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Electronic Theses, Treatises and Dissertations
The Graduate School
2014
To Plea or Not to Plea: The Role of the
Courtroom Workgroup in Certain and
Efficient Felony Case Processing
Christi Metcalfe
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FLORIDA STATE UNIVERSITY
COLLEGE OF CRIMINOLOGY AND CRIMINAL JUSTICE
TO PLEA OR NOT TO PLEA: THE ROL
E OF THE COURTROOM WORKGROUP IN
CERTAIN AND EFFICIENT FELONY CASE PROCESSING
By
CHRISTI METCALFE
A Dissertation
submitted to the
College of inology
Crim
and Criminal Justice
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy.
Degree Awarded:
Summer Semester, 2014
' 2014 Christi Metcalfe
Christi Metcalfe defended this April
dissertation
28,
2014. on
The members of the supervisory committee were:
Marc G. Gertz
Professor Directing Dissertation
JamesS.Bowman
University Representative
TedChiricos
Committee Member
Carter Hay
Committee Member
TheGraduate School has verified and approved
-named the
committee
above members, and
certifies that the dissertation has been approved in accordance with univers
ii
In loving memory of my uncle, Joseph Henry Falco
iii
ACKNOWLEDGEMENTS
This dissertation would have not been possible without the help and sup
important people.
I want to thank
Dr. Marc Gertz for his unwavering and unconditional support. I know he
countless hours worrying about me and know
my career,
it is because
but
he Ialways wants the
best for me. He is there whenever
which
I is
need
more
him,
than most students can hope for
mentor. He has taught me ahow
respected
to be
scholar
enthusiastic
and teacher, while
instilling
an appreciation world
for the
that exists beyond the university and classroom.
Dr. Ted Chiricos
, who has not only taught
, but shown
me e by
m example
,what it is to be
an insightful
nd thoughtful
a
scholar
one who
can make a difference in the field.
Dr. Carter, Hay
who
has offe
redme invaluable
guidance throughout my time in the
graduate program from my first year to Comprehensive Exams and now with the
Dr. James Bowman
for the time and care he has put into my dissertation. My
is not knowing him
this
until
past year.
My husband for putting up with my craziness. He instantly
am having
anotices when
stressful day or need more time to get something
patiently
done,
, picks
and up
ever
in so
my
place while I get my much needed rest or finish
do not my
know
project.
what I I
would do
without him.
My mom and dad who are always me
cheering
on to bigger and better
. They
things
taught me to always believe in myself and never give up, even when things ar
know that my drive and ambitionthem.
comes from
My brother and sister for making
. Having
methemaround
laugh
always makes for an
adventure, but no matter what, they are always there when I need them.
My grandparents for their endless support. I know they are so proud of
They want
the best for me, and it makes me want the best for myself.
My relatives
and friends
who I can always count on through the good and bad. Th
me grounded and I am so grateful for that.
The public defender s office for their willingness
to accomplish
to work this
with me
dissertation. None of this would have been possible without their help.
iv
TABLE OF CONTENTS
List of Tables
................................
................................
................................
..........................
vii
List of Figures
................................
................................
................................
..........................
ix
Abstract
................................
................................
................................
..............................
............
x
1. INTRODUCTION................................
................................
................................
.....................
1
1.1
1.2
1.3
1.4
Criminal Court Communities
................................
................................
........................
2
Current Study
................................
................................
................................
.....................
5
Limitations
ior
ofResearch
Pr ................................
................................
........................
6
Summary
................................
................................
................................
........................
8
2. THEORETICAL AND EMPIRICAL BACKGROUND................................
.......................
10
2.1
2.2
2.3
2.4
Court Bureaucracies
................................
................................
..............................
...........
10
Court Actor Action................................
Systems
................................
.........................
16
Plea Process
................................
................................
................................
......................
22
Opportunities for Future
................................
Research ................................
.....................
25
3. DATA AND METHODS ................................
................................
..............................
..........
27
3.1
3.2
3.3
3.4
Data Collection
................................
................................
................................
..................
27
The Cour
t Community
................................
................................
............................
.......
29
Propositions
................................
................................
................................
......................
31
Research Design
................................
................................
................................
.................
35
4. MEASURES................................
................................
................................
.........................
37
4.1 Ce
rtainty
and Efficiency Measures
................................
................................
....................
37
4.2 Court Actor Measures
................................
................................
.............................
........
38
3.3 Case and Defendant ................................
Measures
................................
.......................
41
5. THE GROUP EFFECTS OF COURTROOM ACTOR FAMILIARITY, SIMILARITY, AND
INFLUENCE ................................
................................
................................
..........................
.44
5.1Purpose and Propositions
................................
................................
...........................
...
44
5.2 Analysis
................................
................................
................................
.........................
45
5.3 Descriptive Statistics
................................
................................
..............................
..........
48
5.4 Results
................................
................................
................................
..........................
50
5.5 Sensitivity Analyses
................................
................................
..............................
...........
58
5.6 Discussion and Conclusion
................................
................................
..........................
59
v
6. THE INDIVI
DUAL EFFECTS OF COURTROOM ACTOR INFLUENCE, RACE, AND
GENDER ................................
................................
................................
............................
.......
64
6.1Purpose and
ropositions
P
................................
................................
...........................
...
64
5.2 Analysis
................................
................................
................................
.........................
66
5.3 Descriptivetic
Statis
s................................
................................
..............................
..........
66
5.4 Results
................................
................................
................................
..........................
68
5.5 Sensitivity Analyses
................................
................................
..............................
...........
73
5.6 Discussion and Conclusion
................................
................................
..........................
74
7. DISCUSSION AND CONCLUSION
................................
................................
......................
78
APPENDICES
A.US SUPREME COURT CASES CITED................................
..............................
...........
85
B.CHAPTER 5 MODEL COMPARISONS ................................
..............................
...........
86
C. RB
I APPROVAL LETTER S................................
................................
.........................
90
REFERENCES ................................
................................
................................
.........................
93
BIOGRAPHICAL SKETCH ................................
................................
.............................
.........
98
vi
LIST OF TABLES
Table 3.1:
unty
Co and Sample Cases ................................
by Year
................................
...............
29
Table 3.2: Circuit
lation
Popuand Judgeships................................
by Year
..........................
..
30
Table 3.3: Circuit
Criminal Dispositions................................
by Year
.............................
.........
31
Table 3.4: Proposed
Relationships
................................
................................
..........................
.32
Table 4.1: Example Court Actor Affiliation
................................
Matrix
............................
......
38
Table 4.2: Criminal Punishment Code Offense Levels and
........................
Point Equivalents
42
Tabl
e 5.1: Descriptive ................................
Statistics
................................
...........................
....
47
Table 5.2: Predicting
the Decision to Plea
Days and
to Disposition:
Direct and Indirect
Effects of Judge
Prosecutor Familiari
ty, Similarity, and ...............................
Influence
..............
49
Table 5.3: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effects of Judge
Defense Familiari
ty, Similarity, and ................................
Influence
..................
52
Table 5.4
: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effects of Prosecutor
-Defense Familiarity, Similarity,
..............................
and Influence
...........
54
Table 5.5: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effects of Workg
roup Familiari
ty, Similarity, and................................
Influence
......................
56
Table 5.6: Key Findings Regarding the Measures of Familiarity,
Influence
........
Similarity,
60
a
Tabl
e 6.1: Descriptive ................................
Statistics
................................
...........................
....
67
Table 6.2: Unconditional Models for Plea
the Decision
and Days
o Disposit
to
t ion....................
68
Table 6.3: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effects of Prosecutor Centrality, Experience,
................................
Race, and ..................
Gender 69
Table 6.4: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effec
ts of Defense Centrality,
Experience, Race, and................................
Gender
.....................
71
Table 6.5: Predicting
the Decision to Plea
Days and
to Disposition: Direct and Indirect
Effects of Judge Centrality,
Experience, Race, and................................
Gender
.......................
72
Table 6.6: Key Findings Regarding of
theProsecutor,
Measures Defense, and Judge
Centrality,
Experience, Race, and................................
Gender
................................
.....................
75
vii
Table 7.1: Reported Versus Expected Effects of Courtroom Workgroup Familiari
Similarity, and Influence on the DecisionomtoArrest
Plea and
to Case
Time Dispositi
fr
on.....
80
Table B1: Predicting the Decision to Plea and Days to Disposition: Model Com
the Effects of-Prosecutor
Judge
Familiari
ty, Similarity, and.............................
Influence
.........
86
Table B2: Predicting the Decision to Plea and Days to Disposition: Model Com
the Effe
cts of Judge
-Defense Familiarity, Similarity,
...............................
and Influence
.............
87
Table B3: Predicting the Decision to Plea and Days to Disposition: Model Com
the Effects of Prosecutor
-Defense Familiari
ty, Similarity, and............................
Influence
.....
88
Table B4: Predicting
cision
the De
to Plea and Days to Disposition: Model Comparisons
the Effects of Workgroupty,
Familiari
Similarity, and................................
Influence
..................
89
viii
LIST OFFIGURES
Figure 5.1:
Decision to Plea
Days
andto Disposition Generalized Structural Equation Mo
for the Effects
f Judge
-oProsecutor Familiari
ty, Similarity, and...........................
Influence
...
51
Figure 5.2:
Decision to Plea
Days
andto Disposition Generalized Structural Equation Mo
for the Effects -of
Defense
Judge Familiari
ty, Similarity, and............................
Influence
.......
53
Figure 5.3:
Decision toa Ple
and
Days to Disposition Generalized Structural Equation Mo
for the Effects of Prosecutor
Defense Familiari
ty, Similarity, and.........................
Influence
55
Figure 5.4:
Decision to Plea
Days
andto Disposition Generalized Structural Equation Mo
for the Effects
orkgroup
of W Familiarity, Similarity,
...............................
and Influence
............
57
ix
ABSTRACT
Theory and research suggests that criminal courts operate as organized
where both bureaucratic influences and court actor action systems play an im
processing
Eisenstein
(
& Jacob, 1977; Eisenstein, Flemming, & Nardulli, 1988; F
Nardulli, & Eisenstein, 1992; Nardulli, Eisenstein, & Flemming, 1988; Ulmer,
Specifically, these factors are expected to impact operational nd
certainty wit
the efficient management of cases (Heumann, 1981; Pollitz Worden, 1990; Skol
Thompson, 197). While a significant amount of research focuses on the extern
organizational influences placed on criminal courts,
een devoted
less attention
to the effect
has b
of workgroup interaction and influence (Hoskins Haynes, Ruback, & Cusick, 20
al., 1988; Pollitz Worden, 1995).
In order to address this issue, the current study uses a sample of felo
cases fromourthouse
a c
in the Southeast to assess the impact of courtroom actor
and efficiency in case processing, operationalized as the decision to plea a
case disposition. The study (1) determines the extent
similarity,
to whichand
familiarity,
influence among the judge, prosecutor, and defense attorney impact certainty
assesses whether there is variation in certainty and efficiency of case proc
and (3) evaluates the impact of
urtindividual
actor characteristics
co
on variation in case
processing. Attending to the limitations of prior research, the study quanti
interaction and influence, focuses on an earlier phase of case processing, l
their respective
ses, ca
and determines the contribution of court actors to the gui
The findings indicate that court actor familiarity and experience have
certainty and efficiency in case processing. Additionally,
n to plea
variation
and in the
time to disposition is detected across judges, prosecutors, and defense atto
influence is able to explain some of this variation.
ns, the
Contrary
findings
to expectatio
reveal
that
familiarity and influence
defensetorney
of
at scan hinder certain and efficient case
processing. The implications of these findings and potential avenues for fut
discussed.
x
CHAPTER 1
INTRODUCTION
In society, criminal trial courts are looked upon as
Forthe
some,
arbitrators
the
of
courts exist solely for punitive
to provide
reasons
justice to victims of crimes through
incarceration of offenders. Others believe that the system was designed to e
the accused until proven guilty, and
vedjustice
throughis
the
achie
defendant s right to due
process
(Packer, 1964; Neubauer & Fradella,
. Despite
2011)
the goals perceived by those
external to the courthouse,
andtrial
the actors
courts within
havethem
their own goals to attain,
most of which are contrary
tothe
se externally perceived responsibilities
(Blumberg, 1967;
Eisenstein & Jacob, 1977). Although trial courts are an important component
justice system, they are at their foundation organizations like any other (T
have actors and bureaucratic needs that influence the core functions, includ
incarceration of offenders and the guarantees of due process.
While multiple scholars
empirically
haveexamined trial courts in the context of the
organizationalure
struct
, focusing
mostly on the effect
caseload,
of
organization size, and tria
rate
on sentencing
(e.g.,
Johnson, 2005; Johnson et al., 2008; Ulmer & Bradley, 2006;
Johnson, 2004; Ulmer et al., 2010; Ulmer
),there
etare
al.,
two
2011
main organizati
onal
assumptions
of courts
that
are rarely considered.
fewstudies
First,
have focused on the
bureaucraticsto
need
plea
cases
and efficiently process
. Seeing
cases
that 95 to 97 percent of
cases result in aadisposition
plea
that
muchlower
has
due proce
ss standards than it
a trial
is
necessary to better understand
contributing
the factors
to a predominately
plea
-basedsystem
(Blumberg, 1967)
.Second,n court
i
communities
, the actors
identified
are as d
ariving force
behind
thecore perations
o
(Eisentein
& Jacob, 1977; Eisenstein et al., 1988; Flemming e
1992; Nardulli et al., 1988;. Ulmer,
Yet, few
1997)
studies explore the effects of courtr
actors and even fewer
able are
(a)
to quantify the effects of the interaction and interde
of court
actors (Hoskins Haynes, Ruback,
k, 2010;
& Cusic
Pollitz Worden, 1995)
link and (b)
court actors to their respective
(e.g., Anderson
cases
& Spohn, 2010; Johnson, 2006; Spohn
Fornango, 2009)
.
Using the court community perspective, an organizational
ework for understanding
fram
courts, this dissertation
contributes to the existing research
the contribution
by assessing of
1
courtroom actors
the to
maintenance
certai
of
n and efficient felony case
manifested
processing,
inthe decision to plea andmthe
arrest
timeto
fro
case disposition.
The chapter begins by
introducing the court community perspective and the prior research
purpose pertainin
of the study
discussed
is further
, along with
the limitations of prior
and research
how
the current
study
intends to overcome some of these limitations.
1.1 Criminal
Court o
Cmmunities
Criminal courts are thought of as court communities, because they posse
common workplace and interdependence of the actors involved. This perspectiv
crim
inal courts are organizations, and courtroom
groupsworkgroups
of judges, prosecutors,
and defense attorneys
are at the core of this system (Eisenstein, Flemming,
;
& Nar
Ulmer, 1997
). Case outcomes are produced through the ourtroom
interactions
workgroup
of the c
members as they adapt to maintain more certain case outcomes and efficient c
within the workplace (Eisenstein & Jacob, 1977; Flemming, Nardulli, & Eisens
Therefore,
the central characteristicsity,
of a including
court commun
its technology for
processing cases,
evolve through the actions and interactions of courthous
(Flemming et al., 1992, p. 199).
This school of thought was initially derived from the work of Blumberg
Skolnick (196
5, 1967), who were the first to acknowledge the organizational beh
criminal courts and how judges, prosecutors, and defense attorneys
buted within th
to the organization. As an employee
courthouse,
in the
Blumberg (1967) pointed
hat in aout t
workplace premised on the notion of adversarial relationships between the pr
defense, there is more cooperation than conflict. Through observation of cou
became clear that there is more to be gained
om cooperating
by the actors
with fr
each other, so
they often abandon
the goalsjustice
of
and due process for the organizational goals o
and efficiency in the form of a bargained justice (Blumberg, 1967; Skolnick,
These two organizational goals
ttained
can through
be a
one primary mechanism
a plea
of guilty. When a defendant pleads guilty, the defense attorney can ensure a
for the defendant, the prosecutor can guarantee a conviction, and the judge
overturned on appeal,
all of which add certainty to the system. In addition, it is
2
pleading guilty expedites the processing of the case by avoiding a trial, re
efficiency.
This increased efficiency may make for a more effective
es aresystem, wher
processed in a timely manner, but it may also come at the detriment of due p
defendants.
Despite this possibility
, negotiations are the most commonly used technique
criminal cases
because they reduce uncertainty and
ciency.
increase
The result
effi
a routinized
is
system of case processing for what can be classified as
normal crimes
(Eis
1977; Sudnow, 1965). Crimes that arouse public attention or receive special
such as murder, are e
not
and
routin
the processing of these cases fulfills more of the
goals of justice and due process, and not necessarily the organizational goa
The attainment of certainty and efficiency in the processing of
normal
depends on the ation
negoti
capability
of the courtroom workgroup. Eisenstein & Jacob (19
contend that workgroup familiarity and stability facilitate negotiation. Jud
defense attorneys who are regulars or
repeat players
in e,
thebecause
system are at
they are likely to be more familiar with one another and acquainted with the
(Eisenstein et al., 1988; Galanter, 1974). In addition, Eisenstein et al. (1
significant attribute of a communitypower
is the
of status
its members.
and
Within courtroom
workgroups, some members have more influence than others in terms of both th
experience. The interaction and influence of members of the workgroup become
of understanding case processing
and, in particular, the ability to enter into negotia
result in a plea of guilty by the defendant.
Research involving the court community perspective has generally taken
directions. The first body of research pertaining
ies emphasizes
to court communit
the court s
social world by studying the interactions between courthouses and their envi
1997). In this view, the courtroom workgroup reflects and responds to the en
courthouse, and this affects conflict
the amount
or of
cooperation that exists (Burstein,
Ulmer (2012) states
courtroom workgroup actors
interpretations and acts ar
maintain or change) local court communities, which are in turn
-cultural
embedded in l
contexts(p. 8).
Therefore, these studies focus on the variation of case pro
courthouses in different circuits, districts, and counties. These circuits,
expected to have different norms and characteristics,
caseload, including
trial rate,
thecourt siz
3
and external context, all of which influence the decision to incarcerate, de
recommended sentences, and sentence length (e.g., Johnson, 2005; Ulmer, 1997
In this first body of research,
ction and
the influence
intera
of courtroom workgroups i
implied. Therefore, if case processing varies across circuits, districts, or
relationship between legal and extralegal case factors and case processing v
assumed thatcourtroom
the
workgroup is operating differently based on the organi
environmental constraints placed on them. Courthouse characteristics and ext
the political, socioeconomic, and cultural environment,s are
variation
expected
in to expl
courtroom workgroup behavior. In accordance with these propositions, studies
case processing does significantly vary across circuits, districts, and coun
caseload, trial rate, court size, es,
sentencing
and other
guidelin
external pressures can expla
some of this variation (e.g., Harris & Jessilow, 2000; Johnson, 2005, 2006;
Kramer, 2008; Kautt, 2002; Ulmer & Bradley, 2006; Ulmer & Johnson, 2004; Ulm
& Johnson, 2010; Ulmer,
ight, L& Kramer, 2011).
A second, smaller body of research about court communities focuses more
operations of the courthouse to explain courtroom workgroup interaction. Mos
relies on interviews and observational
rt communities
data of cou
to explain the impact of
familiarity, stability,
similarity,
and influence among judges, prosecutors, and defense a
on case processing norms (Eisenstein & Jacob, 1977; Eisenstein et al., 1988;
1992; Thompson Hephner,
2002; Ulmer, 1995). Only a couple studies in this area ha
attempted to operationalize courtroom
nteraction,
workgroup
and
i among those
do, the
who
measures are
onlybased on the relationship between the judge and prosecutor (Hos
et al., 2010;
Pollitz Worden, 1995; Nardulli, Flemming, & Eisenstein, 1988). Thes
suggested that courtroom workgroup familiarity and stability does facilitate
processing decisions, including incarceration, restitution,
encing recommendations
fines, and sent
(Eisenstein et al., 1988; Hoskins Haynes et al., 2010; Pollitz Worden, 1995;
2002; Ulmer, 1995).
In order to better understand the influence of certain actors, some add
this area has alsot looked
cases handled
a
by specific judges and prosecutors to dete
is variation in case processing across actors (e.g., Anderson & Spohn, 2010;
2009). Characteristics of the actors are then used to trythose
to explain
studiesthis va
4
that are unable to link cases to specific actors, aggregate characteristics
circuit, district, or county level are used to try to explain variation in c
Farrell, Ward, & Rousseau, 2009; &Ward,
Rousseau,
Farrell,
2009). Research applying these
strategies have found that there is some variation in guidelines departures
prosecutors and judges, and
-related
both work
and physical characteristics of these act
as race, gender,
and age, can explain a degree of this variation (e.g., Anderson &
Johnson, 2006; Ward et al., 2009; Schanzenbach, 2005; Steffensmeier & Britt,
Wooldredge, 2010). Because the quantitative effects of courtroom interaction
case processing are largely unknown, it is this second body of court communi
the main focus of this dissertation.
