
1.67
1.09
0.77
0.18
0.47
0.66
0.23
0.17
106
Table 5.8 continued
Moderator Variable
Offense Specific
No
Yes
k
ES
3
12
0.66
0.39
0.33
0.23
1.00
0.55
0.17
0.08
SSO Use
No
Yes
13
2
0.44
0.70
0.28
0.11
0.61
1.29
0.08
0.30
Interaction Effect Use
No
Yes
14
1
0.41
1.16
0.27
0.41
0.56
1.91
0.07
0.38
Continuous Data Collection
No
Yes
1
14
0.02
0.48
-0.92
0.31
0.04
0.64
0.48
0.08
Offender Race Recorded
No
Yes
6
9
0.48
0.49
0.20
0.25
0.77
0.73
0.15
0.12
Offender Gender Recorded
No
Yes
8
7
0.50
0.47
0.23
0.22
0.77
0.72
0.14
0.13
Offender Age Recorded
No
Yes
5
10
0.55
0.46
0.23
0.23
0.88
0.68
0.17
0.11
Officer Race Recorded
No
Yes
14
1
0.48
0.02
0.31
-0.92
0.64
0.96
0.08
0.48
Officer Gender Recorded
No
Yes
13
2
0.54
0.18
0.36
-0.18
0.72
0.54
0.09
0.19
107
95% CI Min 95% CI Max
SE
Table 5.8 continued
Moderator Variable
Seriousness Recorded
No
Yes
k
ES
12
3
0.40
0.96
0.25
0.43
0.55
1.49
0.07
0.27
Evidence Recorded
No
Yes
12
3
0.48
0.35
0.31
-0.27
0.65
0.97
0.09
0.32
Intoxication Recorded
No
Yes
7
8
0.50
0.44
0.28
0.14
0.72
0.74
0.11
0.15
Victim Preference Recorded
No
Yes
8
7
0.38
0.73
0.23
0.38
0.54
1.07
0.08
0.18
Demeanor Recorded
No
Yes
13
2
0.44
0.70
0.28
0.11
0.61
1.29
0.09
0.30
Interaction Phase Crime Recorded
No
Yes
11
4
0.41
0.73
0.25
0.31
0.57
1.14
0.08
0.21
Compliance Recorded
No
Yes
14
1
0.48
0.02
0.31
-0.92
0.64
0.96
0.08
0.48
Organization Recorded
No
Yes
14
1
0.52
0.21
0.34
-0.20
0.71
0.61
0.09
0.21
Community Recorded
No
Yes
13
2
0.45
0.57
0.29
0.02
0.62
1.12
0.09
0.28
108
95% CI Min 95% CI Max
SE
Table 5.9: Suspect Demeanor Moderator Analysis
Moderator Variable
k
Discipline
Criminal Justice/Criminology
10
Psychology
0
Sociology
5
Social Work
0
Mixed
0
Others
1
0.70
0.00
0.37
0.00
0.00
1.03
0.35
0.00
-0.34
0.00
0.00
0.11
1.06
0.00
1.08
0.00
0.00
1.95
0.18
0.00
0.36
0.00
0.00
0.47
Publication Decade
1970
1980
1990
2000
1
0
9
6
1.03
0.00
0.52
0.73
0.08
0.00
0.03
0.30
0.20
0.00
1.01
1.17
0.49
0.00
0.25
0.22
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
1
4
6
2
0
3
0.01
0.62
0.52
1.00
0.00
0.90
-0.73
0.02
-0.01
0.28
0.00
0.43
0.74
0.12
1.06
1.72
0.00
1.37
0.38
0.31
0.27
0.37
0.00
0.24
Data Gathered
1960
1970
1980
1990
2000
1
5
3
6
1
1.03
1.09
0.07
0.67
0.89
0.32
0.45
-0.43
0.28
0.08
1.74
1.73
0.56
1.06
1.70
0.36
0.33
0.25
0.20
0.41
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
13
2
1
0.74
0.51
0.89
0.30
-0.35
-0.39
1.19
1.36
2.17
0.23
0.44
0.65
109
ES
95% CI Min 95% CI Max
SE
Table 5.9 continued
Moderator Variable
Agency Number
1
2
3
20
24
k
ES
12
1
1
1
1
0.48
0.49
1.03
1.26
1.17
0.18
-0.27
0.32
-0.02
0.39
0.79
1.25
1.74
2.55
1.95
0.16
0.39
0.36
0.65
0.40
Offense Specific
No
Yes
10
6
0.70
0.74
0.24
0.05
1.16
1.44
0.24
0.36
Control Variables
No
Yes
3
13
1.09
0.57
0.44
0.26
1.75
0.88
0.33
0.16
SSO Use
No
Yes
3
13
0.61
0.74
-0.21
0.30
1.43
1.18
0.42
0.22
Interaction Effect Use
No
Yes
11
5
0.77
0.52
0.37
-0.09
1.18
1.12
0.21
0.31
Continuous Data Collection
No
Yes
1
15
0.49
0.72
-0.63
0.36
1.61
1.08
0.57
0.18
Offender Race Recorded
No
Yes
1
15
0.20
0.74
-1.00
0.39
1.40
1.09
0.61
0.18
Offender Gender Recorded
No
Yes
2
14
0.08
0.81
-0.57
0.49
0.74
1.14
0.34
0.16
110
95% CI Min 95% CI Max
SE
Table 5.9 continued
Moderator Variable
Offender Age Recorded
No
Yes
k
ES
3
13
0.71
0.69
-0.11
0.32
1.54
1.06
0.42
0.19
Offender Ethnicity Recorded
No
Yes
13
3
0.80
0.33
0.44
-0.31
1.17
0.96
0.19
0.33
Officer Race Recorded
No
Yes
13
3
0.80
0.46
0.35
-0.31
1.26
1.23
0.23
0.39
Officer Gender Recorded
No
Yes
13
3
0.80
0.46
0.35
-0.31
1.26
1.23
0.23
0.39
Seriousness Recorded
No
Yes
7
9
0.70
0.69
0.17
0.26
1.24
1.11
0.27
0.22
Evidence Recorded
No
Yes
10
6
0.65
0.78
0.13
0.22
1.17
1.34
0.26
0.29
Intoxication Recorded
No
Yes
8
8
0.47
0.96
-0.11
0.45
0.96
1.47
0.27
0.26
Weapon Use Recorded
No
Yes
13
3
0.71
0.64
0.33
0.00
1.09
1.27
0.19
0.32
Victim Preference Recorded
No
Yes
9
7
0.28
0.97
0.01
0.68
0.56
1.26
0.14
0.15
111
95% CI Min 95% CI Max
SE
Table 5.9 continued
Moderator Variable
Interaction Phase Crime Recorded
No
Yes
k
ES
7
9
0.71
0.66
0.27
0.18
1.14
1.14
0.22
0.24
Compliance Recorded
No
Yes
9
7
0.77
0.62
0.29
-0.05
1.25
1.28
0.25
0.34
Community Recorded
No
Yes
14
2
0.71
0.64
0.34
-0.19
1.08
1.46
0.19
0.42
112
95% CI Min 95% CI Max
SE
Sentencing
Sample characteristics
Table 5.10 describes the sample characteristics for the sentencing study analysis. As
shown in Table 5.10, the average sample size contained 49,382.72 cases and across all 46
studies, 2,271,605 cases were studied. The mean number of court systems involved in each
study was 1.83. The mean length of the study period was 37.89 months. Approximately 30
percent of studies reported at least one analysis with interaction effects, and nearly all studies
used presented information on control variables and used continuous data collection, 95.70 and
87.00 percent respectively. Finally, approximately 33 percent of studies collected information
on offense specific cases.
Table 5.10 also describes what variables were collected in various studies. These
variables were separated into 4 groups: suspect characteristics, judge characteristics, seriousness
characteristics, and other characteristics. For suspects, the majority of studies collected
information on race, gender, and age of suspects, 84.80, 69.60, and 87.00 percent respectively.
Approximately 35 and 20 percent of studies reported information on suspect ethnicity, and
suspect employment.
Judge characteristics were not commonly recorded. Judge race was most common. Judge
race was recorded in 10.90 percent of studies. Judge gender was less common with only 6.40
percent of studies reporting information on the gender of the judge. Judicial experience was also
not a common variable. Only 6.5 percent of studies recorded judge experience measures.
Measures of seriousness generally took the form of two variables, seriousness of the
offense and prior record. Each was recorded in 91.30 and 95.70 percent of studies respectively.
Weapon use information was recorded in approximately 40 percent of studies, and drug use was
113
rarely recorded, with only 6.5 percent of studies measuring drug use. The average number of
seriousness measures was 2.00 per study. Overall, other court characteristics were also not
common. Federal court study, court organization measures, and community measures were each
recorded in approximately 6.5 percent of cases. Mode of conviction and attorney type measures
were more common, and were reported in 60.90 and 32.60 percent respectively of all studies.
114
Table 5.10: Sentencing Sample Characteristics
Variable
General Study Characteristics
Number of Court Systems
Offense Specific
Control Variables
Interaction Effects
Length
Continuous Data Collection
Size
Mean
S
1.83
32.60
95.70
28.30
37.89
87.00
49,382.72
3.97
Defendant Characteristics
Defendant Race Measure
Defendant Gender Measure
Defendant Age Measure
Defendant Ethnicity Measure
Defendant Mentally Ill Measure
Defendant Employment Measure
84.80
69.60
87.00
34.80
0.00
21.70
Judge Characteristics
Judge Race Measure*
Judge Gender Measure
Experience
10.90
6.40
6.50
Seriousness Characteristics
Seriousness Measure
Weapon Use Measure
Drug Use Measure
Prior Record Measure
Total Seriousness Measures
91.30
41.30
06.50
95.70
2.00
Other Court Characteristics
Federal vs. Local
Mode of Conviction Measure
Attorney Type Measure
Court Organization Measures
Community Measures
6.40
60.90
32.60
6.50
6.50
N
32.65
136,070.28
0.87
46
*one study included information on ethnicity of the judge but it was included with race
115
Total
2,271,605
Effect sizes
Table 5.11 presents the findings from the effect size analysis. Defendant race, gender
and ethnicity are each significant. Specifically, black defendants were 1.23 times more likely to
be incarcerated over white defendants, males were 1.63 times more likely to be incarcerated than
females and Hispanics were 1.29 times more likely to be incarcerated than non-Hispanics.
However, age and employment status were not significant predictors of incarceration.
Additionally, like arrest, judge characteristics were not significantly related to the outcome of
interest. It is noteworthy that the effect size for judge race approached significance, .073, and
that black judges were more lenient than white judges. This finding is the only criminal justice
actor variable that approaches significance.
Prior record had a stronger overall effect than seriousness of the offense. Specifically,
those with longer prior records were almost 2 times more likely to be incarcerated. Seriousness
of the offense increased the odds of incarceration by approximately 1.5 times. Drug use also
increased incarceration (1.45 times). With regard to other court characteristics, only mode of
conviction was significant. Those that opted for a plea were 1.29 times less likely to be
incarcerated. Attorney type and court size correlations were not significant but were in the
expected directions.
116
Table 5.11: Sentencing Mean Effect Sizes
W
Mean
ES
95% CI
Min
95% CI
Max
0.17
0.21
(0.28)
0.11
(0.27)
0.30
(0.29)
2243915
0.48
0.489
(0.29)
0.334
(0.29)
0.645
(0.31)
39
2214935
-0.02
0.001
(-0.002)
-0.004
(-0.002)
0.002
(-0.001)
Suspect Ethnicity***
16
1481682
0.22
0.25
(0.16)
0.16
(0.13)
0.34
(0.18)
Suspect Employment
10
20694
0.45
0.45
(0.45
-0.17
(-0.17)
1.07
(1.07)
Judge Characteristics
Judge Race
7
268988
-0.17
-0.16
(-0.10)
-0.33
(-0.15)
0.01
(-0.06)
Judge Gender
5
252702
0.09
0.14
(0.002)
-0.10
(0.00)
0.39
(0.004)
Judge Experience
4
200920
0.00
-0.001
(0.01)
-0.02
(0.01)
0.02
(0.01)
Seriousness Characteristics
Seriousness***
24
1991280
0.58
0.39
(0.02)
0.34
(0.02)
0.43
(0.02)
Without outliers***
22
1438972
0.35
0.25
(0.02)
0.22
(0.02)
0.29
(0.02)
Variable
k
N
Mean ES
Defendant Characteristics
Suspect Black***
39
2247871
Suspect Male***
32
Suspect Age
117
Table 5.11 continued
W
Mean
ES
0.67
(0.23)
95% CI
Min
95% CI
Max
0.54
(0.22)
0.81
(0.24)
0.61
(0.22)
0.48
(0.21)
0.74
(0.23)
0.55
0.37
(0.01)
0.04
(-0.001)
0.71
(0.01)
974747
-0.82
-0.83
(-0.69)
-2.05
(-0.74)
0.40
(-0.65)
326882
-0.04
-0.02
(-0.03)
-0.19
(-0.08)
0.15
(0.01)
Mode of Conviction*
28 1292317
-0.08
-0.26
(-0.02)
-0.46
(-0.04)
-0.06
(0.01)
Without outliers*
26
-0.11
-0.20
0.004
-0.38
(-0.02)
-0.02
(0.03)
Variable
k
N
Prior Record***
44 2214373
0.73
Without outliers***
42 2193655
0.66
Drug Use*
4
17164
Other Court/Case Characteristics
Court Size
5
Counsel Type
15
905324
Mean ES
(Fixed effects results in parentheses)
* sig p< .05
** sig p<.01
Fail-safe N analysis
Consistent with the fail-safe N results for the arrest analysis, the fail-safe N values for
sentencing are generally stable. Fail-safe N values for suspect race and gender were 47 and 70
respectively. Ethnicity was the least stable finding in the suspect characteristics domain with 29
additional studies needed to nullify the ethnicity effect size. Seriousness of the offense and prior
record were both very stable findings. Their fail-safe N values were 129 and 160. The effect
118
size for drug use produced the smallest fail-safe N value of any decision point; the value is less
than 1. Clearly, the results for drug use should be interpreted with caution. Finally, the fail-safe
N value for the mode of conviction effect size was 5. Like arrest, the decision-specific variables
produced the smallest fail-safe N values. Overall, these findings, like arrest, are stable and can
be viewed with confidence, except where noted. The mean fail-safe N value was 90 additional
effect sizes.