1.2Current tudy
S
Drawing on the court community perspective, the purpose of the current
the effect
courtroom
of
workgroups on the achievement of certainty and efficiency
processing using a sample of felony cases in a Southeastern courthouse
The
from
study addresses two aspects of courtthe
organizations
court actor action the
system
pleaand
processin order to determine the extent to which court actors have contribut
dominated by pleas.
To measure
court actor
interaction, the concepts of familiarity, simi
and influence among the judge, prosecutor,
orney
andare
defense
quantified
att to better
understand the impact of workgroups on case management. In addition, the imp
individual actors in differences
explainingcase
in processing is assessed. Because of the
relevancecase
of disposition
in the achievemen
t of
het courts
organizational
the goals,
outcomes focused
onarethe decision to plea, a measure of certainty within the cou
and the time from arrest to case
a measure
disposition,
of system efficiency.
The following
research questions
ddressed:
are a
1. To what extent does familiarity, similarity,groups
and influence
judge
of s,
among
prosecutor
s, and defense attorney
simpact the decision to plea and the time from a
case disposition?
2. Is there variation
individual
across
actors in the
ision
decto plea and time to dispositio
3. If so, how much
of this
variation can be explained
actor
by
influence
court
?
5
1.3 Limitations of esearch
Prior R
Among the studies that exist concerning courtroom actor interaction, th
important limitations
: (1) there are few quantitative assessments of courtroom inte
influence, (2) little attention has been devoted to the earlier phases of ca
studies explore why there is a dominance of guilty pleas
em,in(4)
thethe
criminal
goal cou
of efficiency in case processing is often ignored, and (5) most studies cann
prosecutors, and defense attorneys to specific cases.
Eisenstein and Jacob (1977) stressed the importance of interaction and
the ourtroom
c
workgroup in explaining case processing. Only three studies to
able to quantify some of these workgroup characteristics (Hoskins Haynes et
et al., 1988; Pollitz Worden, 1995). Most of what rtance
is known
of about
courtroom
the impo
workgroup interaction and influence is based on qualitative work. While obse
interviews were an important component in the development of the court commu
(Eisenstein & Jacob, 1977; Eisenstein
88; Flemming
et al.,et19al., 1992), the reliability
findings depends, in part, on the ability to identify similar relationships
means that
analyses should explicitly test for the interactions among actor
(Bushway & rst,
Fo
2013, p. 217; Gertz, 1980).
Bushway and Forst (2013) note that
most cases are resolved at the stag
screening and by plea bargains yet research on these critical stages is limi
criminology literature
(p. 217). The
y perspective
court communit
emphasizes the importance
both certainty and efficiency within case processing. Plea bargaining is tho
both of these goals (Eisenstein Research
& Jacob, that
1977).
looks at the influence of the
on case processing
focuses more on the decision to incarcerate, guidelines departu
sentence length. Qualitative studies highlight the importance of the workgro
negotiations (Thompson Hephner, 2002; Ulmer, 1995), but there
sessments
are no quantit
of actor interaction and influence on the decision to plea.
One key reason that it has been difficult to study the decision to plea
variation in case disposition within large data sets. In theseplea
data sets, it
cases make up 95 to 97 percent of the data. Testing the effects of case, wor
factors on the decision to plea requires a sample of cases with more variati
6
this reason, trial cases would have
ledtoinbecomparison
oversamp to plea cases to allow f
more accurate predictions in analyses. Because so many cases result in a ple
that predicting the decision to plea is
of irrelevant
its inevitability
.The
because
reason for this
predominance
is largely unknown, and it is proposed that court actors have some
it (Bushway & Forst, 2013; Eisenstein & Jacob, 1977; Eisenstein et al., 1988
1992; Gertz, 1980; Nardulli et al., 1988; Ulmer, 1997).
termine
It the
is important
extent to to d
which court actors drive the plea system by increasing
of the
guilty
.likelihood of a p
Accepting a plea, instead of going to trial, is recognized as an effici
processing, because it reduces the time
t andbetween
case disposition.
arres
Courtroom
workgroup familiarity is expected to increase the likelihood of plea negotia
in turn, reduce the time to case disposition (Eisenstein
It et
is al.,
important
1988; Gertz
to consider efficiency
ecause although
b
it has its benefits, increases in efficiency
suggest a tendency to overstep the boundaries
Sacksofand
dueAckerman
process.(2012) found
that case and defendant characteristics have an effect on the
n days to dispos
theoretical propositions, workgroup interaction and influence should have a
efficiency of case processing since their familiarity encourages plea negoti
Lastly, few studies are able to link actors toerson
specific
& Spohn,
cases (e.g., An
2010; Spohn & Fornango, 2009). Studying the effect
sis difficult,
of workgroup
and almost
impossible, without the ability to bridge this connection. In order to bette
across actors, researchers need to be
theable
individual
to identify
actors responsible for giv
cases (Bushway & Forst, 2013). Among those studies that can connect actors t
isonlyon explaining case processing variation across judges or prosecutors. C
produced through
joint
the interaction of judges, prosecutors, and defense attorne
2012), so all three actors should really be taken into account. In addition,
consider the possibility that the decision to plea anddisposition
the time from
can arrest
vary across actors as well.
In short, the research to date suffers from a lack of quantification of
interaction and influence, focus on earlier phases of cases processing (e.g.
variation in case
sition
dispo
in order to accurately predict it in analyses, specifi
the court actor, plea, and efficiency link, and data sets that can identify
defense attorney for Thecurrent
each case. study addresses several
e limitations
of.thes
7
Of most importance,
this study
oneisof only three quantitative tests of courtroo
workgroup interaction and influence. Unlike the prior studies, which only in
workgroup measures involving the judge and prosecutor,
torney is
thealso
defense
accounted
at
for here. Testing the impactsis
of workgroup
also made possible through the linking of ac
their respective cases. Because of this, cases can be analyzed as nested wit
(b) groups of prosecutors attorneys,
and defense(c) groups of judges and prosecutors, (
of judges and defense attorneys, and (e) individual courtroom workgroup memb
variation in case disposition and time to disposition across courtroom actor
In addition, the
weredata
collected so that all trial cases from 2002 to 2010
within the data set and only a random sample of plea cases from this same ti
incorporated. This oversample of trial cases was done to allow for enough va
case disposition. The ability to predict case disposition, and account for i
disposition, allows a more complete test of the claim that workgroup familia
certainty and efficiency within the system (Eisenstein
977). The focus
& Jacob,
on case
1
disposition also adds insight into the earlier stages of court processing, a
limited in the criminology literature (Bushway
, and &offers
Forst,the
2013)
opportunity to
evaluate the extent to which
ups have
workgro
contributed-adversarial
to a non
system
.
1.4 Summary
Due to the lack of quantitative research linking courtroom workgroup in
processing, this dissertation focuses on the effect of familiarity, similari
decision to plea and time from arrest to case disposition. Based on theoreti
the court community perspective, it is determined whether these characterist
workgroups contribute to (a) more certain caseeas
outcomes,
of guilt,
through
and (b)
pl greater
system efficiency, by reducing the time to case disposition. Remedying the l
research, the current study focuses on the decision to plea, an earlier phas
and links actors to their
tive cases.
respec
The remaining chapters proceed as follows. Chapter
-depth2 overview
provides of
an in
the theoretical work involving the court community perspective, including a
research that has tested some of the key theoretical
n light of
propositions.
the limitations
I
of
8
prior research, Chapter 3 discusses the current study and how it overcomes s
Details are provided about the data collection, final sample, and research d
the methods used to create
ourtroom
the workgroup
c
measures are explained, including m
derived from affiliation network analyses of courtroom actors. Chapter 5 and
effect of workgroups, pairs of actors, and individual actors on the decision
disposition. Multiple strategies are used for these analyses to account for
within specific cases. Chapter 7 provides a general overview of the key find
some important directions for future research.
9
CHAPTER 2
THEORE TICAL AND EMPIRICAL BACKGROUND
Any criminal trial court community has two main components: (1) a court
and (2) an action system composed of court actors. As previously noted, theo
regarding the organizational behavior
al courts
of crimin
have generally evolved into the st
either of these two areas and how they affect case processing. To have a bet
the current literature regarding criminal trial courts, it is necessary to a
researc
h. For the purposes of this dissertation, it is also essential to recog
the plea process in the context of this theoretical and empirical research.
The chapter begins by reviewing court bureaucracies and actions
he
systems
research pertaining to these two components of criminal court communities. T
relevance of the plea process in the context of the court community perspect
along with the research to date that has predicted
to plea and
thetime
decision
to disposition.
Lastly, opportunities
for future research are identified.
2.1 Court Bureaucrac
ies
Criminal trial courts are organizations, and as such, they have bureauc
(Blumberg, 1967; Skolnick, 1967; Thompson,
bureaucracy
1967). The
of the court directly
responds to the culture of the court, internal operations, and external impo
expands on the theoretical background regarding court bureaucracies and the
connected aspects of eaucracies
court bur to case processing outcomes.
It is theorized that each criminal courthouse possesses a certain cultu
understandings by which operations are conducted on a regular basis (Louis,
1983). This culture ofisthe
important,
court
because it shapes the behavior of indivi
the courthouse and the character of the court community (Eisenstein et al.,
courts are their own social worlds, with each court having its own particula
from one courthouse to the next (Ulmer, 1997).
These norms vary across courthouses, because court communities function
context of local
olitical
p
arrangementswide
and legal
state structures (Ulmer, 1997). Therefo
10
there exists a meansnge
of between
excha a courthouse and its outside environment (Co
Seron, 1990). Clients, suppliers, and regulatory groups outside the courthou
the court s operations (Flemming et al., 1992; Thompson, 1967).
For instanc
defendants, as clients to the court, can place pressure on the courts to ope
(Burstein, 1979). The police and legislators, as suppliers to the system, ca
on court functioning, along with appellate
as courts,
regulators
who of
serve
the trial process
(Burstein, 1979; Eisenstein et al., 1988). In addition, criminal courts have
support for particular sentencing policies, elections, and media coverage (E
Mears, 1998).
Ult
imately, the court community responds to the internal norms of the c
the prosecutor, defense attorney, and judicial offices
referred
in to
theascourthouse
sponsoring
agenciesand the external constraints placed on the
r community
court from(Eisenstein
its large
et al., 1988; Ulmer, 1997). Because of this, courthouses are expected to var
the next, since there are different external pressures within various contex
influence the internal norms.
upposed
It is
that
pres
environmental factors influence inter
norms through the court actors
involvement in
webs of interdependence and
each other, political constituencies, and sometimes with news and media
(Ul
The social dworl
of the court is typically characterized along dimensions o
pressure, trial conviction rate, and organization size (Johnson, 2005; Ulmer
Caseload pressure is operationalized as the average numbertrict,
of cases sentence
circuit, or county in a particular year divided by the number of judgeships.
are calculated by dividing the average number of trials per year by the aver
sentenced in each, district
circuit, orper
county
ye
ar. Court size is typically measured as th
number of authorized judgeships in the district, circuit, or county.
It is assumed that these factors of the court s social world influence
interaction among the courtroom actors
certain
that case
facilitate
processing norms. For
example, in a court where the norm is to plea bargain because of large casel
harsher sentence would be expected for those who go to trial (Ulmer & Johnso
addition, the norm of informal
essing
case
through
proc plea negotiations can create dispa
sentencing among defendants with different demographic characteristics becau
discretion given to actors in an informal system (Albonetti, 1991; Seffensme
11
Streifel,
1979). In each of these circumstances, the interaction and influence o
workgroup as an important component of the court s social world is implied t
outcomes.
2.1.1 The Effect of Court Bureaucracies on Case Outcomes
The majorit
y of research focusing on bureaucratic components of courthouse
hierarchical modeling techniques, so that variation can be detected across c
counties (Johnson, 2005; Johnson et al., 2008; Ulmer et al.,
Cases
2010;
are Ulmer et
considered as nested within their respective geographical areas, and both or
environmental factors of these areas are used to predict both case outcomes
between legal or extralegal case factors
mes. and
Taken
case
together,
outco
this research has
drawn some key conclusions about the social world of the court and its effec
as detailed below.
Because of the focus of this dissertation, it is important to note, bef
research
o date,
t
that these studies consider the decision to plea as a legal f
independent variable) that has an effect on the outcome achieved. In tests u
modelling, the decision to plea is set to vary
ts,across
or counties,
circuits,
anddistric
then
organizational and environmental factors are used to try to explain the rela
decision to plea and case outcomes across these areas. The decision to plea
byproduct of courthouse norms,
ore,
andexpected
theref to vary from courthouse to courth
With this in mind, the research to date has typically focused on three
guidelines departures, the decision to incarcerate, and sentence length. Con
caseload has been ly
positive
linked to(a)
both
downward departures,
orsentences below
guideline recommendations,
(b)substantial
and
assistance ordownward
departures,departures
for defendants who are deemed to have given
substantial assistance
to law
(Johnson, 5;
200Johnson et al., 2008, p. 740). Among trial cases, in particular
caseload pressure actually reduce the odds of a downward departure, suggesti
for those who do not plead guilty (Johnson, 2005; Johnson
, higher
et al.,
trial
2008).
rates In
are associated with sentences above guidelines recommendations, regardless o
defendant was convicted at trial or by a plea of guilty (Johnson,
-sized2005). Rel
courts, large courts have increased
ownward
odds
departure
of a d and decreased odds of an
12
upward departure (Johnson, 2005), which supports the contention that large c
media visibility, routine case processing, increased bureaucratization, and
bargaining (Eisenstei
n et al., 1988; Johnson et al., 2008; Ulmer, 1997). Similarly
Ulmer (2002) found that larger counties have almost 3.5 times greater odds o
downward departures than smaller counties.
In terms of the decision to incarcerate,
garding findings
the effect
re of caseload have
been mixed. Among federal districts, larger caseloads increase the odds of i
defendants (Ulmer et al., 2011). However, studies using data from Pennsylvan
reported that larger caseloads
e the likelihood
reduc
of incarceration (Ulmer & Johnson,
Ulmer & Bradley, 2006). This relationship would be expected if caseload incr
accepting a plea over going to trial, such that pleading guilty often leads
(Ulmer& Johnson, 2004; Ulmer et al., 2010). It is not surprising, then, that
(2004) also found that the trial penalty for the decision to incarcerate is
higher caseload pressure. Higher trial ratesinked
have to
been
thepositively
decision to
l
incarcerate as well (Johnson, 2005), while larger courts are less likely to
to time in jail or prison (Johnson, 2006; Ulmer & Johnson, 2004).
Studies exploring the effect of organizational
length
factors
have on
found
sentence
that
caseload is negatively related to the time spent in jail or prison (Ulmer &
et al., 2010). Similar to findings regarding the decision to incarcerate, tr
length modestly increase
urtwith
caseloads
co
(Ulmer & Bradley, 2006; Ulmer et al., 20
Alternatively, Ulmer et al. (2010) reported that higher trial rates reduce t
on sentence length. Much like the effects of trial rates, the
is trial penalty
significantly less than in smaller courts (Johnson, 2005). However, Ulmer &
found that serious violent offenses in large urban courts actually receive g
penalties than in smaller courts.
Although the majority of
rch
the
in resea
this area treats the mode of disposition
independent variable, it is important to recognize that Wooldredge (1989) di
of caseload, trial conviction rate, and organization size on the guilty plea
stud
ies above that use hierarchal modeling to assess cases nested within diff
areas, his unit of analysis was circuit courts. Specifically, he looked at 1
and found that increases in the caseloadial
pressure
conviction
and the
ratetrcorrespond with
13
higher felony guilty plea rates across circuit courts, while organization si
to the guilty plea rate.
Aside from the caseload, trial conviction rate, and court size, other o
pertinent to the social world of courts have also been explored. While sever
looked at guidelines departures as an outcome of interest (e.g., Johnson, 20
2008; Kramer & Ulmer, 2002), research has also assessed
guidelinethe
departure
effect of
rates
and guideline compliance rates as elements of the court social world that ca
outcomes. For example, Johnson (2006) found that guideline departure rates a
sentence severity, such that higher
s result
departure
in a rate
lower likelihood of incarcera
a three
-level analysis of sentence length, Kautt (2002) found that in districts
guidelines compliance rates, the effect of offense seriousness on sentence l
the ratewarding
of a
substantial assistance departures in districts is associated
length penalty for those who go to trial.
Dixon (1995) also studied the court organization in a slightly differen
sample of felony cases from the
State
Minnesota
Court Administrator
-tracking
s casesystem. In
his study, he focused on two organizational aspects of the court s bureaucra
complexity of the division of labor, or whether the criminal court where the
operates whereset
oneof criminal
-docket judges hear all the cases, and the prosecutor
have more than three divisions or departments, and (2) the decentralization
or whether the case was processed in a court using a decentralized
r system, such
master ca
that judges are responsible for the performance of a particular task for a c
prosecutor can make a decision about a plea. He found that in courts with hi
prosecutorial bureaucratization, was
pleasignificantly
bargaining less likely to result in
incarceration and a longer prison sentence. He concluded that the results ar
Eisenstein et al. (1988), since greater division of labor and decentralized
to aid informal
discretionary
and
case processing among workgroups.
2.1.2 The Effect of Environmental Factors on Court Bureaucracies and Case Ou
Aside from organizational factors, research has also looked at the effe
political environment outside
courthouse
the on the internal courthouse norms. Because
legislature is a key supplier of the court organization (Thompson, 1967), so
14
assessed the impact of sentencing laws within districts, circuits, or counti
Using onviction
c
and sentencing data
-time for
adult
first
offenders sentenced in Washington
State between 1985 and 1995 for drug crimes, Engen and Steen (2000) found th
sentences varied in accordance with changes in sentencing
was laws,
contingent
and this cha
upon the mode of conviction. In a similar fashion, Ulmer et al. (2011) looke
sentencing pre and post the PROTECT
Booker
, Act,
and
Gall
, but they could not find evidence
to support increased discretion following
of the
implementation
law and court decisions. If
anything, discretion actually decreased compared to the earlier period. In a
laws, Harris and Jesilow (2000) interviewed and surveyed members of courtroo
six California counties
ermine
to the
det impact of Three Strikes laws on the behavior
courtroom workgroups. They found that these laws limit discretion of workgro
undermine plea bargaining by making it difficult to
, since
predict
defense
case outcomes
attorneys do not know
en a wh
prosecutor or judge will not
. count a prior
Instead of focusing on the impact of specific laws, some studies have d
attention to the effect of the political climate on court operations. Johnso
the political sm
liberali
of federal districts by using the American Civil Liberties U
scores for the voting records of US Senators in the state where the district
districts that were more politically liberal were more
departures.
likely to In
offer downw
Pennsylvania courts, the percent Republican in a district was related to sen
unrelated to the decision to incarcerate (Ulmer & Johnson, 2004). As an elem
crime
political agendas, Johnson (2006)
t localfound
jail tha
capacity increases the odds
being incarcerated as well.
2.1.3 Summary
Together, the research to date suggests that courts do possess distinct
both internal and external pressures shape courthousevaries
norms.across
Court processin
circuits, districts, or counties, and the court s bureaucracy and organizati
including the caseload, trial conviction rate, and court size, can explain s
most cases, there is a
upward
penalty
departure,
incarceration, or longerfor
sentence
those
defendants who do not assist the court organization by accepting a plea of g
political and legal pressures from outside the courthouse influence case out
15
2.2 Court Actor Action Systems
In addition to bureaucracies, court communities have action systems inv
actors in the courthouse (Eisenstein & Jacob, 1977; Parsons, 1961, 1969). Th
the theoretical relevance of these action systemscase
and processing.
how they influence
The
empirical research to date is also reviewed.
According to Flemming et al. (1992),
court communities are constructed
members. The central characteristics of a court community evolve through the
interactionsurthouse
of co
participants
(p. 199). The theory and research reviewe
espouses that courts are products of internal norms influenced by both spons
external constraints. An essential component of these sponsoring
w howorkagencies is
within them, including judges, prosecutors, and defense attorneys. Criminal
organizations that administer criminal justice, but they are also systems of
cooperation, exchange, and adaptation (Feeley,
s, 1961,
1973;
1969).
Parson
The more smoothly
the actors within the courthouse can work together, the more likely it is th
achieve its goals (Blumberg, 1967; Skolnick, 1967). For this reason, the cou
may be the most appropriate
concept from the court community perspective to explain
in criminal trial courts (Clynch & Neubauer, 1981).
Viewing criminal courts as action systems suggests that they are cooper
organizations with adaptive social structures
racting
made
individuals,
up of inte subgroups, and
formal and informal relationships (Selznick, 1948). While each actor within
her own role, the actors as a whole share a responsibility in the common goa
cases, including thence
maintena
of certainty and efficiency within the system (Feele
Thompson, 1967). The dominance of a particular case processing strategy depe
community familiarity and stability, attitudes and ideologies, resources and
andcommitments (Ulmer, 1997). Because of this, the decision to prosecute and
proceeds thereafter has a lot to do with the cooperation of the actors invol
(Cole, 1970).
Judges, prosecutors, and defense attorneysm operate
workgroups,
in courtroo
where a
single judge, prosecutor, and defense attorney is assigned to each case. Cou
are similar to organized groups in many respects (Eisenstein & Jacob, 1977).
16
both authority relationships relationships
and influence (that can then modify the authori
relationships). An important attribute of a community is the structure of st
its members (Eisenstein et al., 1988). In any courthouse community, status a
variables, onstants
not c
(Flemming et al., 1992). The pattern of influence varies
participants
possession of resources and how they use them. The two main fa
determine influence in workgroups are (a) the role of the participant and (b
parti
cipant, which is essentially the amount of experience (Eisenstein et al.,
of status and influence within workgroups can then potentially explain the a
particular case processing norms.
Second, organized groups gether
are held
by to
common goals (Eisenstein & Jacob,
1977). Courtroom workgroups have a shared perspective that tends to undermin
(Blumberg, 1967). When contingencies and
begin
constraints
to interfere
their
with
goals,
especially those of certainty
fficiency
andine case processing, they adapt as a unit in
attain these goals (Parsons, 1961, 1969; Thompson, 1967). For this reason, c
would be expected to reflect more of the common courthouse goals in workgrou
level cohesion
of
between the members (Clynch & Neubauer, 1981).
Third, courtroom workgroups use a variety of work techniques and engage
tasks. Organizations employ particular techniques based on bureaucratic need
1967). In courthouses,
the three primary techniques are: (a) unilateral decisions,
proceedings, and (c) negotiations or plea bargaining (Eisenstein & Jacob, 19
specialization of tasks within workgroups leads to routinized procedures of
through one of these three techniques (Eisenstein & Jacob, 1977).
Finally, courtroom workgroups have different degrees of stability and f
(Eisenstein & Jacob, 1977; Eisenstein et al., 1988). The more workgroup memb
with one another,
the better they can negotiate, and avoid the formalities of unil
and adversarial proceedings (Eisenstein & Jacob, 1977). This aspect of organ
attention to the importance
-regulars
of lawyer
(Blumberg, 1967). Galanter
recognizes
(1974)
that
there are repeat players within courthouses who engage in many similar cases
result, have more opportunities to develop informal relations with other ins
Longer, established relationshipsers
between
withinmemb
the system reduce the likelihood
formal case processing through adjudication and litigation (Galanter, 1974).
17
defense attorneys move from
regular
to
occasional
status, their membersh
questionable (Eisenstein
l., 1988).
et a Attorneys who are seen as outsiders become pro
the system, because they are unfamiliar with the routines of the repeat play
1988).
As organized groups, courtroom workgroups possess each of ile
these charact
workgroups have different techniques for processing cases, the method adapte
workgroup and its goals (Eisenstein & Jacob, 1977). The decision between a p
an adversarial proceeding reflects the goals
ization,
of thebecause
court organ
it is, essentiall
the choice between certainty and uncertainty of a case outcome. In addition,
believed to be a key aspect of system efficiency. Based on the characteristi
groups, the achievement
ertainty
of c and efficiency in case processing through plea
seems to depend on the level of organization among the courtroom workgroup.
includes the status and influence among the workgroup members, and the famil
stabil
ity within the group. Because of this, research concerning the influence
prosecutors, and defense attorneys has devoted particular attention to the e
familiarity, stability, similarity, and influence this
on case
workprocessing.
is based onMost o
observational data and interviews (Blumberg, 1967; Eisenstein & Jacob, 1977)
more recent work is highlighted below.
2.2.1 Qualitative Studies of Court Actor Interaction
Stemming from the original work of Jacob
Eisenstein
(1977),
andEisenstein et al. (198
found that location in the same building and participation in a common
grap
familiarity, even among people new to the courthouse. Additionally, differen
distribution of age and mong
experience
judges,
a prosecutors, and defense attorneys appe
affect the court community in subtle ways. For instance, large generation ga
from prosecutors and defense attorneys inhibited communication and impeded c
interac
tion. Ulmer (1995) also interviewed and observed court communities in t
Pennsylvania counties. He found evidence that workgroup familiarity and stab
strong informal case processing norms and heavy reliance onnoted
plea negotiation
that robust shared pasts seemed to facilitate negotiation. Based on the find
shared and common pasts help manage uncertainties in decision making.
18
In a slightly different fashion, Thompson Hephner (2002)
e study
conducted a qu
of drug cases in two workgroups in a state district felony court. When the s
defense attorney, and judge were responsible for adjudication of similar def
that the case disposition was more likely
a trial.
to be He
a plea
also than
recognized that the
court appointed defense attorney was an integral part of the courtroom workg
attorney had informal relationships with the judges and prosecutors assigned
2.2.2 Quantitative Studies of Court
raction
Actor Inte
In addition to this qualitative work, three studies have quantified wor
and stability to test its effect on case processing. In the first of these s
(1988) attempted to linearly assess the
n impact
charge of
reductions
actors o and sentences in
plea cases with familiar workgroups, or where the same judge, prosecutor, an
handled at least five cases together in the county sample. First, they used
variables depicting each
judge
relevant
and prosecutor in a county,
modest
andeffects
found on
charge reductions
sentencing.
and
Second, they incorporated attitudinal and backgro
measures to assess traits of judges and prosecutors, including belief in pun
due proces
s, political affiliation, time served, and ties to the community. Th
and prosecutors in relation to levels of Machiavellianism (with high Machiav
manipulative and purposeful) and responsiveness, for
while
judicial
also controlling
involvement
in pretrial negotiations and prosecutor trial competence. When incorporating
measures, they still concluded that judges and prosecutors did not constitut
of any large disparities a
within
process
theinple
the counties studied. Unlike the fairl
findings for judges and prosecutors, they did find support for the notion th
attorneys are creators
tectors
and pro
of norms, such
clients
that
of attorney
the counties had higher conviction rates -than
regulars.
clients of
regulars
in thre
non
In the second study concerning workgroup familiarity and stability, sup
were surveyed to predict their receptivity to prosecutors
sentencing recomm
Worden, 1995). Workgroup stability was controlled for in the analyses by mea
particular prosecutor was assigned a year or more to a specific judge. This
positively, but not significantly, related
s to
to accept
judges recommendations.
willingnes
19
Most recently, Hoskins Haynes et al. (2010) assessed whether similarity
stability of the judge and district attorney in various counties influences
incarcerate, levy fines, and impose
n. Similarity
restitutio
between judges and district atto
was determined along multiple dimensions, including gender, age, college att
attended, and political party. Proximity was measured by determining whether
judge and district
attorney had offices in the same building. Stability was based o
years the judge and county district attorney worked together within the same
indicated that defendants were more likely to be incarcerated
h less gender,
in counties
age,
wi
law school, and political party similarity, and more college similarity, bet
district attorney.
stability
Also, decreased the imposition
,although
of proximity
fines
and college
similarity increased
while
fines,
theposition
im
of restitution
reducedwas
by stability and law
school similarity
.Their findings suggested
, at times,
that
lack of closeness between judges and
district attorneys results in harsher penalties for defendants.
2.2.3 The Effect of Court Actors
ase Outcomes
on C
While these studies focus specifically on courtroomthe
workgroup
majorityinteracti
ofresearch dedicated to understanding the effect of courtroom workgroups loo
of individual actors separately, particularly
t has beenthe
recognized
judge. I that only a small
amount of variation in sentencing (about 3.3 percent to 5 percent) can be ex
differences between judges (Anderson & Spohn, 2010; Johnson, 2006), and most
variation between judges can bedefendant
explainedand
by-level
case factors (Anderson &
Spohn, 2010). Despite the relatively small amount of variation, research in
incorporates judge characteristics to predict case outcomes.
A majority of this research focuses on
dge.
theThere
race of
is the
evidence
ju
that
minority judges are less punitive in sentencing (Johnson, 2006; Spohn, 1990;
Britt, 2001), but the difference between Black and White judges is almost ne
instance, Steffensmeier & Brittthat
(2001)
Black
found
judges in Pennsylvania from 1991 t
1994 gave sentences that were only about a month shorter, and Black judges a
more punitive toward offenders convicted of more serious crimes and/or drug
(1990) also recognized
hat there
are
t minimal differences
between Black and White judges, and
both Black and White judges sentence Black offenders more harshly than White
20
Some of this research has also evaluated the effect of district level v
represen
tation among judges on the decision to incarcerate and sentence length
regarding the effect of Black representation is mixed. Ward et al. (2009) re
of Black judges in a district did not have an incarcerate
effect on the
or decision
sentence to
length, but district variation in the balance of Black representation among
relationship between defendant race and imprisonment decisions. Schanzenbach
found little impact on the average
when there
sentence
is a greater proportion of Black jud
but the proportion of minority judges on the bench has some effect on minori
Alternatively, Farrell et al. (2009) found that districts with greater repre
are less
ely
lik
to sentence defendants to prison, and increased representation o
does not appear to affect racial disparities in imprisonment.
Some additional judicial characteristics, other than race, have also be
case outcomes.xample,
For e older judges are less punitive in sentencing,
who
along wi
are former defense attorneys and/or have sentenced a larger numbers of offen
Spohn, 2010; Johnson, 2006). In turn, the tenure of a judge
d sentence
is associated wi
severity. Judge gender and political party have little impact on average sen
proportion of female judges on the bench does seem to lessen the sex dispari
(Schanzenbach, 2005).
While research about judges
ore common
is m than research about prosecutors and
defense attorneys, recent attention devoted to prosecutorial discretion has
prosecutors as well. It has been established that offenders are less likely
and recei
ve shorter sentences in districts with greater Black representation am
(Farrell et al., 2009; Ward et al., 2009). Also, increased representation of
decreases the disparity between White and Black imprisonment,
representation
meaning Black
among prosecutors conditions the relationship between defendant race and inc
et al., 2009; Ward et al., 2009). When linking prosecutors to their respecti
district courts, it has also been
bstantial
found assistance
that su
departures vary across
prosecutors (Spohn & Fornango, 2009). The prosecutor s gender, caseload, per
cases, and percentage of violent cases are unable to explain differences in
substantial assistance
artures.
dep
21
Research regarding the effect of defense attorneys is mostly nonexisten
defense attorney effect typically considered within sentencing research is w
attorney was publicly or privately attained (Anderson
Dixon, &
1995;
Spohn,
Ulmer
2010;
et al.,
2010; Williams, 2003).
In a recent study, it was found that defense attorne
significant predictor of the Release on Recognizance (ROR) decision, plea ba
incarceration decision, or sentence
rtley,length
Ventura
(HaMiller, & Spohn, 2010). When
looking at interactions, private attorneys were significantly more likely to
primary charge reduced regardless of whether the defendant was employed or u
and/or detained or Those
released.
who were released on bail and/or exercised their ri
jury trial were more likely to be incarcerated if they were represented by a
addition to type of defense attorney, Farell et al. (2009) looked
ong
at Black r
defense attorneys. They identified that districts with a greater representat
attorneys are more likely to sentence defendants to prison, but increased re
defense attorneys does not appear to affect
ities in
racial
imprisonment.
dispar
2.2.4 Summary
Overall, the qualitative studies of courtroom workgroup interaction sug
familiarity, stability, similarity, and influence of the members has an effe
including the mode of disposition.
e findingsTh
from quantitative studies are less concl
regarding the impact of courtroom workgroups. In addition, there does appear
sentencing across courtroom actors. Race of the judge, prosecutor, and defen
have modest
a
impact on case outcomes, while far less information is known abo
characteristics that may influence case processing.
2.3 Plea Process
The plea process is a consequence of the court organization s bureaucra
system, two
the main components of the criminal court community reviewed above.
highlights the theoretical importance of the plea process. Considering that
this dissertation is on court actors, particular attention
he court sis
action
devoted to how
22
system interplays with the plea process. Factors that have been identified a
decision to plea and time to disposition are also discussed.
On a daily basis, trial courts adapt to maintain theiency
goalsinof certainty
case processing (Parsons, 1961, 1969). A plea of guilty is one such adaptati
accomplish both of these goals (Heumann, 1981). Because of this, the settlem
guilty has become the main device in which allemmembers
share an
of interest
the syst of
administrative convenience
it saves time, effort, and gets a more certain deal for
(Skolnick, 1967). Guilty pleas ensure convictions of the accused and operati
predictable environment, all with
enditure
a minimal
of exp
resources (Cole, 1976).
Due to the advantages of a guilty plea, plea bargaining is recognized a
the court system and an important component of case processing. The sponsori
judge, prosecutor, and defense
offices
attorney
in courthouses dictate the norms of plea
bargaining. In particular, it is theorized that plea bargaining norms genera
agencies represent adaptations to the degree of cooperation and competition
involved (Polli
tz Worden, 1990). Higher levels of cooperation between actors are
facilitate a system of informal case processing (Cole, 1970; Pollitz Worden,
1967; Selznick, 1948).
The level of cooperation in the system is
amiliarity
influenced
and
by stability
the f
of
courtroom actors. Ulmer (1997) specifically notes that the dominance of a pa
processing strategy depends on things like familiarity and stability of cour
membership. The actors familiar to the
one system,
another or
andrepeat players, can more
easily predict trial outcomes and set sentences based on predicted trial out
Familiarity among these actors facilitates a routinized system of case proce
recognized protoco
l for specific cases. This process allows for the introduction
into the system, which would result in increased pleas and more efficient ca
(Eisenstein & Jacob, 1977; Eisenstein et al., 1988).
Similarity between actors
so help
can explain
al
cooperation in plea negotiations.
Eisenstein et al. (1988) specifically noted that large generation gaps betwe
and defense attorneys inhibit communication. Hoskins Haynes et al. (2010) al
gender, age,
lege,
col law school, and political party similarity can affect case
then seem that greater similarity between actors induces greater cooperation
23
value the contributions of similar others (Hinds, Carly,
ey, 2000;
Krackhardt,
Hoskins & Whol
Haynes et al., 2010), which should result in increased odds of a plea of gui
case disposition.
Lastly, the influence of particular actors can also affect the level of
turn, plea negotiations.
e role of
Th a participant in the workgroup and his or her ag
experience can explain willingness to cooperate (Eisenstein et al., 1988). O
experienced attorneys may favor the speed and finality of a plea of guilt. D
in the
workgroup, they can influence the other members into working together t
common goals of the group.
2.3.1 Predicting the Decision to Plea
Although plea bargaining is recognized as a significant aspect of case
few studies ly
actual
predict
the mode disposition.
of
Because of this, little is known abo
factors that influence a defendant s choice to plead guilty or go to trial.
do exist, there is general agreement about certain teristics
legal and of
extralegal
cases that
char
affect the decision to plead guilty. Plea and sentence negotiations are less
is more serious and the offender has a more extensive prior record (Albonett
Ball, 2008; Meyer & Gray, he
1997;
& Moore,
Miet 1986). Blacks, Hispanics, and males ar
less likely to enter into both negotiated
-negotiatedand
pleas,
non meaning that Whites and
females are more likely to plead guilty (Albonetti, 1990; Frenzel & Ball, 20
there are
arger
l numbers of witnesses and the accused used a weapon and/or was d
more likely to go to trial, while physical evidence and confessing to the of
likelihood of a plea of guilt (Albonetti, 1990). The
rtroom
statistical
actor
effects of
interaction and influence on the decision to plea or go to trial are still l
2.3.2 Time to Case Disposition
While pleas of guilt are said to add certainty to the system, they are
efficient means of
rocessing.
case p
This means that the time from arrest to case dis
should be shorter for those who plead guilty than those who go to trial. Res
whether plea bargaining increases efficiency, and if courtroom workgroups in
process. Gertz (1980) did compare the length of time needed to dispose of ca
24
courthouses and found that time to case disposition was much shorter
in what
oriented courts
(p. 46). Sacks & Ackerman (2012)f assessed
other legal
the and
effect o
extralegal factors on the number of days that elapsed between the time when
date of the final disposition. They found that the time to disposition incre
Blacks, Hispanics, private dattorneys,
more charges.
an Alternatively, property offenses,
offenses, and cases where the defendant was detained had a shorter time to d
2.4OpportunitiesFuture
for
Research
The review of prior research reveals
opportunities
importanttothe
extend
current
understanding of court communities
. In particular, there are several theoretical prop
left largely unexplored in the literature that considers the actions systems
noted previously, it is this area
hat will
of research
be the focus
t
of this dissertation.
First, there need to be more studies pertaining to the earlier phases o
Ulmer et al. (2010) note that
factors
there
that
areinfluence trial
priorpenalties
to convictions,
but cannot be measure
d by sentencing outcome data. By jumping to the sentence phas
criminal justice decisions are largely ignored (Ulmer, 2012). Considering th
plea has a fairly large impact on sentencing (Ulmer & Johnson,
it 2004;
is
Ulmer e
important to understand the factors that influence a defendant s choice to p
The decision to plead guilty is also a measure of operational certainty
Jacob (1977) recognize that the instrumental expression
goals of of
thethe
court
internal
is
reducing or controlling uncertainty. They theorize that the incentive to red
encourages
courtroom actors to work together. Trials produce uncertainty becaus
substantial investment of time and effort
rantee with
of a no
result.
gua
argued
t
It
hat
isplea
bargaining has become the norm in order to maintain certainty within the sys
majority of cases resolved through a plea of guilt, instead of a trial (Blum
Neubauer, 1981; stein
Eisen & Jacob, 1977; Pollitz Worden, 1995; Ulmer, 1995). Pre
decision to plea adds insight into the factors that have contributed to this
The extent to which workgroups contribute to a system dominated
still largely
by pleas of
unknown.
25
Second, Skolnick (1965) talks about a model of bargained justice in the
is based on organizational efficiency. It is proposed that actors within the
ideological goals in the interests
ion andofefficiency
product
(Blumberg, 1967; Eisenstein
1988). Settlement of cases through plea negotiations is recognized as a way
efficiency of case processing (Flemming et al., 1992; Packer, 1968; Seron, 1
research has yet
termine
to de whether pleading guilty actually increases efficiency
argued
that court actor cooperation plays a large part in the maintenance of e
court system (Eisenstein & Jacob, 1977; Gertz, 1980;
it is
Nardulli,
t no
known 1979), yet
whether
courtroom workgroup interaction and
influences
cooperation
the efficient processing
cases.
Third, more needs to be known about the variation in certainty and effi
groups of judges, prosecutors, and defense
ividual
attorneys,
judges,
andprosecutors,
ind
and
defense attorneys. Theory suggests that certainty and efficiency depend on c
courtroom workgroup (Blumberg, 1967; Eisenstein
Case
& Jacob,
processing
1977).
should then
vary across workgroups and
dualindivi
actors, with actor characteristics being able to
some of this variation.
Studies should incorporate measures of courtroom workgroup i
experience, and demographics to determine if these
variation
factors
case
in influence
processing
outcomes
.
Lastly, qualitative work suggests that familiarity, stability, and infl
workgroup affects case processing strategies (Eisenstein & Jacob, 1977; Ulme
Thompson Hephner, 2002). Quantitative studies
redthat
courtroom
have measu
workgroup
interaction are not as conclusive (Hoskins Haynes et al., 2010; Nardulli et
Worden, 1995). Better operationalizations of courtroom interaction are neede
impact on
the court system
. Hoskins Hayn
es et al. (2010) have the most complete measure
this interaction, but they do not include interactions with defense attorney
Considering the importance of defense attorney
regulars
(Nardulli et al.,
to conside
r the effect of all three actors. Also, in determining the influence
workgroups on case processing, court actors need to be linked to their respe
then needed that not only capture courtroom workgroup
link
interaction,
judges,
but also
prosecutors, and defense attorneys to their cases.
26
CHAPTER 3
DATA AND METHODS
The current study uses the court community perspective to assess the im
courtroom workgroup
son certainty and efficiency in case processing.
g on By concentra
certainty and efficiency,
itfocuses on earlier phases of case processing, including th
plead guilty and the time from arrest to case disposition. Variation in cert
looked at across
(a) workgroups,
(b) groups of
ges
jud
and prosecutors,
(c) groups of judges and
defense attorneys,
(d) groups of prosecutors and defense
(e)attorneys,
individualand
actors.
Quantitative measures of workgroup characteristics and interaction are then
this variation.
These tests are designed to determine whether workgroup
similarity
, familiarit
and influence facilitate informal case processing by increasing the likeliho
and reducing the time to case disposition.
3.1 Data Collection
The data
sed
u for this dissertation
erecollected
w
from public defender cases files i
county courthouse in the Southeast. From August 2012 until May 2013, case, d
attorney information was gathered for 500 felony plea cases
in and
the 411 felony t
largest of the circuit s counties. Trial cases were purposely oversampled to
variation in the mode of disposition, since the decision to plea in most dat
with only 3 to 5 percent of cases going
ter to
sample
trial.
of A
trials
grea also permits
comparisons between trial and plea cases. The sample of trials represents th
closed cases that went to trial between 2002 and 2010, while the sample of p
a random sample of 500 ses
closed
between
ca
2002 and 2010 that ended in
f guilty
either a plea
or a plea of nolo contendere
. For the purposes of the study, cases that had multiple
for different counts against the defendant in a single case were not include
The sampling
amefrwas developed by the record department in the public def
office. For the sample of trials, a list was provided with all felony trial
that were closed. As stated, every one of these trials wasthe
included
trials in the s
were collected, the records department provided a list of all felony cases f
27
32,832). Because of the coding of the plea cases in their system, they were
parse outpleas
the from the. trials
This list
uploaded
was
into STATA and a random sample of
500 cases was drawn. Within the initial random sample, there were several tr
already been coded. These cases were replaced with another random sample. Th
continued until the
included
sample 500 plea cases.