Moderating variable analysis
A series of moderating variable analyses were also performed for the sentencing decision.
Like the arrest analyses, these analyses were limited to a handful of variables that had a
sufficient number of effect sizes for meaningful analysis. The results for each analysis are
displayed in Tables 5.12-5.17.
Table 5.12 displays the results of the moderating variable analysis for defendant race.
There is a similar pattern for the moderator analysis of sentencing outcomes as the arrest
outcome. For defendant race, the majority of mean effect sizes were significant across
moderator categories and the analysis follows the same pattern that African Americans were
more severely punished. However, there are several other noteworthy findings. Race was not a
significant predictor of sentencing for studies that used multiple study locations. These multiple
location studies were often studies of sentencing at the federal level. Race was also not
significantly related to sentencing for studies that used data gathered in the 1970s or the 1980s.
Race was also not related to sentencing in offense specific studies or studies that did not use
continuous data. Race was not significantly related to sentencing in studies that measured
suspect employment or counsel type.
119
Table 5.13 displays the results of the moderating variable analysis for defendant gender.
Results are similar to previous moderator analyses. The majority of mean effect sizes are
significant across moderator categories. Those that are not significant are generally the result of
a small number of effect sizes used to generate the mean effect size. Males were not
significantly more likely to be sentenced to prison in studies conducted in the Northeast, South,
Pacific, or those using multiple locations or the federal level. Gender was not a significant
predictor of sentencing in studies based on data gathered in the 1970s or the 1980s. Like the
suspect race analysis, gender was not a significant predictor of sentencing in studies that
measured suspect employment.
Table 5.14 displays the results for defendant ethnicity moderator analysis. Unlike race
and gender, the ethnicity analysis is based on a much smaller number of effect sizes. The
majority of mean effect sizes were significant across moderator categories. Those that were not
were generally limited to only a few effect sizes to create the mean effect sizes. However, unlike
the previous two analyses, Hispanics were significantly more likely to be incarcerated only in
studies that had multiple locations or Federal court. There was little variation in the other
variable categories.
Table 5.15 displays the results for the moderator analysis for prior record. Generally,
patterns here were similar to previous moderator discussions. Prior record was not a significant
predictor of sentencing for studies published since 2010 and studies conducted in the Southwest.
It should be noted that these findings are based on a small number of effect sizes. All other mean
effect sizes were significant across moderator categories. Those that were not were based on a
negligible number of effect sizes.
120
The final two moderator analyses were conducted on attorney type and mode of
conviction. Table 5.16 displays the results for attorney type and Table 5.17 displays the results
for mode of conviction. Mean effect sizes for attorney type are generally not significant across
moderator categories. However the one exception was in the data gathered moderator. The
results show that studies conducted in the 1960s were significantly more likely to find that those
represented by private attorneys were more likely to be incarcerated. In the 1970s defendants
were significantly more likely to be incarcerated when represented by public defenders or court
appointed counsel. Mean effect sizes for mode of conviction were generally significant. Those
that were not significant were often based on a small number of effect sizes. Studies published
between 2000 and 2009, and studies based on data collected in the South, found that people that
pled guilty were significantly less likely to be sentenced to prison. Significant mean effect sizes
were found for studies based on data from the 1990s indicating those that took a plea were less
likely to be incarcerated. Pleas also produced significantly less prison sentences in studies that
were offense specific.
121
Table 5.12: Defendant Race Moderator Analysis
Moderator Variable
k ES
Discipline
Criminology/Criminal Justice
21 0.20
Psychology
0 0.00
Sociology
11 0.19
Social Work
0 0.00
Mixed
2 0.34
Other
1 0.34
95% CI Min
95% CI Max
SE
0.12
0.00
0.08
0.00
-0.04
-0.01
0.28
0.00
0.30
0.00
0.54
0.68
0.04
0.00
0.06
0.00
0.15
0.17
Publication Decade
1980
1990
2000
2010
6
8
21
3
0.28
0.18
0.16
0.44
0.07
0.03
0.08
0.25
0.48
0.33
0.25
0.64
0.10
0.08
0.04
0.10
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
4
7
16
3
1
8
0.36
0.19
0.17
0.34
0.48
0.11
0.20
0.06
0.06
0.17
0.17
-0.01
0.52
0.32
0.29
0.51
0.79
0.23
0.07
0.07
0.06
0.09
0.16
0.06
Data Gathered
1960
1970
1980
1990
2000
2
10
3
18
6
0.54
0.12
0.75
0.20
0.31
0.08
-0.09
-0.24
0.07
0.11
0.99
0.34
0.39
0.32
0.51
0.23
0.11
0.16
0.06
0.10
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
Other
30
7
1
1
0.20
0.22
0.17
0.35
0.10
0.03
-0.28
-0.10
0.30
0.41
0.62
0.80
0.05
0.10
0.23
0.23
122
Table 5.12: continued
Moderator Variable
Agency Number
1
17
23
k
95% CI Min
95% CI Max
SE
37 0.21
1 0.25
1 0.18
0.11
-0.27
-0.35
0.31
0.77
0.71
0.05
0.27
0.27
Offense Specific
No
Yes
30 0.21
9 0.21
0.10
-0.03
0.31
0.46
0.05
0.13
Control Variables
No
Yes
2 0.26
36 0.21
-0.31
0.12
0.66
0.31
0.20
0.05
Continuous Data Collection
No
Yes
6 0.10
33 0.23
-0.14
0.12
0.35
0.33
0.13
0.05
Defendant Gender Recorded
No
Yes
8 0.30
31 0.20
0.02
0.10
0.57
0.30
0.14
0.05
Defendant Age Recorded
No
Yes
7 0.28
32 0.19
0.05
0.09
0.50
0.30
0.11
0.05
Defendant Ethnicity Recorded
No
Yes
25 0.17
14 0.26
0.07
0.14
0.28
0.39
0.05
0.06
Defendant Employment Status Recorded
No
Yes
32 0.23
7 0.06
0.13
-0.21
0.33
0.34
0.05
0.14
Seriousness Recorded
No
Yes
4 0.37
35 0.19
0.06
0.08
0.68
0.29
0.16
0.05
123
ES
Table 5.12: continued
Moderator Variable
Prior Record Recorded
No
Yes
k
95% CI Min
95% CI Max
SE
2 0.35
37 0.20
-0.03
0.10
0.73
0.30
0.19
0.05
Mode of Conviction Recorded
No
Yes
20 0.22
18 0.18
0.07
0.04
0.36
0.32
0.07
0.07
Counsel Type Recorded
No
Yes
31 0.21
7 0.17
0.11
-0.03
0.32
0.37
0.05
0.10
37 0.23
2
0.13
0.13
0.33
0.05
-0.51
0.24
0.19
0.11
-0.11
0.31
0.50
0.05
0.15
Organization Recorded
No
Yes
Community Recorded
No
Yes
ES
36 0.21
3 0.19
124
Table 5.13: Defendant Gender Moderator Analysis
Moderator Variable
k
Discipline
Criminology/Criminal Justice
20
Psychology
0
Sociology
8
Social Work
0
Mixed
2
Other
1
0.51
0.00
0.45
0.00
0.18
0.74
0.30
0.00
0.14
0.00
0.37
-0.19
0.72
0.00
0.76
0.00
-0.54
1.68
0.11
0.00
0.16
0.00
0.90
0.48
Publication Decade
1980
1990
2000
2010
1
6
21
3
0.13
0.46
0.49
0.66
-0.48
0.17
0.34
0.29
0.74
0.75
0.64
1.02
0.31
0.15
0.08
0.19
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
3
6
12
4
1
6
0.45
0.37
0.66
0.68
0.45
0.21
-0.07
-0.01
0.38
0.21
-0.43
-0.16
0.98
0.75
0.94
1.16
1.33
0.58
0.27
0.19
0.14
0.24
0.45
0.19
Data Gathered
1970
1980
1990
2000
4
4
18
6
0.30
0.46
0.52
0.52
-0.18
-0.02
0.30
0.16
0.79
0.95
0.73
0.88
0.25
0.25
0.11
0.18
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
Other
23
7
1
1
0.50
0.41
0.71
0.47
0.33
0.12
-0.02
-0.26
0.67
0.71
1.44
1.20
0.09
0.15
0.37
0.37
125
ES
95% CI Min 95% CI Max
SE
Table 5.13: continued
Moderator Variable
Agency Number
1
17
23
30 0.49
1 0.53
1 0.51
0.32
-0.29
-0.32
0.65
1.36
1.34
0.08
0.42
0.42
Offense Specific
No
Yes
29 0.19
3 0.45
0.33
-0.10
0.66
0.99
0.08
0.28
Continuous Data Collection
No
Yes
6 0.43
26 0.50
0.07
0.33
0.79
0.68
0.19
0.09
Defendant Race Recorded
No
Yes
1 0.77
31 0.48
-0.29
0.33
0.18
0.64
0.54
0.08
Defendant Age Recorded
No
Yes
4 0.66
28 0.47
0.21
0.30
1.11
0.63
0.23
0.08
Defendant Ethnicity Recorded
No
Yes
18 0.48
14 0.50
0.26
0.27
0.70
0.73
0.11
0.12
Defendant Employment Status Recorded
No
Yes
27 0.50
5 0.43
0.33
-0.01
0.66
0.87
0.08
0.22
Seriousness Recorded
No
Yes
3 0.51
29 0.49
-0.01
0.33
1.03
0.65
0.27
0.08
Prior Record Recorded
No
Yes
2 0.19
30 0.51
-0.40
0.35
0.78
0.67
0.30
0.08
k
126
ES
95% CI Min 95% CI Max
SE
Table 5.13: continued
Moderator Variable
Mode of Conviction Recorded
No
Yes
14 0.50
16 0.46
0.25
0.24
0.75
0.68
0.13
0.11
Organization Recorded
No
Yes
30 0.50
2 0.32
0.34
-0.25
0.67
0.90
0.08
0.30
Community Recorded
No
Yes
29 0.50
3 0.37
0.34
-0.11
0.67
0.85
0.09
0.25
k
127
ES
95% CI Min 95% CI Max
SE
Table 5.14: Defendant Ethnicity Moderator Analysis
Moderator Variable
k
Discipline
Criminology/Criminal Justice
11
Psychology
0
Sociology
4
Social Work
0
Mixed
0
Other
0
ES
95% CI Min 95% CI Max
SE
0.33
0.00
0.15
0.00
0.00
0.00
0.23
0.00
-0.01
0.00
0.00
0.00
0.43
0.00
0.30
0.00
0.00
0.00
0.05
0.00
0.08
0.00
0.00
0.00
Publication Decade
1990
2000
2010
2 -0.14
11 0.32
3 0.15
-0.47
0.21
-0.07
0.20
0.43
0.37
0.17
0.06
0.17
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
3
2
3
3
0
5
0.22
0.26
0.28
0.21
0.00
0.27
-0.02
-0.04
.-.00946
-0.03
0.00
0.09
0.46
0.56
0.57
0.44
0.00
0.46
0.12
0.15
0.15
0.12
0.00
0.09
Data Gathered
1980
1990
2000
2
11
3
0.19
0.27
0.19
-0.15
0.15
-0.04
0.53
0.39
0.42
0.17
0.06
0.12
Publication Type
Journal
Graduate Dissertation/Thesis
14
2
0.26
0.21
0.15
-0.03
0.37
0.46
0.06
0.13
Agency Number
1
17
23
14
1
1
0.24
0.27
0.30
0.14
-0.05
-0.04
0.35
0.60
0.64
0.05
0.17
0.17
128
Table 5.14: continued
Moderator Variable
Continuous Data Collection
No
Yes
2 0.29
14 0.24
0.06
0.14
0.52
0.34
0.12
0.05
Defendant Race Recorded
No
Yes
1 0.22
15 0.25
-0.21
0.16
0.64
0.53
0.22
0.05
Defendant Gender Recorded
No
Yes
1 0.09
15 0.26
-0.27
0.15
0.45
0.37
0.18
0.06
Defendant Age Recorded
No
Yes
2 0.24
14 0.25
-0.02
0.14
0.50
0.37
0.13
0.06