Initially, the goal was to collect 500 From
pleas
2002
and to
500
,
2010
trials.
there were
only 411 trials. The records were more difficult to produce in the computer
earlier years, so it was -decided
off the data
to cut
collection at 2002. Also, the office k
physical case files up until 2002. For years prior to 2002, the case files w
warehouse -off
site. While most of the data were collected using computer records
records wereetimes
som
accessed for clarification purposes. The year 2010 was ch
cap, because few cases in 2011 and 2012 were closed. To avoid these added co
years were restricted from 2002 to 2010.
Of particular importance
datain
collect
the
ionwas the identification of the judge,
prosecutor, and defense attorney involved in each case until the incarcerati
sentencing. In only a small minority of the cases, it was not the same three
the 911 casescoded
was based on case files from the public defender s office, th
involved in the cases were given numericstate
codes.
s website,
Bar
Using the
the year of
admittance to
Barwas
the collected for each attorney and judge. of
Thethe
gender and rac
attorneys and judges
verified
were through internet searches and by consulting
-time
with
public defender. Through network analyses, discussed in the next chapter, me
courtroom actor interaction and influence were created.
Table 3.1 depicts
the breakdown of the cases in the county studied by year. T
contains about 3 percent of all felony cases that came to the public defende
year. Because of the sampling
rategy, about
st
half of the sample is composed
the
of pleas
other half is composed of trials, except for 2005, 2006, and 2008, where the
than trials in the sample. If there are 411 trial cases between 2002 and 201
overall, only about 1.25 percent of all felony
e time cases
periodduring
went to
th trial. Becaus
of recording issues, this number may be underestimated. This number also doe
where some defendants went to trial on certain counts and pled guilty for ot
these are only felony
thatcases
went through the public defender s office, and the r
28
cases are not necessarily all pleas. For instance, the total number of cases
were dropped.
Table 3.1 County and Sample Cases by Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total
Cases Reported b
PD’s Office Cases in Samp
3,657
96
3,453
90
3,628
93
3,710
105
4,197
124
3,839
108
3,691
124
3,375
91
3,282
80
32,832
911
Plea Cases i Trial Cases i
Sample
Sample
50
46
50
40
54
39
67
38
63
61
57
51
65
59
41
50
53
27
500
411
NOTE: PD = Public Defender.
SOURCE: Public Defender s ecords.
Office r
3.2 The Court Community
As noted, some of the research involving court communities has evaluate
processing varies from circuit
andwhether
to circuit
circuit characteristics can explain this
variation.
While this study focuses
internal
on theaction system
a single
of county courthouse in
a circuit in the Southeast, it is important to place this courthouse into it
resid
es in a circuit with particular characteristics that create certain exte
constraints on its operations.
Characteristics of the circuit, as opposed to the county
discussed since the circuit court is the courtoroffelony
original
cases.
jurisdiction
The
f
courthouse houses both circuit court judges and county court judges.
The circuit has a certain number
who can
of hear
judges
felony cases. The number of
circuit
judges varies from year to year depending upon the population
of the
and casel
particular Theyare
area. elected in nonpartisan contested elections and serve for
Table 3.2 reports the population of the circuit and the resulting number of
29
th
fiscal year st
(July
toJune1 30
). Based onpopulation
the
and number of judgeships, the cir
is classified as relatively small compared to the other circuits in the stat
Table 3.2 Circuit Population and Judgeships by Year
2002-2003
2003-2004
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
Population
347,678
355,9
05
362,104
372,713
385,142
385,361
387,671
393,437
Number of
Circuit Judge
15
15
15
16
16
16
16
16
SOURCE: Publically available court statistics
which the circuit resides.
Table 3.3
provides a ral
geneidea of the caseload pressure experienced by the
which the county resides.
Itincludes dispositions for all defendants; not just those
who came through the public defender
Dispositions
s office.correspond
to each defendant for
whom a final judicial decision is rendered terminating a criminal proceeding
dismissal, or a judgment, stating the specific sentence (in the case of a co
about 85 percent of all defendants were disposed
trial,
by while
pleas about
prior 3.5
to percent
of all defendants were disposed by jury trials.
There are also multiple dimensions external to the courthouse that shou
2002 to 2010, about 24 to 25 percent
in the
of the
circuit
were
voters
Republican,esting
sugg that
the circuit is more liberal than
Elections
conservative.
for circuit judges were held in 2002
2006, 2008, and 2010. Elections for the State Attorney and Public Defender w
and 2008. In both elections, the Public
ent uncontested,
Defender w while the State Attorney
went uncontested in 2008. There
prisons
are within
six
the circuit that can house up to 8
inmates at maximum capacity.
institutions
Of the
, there isonefemale
only
prison,
oneand
of the
sixprisons did
ot open
n
until the summer of 2005. There were two significant chan
sentencing laws over the time period. Beginning in July 2001, the responsibi
guidelines and criminal punishment code scoresheets for ced
felony
solely
defendants wa
30
with the State Attorney s Office. The scoresheet
records
thenumerically
level of the offender s
crime and is used at the offender s sentencing. In 2005, penalties were enha
crimes against children and lewd and lascivious
ion of a child.
molestat
Table 3.3 Circuit Criminal Dispositions by Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total
Defendants Convictions Defendants
Total
Pleas Befor Disposed
after Jury Disposed by Defendants
a
b
c
Before Trial
Jury Trial
Disposed
Trial
Trial
5,846
4,852
5,626
132
220
5,909
5,028
5,712
136
197
5,563
4,714
5,411
96
152
5,381
4,545
5,237
103
144
6,112
5,073
5,930
120
182
6,104
4,990
5,884
155
220
5,554
4,598
5,339
153
215
5,143
4,136
4,906
164
237
4,690
3,723
4,471
163
219
42,559
1,222
48,516
1,786
50,302
NOTE S:a Defendants disposed before trial includes dismissals, transfers, pleas, a
b
Defendants disposed by jury trial include dismissals, acquittals, pleas, and co
c
Total defendant
s disposed are the overall number of cases disposed before trial and
SOURCE: Publically available court statistics for the state in which the circuit
3.3Propositions
Based on the theory surrounding court communities,
l propositions
severa
are considered
regarding court actor action systems. Table 3.4 portrays each of the posited
below.
Familiarity and stability among the courtroom actors is expected to fac
efficient case processing
norms (Eisenstein & Jacob, 1977; Eisenstein et al., 1988).
and stability increase through the interaction of the actors in various case
proposition, increases in the following case factors are expected
of a to increas
plea of guilty and decrease the time from arrest to case disposition:
31
Table 3.4
Proposed
Relationships
Time from
Arrest to
Decision t
Indirect
Case
Plea
Disposition Effect
Familiarity
Judge and prosecutor interaction
+
Judge and defense
attorney interaction
+
Prosecutor and defense attorney int
+
Judge, prosecutor, and defense att
+
interaction
Similarity
Judge and prosecutor experience di
Judge and defense attorney experie
Prosecutor and defense attorney ex
difference
Judge and prosecutor race similar
+
Judge and defense attorney race si
+
Prosecutor and defense attorney ra
+
Judge, prosecutor, and defensethe
att
+
same race
Judge and prosecutor
gender
similar
+
Judge and defense attorney
gender
similar
+
Prosecutor and defense attorney
similar
gende +
Judge, prosecutor, and defensethe
att
+
same gender
Influence
Prosecutor ore
is mcentralthe
than
judge
+
Defense attorney is more central t
+
Judge and prosecutor average exper
+
Judge and defense attorney average
+
Prosecutor and defense attorney ave
+
experience
Judge, osecutor,
pr
and defense attorn
+
experience
Prosecutor centrality
+
Defense centrality
+
Judge centrality
Prosecutor experience
+
Defense experience
+
Judge experience
+
Efficiency
Defendant pled guilty
o contendere
or n
N/A
32
-
-
+
+
+
+
+
+
-
-
-
-
-
-
-
-
+
-
+
-
-
N/A
1. The number of times
judge
the
andprosecutor
in a case work together
2. The number of times
judge
the
anddefense attorney
in a case work together
3. The number of times
prosecutor
the anddefense attorney
in a case work together
4. The nu
mber of times
judge
the
,prosecutor
, and
defense attorney
in a case work together
In addition to familiarity, similarity between actors can affect case p
(Eisenstein et al., 1988; Hoskins Haynes et al., 2010). Eisenstein
that
et al. (1
differences in the distribution of age and experience among judges, prosecut
attorneys seemed to affect the court community. Therefore, the following cas
expected to lower the
f aodds
pleao of guilty
locontendere
or no and increase the time from
arrest to case disposition:
1. A larger difference in experience
judge
between
andprosecutor
the
2. A larger difference in experience
judge
between
anddefense
the attorney
3. A larger difference in experience
prosecutor
between
anddefense
the
attorney
Alternatively, based on the work of Hoskins Haynes and colleagues (2010), th
factors are expected to increase the odds of a plea of guilty and decrease t
case disposition:
1. Thejudge
andprosecutor
arehe
t same race
2. Thejudge
anddefense attorney
are the same race
3. Theprosecutor
anddefense attorney
are the same race
4. Thejudge
,prosecutor
, and
defense attorney
are the same race
5. Thejudge
andprosecutor
are the same gender
6. Thejudge
anddefense attorney
are the same gender
7. Theprosecutor
anddefense attorney
are the same gender
8. Thejudge
,prosecutor
, and
defense attorney
are the same gender
In addition to familiarity and similarity, it is predicted that actor i
effect on plea negotiation
s, in terms of both their role and experience (Eisenstein e
The centrality of each of the actors can define their role, such that more c
they are tied to more people in the system, can have a greater
cessing.influence
This
on
is important, because each actor has a vested interest in plea bargaining, b
processing is really initiated through the negotiations of the prosecutor an
Cases without agreed recommendations between
ecutor the
and pros
defense attorney create
33
uncertainty in the court and place the judge in a position of greater discre
the judge puts a stamp of approval on the negotiated charge and sentence (Ge
of the prosecutor
inspreparing
role
the guideline scoresheet, prosecutorial discret
especially important in dictating certain case processing norms (Bushway & F
McCoy, 2005; Ulmer, 2012). With increased experience, though, allt the actors
to them and the system when a defendant pleads guilty (Blumberg, 1967). Case
characteristics listed below should
odds
have
of increased
a plea of guilty or nolo
and contender
a decreased time to case disposition:
1. Theprosecutor
is more central
than the
judge
2. Thedefense attorney
is more central than
judgethe
3. Thejudge
andprosecutor
have a higher average experience
4. Thejudge
anddefense attorney
have a higher average experience
5. Theprosecutor
anddefense attorney
have a higher average
nce
experie
6. Thejudge
,prosecutor
, and
defense attorney
have a higher average experience
7. Theprosecutor
has more years of experience
8. Thedefense attorney
has more years of experience
9. Thejudge
has more years of experience
10.Theprosecutor
has greater centrality.
11.Thedefense attorney
has greater centrality.
Alternatively,
cases with the following characteristic should beplea
lessoflikely to r
guilty or nolo contendere
andhave a longer time to disposition:
12.Thejudge
has greater centrality.
Lastly, it
roposed
is p that cases settled through a plea
theofefficiency
guilty increase
of
case processing (Flemming et al., 1992; Packer, 1968; Seron, 1990). Because
predicted that there will be a shorter time from arrest to
s where
case disposition
the
def
endant pled guilty or no instead
contendere
of going Also,
to trial.
courtroom workgroup
familiarity, similarity, and influence should add certainty to the system, b
likelihood of a plea, whichpresumably
should lead
then great
to
efficiency, or less time to ca
disposition. Stated differently, the decision to plea should mediate the rel
courtroom workgroup interaction and influence and the time to case dispositi
addition to the direct
s noted,
effect
a negative indirect effect is expected in cases w
34
1. Thejudge
andprosecutor
,judge
anddefense attorney
,prosecutor
anddefense attorney
,
andjudge
,prosecutor
, and
defense attorney
work together more often.
2. Thejudge
andprosecutor
,judge
anddefense attorney
,prosecutor
anddefense attorney
,
andjudge
,prosecutor
, and
defense attorney
are the same race.
3. Thejudge
andprosecutor
,judge
anddefense attorney
,prosecutor
anddefense attorney
,
andjudge
,prosecutor
, and
defense attorney
are themesagender.
4. Theprosecutor
is more central than
judge
. the
5. Thedefense attorney
is more central than
judge
. the
6. Thejudge
andprosecutor
,judge
anddefense attorney
,prosecutor
anddefense attorney
,
andjudge
,prosecutor
, and
defense attorney
have a higher
average experience.
7. Theprosecutor
has a greater centrality.
8. Thedefense attorney
has a greater centrality.
9. Theprosecutor
is more experienced.
10.Thedefense attorney
is more experienced.
11.Thejudge
is more experienced.
Alternatively, a positivectindirect
is expected
effe in cases where:
12.Thejudge
andprosecutor
,judge
anddefense attorney
, and
prosecutor
anddefense
attorney
have a larger difference in experience.
13.Thejudge
has a greater centrality.
3.4 Research Design
One of the main featuresdyofisthis
the stu
creation of measures of courtroom
workgroup familiarity, similarity, and influence. Network analysis was requi
create some of these measures, while several others were created from existi
known about the prosecutors,
defense attorneys, and judges in the data set. Chapter 4
how each of the measures was created. It is important to note that these mea
only from the sample. Measures of interaction and centrality are
ritybased on th
and influence in the sample of cases drawn. While it would obviously be idea
same measures using all cases from a particular year or years, this study pr
35
quantifying courtroom workgroup interaction
e by and
creating
influenc
these measures and
testing their effect within a smaller sample.
After the measures are discussed, Chapter 5 and 6 will test the impact
on certainty (the decision to plea) and efficiency (timen)
from
in case
arrest to case
processing. In each of the models presented, cases are the unit of analysis.
attorneys can participate in the same cases, the analyses must account for t
For this reason, two sets of modelsone
areinconducted
each chapter. The first set of ana
found in Chapter 5, considers the nesting of cases within workgroups of judg
defense attorneys, as well as groups of judges and prosecutors, judges and d
prosecutors
d defense
an
attorneys. Each of these models will be clustered by wor
each of the subsequent groups, respectively, in separate analyses. In the wo
workgroup measures will be included. In the judge and prosecutor
res of model, only
courtroom interaction and influence specific to the judge and prosecutor wil
same idea would then apply for the last two groups as well.
After the presentation of this first set of models, a second set of mod
in Chapter 6 for each dependent variable that assesses the effect of individ
outcomes.
Muli-level modelling
will be used in these analyses since there are fairly
numbers of cases nested within individual actors,
ecessarily
while this
true is
fornot
then groups
of actors. Courtroom actor measures specific to individual
raceand sex
,will
actors,
be such as
included
, along with court actor measures that vary by year, including
.
central
These models will demonstrate
whether there is variation in the decision to plea and
from arrest to case disposition across actors, and if individual actor chara
some of this variation.
Together, these analyses will demonstrate the rtroom
full impact
workgroup
of the
on cou
both the decision to plea and the time from arrest to case disposition. Inte
prosecutors, and defense attorneys from the courthouse under study will be u
add insight into some of the findings.
lications
The
of imp
the findings for future research
understanding of courthouse operations will then be discussed.
36
CHAPTER 4
MEASURES
In order to assess the impactworkgroups
of courtroom
certainty
on
and efficiency in case
processing, several
es measur
were utilized. As noted previously, network analysis wa
create some of the measures, while others were derived from the data collect
are the operationalizations for each of the concepts used in the
,
subsequent
including the dependent variables, key independent variables, and control va
4.1 Certainty and Efficiency Measures
It is proposed that certainty and efficiency are dependent on courtroom
and influence. Therefore, ndent
the two
variables
depe
of interest in this
Decision
studytoare the
PleaandDays to Disposition.
TheDecision to is
Plea
used to measure certainty, since accep
a plea, instead of going to trial, is expected to add more certainty to the
Jacob, 1977). Cases in which the defendant accepted a plea of guilty or nolo
coded 1, while cases that were disposed of through a jury or bench trial wer
previously indicated, cases where some counts wereand
disposed
others of
were
by a plea
disposed of by a trial were not included.
Efficiency was measured by determining
Days to Disposition
the . For each case, the total
number of days from the day the defendant was arrested to the day s/he was d
not guilty
ther
(eithrough a plea or after the culmination of a trial) was calcul
was chosen because a defendant could accept a plea agreement at any point du
process. The values for the variable were log transformed
mal distribution.
to create a more n
While not often measured in prior research (Sacks
Days
& Ackerman,
to Disposition
is
2012),
a
way of capturing speed and finality in case processing, with lower values in
efficiency.
For the purposes here, itorisofanincreased
indicat speed in the carrying out o
process, which could, in fact, effect the extent to which due process rights
defendants.
37
4.2Court Actor Measures
4.2.1. Court Actor Familiarity
Network analysis was used to create
asures the
of court
me
actor familiarity. It is
commonly used with
-mode
one data, meaning that each person in the data set can be
specific network of other individuals in the same data set. For example, a d
to his/her peers
establish
to
delinquent peer networks (e.g. McGloin
he &data
Piquero, 201
for this study
different
are
because -mode,
it is and
two therefore, involves affiliations. Af
data consist of a set of binary relationships between
items
members
(Borgatti
of two
& sets of
Halgin, 2011). In the data used here, there are both actors and cases, and t
relationship of attendance between the two of them.
When the data were initially collected, the prosecutor, defense attorne
workedon each case up until the chosen mode of disposition were recorded. Af
collection, each of these actors was given a numeric code. This meant that t
identification numbers were assigned to each case. The case
n information
and actor identi
had to be transformed into an affiliation matrix for network analysis purpos
attorney or judge was listed as a row, and each case was listed as a column.
designed to indicate attendance of attorneys
particular
or judges
cases.in
In the matrix, 1s we
placed for each case the attorney or judge participated in, and 0s were assi
example, if there were a sample of 8 cases and 2 attorneys, and Attorney 1 p
1, 3, and 5, and
neyAttor
2 participated in cases 2, 6, and 7, theTable
matrix
4.1.
would loo
Table 4.1 Example CourtAffiliation
Actor Matrix
Attorney
Attorney
Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8
1
0
1
0
1
0
0
0
0
1
0
0
0
1
1
0
This matrix
was uploaded into UCINET, a network analysis software (Borgatt
& Freeman, 2002). Using a simple transformation of the matrix, it was determ
times each of the actors worked with each other in the data set.
to This inform
create three measures of courtroom workgroup familiarity. Each case was assi
of interactions that occurred between the (a) judge and prosecutor, (b) judg
38
attorney, and (c) prosecutor and defense attorney.
untsThese
represent
interaction
measures
co of
Judge
-Prosecutor Familiarity
,Judge
-Defense Familiarity
, and
Prosecutor
-Defense Familiarity
for each case.
It is importan
t to note that theseonly
measures
capture
thequantity of interaction,
notthequality of interaction.
Thefamiliarity measures vary by year in the data set. For instance, def
worked with judge 187 a total of 2 times in 2002, and a total of 12 times be
Therefore, a case involving both of these actors
Judg
ine-Defense
2002 would
Familiarity
have a
value of 2, while a case involving the two court actors
Judge
-Defense
in 2003 would have a
Familiarity
value of 12. This takes into account that familiarity between actors
year to year.
In addition to actor pairs,
ure wasa created
meas
to identify the number of times t
judge, prosecutor, and defense attorney in a case worked together as a workg
this, workgroup codes were created by combining each of the court actor iden
In a workgr
oup with prosecutor 2, defense attorney 90, and judge 200, the workg
would be 290200. Excel was then used to identify the number of times this co
data set in order to calculate workgroup interaction.Work
This
group
measure, designat
Familiarity
, also varies by year to accurately reflect changes in interaction a
from year to year.
4.2.2
Court Actor Similarity
Unlike the familiarity measures, which were primarily created using a n
software, the
ilarity
sim measures were designed based on information collected ab
actors in the data set. First, the year that each attorney or judge was swor
collected. This year was subtracted from the year years
of theofcase
experience
to obtain
forthe
each actor. Then, for each case, the defense attorney s years of experience
the prosecutor s years of experience, the prosecutor s years of experience w
the judge s years of experience,
defense
and
attorney
the
s years of experience was subtra
from the judge s years of experience. The absolute values of these calculati
that higher values mean that there is a greater difference in experience bet
three
measures were designated
Judge
-Prosecutor
Experience ,Judge
Gap -Defense
Experience
Gap,andProsecutor
-Defense
Experience Gap,
respectively.
39
In addition to experience, the
derrace
of each
and gen
actor was Gender
collected.
was
codedfemale
(1) and
male(0), and
racewas coded
Black(1) and
non-Black(0).
The
corresponding measures
were designated
Prosecutor Female, Defense
,Judge
Female
Female
,
Prosecutor Black, Defense
andJudge
Black,Black
.Foreach case, four measures of gender
similarity and asures
four meof race similarity were created. For
Judge
the
first measure
Prosecutor Gender Similarity,
the case was given a 1 if the judge and prosecutor in t
of the same gender (male and male, or female and female) and 0 otherwise. Fo
measure,
Judge
-Defense Gender Similarity
, the case was given a 1 if the judge and defen
attorney in the case were of the same gender and 0 otherwise.
Prosecutor
For
- the third
Defense Gender Similarity,
the case was given a 1 if thed prosecutor
defense attorney
an
in the
case were the same gender and 0 if they were not the same gender. For the fo
Workgroup Gender Similarity
, cases where all three of the courtroom workgroup membe
of the same gender (all males or eall
coded
females)
1 and wer
0 otherwise. These same
measures were created based on race as well, such that cases were coded 1 if
the same race (either all-Black),
Black orand
non0 if the actors were different races.
measures are designated
Judge
-Prosecutor
as
Race Similarity,
-Defense
Judge
Race Similarity,
Prosecutor
-Defense Race Similarity,
andWorkgroup Race Similarity.
4.2.3 Court Actor Influence
According to Eisenstein et al. (1988), influence is determined by role
part
icular, it
proposed
was that when the prosecutor and defense attorney have more i
over the judge, informal case processing is more likely. In order to determi
influence, network analysis of the affiliation matrix
ss the
wasdegree
conducted
centrality
to asse
of each actor in the data set. The degree centrality of an actor is a score
on the number of cases with which s/he is affiliated (Borgatti & Halgin, 201
These scores are designated
Judge Ce
ntrality, Prosecutor Centrality,
andDefense Centrality.
For
each case, the centrality of the judge was subtracted from the centrality of
centrality of the defense attorney. Two dichotomous
Prosecutormeasures,
More Central
and
Defen
se More Central
, w
ere created to indicate whether the prosecutor and defense
respectively, were more central than the judge in a case.
Prosecutor
For the
More
first meas
Central
, the case
as coded
w
1 if the prosecutor
more central
were he
than
judge
t
and 0 otherwise.
40
For the second measure,
Defense More Central
, the case was
ed cod
1 if the defense attorney
weremore central than the judge and 0 otherwise. Similar to the familiarity
values varied
pending
de
on the year of
e and
theare
casmeasures of quantity, not quality.
Since influence is also based Judge
on experience,
Experience, Prosecutor Experience,
andDefense Experience
werecalculated for the judge, prosecutor, and defense attor
case by subtractingof
the
admittance
year
to the bar from the year of the case, as in
above. A value
Judge
for Experience, Prosecutor andDefense
Experience,Experience
was
assigned to each case, and because the values were obtained by subtracting t
admittance from
he year
t
of the case, the experience measures varied from year to
attorneys and judges
who participated in cases in multiple
Judge
-Prosecutor
years. Average
Experience, Judge
-Defense Average Experience,
Prosecutor
-Defense Average Experience,
and
Wo rkgroup
Average
Experience
were also calculated as the average experience
judge
of the
and prosecutor,
) judge
(b and defense (c)
attorney,
prosecutor and defense
andattorney,
(d)
judge, prosecutor, and defense attorney in each case, respectively.
4.3Case and Defendant Measures
In addition to the measures of court actor familiarity, similarity, and
case and defendant characteristics were included as
Offense
potential
Seriousness
confounders.
was a score given to each case
the based
severity
on of the charge(s) against the defenda
count the defendant was charged with was a violation of a specific state sta
criminal punishment code for the state, the level of the offense was determi
statute.ryEve
offense level equated to a certainOffense
number Seriousness
of points.
is the total
number of points the defendant scored for a particular case based on all cou
in Table 4.2 is the offense level and point equivalents
imary offense
for (first
both the
count)
pr
and subsequent offenses (secondary counts). Therefore, if the primary offens
level 6 and there was a secondary offense that was
Offense
a misdemeanor,
Seriousness
scorethe
would be 36.2.
Prior Record
is a meas
ure of the defendant s criminal history. The court from
data were collected -sentence
had a preinvestigation interview with the defendant in e
case. Defendants were given a 3 if they had no prior convictions, 0 if they
41
convictions,
-1 if they had a case pending or more than one
-2 if
failure
they had
to appear,
prior
felony convictions,
-3 ifand
they had been incarcerated in prison within the past 5
result, scores ranged
-5 tofrom
3. If a defendant
Pri
or had
Record
aof-5, s/he had a case
pending or more than one failure to appear, prior felony convictions, and a
5 years. If the defendant had a prior record of 2, s/he had no prior convict
pending or prior failures
o appear.t For the purposes of the study, this scale was re
score of 3 is 1, 2 is
-1 is
2, -4,
20 is
is -5,
33,is -6,
4 is 7, -5and
is 8. Therefore,
Prior Record
ranges from a score of 1 to 8, with higher values indicating
e prior record.
a more extensiv
Table 4.2 Criminal Punishment Code Offense Levels and Point Equivalent
Primary Offense
Offense Level
Points
1
2
3
4
5
6
7
8
9
10
Secondary Offense(s)
Offense Level
Points
M
0.2
1
0.7
2
1.2
3
2.4
4
3.6
5
5.4
6
18
7
28
8
37
9
46
10
58
4
10
16
22
28
36
56
74
92
116
NOTES:M = Misdemeanor
SOURCE :State Criminal Punishment Code Scoresheet Preparation Manual
Multiple Counts
is a dichotomous variable indicating whether the case has m
count (1) or just one .offense
If the (0)
case involved an offense where the defendant
Weapon, the case was coded 1, otherwise, the case was
Prior
coded
Record
does
0. Since
not the
take into account whether theondefendant
probation,
is a dichotomous variable was create
indic
ate whether the defendant was on probation at the timentofoffense.
the arrest for
If the defendant
On were
Probation
, the case was coded 1, and 0 otherwise. The last le
variable accounted
for is whether the defendant
detained
were
in the
e. Cases
cas in which the
defendant was
Detained
in jail prior to disposition are coded 1, while cases where
bonded out, received a pretrial release, or were released on recognizance (R
ROR is granted by a judge andiminal
allowsdefendant
a cr
to be released
-trial without
pre
42
posting bail based on the defendant s past history, roots in the community,
crime committed.
Aside from these legal variables, some additional extralegal variables
potent
ial confounders. The first of these is race. The computer system in the
defendants as Black and White. Because of the possibility that some of the d
White could be Hispanic, race was dichotomized
Black(1) and -as
non
Black (0).
ender
G was
also taken into consideration, with
Maledefendants
cases involving
coded as 1 and those
involving female defendants Ageis
coded aascontinuous
0.
measure of the defendant s ag
years. Lastly, dummy variables were createdrsfor
foreach
the of
cases
the under
yea
study, wit
2002 being the reference category.
43
CHAPTER 5
THE GROUP EFFECTS OF COURTROOM ACTOR FAMILIARITY,
SIMILARITY, AND INFLUENCE
This cha
pter is the first ofs two
chapters.
analysi
study
Thepresented hereesonthe
focus
action
systems of courtroom
, and
actors
the effect this has on the decision to plea an
disposition.
Separate models are reported for measures of interaction and influen
judges and prosecutors,
) judges
(b and defense attorneys,
(c) prosecutor
s and defense attorneys,
and (d) judges, prosecutors and defense
The chapter
attorneys.
begins
highlighting
by
the goals
of the present analyses
and identifying
propositions
the
being tested
. The
analytic plan and
descriptive statistics for edare
the measures
reviewed,
us and then the results are presente
discussed.
5.1Purpose and Propositions
The purpose of the analysis
current
is to test the effect of courtroom workgroups o
certainty and efficiency in case
In doing
processing.
so, the extent
which workgroup
to
dynamics
have contributed to
adversarial
a non
system isThree
assessed.
elements of courtroom
workgroups are considered: (a) familiarity, (b) similarity, and (c) influenc
Greater familiarity and similarity between increase
actors isthe
expected
odds oftoa
plea and decrease the time from arrest (Eisenstein
to case disposition
& Jacob, 1977; Hoskins
Haynes et al., 2010).
,there
Further
should
be a greater likelihood of a plea and shorter t
case dispositioncases
amongin which
prosecutor
the
and defense attorney areormore centra
have greater influence
over the judge
(Gertz, 1980).
Also measuring
influence
, cases with more
experienced workgroups are expected to have an increased likelihood of a ple
decreased time
rom arrest
f
to case disposition
. Inddition
a
to these actor effects, accepting
should increase system efficiency by reducing the time to disposition. As a
decision to plea is expected
themeasures
to mediate
of court actorn interactio
and influence on
the days to disposition. In particular,
be a negative
there should
indirect effect
, of familiari
44
gender
similarity
, race similarity, centrality,
on and
time
experience
to case disposition, and a
positive indirect effect between
nce difference
experie and time to case disposition.
By testing these propositions,
hisstudy addresses
t
several gaps noted in prior rese
Of particular importance, it assesses the impact of courtroom
While
workgroups qua
three studies have
fied
quanti
workgroup concepts (Hoskins Haynes et al., 2010; Nardu
1988; Pollitz Worden, 1995), none of these studies include measures of famil
and influence. Hoskins Haynes et al. (2010) come closest by mity,
focusing on simi
and stability. However, their measures are solely based on the relationships
district attorneys
, which excludes defense. attorneys
Also, their
measure of stability is based on
the number of years the actors workedof
together
the number
instead
of times the actors
interacted.
Lastly
, their
measures are at the county level and do not link actors to t
cases.
Along with its tative
quanti design, the current
esonan
studyearlier
focus of
phase
case
processing
(Ulmer, 201
2), and in doing so, recognizes the importance of the intern
organizational goals of controlling uncertainty and maintaining efficiency (
Eisenstein & Jacob, 1977; Eisenstein et al., 1988; Skolnick, 1965). Theory a
that
the current system
, where
cases are predominantly
by settled
pleas of may
guilt,
be
facilitated
byworkgroups
that
are more familiar, similar, (Eisenstein
and influential
& Jacob,
1977; Gertz, Hoskins
1980;
Haynes et al.,
Nardulli,
2010; 1979). This
tests
study
that
proposition.
5.2 Analysis
In order to the
assess
direct effects of courtroom workgroups
Decision to
on and
Plea
the
Days to Disposition
, and the indirect effects of courtroom
theDays
workgroups
to
on
Disposition,
generalized
structural ion
equat
modelling
G SEM)
( was used. The Guse
SEMof
allowed for the computation of simultaneous effects of workgroups on both ce
efficiency in case processing and considered
Decision to
that
may
Plea
the
mediate the
relationship between courtroom
or interaction
act
and system effici
ency. By using GSEM, instead
of structural equation modelling
, the model
(SEM) was able to account for the dichotomo
nature ofDecision
the
to such
Plea,that logistic regression
with a Bernoulli distribution
was
45
used to
redict
p
Decision
the
to while
Plea, OLS regression was used to
Days
predict
to
the
Disposition.
It should be notedEMthat
doesGSnot allow for the addition of covariances
between exogenous variables and does not report goodness rofthe
fitAkaike
statistics,
information criterion (AIC) and Bayesian information
(BIC). As acriterion
result, the data were
also analyzed using SEM, and the results, which were substantively similar t
discussed in more detail in the ysis
Sensitivity
section Anal
below.
Results
are reported separately for the
among(a)
relationships
judges and prosecutors,
)
(b
judges and defense attorneys,
(c) prosecutors and defense
andattorneys,
(d) judges, prosecutors,
and defense attorneys. Each model
r potential
accounts fo
autocorrelation in the clusterin
groups of attorneys and full workgroups, since the same groups of attorneys
be participants in multiple
-level
cases.
modelling
Multi
was not used, because there were
groups and kgroups
wor
where the actors only worked together in one case, and th
large numbers of cases nested within groups and workgroups. For each of the
unstandardized coefficients
and standard errors
are reported. Each model is also
a displayed a
figure containing the relationships of theoretical interest in the current s
As noted in Chapter
Days 4,
to Disposition
was log transformed in order to create a
normal distribution for regression purposes. Because of
preted
this,slightly
the models are
differently in the results section. A one unit increase in an independent va
results in a percent the
increase
Days toin
Disposition
when all the other variables in the mod
are held constant.
In addition, each
models
of the
includes dummy variables for the year of the cas
year 2002 was used as the reference category, and thus, omitted from the ana
dummies were used to account for any external influences in a particular yea
affectede cas
processing in that
These
year.
dummies also
helpaccount for the few cases in the
data set that involve the same
Theyear
defendant.
dummy variables are not reported in the
results
tables shown to allow for a clearer presentation of the results.
Four ca
ses were dropped from the analyses because the race of the judge o
attorney in that case was unknown. Two of these cases were pleas, while the
trials. The models reported are based on a sample of 907 cases,
laredwith
as these f
missing. Correlation matrices among the variables used in each model indicat
potential problems with multicollinearity
.
46
Table 5.1 Descriptive Statistics
Mean
.549
5.061
SD
.498
.823
Min
0
1.792
Max
1
7.359
Offense Seriousness
Prior Record
Multiple Counts
Weapon
On Probation
Detained
Black
Male
Age
46.193
4.763
.600
.173
.191
.472
.73
4
.846
32.884
57.536
2.339
.490
.378
.393
.499
.442
.361
10.990
4
1
0
0
0
0
0
0
16
813
8
1
1
1
1
1
1
76
Judge
-Prosecutor Familiarity
Judge
-Prosecutor Experience Gap
Judge
-Prosecutor Gender Similarity
Judge
-Prosecutor Race Similarity
Prosecutor More Central
Judge
-Prosecutor Average Experience
5.642
17.694
.600
.959
.136
17.573
5.242
9.324
.490
.198
.343
7.230
1
0
0
0
0
5
33
45
1
1
1
37
Judge
-Defense
Judge
-Defense
Judge
-Defense
Judge
-Defense
Defense More
Judge
-Defense
4.433
14.760
.589
.879
.133
20.903
4.197
10.152
.492
.327
.340
7.269
1
0
0
0
0
5.5
24
44
1
1
1
41
3.570
10.985
.563
.867
12.762
3.948
10.317
.496
.340
10.008
1
0
0
0
0
25
41
1
1
39.5
2.003
.376
.852
17.080
1.641
.485
.355
7.299
1
0
0
3.67
11
1
1
37.33
.106
.099
.102
.115
.160
.118
.137
.099
.088
.308
.299
.303
.319
.343
.323
.344
.299
.284
0
0
0
0
0
0
0
0
0
Decision to Plea
Days To Disposition
ged)
(log
Familiarity
Experience Gap
Gender Similarity
Race Similarity
Central
Average Experience
Prosecutor
-Defense
Prosecutor
-Defense
Prosecutor
-Defense
Prosecuto
r-Defense
Prosecutor
-Defense
Workgroup
Workgroup
Workgroup
Workgroup
Year
Year
Year
Year
Year
Year
Year
Year
Year
Familiarity
Experience Gap
Gender Similarity
Race Similarity
Average Experience
Familiarity
Gender Similarity
Race Similarity
Average Experie
nce
2002
2003
2004
2005
2006
2007
2008
2009
2010
NOTES:n = 907;
Abbreviations:
SD = Standard deviation
47
1
1
1
1
1
1
1
1
1
5.3 Descriptive Statistics
Table 5.1 reports the descriptive statistics for the variables used in
percent of the cases in the sample were decided
guilty or
by nolo
a plea
contendere,
of
while th
remaining cases wentOn
toaverage,
trial.
heret were 5.061
logged days from arrest to case
disposition
for any one. case
The average case ffense
had an serious
o
score,of
and
46.193
in
about 60 percent of the
his cases,
score was
t
based on multiple
Withincounts.
the sample, the
mean prior recordfor
score
a case
was4.763, suggesting that
ndant
the in
defe
the average case
had(a)apending case or more than one failure
(b)prior
to appear
felonies.
In
andabout
17.3
percent
of the cases, a weapon was
he defendant
used. T was on probation prior to being ar
in 19.1 percent of thethe
cases,
defendant
and subsequently
was
detained after arrest in abo
47.2 percent of the
In cases.
the majority of the cases
ample,inthe
thedefendants
s
were Black
and male, andaverag
thee case involved a defendant
was about
who 33 years old.
Among the sampled cases, the same prosecutor and defense attorney
o4
inter
times
, while the same judge and prosecutor
out 5 interact
6totimes and
ab the same judge and
defense attorney interact about 4 to 5 times. This means that the judge and
typically more familiar withinone
theanother
courtroom environment
than the other two actors.
Additionally, the same
e actors
thre only interact an average of 2 times in the sampl
While the judge and prosecutor are more familiar than the
the
other actors,
largest experience gap (about 18 years) between them in the sampled cases. T
defense orney
att in a case have an average experience gap of about 11 years, an
defense attorney have an average experience gap
In of
about
about
56.3
15 to
years.
60 percent
of the cases, the prosecutor and defense attorney, judge and defense
prosecutor, and
attorney, respectively, are the same gender, while these actor pairs are the
95.9 percent of the
Among
cases.
the cases
, 37.6 percent have a workgroup that is all the
gender (all male or all female)
entand
have
85.2
a workgroup
perc
that is all the same race
Black or all
-Black).
non
Regarding the measures of,the
influence
prosecutor and defense attorney are more
central than the judge in 13.6 and 13.3 percent of
This
thesuggests
cases,
thatrespectively.
judges are affiliated
more with
cases,
as would be expected,
making them more central figures
than prosecutors and defense
Within
attorneys.
the sample
d cases, the judge and defense attorn
48
have the highest
combined
average experience (about 21leyears),
the prosecutor
whi
and
defense attorney
have a combined
average experience of about 13 years and the judge a
pros
ecutor have a combined
average experience of about
Workgroups
18 years.
have an average
experience of about 17 years.
Table 5.2. Predicting
the Decision to Plea
Days
and
to Disposition: Direct and Indirect
Effects of Judge
-Prosecutor Familiarity, Similarity, and Influence
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
Prior Record
Decis
ion to Plea
Multiple Counts
Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to
ea Pl
Age Decision to Plea
Judge
-Prosecutor Familiarity
Decision to Plea
Judge
-Prosecutor ExperienceDecision
Gap
to Plea
Judge
-Prosecutor Gender Similarity
Decision to Plea
Judge
-Prosecutor Race Similarity
Decision to Plea
Prosecutor More Central
Decision to Plea
Judge
-Prosecutor Average Experience
Decision to Plea
Direct Effects on the Days to Disposition
Decision to Plea
Days to Dispos
ition
Offense Seriousness
Days to Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Judge
-Prosecutor Familiarity
Days to Disposition
Judge
-Prosecutor Experience
Gap Days to Disposition
Judge
-Prosecutor Gender Similarity
Days to Disposition
Judge
-Prosecutor Race Similarity
Days to Disposition
Prosecutor More Central
Days to Disposition
Judge
-Prosecutor Average
ExperienceDays to Dispositi
Indirect Effects
Judge
-Prosecutor Familiarity
Days to Disposition
Judge
-Prosecutor ExperienceDays
Gap to Disposition
Judge
-Prosecutor Gender Similarity
Days to Disposition
Judge
-Prosecutor Race Similarity
Days to Disposition
Prosecutor More Central
Days to Disposition
Judge
-Prosecutor Average Experience
Days to Dispositi
SE
-.021***
-.125**
.360*
.275
.619**
.384*
-.673***
-.868***
-.045***
.061*
-.055***
-.071
.207
.276
.060***
.004
.043
.168
.264
.208
.200
.185
.256
.009
.027
.011
.205
.473
.313
.016
-.451***
.004***
-.009
-.010
.055
-.062
-.333***
.065
.074
.002
-.009*
.003
-.025
-.090
.018
-.037***
.062
.001
.011
.046
.063
.054
.058
.055
.063
.002
.004
.003
.056
.073
.089
.004
-.027*
.025***
.032
-.364
-.125
-.027***
.013
.006
.093
.218
.146
.008
NOTES:n = 907. AIC = 2735.864, BIC1.= All
2976.37
indirect effects and error variances a
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
49
5.4 Results
Tables 5.2 to
report
5.5 the direct and indirect
ofjudgeeffects
and prosec
utor
(Table ),
5.2
judge and defense attorney
(Table ),
5.3
prosecutor and defense(Table
attorney
5.4), and
workgroup (Table
) familiarity,
5.5
similarity,onand
the
Decision
influence
to andDays
Plea
to
Disposition
.The case level effects
Decision
on to
thePlea
are fairly consistent s.
across
In
table
cases where the offense was more serious,a the
more
defendant
extensive
had
prior
,andrecord
the defendantolder
was
, Black, and male, the case was less likely to be decided by
guilty or nolo
endere.
cont This is consistent with at
prior
has research
found Blacks
th
to be less
likely
to take a plea, along with defendants who commit more serious offenses
experience with the criminal justice
Albonetti,
system
1990;
( Frenzel & Meyer
Ball, &2008;
Gray, 1997; Miethe & Moore,
).Alternatively,
1986
in cases
therewhere
were multiple counts,
the defendant was on probation prior
and the
to defendant
arrest, was detained, there were
greater odds of a plea of guilty or nolo contendere.