Defendant Employment Status Recorded
No
Yes
15 0.25
1 0.22
0.16
-0.21
0.35
63799.00
0.05
0.22
Seriousness Recorded
No
Yes
1 0.27
15 0.25
-0.06
0.15
0.60
0.34
0.17
0.05
Prior Record Recorded
No
Yes
2 0.32
14 0.24
0.05
0.15
0.60
0.34
0.14
0.05
Mode of Conviction Recorded
No
Yes
5 0.28
11 0.23
0.12
0.10
0.45
0.36
0.08
0.07
Counsel Type Recorded
No
Yes
13 0.23
3 0.41
0.13
0.16
0.32
0.65
0.05
0.13
k
129
ES
95% CI Min 95% CI Max
SE
Table 5.14: continued
Moderator Variable
Organization Recorded
No
Yes
14 0.27
2 0.11
0.17
-0.16
0.36
0.38
0.05
0.14
Community Recorded
No
Yes
15 0.25
1 0.30
0.15
-0.03
0.34
0.63
0.05
0.17
k
130
ES
95% CI Min 95% CI Max
SE
Table 5.15: Prior Record Moderator Analysis
Moderator Variable
Discipline
Criminology/Criminal Justice
Psychology
Sociology
Social Work
Mixed
Other
k
ES
27
0
10
0
2
1
0.70
0.00
0.74
0.00
0.57
0.20
0.53
0.00
0.44
0.00
-0.17
-0.69
0.88
0.00
1.05
0.00
1.30
1.08
0.09
0.00
0.15
0.00
0.38
0.45
Publication Decade
1980
1990
2000
2010
5
9
26
3
0.72
0.46
0.75
0.48
0.24
0.18
0.59
-0.06
1.21
0.74
0.91
1.02
0.25
0.14
0.08
0.28
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
4
12
15
4
1
8
0.61
0.77
0.75
0.15
2.14
0.49
0.25
0.55
0.54
-0.26
1.43
0.22
0.97
0.99
0.96
0.56
2.85
0.77
0.18
0.11
0.11
0.21
0.36
0.14
Data Gathered
1960
1970
1980
1990
2000
2
9
4
24
5
0.57
0.84
0.30
0.72
0.73
-0.15
0.50
-0.11
0.55
0.32
1.28
1.17
0.72
0.90
1.14
0.37
0.17
0.21
0.09
0.21
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
Other
30
12
1
1
0.63
0.60
1.90
1.32
0.50
0.39
.1.258
0.68
0.76
0.81
2.53
1.96
0.07
0.11
0.32
0.33
131
95% CI Min 95% CI Max
SE
Table 5.15: continued
Moderator Variable
Agency Number
1
17
23
k
ES
42
1
1
0.71
0.35
0.64
0.56
-0.53
-0.24
0.85
1.23
1.52
0.08
0.45
0.45
Offense Specific
No
Yes
29
15
0.66
0.76
0.50
0.51
0.82
1.02
0.08
0.13
Control Variables
No
Yes
2
41
0.55
0.69
-0.04
0.55
1.14
0.82
0.30
0.07
Continuous Data Collection
No
Yes
6
38
0.84
0.67
0.46
0.52
1.21
0.82
0.19
0.08
Defendant Race Recorded
No
Yes
7
37
0.54
0.72
0.22
0.58
0.86
0.87
0.16
0.07
Defendant Gender Recorded
No
Yes
14
30
0.77
0.66
0.51
0.51
1.02
0.82
0.13
0.08
Defendant Age Recorded
No
Yes
7
37
0.57
0.71
0.23
0.57
0.90
0.86
0.17
0.07
Defendant Ethnicity Recorded
No
Yes
30
14
0.82
0.44
0.66
0.22
0.98
0.66
0.08
0.11
Defendant Employment Status Recorded
No
Yes
36
8
0.69
0.68
0.55
0.37
0.83
1.00
0.07
0.16
132
95% CI Min 95% CI Max
SE
Table 5.15: continued
Moderator Variable
Seriousness Recorded
No
Yes
k
ES
4
40
0.54
0.71
0.07
0.56
1.01
0.85
0.24
0.07
Mode of Conviction Recorded
No
Yes
14
30
0.46
0.77
0.25
0.62
0.66
0.91
0.10
0.07
Counsel Type Recorded
No
Yes
30
14
0.65
0.74
0.49
0.49
0.81
1.00
0.08
0.13
Organization Recorded
No
Yes
42
2
0.79
0.30
0.57
-0.34
0.87
0.93
0.08
0.32
Community Recorded
No
Yes
42
2
0.67
0.98
0.54
0.41
0.81
1.54
0.07
0.29
133
95% CI Min 95% CI Max
SE
Table 5.16: Attorney Type Moderator Analysis
Moderator Variable
Discipline
Criminology/Criminal Justice
Psychology
Sociology
Social Work
Mixed
Other
k
ES
95% CI Min
95% CI Max
SE
7
0
3
0
5
0
0.10
0.00
-0.52
0.00
0.15
0.00
-0.22
0.00
-1.05
0.00
-0.44
0.00
0.42
0.00
0.01
0.00
0.73
0.00
0.16
0.00
0.27
0.00
0.30
0.00
Publication Decade
1980
1990
2000
5
2
8
0.05
-0.23
0.01
-0.36
-0.65
-0.23
0.45
0.20
0.25
0.21
0.22
0.12
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
1
5
6
1
0
2
0.48
0.15
-0.17
-1.18
0.00
-0.08
0.12
-0.08
-0.41
-1.86
0.00
-0.32
0.83
0.37
0.07
-0.50
0.00
0.16
0.18
0.11
0.12
0.34
0.00
0.12
Data Gathered
1960
1970
1980
1990
2
5
0
8
0.57
-0.33
0.00
0.02
0.09
-0.64
0.00
-0.20
1.06
-0.02
0.00
0.23
0.25
0.16
0.00
0.11
Publication Type
Journal
Graduate Dissertation/Thesis
14 -0.03
1 -0.08
-0.26
-0.79
0.20
0.63
0.12
0.36
Offense Specific
No
Yes
9
6
-0.25
-0.41
0.23
0.28
0.12
0.18
134
-0.01
-0.07
Table 5.16: continued
Moderator Variable
Control Variables
No
Yes
k
95% CI Min
95% CI Max
SE
1 0.48
14 -0.07
0.06
-0.22
0.89
0.08
0.21
0.08
Defendant Gender Recorded
No
Yes
6
9
0.13
-0.06
-0.17
-0.24
0.43
0.11
0.15
0.09
Defendant Age Recorded
No
Yes
4 0.19
11 -0.10
-0.07
-0.27
0.46
0.07
0.14
0.09
Defendant Ethnicity Recorded
No
Yes
10 0.11
5 -0.28
-0.14
-0.62
0.36
0.05
0.13
0.17
Defendant Employment Status Recorded
No
Yes
14 0.00
1 -0.53
-0.17
-1.36
0.18
0.30
0.09
0.42
Seriousness Recorded
No
Yes
3 -0.01
12 -0.04
-0.44
-0.27
0.42
0.19
0.22
0.12
Mode of Conviction Recorded
No
Yes
5 0.02
10 -0.04
-0.29
-0.25
0.34
0.17
0.16
0.11
Organization Recorded
No
Yes
14 -0.03
1 -0.08
-0.26
-0.79
0.20
0.63
0.12
0.36
135
ES
Table 5.17: Mode of Conviction Moderator Analysis
Moderator Variable
k
ES
Discipline
Criminology/Criminal Justice
17 -0.49
Psychology
0
0.00
Sociology
7
0.22
Social Work
0
0.00
Mixed
1
0.31
Other
0
0.00
95% CI Min 95% CI Max
SE
-0.80
0.00
-0.33
0.00
-1.50
0.00
-0.17
0.00
0.77
0.00
2.11
0.00
0.16
0.00
0.28
0.00
0.92
0.00
Publication Decade
1980
1990
2000
2010
4
7
15
2
0.20
-0.24
-0.32
-0.39
-0.43
-0.71
-0.58
-1.12
0.83
0.23
-0.06
0.33
0.32
0.24
0.13
0.37
Geographic Region
Northeast
South
Midwest
Southwest
Pacific/Northwest
Mixed
5
4
10
1
1
7
-0.07
-1.23
-0.28
0.11
-0.86
0.61
-0.69
-1.96
-0.77
-1.23
-2.26
-0.03
0.56
-0.51
0.22
1.50
0.54
1.26
0.32
0.37
0.25
0.71
0.71
0.33
Data Gathered
1960
1970
1980
1990
2000
2
6
3
13
4
-0.13
0.07
-0.18
-0.44
0.12
-1.22
-0.66
-0.97
-0.85
-0.58
0.96
0.79
0.62
-0.03
0.83
0.56
0.37
0.41
0.21
0.36
Publication Type
Journal
Graduate Dissertation/Thesis
State or Local Report
22
5
1
-0.19
-0.34
-0.04
-0.52
-0.99
-1.43
0.14
0.31
1.35
0.17
0.33
0.71
Agency Number
1
23
27
1
-0.24
-0.62
-0.45
-1.61
-0.04
0.37
0.11
0.51
136
Table 5.17: continued
Moderator Variable
Offense Specific
No
Yes
k
ES
21
7
-0.28
-0.17
-0.48
-0.67
-0.08
0.34
0.10
0.26
Control Variables
No
Yes
1
26
-0.44
-0.24
-1.37
-0.45
0.49
-0.03
0.48
0.11
Continuous Data Collection
No
Yes
3
25
-0.51
-0.23
-1.12
-0.44
0.10
-0.01
0.31
0.11
Defendant Race Recorded
No
Yes
1
27
0.11
-0.27
-0.84
-0.49
1.05
-0.06
0.48
0.11
Defendant Gender Recorded
No
Yes
8
20
0.16
-0.34
-0.39
-0.65
0.71
-0.03
0.28
0.16
Defendant Age Recorded
No
Yes
4
24
-0.14
-0.24
-0.80
-0.53
0.52
0.05
0.34
0.15
Defendant Ethnicity Recorded
No
Yes
15
13
-0.25
-0.18
-0.65
-0.53
0.08
0.17
0.19
0.18
Defendant Employment Status Recorded
No
Yes
25
3
-0.31
0.44
-0.53
-0.32
-0.10
1.20
0.11
0.39
Seriousness Recorded
No
Yes
1
27
0.31
-0.27
-1.30
-0.47
1.92
-0.06
0.82
0.10
137
95% CI Min 95% CI Max
SE
Table 5.17: continued
Moderator Variable
Prior Record Recorded
No
Yes
k
ES
2
26
0.30
-0.31
-0.40
-0.52
0.99
-0.10
0.36
0.11
Counsel Type Recorded
No
Yes
18
10
-0.21
-0.35
-0.46
-0.72
0.04
0.02
0.13
0.19
Organization Recorded
No
Yes
24
4
-0.29
-0.05
-0.52
-0.59
-0.07
0.48
0.11
0.27
Community Recorded
No
Yes
27
1
-0.24
-0.62
-0.45
-1.61
-0.04
0.37
0.11
0.51
95% CI Min 95% CI Max
SE
Parole Revocation
Sample characteristics
Table 5.18 describes the sample characteristics for the parole revocation study analysis.
As shown in Table 5.18, the average sample size was 8,979.16 cases and across all 19 studies,
170,604 cases were studied. The mean number of parole revocation systems involved in each
study was 1.11 agencies. The mean length of study period was 15.33 months. Approximately 15
percent of studies reported at least one analysis with interaction effects, and nearly all studies
used continuous data collection (88.90%). Approximately six percent of studies collected
information on offense specific cases (5.60%).
Table 5.18 also describes what variables were collected in various studies. These
variables are separated into 2 groups: offender characteristics, and seriousness characteristics.
For suspects, the majority of studies collected information on race, gender, and employment
status of offenders, 84.20, 52.60, and 52.60 percent respectively. About 37 percent of studies
138
collected information on offender age. One quarter of studies recorded information on ethnicity
of offenders and 21 percent of studies recorded information on offender education. No studies
included in this analysis recorded measures of parole officer characteristics.
The most common seriousness measure was a risk/prior record measure. Eighty percent
of studies recorded a measure of risk (78.90%). Slightly more than half of studies recorded
information on the seriousness of the offense and roughly one quarter recorded information on a
need measure, 52.60 and 26.30 percent respectively. Very few studies (5.30%) recorded
information on weapon use. The average number of seriousness measures was 1.26 per study.
139
Table 5.18: Parole Revocation Sample Characteristics
Variable
Mean/%
General Study Characteristics
Number of PR Systems
1.11
Offense Specific
5.60
Control Variables
63.20
Interaction Effects
15.80
Length
15.33
Continuous Data Collection
88.90
Size
8,979.16
Suspect Characteristics
Suspect Race Measure
Suspect Gender Measure
Suspect Age Measure
Suspect Ethnicity Measure
Suspect Education Measure
Suspect Employment Measure
84.20
52.60
36.80
26.00
21.10
52.60
Seriousness Characteristics
Seriousness Measure
Evidence Measure
Weapon Use Measure
Risk/Prior Record Measure
Need Measure
Total Serious Measures
52.60
0.00
05.30
78.90
26.30
1.26
N
19
140
S
Total
0.74
7.554
20,856.20
0.45
170,604
Effect sizes
Table 5.19 presents the effect sizes for parole revocation. As shown in table 5.19, only
two variables are significantly related to parole revocation, seriousness and risk. Seriousness
increased the odds of revocation 1.18 times and those parolees with greater risk were 1.15 times
more likely to be revoked. No other measures were significantly related to parole revocation. It
is noteworthy that the effect size for offender race approached significance (p<0.051). This
finding that race was nearly significant is consistent with findings from the other two decisions
that indicated that race was a significant predictor of decision-making. Further, this race finding
may be an artifact of the small number of studies assessed.