Unlike the
Decision to ,Plea
only
Offense Seriousness
andDetained
are significantly
related to
Days
the to Disposition
, with more serious
resulting
cases in a longer time to
disposition, and cases in which the defendant was detained resulting in a sh
disposition. The latter finding is not surprising considering prior research
negative relationship between being detained and the days from the time bail
of the final disposition (Sacks & Ackerman,
As proposed,
2012).
Decision
the
to does
Plea
significantly reduce
Days the
to Disposition
, suggestingpleading
that
guilty or nolo contendere
increases system efficiency.
In Table ,Judge
5.2 -Prosecutor Familiarity
significantly increases the likelihood of
.05
case resulting
plea
in of
a guilty
b = .061,
(
p), which is in line with expectations. A g
combined average experience between the judge and prosecutor also increases
pleab (= .060, .001
p ). Alternatively, a larger gap in experience
the judge and
between
.001
prosecutor reduces the likelihood
b =-.055,
of a pplea
).For
( the
Days to Disposition
,
Judge
-Prosecutor Familiarity
andJudge
-Prosecutor Average Experience
are negative and
significant
,such that
greater interaction between
e and
the
prosecutor
judg a greater
and
combined average experience of the judge and prosecutor reduces the days to
50
about
.9 and 3.7 percent additional
for every interaction
and 1 year increase
average
in
experience of the two
, respectively
actors.
NOTES:n = 907. Not all paths are shown. Unstandardized coefficients are reported.
* p £.05; ** £.01;
p
*** £.001
p
Figure 5.1.
Decision to Plea
Days
andto Disposition
Generalized
Structural Equation
Model for the Effects -of
Prosecutor
Judge
Familiarity, Similarity, and Influence
In addition, the indirect effects are detailed in Table 5.2 and modelle
There is a negative
and significant
indirect effect Judge
between
-Prosecutor Familiarity
and
Days to Disposition
(b =
-.027, p.05)
.There also
is a negative indirectJudge
effect
of
Prosecutor Average Experience
(b =
-.027, p.001)
,and a positive indirect
Judge
effect
of
Prosecutor Experience
(b Gap
= .025, .001)
p onDays to Disposition
, as expected.
Put
differently,
famili
arity and influence
of the judge and prosecutor facilitates system effi
increasing the likelihood of a plea, while dissimilarity in experience betwe
the opposite.
51
Table 5.3. Predicting
the Decision to Plea
Daysand
to Disp
osition: Direct and Indirect
Effects of Judge
-Defense Familiarity, Similarity, and Influence
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
Prior Record
Decision to Plea
Multiple nts
Cou Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to Plea
Age Decision to
leaP
Judge
-Defense Familiarity
Decision to Plea
Judge
-Defense Experience Gap
Decision to Plea
Judge
-Defense Gender Similarity
Decision to Plea
Judge
-Defense Race Similarity
Decision to Plea
Defense More Central
Decision to Plea
Judge
-Defense Average Experience
Decision to Plea
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
Offense Seriousness
Days ot Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Judge
-Defense Familiarity
Days to Disposition
Judge
-Defense Experience Gap
Days to Disposition
Judge
-Defense Gend
er Similarity
Days to Disposition
Judge
-Defense Race Similarity
Days to Disposition
Defense More Central
Days to Disposition
Judge
-Defense Average Experience
Days to Dispositio
Indirect Effects
Judge-Defense Familiarity
Days to Disposition
Judge
-Defense Experience Gap
Days to Disposition
Judge
-Defense Gender Similarity
Days to Disposition
Judge
-Defense Race Similarity
Days to Disposition
D efense More Central
Days to Disposition
Judge
-Defense Average Experience
Days to Dispositio
SE
-.022***
-.158***
.314*
-.298
.641**
.486*
-.701***
-.832***
-.041***
-.104***
-.038***
.463*
-.123
-.905**
.044**
.004
.043
.160
.259
.234
.201
.204
.218
.009
.025
.012
.216
.259
.308
.016
-.565***
.004***
-.002
.006
.042
-.052
-.359***
.050
.078
.001
-.012
-.005
-.100
.010
.060
-.024***
.062
.001
.011
.048
.063
.061
.056
.049
.065
.002
.009
.003
.064
.070
.106
.005
.059***
.022**
-.261*
.069
.511**
-.025**
.014
.007
.125
.147
.179
.009
NOTES:n = 907. AIC = 2841.979, BIC = 3082.486. All indirect effects and error
ABBREVIATIONS:b = unsta
ndardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
The dir
ect and indirect effects
judge and
of defense attorney familiarity, similari
influence are reported in
Judge
Table
-Defense
5.3. Familiarity
is negatively nificantly
and sig
related to
Decision
the
to (Plea
b =-.104, p.001
). This is contrary to the expectation tha
familiarity increases certainty in case processing. With regard to similarit
52
gender similarity increase the likelihood of a case resulting in a guilty pl
defense attorney is more central,
is a reduced
therelikelihood
of a plea
b =-.905,
(
p.01
), while
greater combined average experience of the judge and defense attorney increa
of a plea
b = (.044, .01
p ).Judge
-Defense Average Experi
enceis the only courtroom actor
interaction and influence measure that is significantly
Days to Disposition
related
(b =- to the
.024, p.001
). A greater average experience between
ncreases
the twoefficiency.
actors i
NOTES:n = 907. Not all paths are shown. Unstandardized coefficients are reported.
* p £.05; ** £.01;
p
*** £.001
p
Figure 5.2.
Decision to Plea
D ays
andto Disposition
Generalized
Structural Equation
Model for the Effects -of
Defense
Judge Familiarity, Similarity, and Influence
Regarding the indirect effects (see Table 5.3 and Figure 5.2), as predi
positive and significant indirect
Judge
effect
-Defense
between
Experience
andDays
Gap
to
Disposition
(b = .022, .01)
p ,while there are negative and significantJudge
indirect
effec
Defense Gender Similarity
(b =
-.261, p.05)
andJudge
-Defense Average Experience
(b =.025, p.01)
. Unexpectedly, familiarity between
and defense
the judge
rney
attodecreases the
likelihood of a plea, while a plea decreases the days to disposition, result
53
Table 5.4. Predicting
the Decision to Plea
Daysand
to Disposition: Direct and Indirect
Effects of Prosecutor
-Defense Familiarity, ty,
Similari
and Influence
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
-.023***
Prior Record
Decision to Plea
-.122**
Multiple Counts
Decision to Plea
.382*
Weapon Decision to Plea
-.181
On ProbationDecision to Plea
.629**
Detained Decision to Plea
.410*
Black Decision to Plea
-.689***
Male Decision to Plea
-.861***
Age Decision to Plea
-.043***
Prosecutor
-Defense Familiarity
Decision to Plea
-.092*
Prosecutor
-Defense Experience Gap
Decision to Plea
-.052***
Prosecutor
-Defense Gender Similarity
Decision to Plea
.219
Prosecutor
-Defense Race Similarity
Decision to Plea
-.032
Prosecutor
-Defense Average Experience
Decision
to Plea
.117***
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
-.451***
Offense Seriousness
Days to Disposition
.004***
Prior Record
Days to Disposition
-.005
Multiple Counts
Days to Dispos
ition
-.019
Weapon Days to Disposition
.017
On ProbationDays to Disposition
-.093
Detained Days to Disposition
-.327***
Black Days to Disposition
.062
Male Days to Disposition
.060
Age Days to Disposition
.002
Prosecutor
-Defense Familiarity
Days to Disposition
-.031***
Prosecutor
-Defense Experience Gap
Days to Dispositio
.011***
Prosecutor
-Defense Gender Similarity
Days to Dispositio -.114*
Prosecutor
-Defense Race Similarity
Days to Disposition -.056
Prosecutor
-Defense Average Experience
Days to Dispositio-.020***
Indirect Effects
Prosecutor
-Defense Familiarity
Days to Disposition
.041*
Prosecutor
-Defense Experience Gap
Days to Dispositio
.023***
Prosecutor
-Defense Gender Similarity
Days to Dispositio -.099
Prosecutor
-Defense Race Similarity
Days to Disposition .014
Prosecutor
-Defense Average Experience
Days to Dispositio-.053**
SE
.004
.041
.173
.248
.233
.211
.194
.253
.010
.038
.014
.185
.271
.022
.054
.001
.010
.050
.061
.058
.053
.053
.062
.002
.008
.003
.055
.063
.003
.017
.006
.083
.122
.011
NOTES:n = 907. AIC = 2710.183,
= 2941.070.
BIC
All indirect effects and error variance
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
positive
indirect effect Judge
between
-Defense Familiarity
andDays to Dispositi
on(b = .059, p
.001)
.Similarly, when the defense attorney is more central than the judge, t
likely to result in a plea, and a plea decreases the time to disposition, so
positive indirect effect
Defense
between
More
Central
andDays to Disposition
(b = .511, p
.01).
54
NOTES:n = 907. Not ths
all are
pa shown. Unstandardized coefficients are reported.
* p £.05; ** £.01;
p
*** £.001
p
Figure 5.3.
Decision to Plea
Days
andto Disposition
Generalized
Structural Equation
Model for the Effects of Prosecutor
-Defense Familiarity, Similarity, and Influence
Similar to Table 5.3,and
Table
Figure
5.4showthat
5.3
Prosecutor
-Defense Familiarity
andProsecutor
-Defense Experience
are
Gap
negatively and significantly
elated toDecision
the
r
b =,p
to Plea
(b =-.092, p.05, and-.052
.001,
respectively)
,while
Prosecutor
-Defense
Average Experience
is positive and significant
(b = .117, .001
p ). This means that when the
prosecutor and defense attorney are more familiar with
s less
each
likely
other,
tothe
result
case
in a plea, which istocontrary
expectations
.Inline withpropositions
the
, cases in which the
prosecutor
and defense attorney
a greater
have experience
, orgap
are more dissimilar in terms
experience,
are less likely
result
in
to p
alea
. Also, cases where
prosecutor
the
and defense
attorney have
greater
a
combined average experience are more likely
. These
to result in
findings suggest
experience
that
similarity and
between
influence
the prosecutor and defense
attorney
inc
rease
certainty in case processing, while
between
familiarity
the two does not.
Although
thefamiliarity/certainty relationship was not in the expected di
familiarity between the prosecutor and defense attorney
Days to does
Disposition
reduce
by
the
about.1percent
3
for additional
every interaction between the two attorneys. Also, as ex
the experience gap between the prosecutor and defense
Days
attorney
to
increases the
55
Disposition
(b = .011, .001
p ), while their average experience
Days
decreases
to
the
Disposition
(b =-.020, p.001
). In addition, gender similarity between the prosecuto
defense attorney significantly reduces the time to disposition,
of case
increasing t
processing
(b =-.114. p.05
).
Table 5.5. Predicting
the Decision to Plea
Daysand
to Disposition: Direct and Indirect
Effects of Workgroup Familiarity, Similarity, and Influence
b
Direct Effects on the Decision to Plea
Offense Serious
ness Decision to Plea
Prior Record
Decision to Plea
Multiple Counts
Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to Plea
Age Decision to Plea
Workgroup Familiarity
Decision to Plea
Workgroup Gender Similarity
Decision to Plea
Workgroup Race Similarity
D ecision to Plea
Workgroup Average Experience
Decision to Plea
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
Offense Seriousness
Days to Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Workgroup Familiarity
Days to Disposition
Workgroup Gender Similarity
Days to Disposition
Workgroup Race Similarity
Days to Disposition
Workgroup Average Experience
Days to Disposition
Indirect Effects
Workgroup Familiarity
Days to Disposition
Workgroup Gender Similarity
Days to Disposition
Workgroup Race Similarity
Days to Disposition
Workgroup Average Experience
Days to Disposition
SE
-.022***
-.136***
.291
-.269
.655**
.480*
-.688***
-.853***
-.043***
-.097
.349
.046
.082***
.004
.040
.169
.251
.222
.190
.186
.262
.009
.056
.200
.231
.014
-.468***
.004***
-.006
-.007
.028
-.071
-.331***
.058
.075
.001
-.041**
-.166**
-.008
-.029***
.052
.001
.011
.047
.062
.057
.052
.052
.066
.002
.014
.056
.060
.004
.045
-.163
-.217
-.038***
.026
.095
.108
.007
NOTES:n = 907. AIC = 2773.057, BIC = 2994.324. All indirect effects and error
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
Demonstrated
by Figure 5.3, familiarity between the prosecutor and defense
decreases the likelihood of a plea, although a plea decreases the time to di
56
there is an unexpected significant
ve indirect
and positi
effect
Prosecutor
of-Defense Familiarity
onDays to Disposition
(b = .041, .05)
p .Alternatively, there is an expected positive in
effect between
Prosecutor
-Defense Experience
andDays
Gap
to Disposition
(b = .023, .001)
p ,
and a nega
tive indirect effect
Prosecutor
between
-Defense Average Experience
andDays to
Disposition
(b =
-.053, p.01).
Lastly,
Table 5.5
indicates Workgroup
that
Familiarity
does not increase
likelihood
the of
a plea of guilty or nolo contendere,
rease
butefficiency,
it does incby decreasing
Days to the
Disposition
(b =-.041, p.01
).Workgroup Average Experience
increases
the likelihood of a
pleab (= .082, .001
p ), adding certainty to the system,
Workgroup
and Gender
both
Similarity
and- b =
andWorkgroup Average
xperience
E
reduce the time to disposition
b =-.166, p.01,
(
.029, p.001
, respectively),
suggesting that gender and
similarity
influence
increase efficiency.
As indicated in Table 5.5 and displayed in Figure 5.4, the
effect
only
issignificant
between
Workgroup Average Experience
andDays to Disposition
(b =
-.038, p.001)
, and
t i
is
in the expected direction.
NOTES:n = 907. Not all paths are shown. Unstandardized coefficients are reported.
* p £.05; ** £.01;
p
*** £.001
p
Figure 5.4.
Decision to Plea
Days
andto Disposition
Generalized
Structural Equation
Model for the Effects of Workgroup
liarity,
FamiSimilarity, and Influence
57
5.5 Sensitivity Analyses
Several subsequent analyses were conducted to
ustness
determine
of the
theresults.
rob
First, the models were recalculated ,without
although
clustering
this does not take into accou
that groups of actors can be involved.The
in multiple
results were
cases
substantively similar
those reported, with a few
s. exception
Particularly, in-Defense
the Judge
Attorney model, the
direct effects
Judge
of
Defense Familiarity,
-Defense
Judge ExperienceandJudge
Gap, -Defense
Gender Similarity
on the
Days to Disposition
became negative and significant.
Second,
the models were
analyzed
re
separately using logistic regression to pre
Decision to and
PleaOLS regression to predict
Days to the
Disposition.
Again,het results were
substantively similar
tothose reported.
TheDecision to was
Pleaalso dropped in and out of the
models predicting
Daysthe
to Disposition
in order to detect indirect effects. There was
indication of indirect effects, but the results are notlly
as conclusive
substantively and
as the path models.
Third,
Prosecutor More Central
andDefense Mor
e Central
were converted into
continuous measures.
Judge Centrality
was subtractedProsecutor
from
Centrality,
andJudge
Centrality
was subtractedDefense
from
Centrality,
such that higher values indicated that th
prosecutor and defense attorney
central
were more
then the judge, respectively. The resu
substantively similar
forProsecutor More Central
,butthe direct and indirect
Defense
effects of
MoreCentral
areno longer significant
in the model containing
thejudge and defense attorney
familiar
ity, similarity, and influence
(see Table
measures
5.3 for )
reference
.
Fourth
,the models were reanalyzed using structural equation modelling (S
robust weighted least squares. Using SEM does not
the take
categorical
into account
nature of
themedia
ting variable
, but it does allow for the addition of covariances and repor
fit statistics other than the
.In AIC
these
andanalyses,
BIC
the models and data fit well,
SRMRs of .023 and .024. Additionally, the coefficients
n indicated
of determinatio
that the
models explained between 40.4 and 47.5 percent of
Decision
the variation
to and
Plea in the
Days to Disposition
. The results were substantively similar to those presented wi
differences.
Judge
-Prosecutor Race Similarity
andWorkgroup Gender Similarity
emerged as
positive significant
and
predictor
sof the
Decision to The
Plea.
indirect effect
Judge
- of
58
Prosecutor Familiarity
became non
-significant, while the indirect
Judge
-Prosecutor
effect of
Race Similarity
becamenegative s
and
ignificant.
As mentioned
, the models also
were
analyzed in MPLUS using a robust weighted leas
squares estimator
(WLSMV) .These analyses did account for the dichotomous nature o
mediator. There were some differences between this orted.
model
Judge
and
-Defense
the one rep
Gender Similarity
andProsecutor
-Defense Familiarity
were not significant predictors of the
Decision to Judge
Plea.
-Defense Familiarity
andJudge
-Defense Experience
emerged
Gap
as
negative and significant predictors
Days to Disp
of
osition,
thewhile
Prosecutor
-Defense Gender
Similarity
became a non
-significant predictor
Daysoftothe
Disposition.
Lastly, the indirect
effectsJudge
of -Defense Gender Similarity
andProsecutor
-Defense Familiarity
were non
signific
ant.
Comparison tables
the of
GSEM, SEM, WLSMV, and LOGIT/OLS models can be
found in Appendix B.
Lastly, the relationshipsDecision
between to
theandJudge
Plea -Prosecutor Experience
Gap, Judge
-Defense ExperienceandProsecutor
Gap,
-Defense Experience
were
Gapexamined
further
or fevidence
possible
of threshold effects
tipping
or point
s. Scatter plots were observed
between the actual values of the experience gap Decision
measures to
and
and
Plea
both
thethe
predicted probabilities
of the
Decision to .Plea
Average predicted
bilities
proba were also
calculated at increments of five years using the sample values .of the other
The plots and descriptive statistics indicated linear relationships between
measures andDecision
the
to ,Plea
exceptrethe
was a slight indication
-linearity
of non
for the
measure Prosecutor
of
-Defense ExperienceA Gap.
squared version of this measure was plac
in the model and the analyses confirmed that it was not significant.
5.6Discussion and Conclusion
The studyasw designed to assess the impact of familiarity, similarity, a
amonggroups judges,
of
prosecutors, and defense attorneys on certainty and effi
processing. The results indicated that actors do
arly
have
phases
an case
impact
of
in the e
processing
.Table 5.6
presents the key findings regarding the measures of judge, pr
defense attorney interaction and influence.
59
Experience among theappears
actors toa be
key factor in predicting certainty and
efficiency in case
sing
proces
, as proposed
.In particular
, larger gaps in experience between the
(a) prosecutor and defense attorney, (b) judge and prosecutor, and (c) judge
reduce the
likelihood
of a guilty
, plea
and by consequence, indirectly fficiency
hinder. system e
These findings are in line with Eisenstein et al. s (1988) observation that
distribution of experience among judges, prosecutors, and defense attorneys
court community.
In addition to the experience
a greater
gap,
average experience among the
actors
increases
the likelihood of a plea
sthe
and days
reduce
to disposition.
There is also
evidence to show that by increasing the decision to plea, average experience
system efficiency.
Table5.6Key Findings Regarding the Measures of Familiarity, Similarity, and
Judge
-Prosecutor Familiarity
Judge
-Prosecutor Experience Gap
Judge
-Prosecutor Gender Similarity
Judge
-Prosecutor Race Similarity
Prosecutor More Central
Judge
-Prosecutor Average Experience
Decision t Days to
Plea
Dispositio
+
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
+
-
Indirect
Effects
+
n.s.
n.s.
n.s.
-
Judge
-Defense Familiarity
Judge
-Defense Experience Gap
Judge
-Defense Gender Similarity
Judge
-Defense Race Similarity
Defense
MoreCentral
Judge
-Defense Average Experience
+
n.s.
+
n.s.
n.s.
n.s.
n.s.
n.s.
-
+
+
n.s.
+
-
Prosecutor
-Defense
Prosecutor
-Defense
Prosecutor
-Defense
Prosecutor
-Defense
Prosecutor
-Defense
n.s
n.s.
+
+
n.s.
-
+
+
n.s,
n.s.
-
n.s.
n.s.
n.s.
+
n.s.
-
n.s.
n.s.
n.s.