Table 5.19: Mean Effect Sizes Parole
Revocation
N
Mean
ES
W
Mean
ES
95% CI 95% CI
Min
Max
Variable
k
Offender Characteristics
Offender Black
16 157177
0.76
0.22
(0.19)
0.00
(0.13)
0.44
(0.25)
Without outliers
15 126541
0.15
0.15
(0.19)
0.00
(0.13)
0.31
(0.25)
Offender Male
10 149928
-0.39
-0.04
(0.11)
-0.36
(0.06)
0.29
(0.16)
Offender Age
7
21385
-0.16
-0.02
(-0.02)
-0.05
(-0.03)
0.01
(-0.01)
Offender Ethnicity
5
115738
-0.01
0.04
(0.13)
-0.13
(0.09)
0.21
(0.17)
Offender Employment
10
19689
-0.29
-0.29
(-0.24)
-0.75
(-0.38)
0.17
(-0.11)
141
Table 5.19 Continued
Variable
k
N
Mean
ES
Offender Education
6
45652
-0.49
W
Mean
ES
-0.28
(-0.18)
95% CI 95% CI
Min
Max
-0.83
(-0.37)
0.26
(0.01)
Seriousness Characteristics
Seriousness**
10
154527
-0.12
0.17
(0.20)
0.05
(0.16)
0.28
(0.25)
Risk/Prior Record***
16
167528
0.63
0.14
(0.05)
0.09
(0.05)
0.20
(0.05)
Without Outliers***
15
136892
0.38
0.13
(0.05)
0.08
(0.05)
0.19
(0.05)
Need**
4
1095
0.14
0.47
(0.49)
0.16
(0.25)
0.79
(0.73)
(Fixed effects results in parentheses)
* sig p< .05
** sig p<.01
*** sig p< .001
Fail-safe N analysis
The results of the parole revocation fail-safe N analysis indicate that the results were less
stable than results of the other decisions. This is likely due to the smaller k values. Risk had the
largest fail-safe N value; 25 additional studies would be necessary to nullify the risk mean effect
size. There was no substantive difference between the risk fail-safe N analysis with or without
outliers. Seriousness and need had similar fail-safe N values, 4 and 2 respectively. The results
regarding these two variables should be used with caution given the small fail-safe N value. The
value for risk appears to be stable but the values for seriousness and need cannot be considered
stable. The mean fail-safe N value was 14 additional studies.
142
Moderating variable analysis
The moderator analysis for parole revocation is limited to offender race and risk effect
sizes. The limited analysis is due to the small number of studies in parole revocation generally
and offender race and risk were the only two variables that had a sufficient number of effect
sizes for meaningful moderator analysis. As with previous moderator variable analyses
discussed above, the general trends of significance mirror those at the mean effect size level.
Thus, across moderator categories, mean effect sizes for offender race are generally not
significant. However, there were some exceptions. The full results are found in Table 5.20.
Studies conducted by those working in either “sociology” or “social work” categories produced a
significant mean effect size. Those two groups were found that black individuals were more
likely to be revocated. Significant mean effect sizes were also found in studies that were
conducted in the South. Black individuals in the South were more likely to be revocated.
Studies that had a continuous data collection generated a significant mean effect size. Those
studies that coded both race and a measure of risk had a significant mean effect size. Finally,
studies that measured race as white/non-white produced a significant mean effect size. Like
Pratt (1998), the effects of race on parole revocation may be masked by measurement issues,
while those that measured race as white/black did not produce a significant mean effect size.
Risk was also measured in a sufficient number of studies to perform further analysis. As
with previous moderator analyses, the general pattern of significance follows the mean effect
size significance. In other words, the majority of mean effect sizes across moderator categories
were significant for the risk variable. Table 5.21 displays the full results for this analysis.
Studies based on data gathered between 2000 and 2009 did not produce a significant mean effect
size, but studies based on data collected in the 1980s and 1990s did produce a significant mean
143
effect size. All other categories of moderator variables produced significant effect sizes or were
based on one to two effect size values.
144
Table 5.20: Offender Race Moderator Analysis
Moderator Variable
k
ES
Discipline
Criminal Justice/Criminology
6
-0.15
Psychology
1
0.48
Sociology
2
1.34
Social Work
1
0.99
Mixed
6
0.11
Other
1
0.00
95% CI Min
95% CI Max
SE
-0.56
-0.27
0.60
0.05
-0.22
-1.53
0.26
1.24
2.08
1.92
0.44
1.53
0.21
0.38
0.38
0.48
0.17
0.78
Publication Decade
1970
1980
1990
2000
2010
1
1
3
5
7
0.48
0.99
0.07
0.15
0.21
-0.38
-0.04
-0.48
-0.40
-0.16
1.35
2.01
0.62
0.69
0.58
0.44
0.52
0.28
0.28
0.19
Geographic Region
Northwest
South
Midwest
Southwest
Pacific/Northwest
Mixed
0
3
4
2
6
1
0.00
0.43
0.02
-0.66
0.10
9.91
0.00
0.12
-0.25
-1.40
-0.10
7.41
0.00
0.74
0.30
0.08
0.32
12.40
0.00
0.16
0.14
0.28
0.10
1.27
Data Gathered
1980
1990
2000
2
5
8
0.06
0.22
0.16
-0.62
-0.25
-0.22
0.74
0.68
0.53
0.35
0.24
0.19
Publication Type
Journal
Graduate Dissertation/Thesis
State or local report
13
2
2
0.20
0.72
0.03
-0.05
-0.08
-0.64
0.45
1.52
0.70
0.13
0.41
0.34
Agency Number
1
4
15
1
0.12
9.91
-0.03
7.41
0.27
12.40
0.08
1.27
145
Table 5.20: continued
Moderator Variable
Control Variables
No
Yes
k
ES
95% CI Min
95% CI Max
SE
5
12
0.37
0.17
-0.07
-0.10
0.80
0.44
0.22
0.14
Interaction Effects
No
Yes
14
3
0.28
0.10
0.00
-0.40
0.57
0.59
0.15
0.25
Continuous Data Collection
No
Yes
2
14
-0.31
0.26
-0.84
0.03
0.23
0.50
0.27
0.12
Risk Recorded
No
Yes
4
13
-0.14
0.38
-0.59
0.10
0.30
0.67
0.23
0.14
Need Recorded
No
Yes
13
4
0.16
0.45
-0.09
-0.03
0.41
0.94
0.13
0.25
Race Measure
White/Non-white
White/Black
8
9
0.43
0.13
0.03
-0.15
0.83
0.40
0.20
0.14
Offender Gender
Exclusively Male
Exclusively Female
Mainly Male
4
1
10
0.48
0.21
0.10
0.06
-0.68
-0.19
0.89
1.10
0.39
0.21
0.46
0.15
146
Table 5.21: Risk Moderator Analysis
Moderator Variable
Discipline
Criminal Justice/Criminology
Psychology
Sociology
Social Work
Mixed
Other
k
ES
95% CI Min
95% CI Max
SE
5
0
2
1
7
1
0.32
0.00
0.17
1.62
0.21
-0.38
0.05
0.00
-0.07
0.69
0.09
-1.75
0.60
0.00
0.41
2.28
0.33
0.99
0.14
0.00
0.12
0.34
0.06
0.70
Publication Decade
1980
1990
2000
2010
3
2
5
5
1.46
0.22
0.24
0.09
1.00
0.05
0.06
-0.02
6.27
2.56
2.61
1.64
0.23
0.09
0.09
0.05
Geographic Region
Northwest
South
Midwest
Southwest
Pacific/Northwest
Mixed
1
2
3
2
6
1
1.37
0.23
0.21
1.11
0.12
4.39
0.51
0.06
0.03
0.19
0.01
1.61
2.23
0.41
0.39
2.03
0.22
7.16
0.44
0.09
0.09
0.47
0.05
1.42
Data Gathered
1980
1990
2000
2
5
7
0.21
0.28
0.12
0.00
0.13
-0.01
0.42
0.44
0.25
0.11
0.08
0.07
Publication Type
Journal
Graduate Dissertation/Thesis
State or local report
Other
10
2
3
1
0.12
1.26
0.74
0.80
0.03
0.67
0.42
0.28
0.21
1.84
0.99
1.32
0.04
0.30
0.15
0.26
Agency Number
1
4
14
1
0.20
4.39
0.10
1.61
0.29
7.16
0.05
1.42
147
Table 5.21: continued
Moderator Variable
Control Variables
No
Yes
k
ES
95% CI Min
95% CI Max
SE
6
10
0.84
0.14
0.59
0.04
1.09
0.23
0.13
0.05
Interaction Effects
No
Yes
13
3
0.37
0.07
0.24
-0.09
0.50
0.23
0.07
0.08
Continuous Data Collection
No
Yes
1
14
0.80
0.18
0.27
0.09
1.33
0.28
0.27
0.05
Need Recorded
No
Yes
11
5
0.21
0.45
0.10
0.21
0.32
0.69
0.06
0.12
Race Measure
White/Non-white
White/Black
7
8
0.49
0.17
0.27
0.06
0.71
0.28
0.11
0.06
Offender Race
Mainly Black
Mixed
2
12
0.62
0.15
0.32
0.06
0.93
0.23
0.16
0.05
Offender Gender
Exclusively Male
Exclusively Female
Mainly Male
3
1
10
0.19
-1.42
0.20
0.05
-2.32
0.09
0.33
-0.52
0.32
0.07
0.46
0.06
148
Chapter Summary
This chapter presented the results of the meta-analysis that was conducted as a part of this
dissertation. Across decision points, the majority of studies were published since 1990 and were
published in journals. The majority of studies used data that were gathered relatively recently.
Finally, studies were conducted across the United States and were not limited to a single
jurisdiction.
There was less consistency with independent variables across studies. Race, gender, and
measures of seriousness of the offense were measured often across decisions. Measures of other
variables were more limited. Age was measured in a majority of studies of arrest and sentencing
but not parole revocation. Suspect ethnicity has been a more recent addition to studies of
decision-making and is measured in a few studies across decision points. Criminal justice actor
characteristics were not measured in parole revocation studies, but were measured in a few of the
studies arrest and sentencing. Community characteristics were not measured often at any
decision point.
The effect size analysis indicates that measures of seriousness are significant across
decision points. The correlations between seriousness variables and the three dependent
variables estimated some of the largest mean effect sizes across decision points. Suspect
characteristics were also significant across decision points. Race was significantly related to
arrest and sentencing, but approached significance in the parole revocation analysis. The
findings indicated that black individuals were significantly more likely to be processed further
through the system. Gender was also significant at two decision points, arrest and sentencing.
Males were more likely to be arrested and incarcerated. Ethnicity was also a significant factor.
Hispanic individuals were more likely to be arrested and sentenced to prison.
149
Each decision point also had significant decision-specific results. At arrest, significant
mean effect sizes were found for all measures of suspect demeanor. At sentencing, the mode of
conviction was significant. More specifically, those that chose to take a plea were significantly
less likely to be sentenced to prison than those that went to trial. Finally, the analysis of parole
revocation indicated that those with a greater level of need were more likely to be revoked.
Several system-wide and decision-specific variables were not significant. Criminal
justice actor characteristics and community characteristics were not significant across decision
points. Suspect/defendant age was also not significant across decision points. The decisionspecific variable counsel type was not significant at sentencing. Finally, the moderator analysis
does not appear to nullify the effect size findings.
150
CHAPTER 6
DISCUSSION
This dissertation seeks to address two major limitations within the field of criminal
justice: 1) the lack of extensive reviews regarding decision-making correlates throughout
criminal justice system, and 2) the general perception that criminal justice is an atheoretical field.
Reviews of decision-making correlates are limited in both scope and number; additionally, the
majority of these reviews focus on specific decisions within the system or on a particular
variable’s (such as race and gender) impact on an outcome within the system (see Chapter 2 for a
detailed discussion). While these research endeavors have been valuable contributions to the
field of criminal justice research, they are limited. The only comprehensive system-wide review,
which was a massive and time-consuming undertaking, was conducted by Gottfredson and
Gottfredson in the late 1980s and has yet to be sufficiently updated. System-wide reviews of
decision-making correlates are valuable in that they help us understand what factors influence or
structure discretion. The system-wide focus also presents a fuller picture because the entire
system is examined, as opposed to examining each piece independently.
A second limitation of criminal justice is the general perception that criminal justice is an
atheoretical field. Marenin and Worrall (1998) argue that criminal justice “has not yet achieved
theoretical integrity and coherence” and that the only general consensus within the field is that
theories and concepts are drawn from a variety of other disciplines—such as sociology, biology,
political science—and that these derived theories coalesce and contribute to the “consolidation of
the new discipline” (p. 465). An atheoretical perception reduces legitimacy of criminal justice
theory and suggests a lack of development as a field. The lack of legitimacy may also reduce
interest in the topic amongst scholars, which also leads to the lack of development.
151
This dissertation seeks to address both of these limitations by recreating Gottfredson and
Gottfredson’s work on a smaller scale. First, correlates of decision-making are examined at
multiple decision points in an attempt to examine the “bigger picture” of criminal justice
decision making. Second, the correlates of decision-making are considered in the context of
system-wide versus decision-specific in order to determine if criminal justice theories should be
focused on the criminal justice system as a whole or by looking at each decision point separately.
The purpose of this dissertation has been to examine, in the ten plus years since Marenin and
Worrall’s comments, whether the field of criminal justice has begun to create a theoretical
foundation.
This section of the dissertation—which is broken up into five different sections—will
discuss these findings and comment on their relevance to these two limitations. First, there is a
summary of the findings. Second, there is a discussion of how to organize criminal justice
theory given the results. Third, there is a discussion of a theory that can be gleaned from the
findings of this dissertation. Fourth, there suggestions for future research in criminal justice
theory are presented. Finally, there is a brief discussion of the limitations of this study.
Summary of Findings
The overall findings suggest a lack of consistency in decision-making research across the
system, but there was some degree of similarities with respect to certain variables, such as
measures of seriousness and race. Measures of seriousness were significant across the system.
These measures were generally the strongest predictors of criminal justice processing. These
results indicated that those who committed more serious acts were more likely to be processed
through that decision point. Seriousness measures increased the likelihood of arrest between
1.53 and 6.91 times. The weakest seriousness relationship was between weapon use and arrest.
152
The results indicated that those that used a weapon were 1.53 times more likely to be arrested.
The strongest relationship was between evidence and arrest. Cases with more evidence were
nearly 7 times more likely to end in arrest. For sentencing, the increase in likelihood of
sentencing was between 1.45 and 1.96 times across seriousness measures.18 The weakest
relationship was between drug use and sentencing, which indicated that suspects that used illicit
substances were 1.45 times more likely to be incarcerated. The findings indicated that prior
record was the strongest predictor of incarceration; those with longer prior records were nearly 2
times more likely to be incarcerated. Results regarding seriousness measures for parole
revocation were similar to the other two decision points. Risk, which is essentially prior record,
was the weakest measure of seriousness. The results indicated that those with greater risk were
1.15 times to be revoked. The strongest measure of seriousness was need. Those with greater
need were 1.60 times more likely to be revoked.
These findings support the idea that criminal justice decision making is largely a function
of seriousness of the offense. Furthermore, the idea of two criminal justice systems is supported
by the finding from arrest analysis, which dichotomizes seriousness of offense as felony versus
misdemeanor. This indicates that those suspected of felony crimes are significantly more likely
(2.50 times) to be arrested. Additionally, measures of seriousness were generally the strongest
predictors across decision points and the direction of the relationship was positive. This
information indicates that it may be the case that the duality of criminal justice idea discussed by
the Gottfredsons may be correct. However, the data presented here cannot provide a definitive
answer.
18
The reduced effect of seriousness measures for sentencing may have been due to drug charging as a result of the
“War on Drugs”. Mandatory drug penalties may have caused some defendants to be sentenced to prison when they
would otherwise would not have been sentenced as harshly
153
Race appears to be a significant correlate of criminal justice decision making at multiple
stages of the criminal justice system. Black individuals are significantly more likely to be
processed than white individuals, 1.39 times more likely to be arrested and 1.23 times more
likely to be sentenced to prison. Offender race was nearly significant at parole revocation and
would likely have been significant with the addition of a small number of studies. This finding
may be due to the small number of studies on parole revocation. Additionally, the moderator
analysis indicates that African Americans are more likely to be revoked in the South (1.54 times
more likely). This evidence indicates that race appears to be an important predictor of criminal
justice decision making. Gender and ethnicity were significant at two decision points. Results
indicated that males were more likely to be arrested or sentenced than females, 1.49 and 1.63
times respectively, and Hispanics were more likely to be arrested or sentenced than nonHispanics, 1.25 and 1.29 times respectively. Another important aspect of the finding that black
individuals and Hispanics are more likely to be processed through the system is that it is not
limited temporally. This means, potentially, disparate treatment or discriminatory treatment is
still on going.
Several multi-decision relationships were not significant at any decision point. Age was
not significant at any decision point. It could be that the effect of age was absorbed by measures
of race, gender, and ethnicity. Measures of criminal justice agent characteristics, race and
gender, were also not significant.19 There are many possible explanations for this finding. First,
the lack of influence for criminal justice actor characteristics could be an artifact of the small
number of studies that actually measured these concepts, but the results of available studies
19
Criminal justice actor ethnicity was only measured in a few studies in either arrest or sentencing and was not
included in parole revocation studies. Therefore, criminal justice actor ethnicity was included in with race for metaanalytic purposes. Hispanic was treated as black or non-white and non-Hispanic was treated as white. In the event
that a study measured both officer race and officer ethnicity, officer ethnicity was dropped in favor of the officer
race measure.
154
indicated that agent characteristics were not significantly related to the processing decisions. A
second and related issue is that some studies examined actor race, gender or ethnicity in a way
that was not conducive to meta-analysis. For example, Brown and Frank (2006) examined the
effect of officer race on arrest but did so by creating a multivariate model for white, and a second
model for black officers. The model containing arrests made by both black and white officers
did not include officer race. Thus, their finding that black officers were more likely to arrest
black suspects could not be included in this meta-analysis; there was no data available to
generate an effect size for the relationship between officer race and arrest.
Third, the lack of significance for criminal justice actor predictors is limited evidence of
discrimination in the system. The finding that race, gender, and ethnicity of suspects/defendants
is significant, but those same variables are not significant for criminal justice actors, could
indicate that potential discrimination is structural and not based on individual actors. In other
words, discrimination is potentially institutionalized and not just limited to a few “bad apples”.
The training process may be very thorough and could cause any individual differences to be
reduced or eliminated. Instead the individual criminal justice agent functions as a part of the
workgroup for that particular branch of the criminal justice system (Klinger, 1996).
A final interpretation of the significance of suspect demographic characteristics but not
criminal justice agent demographics is that suspect race, gender and ethnicity may be proxies for
other theoretically relevant but unmeasured variables. None of the studies that measured
criminal justice agent factors also measured factors pertaining to social class or education, and
only a handful of studies measured employment status. However, these variables have been
theorized to be explanations of criminal justice actor behavior. For example, Black (1976)
argues that social status, education (a measure of culture), and employment status (a measure of
155
organization) are critical factors to explaining the behavior of governmental social control.
These other demographic characteristics may be correlated with race, gender, and ethnicity and
act as proxy measures.
Both sentencing and arrest also had unique, decision-specific measures that were
significant. Both measures of demeanor were significant. Specifically, suspects that were
disrespectful or non-compliant were significantly more likely to be arrested. The third demeanor
related variable—interaction-phase crime—was also significant. Based on the findings
presented in this study, it appears that Klinger may have been correct: interaction-phase crime
does significantly increase the odds of arrest. The mean effect size of interaction-phase crime
was almost twice as strong as the mean effect size of demeanor. However, the moderator
analysis showed that regardless of whether a study measured interaction-phase crime, demeanor
was still significant. Suspect mental illness was not significantly related to arrest, but this could
be an artifact of the small number of studies that actually measured suspect mental state. The
direction of the effect size indicated that officers were less likely to arrest mentally ill suspects.
Sentencing also produced a unique finding. Mode of conviction was significantly related
to sentencing. Those that pled guilty were 1.29 times less likely to go to prison. However,
suspect employment, court size, and the type of attorney were not significantly related to
sentencing. Finally, moderator analysis for all three decision points did not really create any
significant modifications to the general mean effect size findings.
Missing variables
Another finding, when looking at the bigger picture of the results, is that there is a clear
lack of consistency in decision-making research. The results generally indicate that seriousness
of the offense and prior record are important at all stages examined, and race, gender, and
156
ethnicity are important at multiple stages. However, when it comes to other correlates, there is
little consistency across research. Potentially relevant variables are simply not measured. This
finding has implications for the “atheoretical” perception of criminal justice research. Without a
clear theory or theories to provide guidance on not only what to measure, but how to measure
concepts, this lack of consistency will continue, which will cause the “atheoretical” perception of
criminal justice research to thrive.
Criminal Justice Theory Organization
Given the above results, and the general lack of effects from moderators, it appears that
correlates are generally consistent across the system. Some measure of seriousness was
significant at all three stages of the criminal justice system. Additionally, several variables were
significant predictors (race, gender, and ethnicity) of at least two decision points. These
variables also performed in the same directions at all stages examined. The effects of factors that
are unique to a particular decision were not as strong as those that are generally applicable.
Factors that were unique to a decision may have only been unique because they were not
measured at other stages of the criminal justice system. Arrest specific factors, demeanor and
mentally ill status, are not exclusive to arrest. Defendants could potentially have a less than
cooperative disposition and judge may enforce a harsher punishment. Interaction-phase crime is
really a measure of seriousness and the strong effect is more applicable in the context of a
measure of seriousness than as a unique demeanor measure.
The effects of unique sentencing factors are virtually non-existent and the one decisionspecific relationship that produced a significant effect size, mode of conviction, was weaker than
any effect size in suspect characteristics, or seriousness measures. While the effects of need
157
were a strong unique factor in parole revocation, it was also a measure of seriousness. This
means that need functions in a similar manner as interaction-phase crime did at arrest.
Given this information, it seems most appropriate to approach criminal justice theory
from a whole-system perspective versus a decision-specific approach. Given the leaning toward
the whole-system approach for theory organization, the idea that criminal justice theory has one
dependent variable, discretionary processing, is also supported. However, due to limited data on
some unique factors, it is not prudent to dismiss them entirely, and it is probably the case that the
majority of the decision making of criminal justice agents is based largely on these whole-system
variables with some influence by decision-specific characteristics.
The findings here do not appear to be consistent with Bernard and Engel’s
conceptualization of criminal justice theory organization. Bernard and Engel’s (2001) attempt
was useful and an important contribution to this area of study, but their organizational approach
may miss higher order, system-wide influences on processing. A new organizational method
based on primary predictor variables would prevent theories with similar theoretical explanations
from being separated simply because they operationalize a dependent variable differently. For
example, Klinger’s ecological theory of policing focuses on police workgroups and the influence
of those workgroup norms on police decisions. Eisenstein and Jacob’s workplace theory of court
processing argues that the courtroom workgroup is a critical component to sentencing. If the
focus is on the dependent variable, then these two theoretically similar theories would not be
grouped together. Focusing on primary correlates as an organizational approach would also
bring criminal justice in line with other social science disciplines, such as criminology and
sociology, and increase the scientific legitimacy of criminal justice as a discipline.
158
The theoretical categories, discussed in chapter two, represent the new organizational
model proposed in this study. Theories should be grouped into the following categories:
situational theories, actor theories, workgroup/organizational theories, community theories, and
conflict theories. This newly proposed model breaks each type of theory into their key predictive
components. Additionally, while actor characteristics, organizational, and community
characteristics were not supported in the effect size analysis, they should not be omitted. These
categories of predictors have not been sufficiently studied to warrant their removal as theory
categories.
Toward a General Theory of Criminal Justice
A general explanation for the behavior of criminal justice actors would have to include
indicators of seriousness and suspect characteristics—race, ethnicity, and gender. There would
also seem to be some effect of unique characteristics that function as part of the general
explanation. Unfortunately, the literature examined here did not provide consistent measures of
community influences or organizational factors. The handful of studies that actually recorded
this information did not measure with any degree of consistency. Court size, the one
organizational factor that was measured somewhat frequently was not significant. However, it
would be inappropriate to dismiss both organizational and community explanations and the
theories based on this level of explanation such as Wilson’s theory of police behavior, and
Duffee’s community theory of criminal justice, based on this one finding. Nevertheless, it
appears that a theory of criminal justice should first focus on the entire criminal justice system.
Second, theories of criminal justice should include measures of seriousness and suspect
characteristics.
159
Future Research
First, there is a general lack of research on certain variables; specifically, there is a lack
of research on the effect of organizational and community factors on decision-making behavior.
The examination of characteristics of criminal justice actors is very limited. Eleven studies of
arrest and seven studies of sentencing examined here also measured the effects of criminal
justice actors on decision making. Suspect employment was only examined in ten sentencing
studies and ten parole revocation studies; this is a relatively small portion when compared to
other factors like race and gender.
A related issue is that other potentially relevant measures, like education and social class,
were virtually unmeasured. Some studies examined a quasi-social class characteristic; however,
any examination was based more on where the arrest or sentencing took place and not an
individual’s social class. Future research may want to improve upon the current state of
decision-making research by striving to incorporate the effect of these relevant but unmeasured
variables. This would aide in potentially disentangling the effects of race, gender, and ethnicity,
and how they may be serving as a proxy measure of other variables like education and social
class.
Second, it appears that prior record/risk is a key factor in criminal justice decision
making. However, prior record/risk has not been measured at the arrest stage. If possible, it
could be useful to incorporate some measure of prior record into examinations of the arrest
decision. This concept could be incorporated into systematic social observation studies by
asking officers if they have had repeated contact with the suspect or by checking arrest records of
suspects to obtain the identity of the arresting officer. Another potential measure could be the
extent of the record check when an officer performs a record check during a stop. Finally, there
160
is a very limited knowledge of ethnicity relative to other suspect characteristics. While the
results are significant, perhaps as time passes and more studies measuring ethnicity are
accumulated the results may change.
Third, while specific variables are limited, there is a general lack of studies on parole
revocation. This important final decision has very little research relative to arrest and
sentencing. Given that this decision potentially pulls someone back into prison, it would be
important to study the factors that influence this decision. However, based on the small number
of studies analyzed here, it would appear that parole revocation is the most neutral and objective
decision. Research in this area can be improved by creating a more uniform measure of
revocation and consistent operational definitions of concepts. Parole revocation as a field of
study was the least developed of any of the decisions examined.