-
Workgroup
Workgroup
Workgroup
Workgroup
Familiarity
Experience Gap
Gender Similarity
Race Similarity
Average Experience
Familiarity
Gender Similarity
Race Similarity
Average Experience
ABBREVIATIONS:+ = positive and significant,
-= negative and significant,
n.s. = -non
significant
These results demonstrate that similarity
erience andinoverall
exp
experience (a measu
of influence)
lend to a more organized courte system
bureaucratic
wher expectations are more
60
likely to beItmet.
is not surprising
, then, in
that
reference to a newer judge in the courthou
one of the public defender s stated:
And I think a lot of the issues we ve had in our courtroom is because w
new judge who doesn t understand what the bailiff has to do on a daily
in order to transport somebody what he has to do. You know, she can t s
him brought up this afternoon at 1:30. Well, he s gotta go through a lo
get that
ne,
do you know. Just, logistically things have started to get bett
because we have had to train our judge in how everybody s job works and
all works together.
The effect of familiarity between
seemsslightly
the actors
more complicated.
amiliarity
F
between the prosecutor
defense
and attorney
and among the workgroup does seem to increase
system efficiency, and familiarity between the judge and prosecutor increase
pleaand system efficiency
, as predicted. r,
Howeve
famili
arity between the
judge
(a)
and defense
attorney
and (b) prosecutor and defense attorney
thelikelihood
decreases
of a plea
, which
indirectly impedes system. efficiency
Both these measures involve a relationship with t
defense attorney. It is
ing,
notthen,
surpris
that cases are also less likely to result i
the defense attorney is more central
.It than
appears
the that
judgeincreased interaction wit
defense attorney and increased influence of the defense and,
attorney
as a hampers cer
consequence, efficiency
in case processing
.
Defense
attorneys are
who more familiar
with the system, in the sense that they hav
worked with other prosecutors and judgesact
more
differently
often, may
those
fromless
familiar with the.Less
system
amiliar
f
defense attorneys may feel the need to go along
judge and prosecutor as a measure of good will that can help
Hessick
them in their f
& Saugani, 2002
), a sentiment not necessarily shared among more experienced and
defense attorneys
.It is also possible that more attorneys
familiar
aredefense
handling the more
serious cases, and therefore, there might be a conflation between familiarit
of a case. For instance, defense
who are
attorneys
more famili
ar with the prosecutor and judge i
a case
may bemore likely to go to trial if the case is more serious.
When talking public
to a defender about the collegiality between the prosecu
defense attorney offices,
No,she
it said,
s kind of separate,
fortunately.
un
I think there are
some, I don t know whether
friends,
they rebut acquaintances outside the courtroom, b
61
philosophies are different,
Inyou
a similar
know. fashion, one of the state attorneys s
Well, uh, you know we geth along
the public
wit defenders. They re kind of a pain, yo
And they re, you know, a lot of them are believers, they feel like they re s
injustice and the imperialistic society
The that
public
we defender
live in. then, continued
as
far as
he tjudge goes, we don t really have that much of a relationship, except
Alternatively, the state attorney from the circuit court insinuated greater
their office and the judges.help
This
explain
may also
the statistical
erences diff
related to
familiarity between the actors.
Lastly, the gender and racial
up of the
makeactors involved in the cases does not
substantial effect on case processing.
milarityGender
between
si the prosecutor and defense
attorney and among
workgroup
the does reduce the days to disposition,
gender
similarity
and
between the judge and defense attorney increases the likelihood
In
of a plea, a
addition, gender similarity between the judge and defense attorney
e
indirectl
to disposition. Alternatively,
ace similarity
does
r
not exert any significant effects on the
to plea or the days to. disposition
Based on these findings, there are several considerations to be made fo
First,
the measuresamiliarity
of f
and influence are based on the quantity of intera
the judge, prosecutor, and defense attorney. This does not reflect the quali
interactions. Future research should consider measuring the
ionships
qualitytoof court
determine its effect on case outcomes. While an actor may
s/hebe influential bec
participated in a lot of cases and worked with a lot of the actors, this doe
s/he is viewed as an influential person
by th
e in
other
the actors
system
.
Second,
not only are the interaction and influence measures based on the
interactions, theyonly
are
based
alsoon cases in the sample. Considering that the case
the full population of trials and a random
as fromsample
2002 to
of 2010,
ple
the cases should
a fairly accurate reflection of the larger
it caseload.
may be theHowever,
case, in some instance
that certain actors are more familiar or more influential than what was capt
cases. Thismething
is so that should be considered in future research, although it
knowing the judge, prosecutor, and defense attorney for all cases in a parti
any given year or years.
62
Third,
future research may want to consider
effects.
moderating
Specifically,
nteractions
i
between case characteristics, defendant profiles, and courtanalyzed
actor involvemen
further
. For instance, familiarity between actors may be more influential
and
in l
whenthe defendant
does not have as extensive of a prior record. In these cases, t
more room to negotiate and interactions between the judge, prosecutor, and d
would matter more.
Lastly
, additional case outcomes should be considered,
t decision
such as
and
the in/ou
sentence length. Most research ineson
thissentencing
area focus
outcomes and finds that the
decision to plea is a significant factor in predicting someone s sentence (U
Ulmer et al., 2010). It may be the
e, prosecutor,
case that judg
and defense attorney interact
and influence affects the decision to plea, which, in turn, affects the inca
length decision
s. Court actor familiarity, similarity, and influence may also hav
sentenci
ng outcomes (Hoskins Haynes et al., 2010). The used
courtinactor
the study
measures
offer a new and innovative way of looking at the effect of court actors on c
inits
early phases and later phases.
63
CHAPTER 6
THE INDIVIDUAL EFFECTS O F COURTROOM ACTOR INFLUENCE,
RACE, AND GENDER
While the former chapter focused on groups ofchapter
actors,
looksthe
at present
individual actors to determine
er certainty
wheth and efficiency
acrossvaries
prosecutors, judges,
and defense attorneys,
to what
and extent court actor characteristics can explain th
Multi
-level models accounting for the nesting of cases within actors are repo
begins by reviewing the propositions being tested and the analytical
llowed
plan us
by a presentation and discussion of the descriptive statistics and findings.
6.1Purpose and Propositions
The purpose of the analysis
current
is twofold: (1) to determine whether there is v
in certainty and efficiency across
rt individual
actors and cou
(2) to assess the degree to wh
court actor characteristics can explain this variation,
Four characteristics
if it in factofexists
court actors
measured
are : (a) centrality, (b) experience, (c) race, and (d) gender
Centrality andience
experare considered measures of influence, such that cen
captures how central an actor is in terms of both his/her involvement with o
multiple cases, and experience reflects the number of years anbar.
actor has bee
More central prosecutors and defense attorneys are expected to directly incr
a plea and
crease
de
the time to disposition,
he opposite
whileprediction
t scan be made regarding
judges who
are more central.
Judges, prosecutors,
defense
and attorneys with greater years of
experience are expected to directly increase the likelihood of a plea and de
disposition.
In addition to these direct effects, negative indirect effects of p
attorney centrality
and prosecutor, defense attorney, and judge experience on the d
disposition are expected, such that these factors are predicted to reduce th
increasing the likelihood of a plea. Alternatively, there
ect effect
should of
be a positi
judge centrality on the days The
to disposition.
race and
gender of each attorney is also
accounted for considering prior research that has notedand
an gender
impact on
of attorne
64
case processing
(Johnson, 2006; Spohn, 1990; ch,
Schanzenba
2005; Steffensmeier & Britt,
2001).
In the present
analysis
, several limitations noted in prior research
There are addres
arefewstudies
to date have
that been able to link actors to their respective cases i
determine whether there
riation
is va
in case processing (e.g.,
across
Anderson
actors
& Spohn,
2010; Johnson,; 2006
Spohn & Fornango,
).For
2009the most hese
part,studies
t
focus on either
judges
or prosecutors, and
give
rarely
attention to defense attorneys. The present rese
link judges, prosecutors, and defense attorneys to their respective cases. I
previous research has considered race, gender,
(Anderson
and experience
& Spohn, 2010;
Johnson, 2006; Spohn & Fornango,
, centrality
2009)
has yet to and
be measured
looked upon as
a factor that can explain variation in case processing.
Centrality is a way to capture the attorney s role within an
the courthous
aspect of influence that
basedsolely
is not
on the
actor s experience
(Eisenstein 1988)
et al.,
. It is
true that more experienced actors have more of an opportunity to be central
centrality depends on involvement with other actorsthe
andequivalent
in cases,
with one is not
the other.
In the data used, thereion
is of
a correlat
.556 between prosecutor centrality and
experience, .528 between defense attorney centrality and experience, and .03
centrality and experience. Since judges tend to be similarly experienced, th
probably mostly driven
y participation
b
and
in cases
interaction with, attorneys
which would
explain the low correlation between judge centrality and experience.
Similar to the study presented in Chapter
chapter
5,
focuses
the current
on an earlier
phase of case processing
, 2012),
(Ulmerand determines the influence of individual ac
achievement of certainty and efficiency within the courthouse, two of the co
goals (Blumberg, 1967; Eisenstein & Jacob, 1977; Eisenstein et
This
al., 1988; Sk
is different
fromprior research that has focused on sentencing
Actors are
outcomes.
said to be an
important factor in
ing
explain
why cases get resolved in a certain way. Considering th
majority of cases are settled
which
byis
a supposed
plea,
to nsure
e
system efficiency
(Cole,
1976; Heumann, 1981; Skolnick,
, this
1967)
study is able to determine the extent to w
likelihood of a case resulting in a plea varies across judges, prosecutors,
and whether actor influence
, gender, and race can explain some of this variation.
65
6.2 Analysis
Because cases are nested withinmultilevel
court actors,
modelling to
wasestimate
used
random intercept .models
Based on the propositions, generalized structural equatio
(GSEM) was conducted
in order to both
obtain
direct and indirect
for the
effects
variables of
interest
. By using GSEM, both the
-level
multi
nature of the data and the dichotomous ple
outcome was accounted for in the
ierarchical
analyses. generalized
H
modelling
linear(HGLM)
with a Bernoulli distribution
was utilized
to predict the decision
, andtohierarchical
plea
linear
modelling (HLM) used
wasto predict the time to. disposition
As mentioned in Chapter 4, four
cas
es were dropped from the
s because
analyse
nformation
i
was missing pertaining to the rac
the judge or defense attorney
s. As
in a
the
result,
case there
a total
wasof 907 cases nested
within 66 prosecutors, 118 defense attorneys, and 35 judges.
It should be recognized that the cases
ted within
are also
years.
nes There are 96 cases
2002, 90 cases in 2003, 93 cases in 2004, 104 cases in 2005, 123 cases in 20
2007, 124 cases in 2008, 90 cases in 2009, and 80 cases in 2010. In each of
attorneys and judgesparticipate
did not
in any cases, others participated in only on
others participated in more
case.
than
Because
one of this,
not
there
actually
are three levels of
nesting, but rather two different types of nesting at the same level.
This is important
to consider, because two of the four court
centrality
actor measures
and experience
actually vary by year. Therefore, for purposes of the analyses,
as case level measures.
The other two court
measures
actor race and gender
do not
vary by
year, and as a result, are considered courtInactor
the end,
levelall
measures.
four measures are
theoretically
designed to assess the impact of court actors on certainty and effi
processing
, despite thisal
statistic
caveat
. Also,
n consideration
i
of the time element, dummy
variables for each of the years were included
2002
in as
thethe
models,
reference
with category.
6.3 Descriptive Statistics
Table 6.1 reports the descriptive statistics for theInvariables
4.9
5
used in
percent of the cases, the case was disposed by a plea of
andthe
guilty or nolo cont
average logged days from arrest to case disposition61days.
for anyThe
one case was 5.0
66
average case had
offense
an
serious score and
of 46.193
a defend
ant wit
h a prior record ,of 4.763
suggesting that
average
the case had a defendant
(a) a with
pending case or more than one fai
to appear and
prior
(b) felonies.
percent
In 60 of the cases,
e werether
multiple counts, and 17.3
percent of the cases weapon.
involvedThe
a defendant was on probation
19.1percent
inof the
cases and detained
47.2percent
in
of the cases. In the majority of the cases, the de
Black and male, and
verage
the acase had a defendant
was about
who 33 years old.
Table .1
6 Descr
iptive Statistics
Mean
Decision to Plea
.549
Days To Disposition
(logged) 5.061
SD
.498
.823
Min
0
1.792
Max
1
7.359
Offense Seriousness
Prior Record
Multiple Counts
Weapon
On Probation
Detained
Black
Male
Age
46.193
4.763
.600
.173
.191
.472
.734
.846
32.884
57.536
2.339
.490
.378
.393
.499
.442
.361
10.990
4
1
0
0
0
0
0
0
16
813
8
1
1
1
1
1
1
76
Judge
Judge
Judge
Judge
1.404
25.714
.012
.302
.972
7.072
.109
.459
0
10
0
0
4.001
46
1
1
.340
9.432
.029
.398
.258
11.040
.167
.490
0
0
0
0
1.047
40
1
1
.326
16.094
.109
.299
.241
13.028
.312
.458
0
0
0
0
.938
42
1
1
.106
.099
.102
.115
.136
.118
.137
.099
.088
.308
.299
.303
.319
.343
.323
.344
.299
.284
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
Centrality
Experience
Black
Female
Prosecutor
Prosecutor
Prosecutor
Prosecutor
Defense
Defense
Defense
Defense
Year
Year
Year
Year
Year
Year
Year
Year
Year
Centrality
Experience
Black
Female
Centrality
Experience
Black
Female
2002
2003
2004
2005
2006
2007
2008
2009
2010
NOTES:n = 808. Judge, prosecutor, and defense centrality were multi
ABBREVIATIONS:SD = Standard deviation
67
The judge was Black in only 1.2 percent of the cases, while
fense the prosecu
attorney were Blackndin10.9
2.9
percent
a
of the cases, respectively.
A female judge was
present in percent
30.2
of the cases, while there wasora in
female
39.8
percent
prosecut
of the
cases and a female defense attorney in 29.9 percent of the cases. Overall, j
average centrality score, prosecutors
followed by and then defense
. This
attorneys
is not
surprising consideringfar
there
fewer
arejudges than prosecutors and defense attorney
turn
-over is not as common on the bench. Judges also ge
had
experience,
the highest avera
followed by defense
attorneys and then prosecutors.
6.4 Results
Table 6.2 Unconditional Models for the Decision to Plea and Days to Dis
Constant
SE
Variance o
Constant SE
Variance o
Residual SE
Chi-Square
ICC
Decision to Plea
Prosecutor Mode .153
.194
Defense Model
1.024*** .249
Judge Model
.353
.191
1.747
3.161
.619
.493
.906
.296
-
-
178.680*** .347
241.880*** .490
44.150*** .158
Days to Dispositio
Prosecutor Mode 5.165*** .061
Defense Model
5.134*** .049
Judge Model
5.010*** .081
.192
.145
.136
.043
.034
.045
.428
.453
.553
.021
.023
.026
302.810*** .309
256.030*** .242
136.800*** .197
NOTES: n = 907.
ABBREVIATIONS: SE = standard error, ICC = interclass correlation
* p £.05; ** £.01;
p
*** £.001
p
Table6.2 repor
ts the
estimates
of the unconditional models
Decision
for the
to and
Plea
theDays to Disposition.
For the
Decision to models,
Plea
the variances
-square
and chi
values
indicate that there is significant
iation inDecision
the
var
to across
Plea
prosecutors, defense
attorneys, and According
judges.
to the interclass
orrelations
c (ICCs), 34.7 percent, 49 perce
and 15.8 percent of the variance
Decision
into
the
is
Plea
due to differences between
prosecutors,
defense attorneys, and judges, respectively. This means that the re
proportion of variance
Decision
in theto within
Plea
cases for each of the models is 65.3
percent, 51 percent, and 84.2 percent.
Days to Disposition
For the
models, the variance
s of the
constants and the
square
chi values also indicate significant variation across pros
68
attorneys, and judges. In these models, 30.9 percent, 24.2 percent, and 19.7
variance inDays
the to Disposition
can be explained
y differences
b
between prosecutors, defe
attorneys, and judges, respectively, while
Days
thetovariance
Disposition
within
in thecases is
69.1 percent, 75.8 percent, and 80.3 percentThese
for the
findings
three indicate
models. that
multi
-level modell
ing is an appropriate technique with the current data.
Table 6.3: Predicting Days to Disposition: Direct and Indirect Effects of
Centrality, Experience, Race, and Gender
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
Prior Record
Decision to Plea
Multiple Counts
Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to Plea
Age Decision to Plea
Prosecutor Centrality
Decision to Plea
Prosecutor Experience
Decision to Plea
Prosecutor Black
Decision to Plea
Prosecutor
Female Decision to Plea
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
Offense Seriousness
Days to Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Prosecutor Centrality
Days to Disposition
Prosecutor Experience
Days to Disposition
Prosecutor Black
Days to Disposition
Prosecutor Female
Days to Disposition
Indirect Effects
Prosecutor Centrality
Days to Disposition
Prosecutor Experience
Days to Disposition
Variances
Decision to Plea
Days to Disposition
Residual
-.018***
-.130**
.339
-.359
.659**
.463*
-.700***
-.807**
-.049***
.621
.060**
-.946
-.161
SE
.003
.044
.190
.249
.243
.203
.212
.274
.009
.675
.020
1.099
.334
-.355***
.003***
-.005
.018
.020
-.079
-.310***
.045
.041
.002
-.342*
-.023***
.049
.004
.010
.043
.056
.054
.045
.047
.058
.002
.150
.005
.238
-.01
6
.293
.088
-.220
-.021**
.241
.007
.884
.069
.341
.347
.020
.017
NOTES:n = 907. AIC
2661.307,
=
BIC = 2892.307. The standard
Offense
error
Seriousness
of Days to
Disposition
was multiplied by 10 to obtain
-zero value.
a non
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
69
The fully
pecified
s
random intercept model predicting the effect of prosec
race, and genderDecision
on the to andDays
Plea
to Disposition
can be found in6.
Table
3.
Focusing first on the case factors, cases in which the offense
defendant
seriousness
has
i
a more extensive prior record, and defendant
and/
orismale
older,
are Black,
less likely to resul
in a plea, while cases where the defendant /or
was
detained
on probation
are more
and likely to
result in a plea. As predicted,
e defendant
cases where
pled
th guilty or nolo contendere are
associated with shorter days to disposition. This is also true of cases in w
detained.
In contrast, more serious cases reduce system efficiency by increasin
disposition.
Inaddition to the case level factors, the cases
findings
with
more
indicate that
experienced prosecutors are more
result
likely
plea
in a(b
to= .060,
p .01)
andreduce the time
to disposition
(b =
-.023, p.001)
, as expected. It is not surprising, then, that prose
experience has a significant indirect
Days effect
to
Disposition
on(b
the
=
-.012, p.01)
.Also
in line with the predictions,
cases involving
pros
ecutors with greater increase
centrality
system
efficiency
(b =-.342, p.05)
, although they do not significantly increase certainty
system. Race and gender of the prosecutor is Decision
unrelatedtoto
andDaysto
Plea
both the
Disposition
.When comparing this model to the unconditional model, it accounts f
percent of the variation
Decision
in the
to across
Plea
prosecutors and 54.7 percent of the
variation in
Days
theto Disposition
across prosecutors. the
Together,
model explains about 33.9
percent of the variation
Days in
to the
Disposition.
Table6.4 reports the effect of defense attorney influence, race, and gend
Decision to andDays
Plea
to Disposition
. The case factors have a similar
tainty
impact on cer
and efficiency in case processing as the previous model. The only difference
cases where the defendant was on probation prior to arrest are negatively as
Days to Disposition
. Besides the case levelresults
factors,
indicate
the
that cases with more
.001)
experienced defense attorneys are more likely
(b =
to.075,
result
p ,
inwhile
a plea
cases
with more central defense attorneys
,unexpectedly
,less
are
likely to result
(b in
=-3.146,
a plea
p
.001)
,but still reduce the days (b
to =
disposition
.442, p.05)
.BothDefense Centrality
and
Defense Experience
have significant indirect Days
effects
to Disposition
on the, such that cases
involving a more central defense attorney hinder system efficiency
(b = by decrea
70
1.590, p.001)
, and cases involving a more experienced defense
system
attorney increas
efficiency by increasing
(b certainty
=
-.038, p.001)
.