Finally, there is a need of greater research that uses a system-wide approach to decision
making. No studies analyzed here actually took cases from arrest to parole revocation. It would
be helpful, and a rich source of data, to be able to analyze cases from arrest to parole revocation
to see if the same factors are influencing decision making. A limitation of the meta-analysis
presented in this dissertation is that the cases examined in arrest are not necessarily the same
cases examined in sentencing or parole revocation. Having data that actually examines
processing from the beginning to the end of the system might be able to put an end to the wholesystem versus decision-specific approach debate.
Limitations
While every effort was made to minimize the limitations of this study, there were several
limitations. First, it is likely that some studies may have been missed. Efforts were made to be
as inclusive as possible, but some of the results should be used with caution. However, the
161
majority of significant mean effect sizes produced stable results. Second, this project was that
only direct effects could be measured. It may be that some of the non-significant factors were
functioning as part of an interaction effect or a non-direct effect. It may be the case that some
effects were masked by unexamined interaction effects. Third, the results are limited by the
quality of the studies included. However, this limitation was mitigated somewhat by the
moderator analysis, which indicated a great deal of uniformity across studies. Finally, as
mentioned above there were some predictors of decision-making that were simply insufficiently
measured. This reduced the ability to explore all facets of the research question and truly
develop a general theory of criminal justice. Finally, two large groups of factors, organizational
and community, were simply not able to be examined.
The third and fourth limitations can be corrected with future research. The solution to
these limitations is for future research to strive to create more uniform measures and models.
More complete models would correct the second limitation because it would improve the overall
quality of the study by having more complete data. Further, standardized measures and models
would provide effect size information that is more directly comparable and less likely to
experience problems with heterogeneity. More compatible information would reduce the risk of
heterogeneity in the study effect sizes. The fourth limitation can also be corrected with this
proposed idea of a standardized model of decision making. By standardizing the measures and
models currently omitted variables would be more likely to be included and therefore a more
definitive decision could be made as to the effect of actor, organizational, and community
characteristics. Variables that could be included in this standardized model would include
system-wide variables: offender race, offender gender, offender ethnicity, seriousness of the
offense, risk/prior record. Additionally, the model would also need to include significant
162
decision-specific variables, where applicable, such as demeanor, and mode of conviction.
Finally, the standardized model should also include agent characteristics, race, gender, ethnicity,
and experience, organizational characteristics, and community characteristics in order to fully
determine if these factors are important to criminal justice processing. Standardized models may
also help to conceptualize new criminal justice theories.
Despite these limitations this dissertation has contributed to the field of decision-making
research. This study examined decision making at multiple decision points, which has not been
done since Gottfredson and Gottfredson’s study. Further, this dissertation improved upon the
Gottfredson’s work by adding meta-analysis to the examination of correlates of decision-making.
The results indicate that seriousness is the chief decision-making component of decision makers
of the criminal justice system. Further, the results indicate, race, gender, and ethnicity are
important factors in decision making. Black individuals, males, and Hispanics appear to be more
likely to be processed through the system.
Finally, this dissertation has also presented results that appear to indicate that criminal
justice theory should focus on a system-wide approach versus creating decision-specific theories.
Correlates that are significant predictors of decision-making are consistent across decision
points. Independent variables that were significant and unique to a particular decision point were
generally unique in application, not in theory. In other words, there were no theoretical reasons
why, for example, a suspect/defendants demeanor would not impact a judge’s decision in the
same way it appears to impact an officer’s exercise of discretion. Finally, moderator analyses do
not seem to negate any of the major findings.
163
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APPENDIX A
Arrest Coding Guide: (All missing data coded as 99)
I. STUDY INFORMATION:
A. Identification Number:
[STUDID] ________
B. Study accepted or rejected? (yes=1; no=0)
[INCLUD] ________
C. Eligibility criteria
1. Study is examining arrest measured dichotomously?
1=YES
0=NO
2. Includes at least one measure of offense severity?
1=YES
0=NO
3. Includes at least one measure of offender characteristics?
1=YES
0=NO
4. Includes at least one measure of officer characteristics?
1=YES
0=NO
Notes [DATASET]: _____________________________________________________________
______________________________________________________________________________
______________________________________________________________________________ D. Article author(s): _____________________________________________________________
E. Title: ______________________________________________________________________
F. Author affiliation: ____________________________________________________________
[AFFIL] __________
1 = University
2 = State Agency
3 = Federal Agency
4 = Mixed
5 = Other _________________
99 = Missing
G. Author discipline: 1 = Criminal Justice/Criminology
2 = Psychology
3 = Sociology
4 = Social Work
5 = Mixed
6 = Other _____________
99 = Missing
[DISCIP] _________
H. Coder:
[CODER] _________
1 = Daniel Lytle
180
0 = Other
99 = Missing
I. Funding Agency: _____________________________________________________________
1 = Unfunded
[FUND] __________
2 = Independent agency/foundation funded
3 = State funded
4 = Federal funded
5 = Other ______________
99 = Missing
J. Publication year:
Decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
[PUBYR] _________
[PUBDEC] ________
K. Geographic region of the study:
1 = Northeast
2 = South
3 = Midwest
4 = Southwest
5 = Pacific/Northwest
6 = Mixed
99 = Missing
[GEOREG] ________
L. Data collection year:
[START] _________
[FINISH] _________
[DATAGA] _______
Data gathered decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
181
M. Publication type:
1 = Journal
2 = Doctoral dissertation
3 = Book
4 = Book chapter
5 = Federal report
6 = State or local report
7 = Conference paper
8 = Other ________________
99 = Missing
[PUBTYP] ________
O. Number of police agencies surveyed:
[AGENUM] _______
II. SAMPLE DEMOGRAPHICS:
A. Offense specific:
Is the current study limited to a type of offense (1=YES, 0=NO) [TYPOFF0] __________ Specific type of offense: [TYPOFF1] __________ 1 = Drug offenses
2 = Property offenses
3 = Violent offenses
4 = DUI
5 = Mixed
6 = Other _______________
99 = Missing/na
Percent arrested
[PERARR] ________
B. Age:
Suspects are:
1 = Exclusively adults
2 = Exclusively juveniles
3 = Mainly adults (over 80%)
4 = Mainly juveniles (over 80%)
5 = Mixed
99 = Missing
[SUSAGE] ________
Mean age of suspects
[MAGESU] ______
182
C. Gender
Suspects are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[SUSGEN] ________
Percent Female suspect:
[PRFSUS] _______
Officers are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[OFFGEN] ________
Percent Female officer:
[PRFOFF] _______
D. Race
Suspects are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[SUSRAC] ________
Percent Suspect Non-white:
[PRNWS] _________
Officers are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[OFFRAC] ________
Percent Officer Non-white
[PRNWO] ________
183
Race is measured as:
1 = White/Non-white
2 = White/Black
3 = Other _________________
99 = Missing/not measured
[RACMEA] _______
E. Ethnicity
Suspects are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[ETHSUS] ________
Percent Hispanic:
[PRHISS] _________
Officers are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[ETHOFF]_________
Percent Hispanic:
[PRHISO] _________
Ethnicity is measured as:
1 = Hispanic/Non-Hispanic
2 = Other ________________
99 = Missing
[ETHMEA] _______
F. Offense Severity
Sample is:
1 = Exclusively felonies
2 = Exclusively misdemeanors
3 = Mainly felonies (Over 80%)
4 = Mainly misdemeanors (Over 80%)
5 = Mixed
99 = Missing
[SERFEL] ________
Percent felony:
[PRFEL] __________
Evidence:
184
Was the amount of evidence measured? (1=YES, 0=NO)
[EVIREC] ________
Citizen intoxication:
Did the study measure citizen intoxication? (1=YES, 0=NO)
[TOXREC] ________
Percent of citizens intoxicated:
[PRTOX] _________
Weapon Use:
Was the weapon use in the encounter recorded? (1=YES, 0=NO)
[WEAPREC] ______
Percent of encounters involving a weapon:
[PRWEAP] ________
Victim request arrest:
Was victim preference recorded? (1=YES, 0=NO)
[VPREFREC] ______
Percent of encounters where victim requested arrest:
[PRVPREF] _______
Total number of offense severity measures:
[SERTOT] ________
G. Demeanor
Suspect demeanor:
Was suspect demeanor recorded? (1=YES, 0=NO)
[DEMREC] _______
Percent non-deferential:
[PRDEM] ________
Interaction-phase crime:
Was IPC recorded? (1=YES, 0=NO)
[IPCREC] _________
Percent of encounters with IPC:
[PRIPC] __________
Suspect compliance:
Was suspect compliance recorded? (1=YES, 0=NO)
[COMREC] _______
Percent of encounters where suspect was non-compliant
[PRCOM] _________
F. Mental illness
Mental illness:
Was suspect mental illness recorded? (1=YES, 0=NO)
Percent mentally ill:
[MIREC] _________
[PRMENIL] _______
G. Other officer characteristics
Education:
Is officer education recorded? (1=YES, 0=NO)
185
[EDREC] _________
Officers are:
1 = Exclusively college educated
2 = Exclusively high school/GED educated
3 = Mainly college educated (Over 80%)
4 = Mainly high school/GED educated (Over 80%)
5 = Mixed
99 = Missing
[COLED] _________
Percent college educated:
[PRCOLED] _______
Experience:
Was a measure of officer experience recorded? (1=YES, 0=NO)
[XPREC] _________
H. Organizational characteristics:
Were organizational level variables recorded? (1=YES, 0=NO)
[ORGREC] ________
Mean number of organizational levels:
[MNLEV] _________
Mean agency size:
[MNSIZE] ________
I. Community characteristics:
Were community level variables recorded? (1=YES, 0=NO)
[UNITREC] _______
III. METHODOLOGICAL RIGOR ASSESSMENT
A. Control Variables included: (1=YES, 0=NO)
[CONVAR] _______
B. Systematic social observation used: (1=YES, 0=NO)
[SSOUSE] ________
C. Includes interaction-­‐effects: (1=YES, 0=NO)
[INEFUSE] _______
D. How long was the study conducted in months: E. Was data collection continuous: (1=YES, 0=NO) IV. SAMPLE SIZE A. Sample size: 186
[LENGTH] ___________ [CONTIN] ____________ [SIZE] ________________ V. EFFECT SIZE DATA A. Suspect Race: Odds ratio (logistic regression) of race and arrest [ORRACE] ___________ Inverse variance: [IVRACE] ____________ Type of statistical test for race and arrest [RACETEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and arrest effect size:
[ESRACE] ________
B. Suspect Gender: Odds ratio (logistic regression) of gender and arrest [ORGEN] ___________ Inverse variance: [IVGEN] ____________ Type of statistical test for gender and arrest [GENTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Gender and arrest effect size:
[ESGEN] ________
C. Suspect Age:
Odds ratio (logistic regression) of age and arrest [ORAGE] ___________ Inverse variance: [IVAGE] ____________ Type of statistical test for age and arrest [AGETEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and arrest effect size:
[ESAGE] ________
D. Suspect Ethnicity:
Odds ratio (logistic regression) of ethnicity and arrest Inverse variance: 187
[ORHISP] ___________ [IVHISP] ____________ Type of statistical test for ethnicity and arrest [HISPTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Ethnicity and arrest effect size:
[ESHISP] ________
E. Officer Race: Odds ratio (logistic regression) of officer race and arrest [ORORAC] ___________ Inverse variance: [IVORAC] ____________ Type of statistical test for officer race and arrest [ORACTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer race and arrest effect size:
[ESORAC] ________
F. Officer Gender: Odds ratio (logistic regression) of officer gender and arrest [OROGEN] ___________ Inverse variance: [IVOGEN] ____________ Type of statistical test for officer gender and arrest [OGENTEST] ________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer gender and arrest effect size:
[ESOGEN] ________
G. Officer Ethnicity:
Odds ratio (logistic regression) of officer ethnicity and arrest [OROHIS] ___________ Inverse variance: [IVOHIS] ____________ Type of statistical test for officer ethnicity and arrest [OHISTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer ethnicity and arrest effect size:
[ESOHIS] ________
188
H. Offense Severity (felony v. misdemeanor):
Odds ratio (logistic regression) of felony and arrest [ORFELO] ___________ Inverse variance: [IVFELO] ____________ Type of statistical test for felony and arrest [FELOTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Felony and arrest effect size:
[ESFELO] ________
I. Evidence:
Odds ratio (logistic regression) of evidence and arrest [OREVID] ___________ Inverse variance: [IVEVID] ____________ Type of statistical test for evidence and arrest [EVIDTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Evidence and arrest effect size:
[ESEVID] ________
J. Citizen Intoxication
Odds ratio (logistic regression) of intoxication and arrest [ORTOX] ___________ Inverse variance: [IVTOX] ____________ Type of statistical test for intoxication and arrest [TOXTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Intoxication and arrest effect size:
[ESTOX] ________
K. Weapon Use
Odds ratio (logistic regression) of weapon use and arrest [ORWU] ___________ Inverse variance: [IVWU] ____________ Type of statistical test for weapon use and arrest [WUTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
189
4: descriptive statistic
99: MISSING
Weapon use and arrest effect size:
[ESWU] ________
L. Victim preference for arrest
Odds ratio (logistic regression) of victim preference and arrest [ORVPREF]___________ Inverse variance: [IVVPREF]____________ Type of statistical test for victim preference and arrest [VPREFTEST]________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Victim preference and arrest effect size:
[ESVPREF]________
M. Demeanor
Odds ratio (logistic regression) of demeanor and arrest [ORSDEM] ___________ Inverse variance: [IVSDEM] ____________ Type of statistical test for demeanor and arrest [SDEMTEST] ________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Demeanor and arrest effect size:
[ESSDEM] ________
N. Interaction Phase Crime
Odds ratio (logistic regression) of IPC and arrest [ORIPC] ___________ Inverse variance: [IVIPC] ____________ Type of statistical test for IPC and arrest [IPCTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
IPC and arrest effect size:
[ESIPC] ________
O. Suspect Compliance
Odds ratio (logistic regression) of compliance and arrest Inverse variance: 190
[ORSCOM] ___________ [IVSCOM] ____________ Type of statistical test for compliance and arrest [SCOMTEST]_________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Compliance and arrest effect size:
[ESSCOM] ________
P. Suspect Mentally Ill
Odds ratio (logistic regression) of mentally ill and arrest [ORSMI] ___________ Inverse variance: [IVSMI] ____________ Type of statistical test for mentally ill and arrest [SMITEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Mentally ill and arrest effect size:
[ESSMI] ________
Q. Officer Education
Odds ratio (logistic regression) of officer education and arrest [OROED] ___________ Inverse variance: [IVOED] ____________ Type of statistical test for officer education and arrest [OEDTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer education and arrest effect size:
[ESOED] ________
R. Officer Experience
Odds ratio (logistic regression) of experience and arrest [ORXP] ___________ Inverse variance: [IVXP] ____________ Type of statistical test for experience and arrest [XPTEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Experience and arrest effect size:
[ESXP] ________
191
S. Rank Structure (number of organizational levels)
Odds ratio (logistic regression) of rank structure and arrest [ORLEVE] ___________ Inverse variance: [IVLEVE] ____________ Type of statistical test for rank structure and arrest [LEVETEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Rank structure and arrest effect size:
[ESLEVE] ________
T. Agency Size
Odds ratio (logistic regression) of agency size and arrest [ORSIZE] ___________ Inverse variance: [IVSIZE] ____________ Type of statistical test for agency size and arrest [SIZETEST] _________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Agency size and arrest effect size:
[ESSIZE] ________
U. Community Characteristics (crime rate)
Odds ratio (logistic regression) of community and arrest [ORCOMM]___________ Inverse variance: [IVCOMM]____________ Type of statistical test for community and arrest [COMMTEST] ________ 1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Community and arrest effect size:
[ESCOMM] _______
192
Sentencing Coding Guide: (All missing data coded as 99)
I. STUDY INFORMATION:
A. Identification Number:
[STUDID] ________
B. Study accepted or rejected? (YES=1; NO=0)
[INCLUD] ________
C. Eligibility criteria
1. Study is examining sentencing measured dichotomously?
1=YES
0=NO
2. Includes at least one measure of offense severity?
1=YES
0=NO
3. Includes at least one measure of offender characteristics?
1=YES
0=NO
4. Includes at least one measure of officer characteristics?
1=YES
0=NO
[DATASET]: __________________________________________________________________
______________________________________________________________________________
______________________________________________________________________________
D. Article author(s): _____________________________________________________________
E. Title: ______________________________________________________________________
F. Author affiliation: ____________________________________________________________
[AFFIL] __________
1 = University
2 = State Agency
3 = Federal Agency
4 = Mixed
5 = Other _________________
99 = Missing
G. Author discipline: 1 = Criminal Justice/Criminology
2 = Psychology
3 = Sociology
4 = Social Work
5 = Mixed
6 = Other _____________
99 = Missing
H. Coder:
1 = Daniel Lytle
0 = Other
99 = Missing
[DISCIP] _________
Fill in name
193
[CODER] _________
__________________
I. Funding Agency: _____________________________________________________________
1 = Unfunded
[FUND] __________
2 = Independent agency/foundation funded
3 = State funded
4 = Federal funded
5 = Other ______________
99 = Missing
J. Publication year:
Decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
[PUBYR] _________
[PUBDEC] ________
K. Geographic region of the study:
1 = Northeast
2 = South
3 = Midwest
4 = Southwest
5 = Pacific/Northwest
6 = Mixed
99 = Missing
[GEOREG] ________
L. Data collection year:
[START] _________
[FINISH] _________
[DATAGA] _______
Data gathered decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
194
M. Publication type:
1 = Journal
2 = Doctoral dissertation
3 = Book
4 = Book chapter
5 = Federal report
6 = State or local report
7 = Conference paper
8 = Other ________________
99 = Missing
[PUBTYP] ________
O. Number of courts surveyed:
[AGENUM] _______
II. SAMPLE DEMOGRAPHICS:
A. Offense specific:
Is the current study limited to a type of offense (1=YES, 0=NO)
Specific type of offense:
1 = Drug offenses
2 = Property offenses
3 = Violent offenses
4 = DUI
5 = Mixed
6 = Other _______________
99 = Missing/na
Percent sentenced to prison
[TYPOFF0] _______
[TYPOFF1] _______
[PERARR] ________
B. Age:
Defendants are:
1 = Exclusively adults
2 = Exclusively juveniles
3 = Mainly adults (over 80%)
4 = Mainly juveniles (over 80%)
5 = Mixed
99 = Missing
[DEFAGE] ________
Mean age of defendants
[MAGEDF] ______
195
C. Gender
Defendants are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[DEFGEN] ________
Percent Female defendants:
[PRFDEF] _______
Judges are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[JUDGEN] ________
Percent Female officer:
[PRFJUD] _______
D. Race
Defendants are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[DEFRAC] ________
Percent Defendants Non-white:
[PRNWD] ________
Judges are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[JUDRAC] ________
Percent judges Non-white
[PRNWJ] ________
196
Race is measured as:
1 = White/Non-white
2 = White/Black
3 = Other _________________
99 = Missing/not measured
[RACMEA] _______
E. Ethnicity
Defendants are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[ETHDEF] ________
Percent Hispanic defendants:
[PRHISD] _________
Judges are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[JUDHIS] _________
Percent Hispanic judge:
[PRHISJ] _________
Ethnicity is measured as:
1 = Hispanic/Non-Hispanic
2 = Other ________________
99 = Missing
[ETHMEA] _______
F. Offense Severity
Sample is:
1 = Exclusively felonies
2 = Exclusively misdemeanors
3 = Mainly felonies (Over 80%)
4 = Mainly misdemeanors (Over 80%)
5 = Mixed
99 = Missing
[SERFEL] ________
Percent felony:
[PRFEL] __________
Evidence:
Was the amount of evidence measured? (1=YES, 0=NO)
[EVIREC] ________
197
Weapon Use:
Was the weapon use in the case recorded? (1=YES, 0=NO)
[WEAPREC] ______
Percent of cases involving a weapon:
[PRWEAP] ________
Drug and Alchohol use:
Was drug and/or alcohol use recorded? (1=YES, 0=NO)
[DRUGREC] ______
Percent of cases involving drugs or alcohol
[PRDRUG] _______
Prior record/criminal history
Criminal history/prior record recorded? (1=YES, 0=NO)
How was prior record measured?
1= 1 or more or none
2= discrete scale
3= count
4= other ________________
Total number of offense severity measures:
F. Mental illness
Mental illness:
Was defendant mental illness recorded? (1=YES, 0=NO)
Percent mentally ill:
[PRIORREC]______
[PRIORMEA] _____
[SERTOT] ________
[MIREC] _________
[PRMENIL] _______
G. Other court characteristics
Experience:
Was a measure of judicial experience recorded? (1=YES, 0=NO) [XPREC] _________
Mean experience
[XPMEAN] _______
Federal vs. local
Cases are:
1 = Exclusively Federal
2 = Exclusively local
3 = Mostly Federal (over 80%)
4 = Mostly local (over 80%)
5 = Mixed
99 = Missing
[FVLREC] ________
Percent Federal
[PRFVL] _________
198
Mode of conviction (plea v. trail conviction)
Cases are:
1 = Exclusively pleas
2 = Exclusively trial convictions
3 = Mostly pleas (80%)
4 = Mostly trial convictions (80%)
5 = Mixed
99 = Missing
Percent plea
[PVTCREC] _______
[PRPVTC] ________
Counsel type
Defendants had:
1 = Exclusively public
2 = Exclusively private
3 = Mostly public (over 80%)
4 = Mostly private (over 80%)
5 = Mixed
99 = Missing
[PVPREC] _______
Percent public defenders
[PRPVP] __________
H. Organizational characteristics:
Were organizational level variables recorded? (1=YES, 0=NO)
Mean court size:
[ORGREC] ________
[MNSIZE] ________
I. Community characteristics:
Were community level variables recorded? (1=YES, 0=NO)
[UNITREC] _______
III. METHODOLOGICAL RIGOR ASSESSMENT
A. Control Variables included: (1=YES, 0=NO)
[CONVAR] _______
B. Includes interaction-effects: (1=YES, 0=NO)
[INEFUSE] _______
C. How long was the study conducted in months:
[LENGTH] _______
D. Was data collection continuous: (1=YES, 0=NO)
[CONTIN] ________
IV. SAMPLE SIZE
A. Sample size:
[SIZE] ___________
V. EFFECT SIZE DATA
199
A. Defendant Race:
Odds ratio (logistic regression) of race and sentence
Inverse variance:
[ORRACE] ________
[IVRACE] ________
Type of statistical test for race and sentence
[RACETEST]______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and sentence effect size:
[ESRACE] ________
B. Defendant Gender:
Odds ratio (logistic regression) of gender and sentence
Inverse variance:
[ORGEN]_________
[IVGEN] _________
Type of statistical test for gender and sentence
[GENTEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Gender and sentence effect size:
[ESGEN] ________
C. Defendant Age:
Odds ratio (logistic regression) of age and sentence
Inverse variance:
[ORAGE] _______
[IVAGE] _________
Type of statistical test for age and sentence
[AGETEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and sentence effect size:
[ESAGE] ________
D. Defendant Ethnicity:
Odds ratio (logistic regression) of ethnicity and sentence
Inverse variance:
[ORHISP] ________
[IVHISP]__________
Type of statistical test for ethnicity and sentence
[HISPTEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
200
Ethnicity and sentence effect size:
[ESHISP] ________
E. Judge Race:
Odds ratio (logistic regression) of judge’s race and sentence
Inverse variance:
[ORJRAC] ________
[IVJRAC] _________
Type of statistical test for judge’s race and sentence
[JRACTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Judge’s race and sentence effect size:
[ESJRAC] ________
F. Judge Gender:
Odds ratio (logistic regression) of judge’s gender and sentence
Inverse variance:
[ORJGEN]_________
[IVJGEN] _________
Type of statistical test for judge’s gender and sentence
[JGENTEST]_______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Judge’s gender and sentence effect size:
G. Judge’s Ethnicity:
Odds ratio (logistic regression) of judge’s ethnicity and
sentence
Inverse variance:
[ESJGEN] ________
[ORJHIS]_________
[IVJHIS] _________
Type of statistical test for judge’s ethnicity and sentence
[JHISTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Judge’s ethnicity and sentence effect size:
H. Offense Severity (felony v. misdemeanor):
Odds ratio (logistic regression) of felony and sentence
Inverse variance:
Type of statistical test for felony and sentence
1: r
5: t test
201
[ESJHIS] ________
[ORFELO] ________
[IVFELO] _________
[FELOTEST] ______
2: ANOVA
3: Chi-square
4: descriptive statistic
6: p value not otherwise specified
7: Φ calculated
99: MISSING
Felony and sentence effect size:
[ESFELO] ________
I. Evidence:
Odds ratio (logistic regression) of evidence and sentence
Inverse variance:
[OREVID] ________
[IVEVID] _________
Type of statistical test for evidence and sentence
[EVIDTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Evidence and sentence effect size:
[ESEVID] ________
J. Weapon Use
Odds ratio (logistic regression) of weapon use and sentence
Inverse variance:
[ORWU] ________
[IVWU]___________
Type of statistical test for weapon use and sentence
[WUTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Weapon use and sentence effect size:
[ESWU] ________
K. Experience
Odds ratio (logistic regression) of experience and sentence
Inverse variance:
[ORXP] _________
[IVXP] __________
Type of statistical test for experience and sentence
[XPTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Experience and sentence effect size:
[ESXP] ________
L. Court size
Odds ratio (logistic regression) of court size and sentence
Inverse variance:
202
[ORSIZE] _________
[IVSIZE]__________
Type of statistical test for court size and sentence
[SIZETEST]_______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Court size and sentence effect size:
[ESSIZE] ________
M. Community Characteristics
Odds ratio (logistic regression) of community and sentence
Inverse variance:
[ORCOMM]_______
[IVCOMM]________
Type of statistical test for community and sentence
[COMMTEST] ________
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Community and sentence effect size:
[ESCOMM] _______
N. Federal v. Local
Odds ratio (logistic regression) of jurisdiction and sentence
Inverse variance:
[ORJUR]________
[IVJUR]_________
Type of statistical test for jurisdiction type and sentence
[JURTEST]________
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Jurisdiction type and sentence effect size:
O. Counsel Type (public v. private)
Odds ratio (logistic regression) of counsel type and sentence
Inverse variance:
[ESJUR] ________
[ORPVP]________
[IVPVP]_________
Type of statistical test for counsel type and sentence
[PVPTEST] ________
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Counsel type and sentence effect size:
[ESPVP] _______
P. Plea type (plea v. trial conviction)
203
Odds ratio (logistic regression) of plea type and sentence
Inverse variance:
[ORPVTC] ________
[IVPVTC] ________
Type of statistical test for plea type and sentence
[PVTCTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Plea type and sentence effect size:
[ESPVTC] _______
Q. Drug and/or Alcohol use
Odds ratio (logistic regression) of Drug/alcohol use
Inverse variance:
[ORDRUG] ______
[IVPVTC]________
Type of statistical test for plea type and sentence
[DRUGTEST] _____
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Plea type and sentence effect size:
[ESDRUG] _______
R. Prior record/Criminal History
Odds ratio (logistic regression) of Drug/alcohol use
Inverse variance:
[ORPRIOR] _______
[IVPRIOR] _______
Type of statistical test for plea type and sentence
[PRIORTEST] _____
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Plea type and sentence effect size:
[ESPRIOR] _______
204
Parole Revocation Coding Guide: (All missing data coded as 99)
I. STUDY INFORMATION:
A. Identification Number:
[STUDID] ________
B. Study accepted or rejected? (yes=1; no=0)
[INCLUD] ________
C. Eligibility criteria
1. Study is examining parole revocation measured dichotomously? 1=YES
0=NO
2. Includes at least one measure of offense severity?
1=YES
0=NO
3. Includes at least one measure of offender characteristics?
1=YES
0=NO
4. Includes at least one measure of officer characteristics?
1=YES
0=NO
Data Set [DATASET]: ___________________________________________________________
______________________________________________________________________________
______________________________________________________________________________
D. Article author(s): _____________________________________________________________
E. Title: ______________________________________________________________________
F. Author affiliation: ____________________________________________________________
[AFFIL] __________
1 = University
2 = State Agency
3 = Federal Agency
4 = Mixed
5 = Other _________________
99 = Missing
G. Author discipline: 1 = Criminal Justice/Criminology
2 = Psychology
3 = Sociology
4 = Social Work
5 = Mixed
6 = Other _____________
99 = Missing
H. Coder:
1 = Daniel Lytle
0 = Other
99 = Missing
[DISCIP] _________
[CODER] _________
If zero, Identify coder __________________
205
I. Funding Agency: _____________________________________________________________
1 = Unfunded
[FUND] __________
2 = Independent agency/foundation funded
3 = State funded
4 = Federal funded
5 = Other ______________
99 = Missing
J. Publication year:
Decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
[PUBYR] _________
[PUBDEC] ________
K. Geographic region of the study:
1 = Northeast
2 = South
3 = Midwest
4 = Southwest
5 = Pacific/Northwest
6 = Mixed
99 = Missing
[GEOREG] ________
L. Data collection year:
[START] _________
[FINISH] _________
[DATAGA] _______
Data gathered decade:
1 = 1950
2 = 1960
3 = 1970
4 = 1980
5 = 1990
6 = 2000
7 = 2010
99 = Missing
(If data collection crosses decade use starting decade for DATAGA)
206
M. Publication type:
1 = Journal
2 = Doctoral dissertation
3 = Book
4 = Book chapter
5 = Federal report
6 = State or local report
7 = Conference paper
8 = Other ________________
99 = Missing
[PUBTYP] ________
O. Number of police agencies surveyed:
[AGENUM] _______
II. SAMPLE DEMOGRAPHICS:
A. Offense specific:
Is the current study limited to a type of offense (1=YES, 0=NO)
Specific type of offense:
1 = Drug offenses
2 = Property offenses
3 = Violent offenses
4 = DUI
5 = Mixed
6 = Other _______________
99 = Missing/na
Percent revocated
[TYPOFF0] _______
[TYPOFF1] _______
[PERARR] ________
B. Age:
Offenders are:
1 = Exclusively adults
2 = Exclusively juveniles
3 = Mainly adults (over 80%)
4 = Mainly juveniles (over 80%)
5 = Mixed
99 = Missing
[OFFAGE] ________
Mean age of offenders
[MAGEOF] ______
207
C. Gender
Parole officers are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[POGEN] ________
Percent Female suspect:
[PRFPO] __________
Offenders are:
1 = Exclusively male
2 = Exclusively female
3 = Mainly male (over 80%)
4 = Mainly female (over 80%)
5 = Mixed
99 = Missing
[OFFGEN] ________
Percent Female offenders:
[PRFOFF] _______
D. Race
Parole officers are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[PORAC] ________
Percent parole officers Non-white:
[PRNWPO] _______
Offenders are:
1 = Exclusively White
2 = Exclusively Black
3 = Mainly White (over 80%)
4 = Mainly Black (over 80%)
5 = Mixed
99 = Missing
[OFFRAC] ________
Percent Offender Non-white
[PRNWO] ________
208
Race is measured as:
1 = White/Non-white
2 = White/Black
3 = Other _________________
99 = Missing/not measured
[RACMEA] _______
E. Ethnicity
Parole officers are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[ETHPO] ________
Percent Hispanic:
[PRHISPO] _______
Offenders are:
1 = Exclusively Hispanic
2 = Exclusively Non-Hispanic
3 = Mainly Hispanic (Over 80%)
4 = Mainly Non-Hispanic (Over 80%)
5 = Mixed
99 = Missing
[ETHOFF] ________
Percent Hispanic:
[PRHISOF]________
Ethnicity is measured as:
1 = Hispanic/Non-Hispanic
2 = Other ________________
99 = Missing
[ETHMEA] _______
F. Offense Severity
Sample is:
1 = Exclusively felonies
2 = Exclusively misdemeanors
3 = Mainly felonies (Over 80%)
4 = Mainly misdemeanors (Over 80%)
5 = Mixed
99 = Missing
[SERFEL] ________
Percent felony:
[PRFEL] __________
Evidence:
Was the amount of evidence measured? (1=YES, 0=NO)
[EVIREC] ________
209
Weapon Use:
Was the weapon use in the encounter recorded? (1=YES, 0=NO)
[WEAPREC] ______
Percent of encounters involving a weapon:
[PRWEAP] ________
Risk:
Criminal History/Risk Recorded? (1=YES, 0=NO)
[RISKREC] _______
Need:
Criminogenic Need Recorded ? (1=YES, 0=NO)
[NEEDREC] ______
Total number of offense severity measures:
[SERTOT] ________
G. Education
Education:
Is offender’s education recorded? (1=YES, 0=NO)
[EDREC] _________
Offenders are:
1 = Exclusively college educated
2 = Exclusively high school/GED educated
3 = Mainly college educated (Over 80%)
4 = Mainly high school/GED educated (Over 80%)
5 = Mixed
99 = Missing
[COLED] _________
Percent college educated offenders:
[PRCOLED] _______
Parole officer’s education recorded? (1=YES, 0=NO)
[POEDREC] _______
Officers are:
1 = Exclusively college educated
2 = Exclusively high school/GED educated
3 = Mainly college educated (Over 80%)
4 = Mainly high school/GED educated (Over 80%)
5 = Mixed
99 = Missing
[COLEDPO] ______
Percent college educated parole officers:
[PRCOLEDPO] ____
Experience:
Was a measure of parole officer experience recorded?
(1=YES, 0=NO)
[XPREC] _________
Mean officer experience
[MNXP] __________
210
H. Employment status:
Offenders are:
1 = Exclusively employed full time or part time
2 = Exclusively not employed
3 = Mostly employed full or part time (over 80%)
4 = Mostly not employed (over 80%)
5 = Mixed
99 = Missing
Percent employed full or part time
[EMPOREC] ______
[PREMP] _________
I. Community characteristics:
Were community level variables recorded? (1=YES, 0=NO)
[UNITREC] _______
J. Program completion:
Were program completion variables recorded? (1=YES, 0=NO)
[PCREC] _________
Percent that have completed programming requirements
[PRPC] ___________
III. METHODOLOGICAL RIGOR ASSESSMENT
A. Control Variables included: (1=YES, 0=NO)
[CONVAR] _______
B. Includes interaction-effects: (1=YES, 0=NO)
[INEFUSE] _______
C. How long was the study conducted in months:
[LENGTH] ________
D. Was data collection continuous: (1=YES, 0=NO)
[CONTIN] ________
IV. SAMPLE SIZE
A. Sample size:
[SIZE] ___________
V. EFFECT SIZE DATA
A. Offender Race:
Odds ratio (logistic regression) of race and parole revocation
Inverse variance:
[ORRACE] ________
[IVRACE] ________
Type of statistical test for race and parole revocation
[RACETEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and parole revocation effect size:
[ESRACE] ________
211
B. Offender Gender:
Odds ratio (logistic regression) of gender and parole
revocation
Inverse variance:
[ORGEN]_________
[IVGEN] _________
Type of statistical test for gender and revocation
[GENTEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Gender and parole revocation effect size:
C. Offender Age:
Odds ratio (logistic regression) of age and parole revocation
Inverse variance:
[ESGEN] ________
[ORAGE] _________
[IVAGE] _________
Type of statistical test for age and parole revocation
[AGETEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and parole revocation effect size:
[ESAGE] ________
D. Offender Ethnicity:
Odds ratio (logistic regression) of ethnicity and parole
revocation
Inverse variance:
[ORHISP]_________
[IVHISP] _________
Type of statistical test for ethnicity and parole revocation
[HISPTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Race and parole revocation effect size:
[ESHISP] ________
E. Offense Severity (felony v. misdemeanor):
Odds ratio (logistic regression) of felony and parole revocation
Inverse variance:
212
[ORFELO] ________
[IVFELO] _________
Type of statistical test for felony and parole revocation
[FELOTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Felony and parole revocation effect size:
F. Evidence:
Odds ratio (logistic regression) of evidence and parole
revocation
Inverse variance:
[ESFELO] ________
[OREVID] ________
[IVEVID] _________
Type of statistical test for evidence and parole revocation
[EVIDTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Evidence and parole revocation effect size:
G. Weapon Use
Odds ratio (logistic regression) of weapon use and parole
revocation
Inverse variance:
[ESEVID] ________
[ORWU] ________
[IVWU] __________
Type of statistical test for weapon use and parole revocation
[WUTEST] ________
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Weapon use and arrest effect size:
[ESWU] ________
H. Parole Officer Education
Odds ratio (logistic regression) of officer education and parole
revocation
Inverse variance:
[ORPOED] ________
[IVPOED] ________
Type of statistical test for officer education and parole
[POEDTEST] ______
revocation
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
213
Officer education and parole revocation effect size:
I. Parole Officer Race
Odds ratio (logistic regression) of officer race and parole
revocation
Inverse variance:
[ESPOED] ________
[ORPORAC] ______
[IVPORAC] _______
Type of statistical test for officer race and parole revocation
[PORACTEST]_____
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer race and parole revocation effect size:
J. Parole Officer Gender:
Odds ratio (logistic regression) of officer gender and parole
revocation
Inverse variance:
[ESPORAC] _______
[ORPOGEN] ______
[IVPOGEN] _______
Type of statistical test for officer gender and parole revocation
[POGENTEST] ____
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer gender and parole revocation effect size:
K. Parole Officer Ethnicity:
Odds ratio (logistic regression) of officer ethnicity and parole
revocation
Inverse variance:
[ESPOGEN] _______
[ORPOETH] ______
[IVPOETH] _______
Type of statistical test for officer ethnicity and parole
[POETHTEST] ____
revocation
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Officer ethnicity and parole revocation effect size:
L. Offender Employment Status
Odds ratio (logistic regression) of employment and parole
revocation
Inverse variance:
214
[ESPOETH] _______
[OREMP] _______
[IVEMP] _________
Type of statistical test for employment and parole revocation
[EMPTEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Employment and parole revocation effect size:
M. Offender Education
Odds ratio (logistic regression) of education and parole
revocation
Inverse variance:
[ESEMP] ________
[OREDU] _________
[IVEDU] _________
Type of statistical test for education and parole revocation
[EDUTEST] _______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Education and parole revocation effect size:
N. Program Completion
Odds ratio (logistic regression) of program completion and
parole revocation
Inverse variance:
[ESEDU] _______
[ORPROG] _______
[IVPROG] ________
Type of statistical test for program completion and parole
[PROGTEST] ______
revocation
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Program completion and parole revocation effect size:
O. Parole Officer Experience
Odds ratio (logistic regression) of experience and parole
revocation
Inverse variance:
[ESPROG] _______
[POXP] ___________
[IVXP] __________
Type of statistical test for experience and parole revocation
[XPTEST] ________
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
215
Experience and parole revocation effect size:
P. Community Characteristics
Odds ratio (logistic regression) of community and parole
revocation
Inverse variance:
[ESXP] ________
[ORCOMM] _______
[IVCOMM] _______
Type of statistical test for community and parole revocation
[COMMTEST] _____
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Community and parole revocation effect size:
Q. Criminal History/Risk
Odds ratio (logistic regression) of risk and parole
revocation
Inverse variance:
[ESCOMM] _______
[ORRISK] ________
[IVRISK] _________
Type of statistical test for risk and parole revocation
[RISKTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Risk and parole revocation effect size:
[ESRISK] _______
R. Need
Odds ratio (logistic regression) of need and parole
revocation
Inverse variance:
[ORNEED] ________
[IVNEED] ________
Type of statistical test for need and parole revocation
[NEEDTEST] ______
1: r
5: t test
2: ANOVA
6: p value not otherwise specified
3: Chi-square
7: Φ calculated
4: descriptive statistic
99: MISSING
Need and parole revocation effect size:
[ESNEED] _______
216