Table 6.4
: Predicting Days to Disposition: Direct and Defense
Indirect
Centrality,
Effects of
Experience, Race, and Gender
b
Direct Effects on the Decision to Plea
Offense Seriousn
ess Decision to Plea
Prior Record
Decision to Plea
Multiple Counts
Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to Plea
Age Decision to Plea
Defense Centrality
Decision to Plea
Defense Experience
Decision to Plea
Defense BlackDecision to Plea
Defense Female
Decision to Plea
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
Offense Seriousness
Days to Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Defense Centrality
Days to Disposition
Defense Experience
Days to Disposition
Defense BlackDays to Disposition
Defense FemaleDays to Disposition
Indi
rect Effects
Defense Centrality
Days to Disposition
Defense Experience
Days to Disposition
Variances
Decision to Plea
Days to Disposition
Residual
-.020***
-.169***
.291
-.461
.727**
.450*
-.825***
-.703*
-.051***
-3.146***
.075***
SE
.003
.049
.208
.281
.268
.226
.235
.300
.010
.857
.020
.186
-.641
.598
.448
-.505***
.003***
-.005
-.001
-.008
-.113*
-.337***
.027
.06
9
.002
-.442*
-.002
.053
.004
.010
.044
.058
.055
.046
.048
.059
.002
.173
.004
.101
-.073
.117
.088
1.590***
-.038***
.464
.011
2.294
.086
.348
.719
.025
.017
NOTES:n = 907. AIC = 2609.393,
C = 2840.280.
BI
The standard
Offense
error Seriousness
of
Days to
Disposition
was multiplied by 10 to obtain
-zero value.
a non
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
In comparison to onditional
the unc
model
, the model presented6.4
in
accounts
Table for
27.5percent
of the variation
Decision
in the to across
Plea
defense attorneys and 27.4 percent
71
the variationDays
in the
to Disposition
across defense attorneys. As a whole,
ains
the model e
about 40.7 percent of the variation
Days to Disposition
in the
. Similar to the prosecutor model,
this model accounts for some of the variation in certainty and efficiency, b
significant variation across attorneys to be explained.
Table 6.5
: Predicting Days to Disposition: Direct and JudgeCentrality,
Indirect Effects of
Experience, Race, and Gender
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
Prior Record
Decision to Plea
Multiple Counts
Decision to Plea
Weapon Decision to Plea
On ProbationDecision to Plea
Detained Decision to Plea
Black Decision to Plea
Male Decision to Plea
Age Decision to Plea
Judge Centrality
Decision to Plea
Judge Experience
Decision to Plea
Judge BlackDecision to Plea
Judge FemaleDecision to Plea
Direct Effects on the
to Disposition
Days
Decision to Plea
Days to Disposition
Offense Seriousness
Days to Disposition
Prior Record
Days to Disposition
Multiple Counts
Days to Disposition
Weapon Days to Disposition
On ProbationDays to Disposition
Detained Days to Disposition
Black Days to Disposition
Male Days to Disposition
Age Days to Disposition
Judge Centrality
Days to Disposition
Judge Experience
Days to Disposition
Judge BlackDays to Disposition
Judge FemaleDays to Disposition
Indirect Effects
Judge Centrality
Days to Disposition
Judge Experience
Daysto Disposition
Variances
Decision to Plea
Days to Disposition
Residual
-.022***
-.145***
.337*
-.320
.549*
.597***
-.603**
-.864***
-.043***
.174
-.003
.528
-.131
SE
.003
.040
.170
.232
.222
.185
.192
.251
.008
.204
.025
1.011
.531
-.511***
.003***
-.001
.025
.07
5
-.050
-.362***
.051
.052
.001
-.254***
-.023*
.047
.004
.009
.044
.057
.054
.045
.048
.059
.002
.065
.010
-.391
.271
.331
.251
-.089
.001
.105
.013
.493
.153
.359
.269
.051
.017
NOTES:n = 907. AIC = 2776.741, BIC = 3007.628. TheOffense
standard
Seriousness
error Days
of
to
Disposition
was multiplied bybtain
10 toao-zero
non value.
ABBREVIATIONS:b = unstandardized coefficient, SE = standard error
* p £.05; ** £.01;
p
*** £.001
p
72
The results
for the influence of judges on certainty and efficiency in case
reported in 6.
Table
5. The effects
of each of the case level Decision
factors on
tothe
and
Plea
Days to Disposition
are substantively similar
6.3 to
and
6Tables
.4.LikeTable6.3,On Probation
is no longer a significant predictor
Days to Disposition.
of the
Unlike Tables
6.3 and
6.4,
Mult
iple Counts
emerges as a positive and significant
Decision
predictor
to When
Plea.
of the
looking at the judge Judge
measures,
Centrality
andJudge Experience
do not emerge as
significant predictors
Decision
of the
to but
Plea,
cases with more central
more experienced
and
b =
judges are associated with
Days fewer
to Disposition
(b =
-.254, p.001 and -.023,
p.05,
respectively)
.Because centrality and experience are Decision
not related
to it
Plea,
to
is the
not
surprising that there
nosignificant
are indirect effect
sof these two factors on system efficien
Similarthe
to other tables, judge race and gender does not influence certainty
case processing.
When compared to the unconditional model, the
6.3 model
explains
in Table
20.4 percent
of the variation
Decision
in the to across
Plea
judges.
Themodel does not increase the variation
explained in
Days
the to Disposition
across judges, although the model as a whole explai
percent of the variation
Days in
to the
Disposition.
This means Judge
that Centrality
andJudge
Experience
, as case level
dictors
pre that vary by year, are explaining some of the va
system efficiency,
Judge
but
Black
andJudge Female
are not. Other
judge
factors
not accounted
forhere may be able
explain
to
this variation.
6.5Sensitivity Analyses
In addition to
M, GSE
separate HGLM and HLM models were conducted
Decision
for the
to Plea
andDays to Disposition
. For the
Days to Disposition
models, the
Decision to was
Plea
left out in each of the initial HLM models and included in each of the subse
to de
tect possible mediating effects. The findings from these analyses were i
reported using GSEM, except GSEM allows for the calculation of indirect effe
mediating effects could only be speculated about in the HLM models.
Followi
ng the recommendations of Enders and Tofighi (2007), the models w
reanalyzed with
case
all
level variables grand mean centered. The results were sub
73
statistically similar to those reported. Of particular importance,
terest
the relat
remained the same in all of the models.
Lastly, the data were reanalyzed including fense
only prosecutors,
attorneys, and
de
judges who
had 3 or more cases.
-level
Multi
modelling assumes a nested structure, which
of most of the cases
data
inset.
the However, some attorneys and judges only particip
or two
case
s. These cases were dropped, leaving 808 cases nested in 56 prosecut
attorneys, and 25 judges. The results of the analyses with the 808 cases wer
similar to those reported. Therefore, the findings using all 907 cases were
6.6Discussion and Conclusion
The purpose ofanalysis
this was to determine if there is variation in certainty
efficiency across individual court
o, actors,
to assess
andthe
if degree
s
to which court acto
characteristics can explain this variation. The findings indicated that ther
explainedboth
incertainty and efficiency of case processing across prosecutors,
attorneys, and judges,
ith more
w variation in the decision to plea and time to disp
prosecutors and defense The
attorneys.
centrality and experience of the court actors wa
explain some of this variation, while the race and gender
e 6.6
of the
reports
actors did
the key findings from the analyses.
Centrality was a key factor in increasing system efficiency across all
was expected that cases with more central judges would slow down case proces
indicated otherwise.
appears
It that increased among
interaction
prosecutors, defense attorneys,
andjudges
with other attorneys and judges, and participation in more cases, be
bureaucratically
by increasing the rate at which cases
Central
are judges
disposed.
hat are
t
more
familiar with the system may understand the bureaucratic
efficiency. As
needs
one of
judge
noted
, the main goal of the courthouse is to
fairly and timely conclude what
brought to us.
While this is true, centrality
efense of
attorney
the d does decrease the
aplea
likelihood of
of guilty
, and as a result, indirectly slows
, which
down the
was system
not expected. However,
this finding is not surprising considering the wing
results
thatindefense
Chapterattorneys
5, sho
who are more central figures
system
in the
seem to have a different impact on case proce
74
than those defense attorneys
are new
who
to the system and are likely under the influe
prosecutor and It
judge.
seems that defense, attorneys
as they continue
to practice,
are more
likely to see the system as
a sentiment
coercive not necessarily shared by state attorn
judges. Regarding
bargaining,
plea
one public defender said,
They say it is a plea of convenience. What does thateans
really mean? To me
they just weren t willing to go through the system and fight it, just
were scared of being found
whenthey
guilty really aren
and the
t
consequences on their family. If they are looking at prison time, then
want to takechance
that at all.
Table 6.6
Key Findings Regarding the Measures of Prosecutor, Defense, and Ju
Centrality, Experience, Race, and Gender
Prosecutor
Prosecutor
Prosecutor
Prosecutor
Defense
Defense
Defense
Defense
Judge
Judge
Judge
Judge
Decision t Days to
Plea
Dispositio
Centra
n.s.
Experie
+
Black
n.s.
n.s.
Female
n.s.
n.s.
Centralit
Experienc
Black
Female
Centrality
Experience
Black
Female
+
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Indirect
Effects
n.s.
-
+
-
n.s.
n.s.
ABBREVIATIONS:+ = positive and significant,
-= negative and significant,
n.s. = -non
significant
Despite this nuance, cases involving
e attorneys
defens
and prosecutors
are more
who
experienced are more likely
n atoplea,
result
which
i indirectly increases system effici
while judge experience directly decreases the time
utors
to disposition.
and defense Prosec
attorneys have
who been around for aemwhile
to besemore familiar with the bureaucratic
environment of
court
the and decide to negotiate their cases to maintain some sort
within the system.
As a public defender explained using an example of a defendant
minimum sentence of 24 months,
75
So, if you negotiate a plea, you can
months,
go below
butthe
if 24
they were to
go to trial, then the judge would have no discretion then to sentence a
between the lowest up to the maximum, unless there s reason for a depar
That s why people don t really want to go to trial,
west because if their lo
permissible sentence is 24 months, and the judge can t do anything abou
they re, you know, a little bit afraid sometimes to go to trial.
In addition,
t is ilikely that more experienced judges have adapted to the bureau
choos
e to
follow the lead
the of
prosecutor in.Ultimately,
the case
court actor centrality and
experience does have an influence, to some extent, on the certain and effici
cases.
Whiletherace the
of court actors has been found entencing
to influence
(Farrell
s
et al.,
2009; Johnson, 2006; Spohn, 1990; Steffensmeier & Britt, 2001; Ward et al.,
prosecutor, judge, and defense attorney did not have an impact on the decisi
to disposition. The race may
of the
partially
actor explain later phases of case processin
not a significant factor in explaining the plea process, an earlier phase of
of courtroom actors has been shown to have little impact on sentencing
istance
and s
departures (Schanzenbach,
Spohn
2005;
& Fornango, 2009), so it is not surprising that
the courtroom actors is an insignificant predictor of the decision to plea a
These findings lend to several considerations
ture research.
for First,
fu
and most
importantly, there still remained a significant amount of variation to be ex
courtroom actors, particularly judges.
that additional
This means individual
court actor factors
should be considered. For
e, prior
instanc
research has accounted for whether a judge w
former defense attorney or had sentenced a large number of offenders (Anders
Johnson, 2006). Research focusing on prosecutors has also considered caseloa
drug cases,
and percentage of violent cases (Spohn& Fornango, 2009). While these
not have a significant effect on substantial assistance departures, they cou
earlier phases of case processing. Nardulli et al.
attitudinal
(1988) also
measures
considered
of
the actors, including belief in punishment, regard for due process, politica
the community.
Second, the study only looked at random intercept models, and did not c
and intercepts as outcomes
odels. It
m may be that some of the relationships between
76
level factors and the outcomes vary across prosecutors, judges, and defense
actor characteristics can then be used to try to explain the variation
ss
in th
actors. For instance, the relationship between offense seriousness and the d
vary across court actors, and factors like perceptions
actor
caseload,
of due process
may be and
able to explain some this variation.
Third, this study
sed is
on ba
data from one courthouse in the Southeast from 20
2010. Replication is needed using data from other courthouses in other regio
time period
to better assess the reliability and external
. It
validity
may bease
of
thethe
c findin
that variation in case processing across prosecutors, judges,
isdifferent
and defense
in
at
other courthouses with different internal and environmental constraints.
Lastl
y, only a couple studies
haveto
assessed
date
variation in case
across
outcomes
court actors, and this research focuses on judges
e.g., and
Anderson
prosecutors
& Spohn,
( 2010;
Johnson, 2006; Spohn & Fornango,
). The2009
data
from the current study
also can
be used to
assess variation
in additional
court outcomes, including
in/outthe
decision and sentence length,
across judges, prosecutors, and defense attorneys. This is something that ca
future to better understand the influence
earlier
of attorneys
and later on
phases of case
processing.
77
CHAPTER 7
DISCUSSION AND CONCLUSION
The study
presented here
usedthe court community perspective
evaluateto
the impact of
courtroom workgroups on the achievement of certain and efficient felony case
believed that criminal courts operate,with
like bureaucratic
organizationsinfrastructures and
action systems composed of key court actors (Blumberg, 1967; Parsons, 1961,
1967). Scholars contend that
smoothly
the more
the actors
in the action work
system
together,
the more likely it court
is that
organization
the
will achieve its goals (Blumberg, 1967
1973; Skolnick, 1967; Thompson,
Certainty
1967).and efficiency are recognized as two k
goals of any organization
(Parsons, 1961, 1969),
for the
and courts,
a plea of guilty
seen as
is
n a
adaptation that can accomplish both of these goals
Because
(Heumann,
of this,
1981).
the study
focused on the decision to plea and time to disposition as operationalizatio
efficiency.
The dominancea of
guilty plea, and ase atime
result,
to disposition,
th
was expected
to depend
on the courtroom workgroup, includingsimilarity,
its familiarity,
and influence
(Eisenstein & Jacob, 1977; Eisenstein et al., 1988; Hoskins
Ulmer,Haynes
1997
).
et al., 2
The research presented he
advances
currentt literature in important ways. It is w
recognized that the majority of cases are resolved through
there little
is
a plea of guilty,
quantitative evidence about the factors that sustain
(Albonetti,
this type
1990;
of Frenzel
system
& Ball
, 2008; Meyer & Gray, 1997; Miethe &.Moore,
Bushway
1986)
and Forst (2013)
recognize that most cases are resolved at arrest screening and by plea barga
limited research about these stages of the criminal justice
the court
process. As note
community perspective
contend
sthat the courtroom workgroup has something to do with
predominant use of pleas within the system (Eisenstein & Jacob, 1977; Eisens
Flemming et al.,
92; 19
Nardulli et al., 1988).
stud
The
y was
current
able to quantify courtroom
workgroup interaction
and connect actors to their respective
to determine
cases in the
order
effect of workgroup familiarity, similarity, and influence.
Additionally,
the court community perspective
contends that trial
rts, as
cou
organizations,
should be operating to maintain both certainty
Relatively
and efficiency.
no
studies to date have looked at the factors that influence system efficiency
Ackerman, 2012), and while it is believed
ilty
that
increases
pleading
system
gu
efficiency
78
(Flemming et al., 1992; Packet, 1968; Seron, 1990), this assumption has neve
verified. Not only are familiar and stable workgroups expected to facilitate
they are also assumedibute
to contr
to system efficiency (Eisenstein et al., 1988; Ge
Therefore, this study incorporated system efficiency and explicitly tested t
disposition and the workgroup
ina case
involved
on efficient case processing.
Several
conclusions can be drawn from the analyses
Table presented.
7.1 reports the
relationships that emerged in the analyses in comparison to the original pro
result is contrary to the original proposition, the expected
parentheses.
relationship is
First,
the use of path models confirmed the interconnection between certainty a
the earlier phases of case processing. Pleading guilty does decrease the tim
factors that increase
ihood
the of
likel
a plea
r add
certainty
(o
into the system)
reduce
indirectly
the time to disposition
increase
system
(or
efficiency)
, and vice .versa
Second,
familiarity between the judge and prosecutor facilitates pleas of
system efficiency, both directly
. However,
and indirectly
familiarity with the defense attor
actually seems to hinder system certainty and efficiency. This finding was s
is suggested that defense attorneys often feel the pressure to go along with
prosecutor asans
a me
of good will for future cases (Hessick & Saugani, 2002). Co
expectation, the findings demonstrated
eis
that
notthis
necessarily
rational true of defense
attorneys who are more familiar and central in the system.
analyses
Even the individu
confirm that cases with more central defense attorneys are less likely to re
having a more central defense attorney does still increase system efficiency
and influential
defense attorneys
ampering
are h
the system in the sense that they add
uncertainties, but their
and
familiarity
influence
does not directly affect system efficiency.
should also be noted that attorney assignment, at least for the more recent
case type. Itbethat
might
familiar defense attorneys tomore
are assigned
serious ,cases
which
are significantly less likely to be pled.
Third, experience has a large impact on case processing. The results se
more experienced attorneys are better
with acquainted
the system and the court s organizati
goals. As a result, they are more likely to uphold certainty and efficiency
entering into plea negotiations. While defense attorney familiarity may nega
systems operatio
ns (from the organizational perspective), more experience does s
79
Table 7.1 Reported Versus Expected Effects of Courtroom Workgroup Familia
Similarity, and Influence on the Decision to Plea and Time from Arrest to C
Time from
Arrest to
Deci
sion to
Case
Indirect
Plea
Disposition Effect
Familiarity
Judge and prosecutor interaction
Judge and defense attorney interac
Prosecutor and defense attorney int
Judge, prosecutor
, and defense attorney
interaction
Similarity
Judge and prosecutor experience di
Judge and defense attorney experie
Prosecutor and defense attorney ex
difference
Judg
e and prosecutor race similar
Judge and defense attorney race si
Prosecutor and defense attorney ra
Judge, prosecutor, and defensethe
att
same race
Judge and prosecutor
gender
similar
Judge and defense attorney
gender
similar
Prosecutor and defense attorney
similar
gende
Judge, prosecutor, and defense
arethe
att
same gender
Influence
Prosecutor is more central
the judge
than
Defense attorney is more central t
Judge and prosecutor average exper
Judge and ense
def attorney average expe
Prosecutor and defense attorney ave
experience
Judge, prosecutor, and defense att
experience
Prosecutor centrality
Defense centrality
Judge centralit
y
Prosecutor experience
Defense experience
Judge experience
Efficiency
Defendant pled guilty or no contende
80
+
-(+)
-(+)
n.s. (+)
-
n.s.
-
n.s. (+)
n.s.+)(
+
+ ()
+ ()
n.s.-) (
+
+
+
n.s.+)(
n.s.+)(
n.s.+)(
n.s. (+)
n.s.-) (
n.s.-) (
n.s.-) (
n.s.-) (
n.s.-) (
n.s.-) (
n.s.-) (
n.s.-) (
n.s.+)(
+
n.s.+)(
n.s.+)(
n.s.-) (
n.s.-) (
-
n.s.-) (
n.s.-) (
n.s.-) (
n.s.+)(
-(+)
+
+
+
n.s.-) (
n.s.-) (
-
n.s.-) (
+ ()
-
+
n.s. (+)
-(+)
n.s.-) (
+
+
n.s.+)(
N/A
-(+)
n.s.-) (
-
n.s.-) (
+ ()
n.s. (+)
n.s.-) (
N/A
the system.
The conflation of centrality with experience
ts that there
sugges
may be some
interactions that should be parsed out further
In addition,
in futureasresearch.
observed by
Eisenstein et al. (1988), greater differences in experience leads to greater
slower system.
Lastly, race andsimilarity,
gender
and individual actor race and gender,
seem to have much of an influence
certain and
on efficient
case processing. On occasion, gender
similarity does increase theaplea
likelihood
and reduce
of
the days to disposition, but fo
mostpart, these extralegal factors do not have an effect.
These findings
particularly
are
important because
they indicate that there is a court a
action system that influences case outcomes, and court actors, to some exten
plea process.
Considering that pleading guilty is a significant predictor of the
(Ulmer & Johnson, 2004; Ulmer et al., 2010), the findings presented are part
They demonstrate that more needs to be known about influence
court actors
on felony
and their
case processing.
With this in there
mind, are
plenty additional
of
avenues to be pursued in future re
This study is based
a sample
on public
of
defender case files from one circuit courthou
Southeast. The sample
thelimits
generalizability of the findings, so repetition is n
different courthouses and different
By using
regions.
a sample of cases, the data does not
all interactions betweenAlso,
attorneys.
because of the focus on one
both
courthouse,
nternal
i
and external
environmental factors cannot be considered. Technically, trial cour
as cases nested within actors, which are nested within courthouse environmen
that es
takthis three
level nesting into consideration
,court community factors such as caseload
trial rate, and organization size can be used as predictors (Johnson, 2005;
Kramer & Ulmer, 2002; Ulmer, 1997; Ulmer & Bradley, 2006; Ulmer & Johnson, 2
et al., 2010; Ulmer
011).
etOther
al., internal
2
courthouse factors, such as the policie
sponsoring agencies, can also be considered. By looking at a single courthou
to determine the extent to which the policies of the sponsoring
cessing
agencies aff
and case outcomes, unless there are differences by year, which was something
These differences can be looked at across courthouses to see if there is var
if policies affect the autonomy of the
kgroups,
courtroom
whichwor
would then ultimately
influence case processing (Eisenstein et al., 1988; Flemming et al., 1992).
81
environmental factors can also be explored, including the influence of legis
justice policy makers
shway(Bu
& Forst, 2013).
This multi
-level modelling design would provide the opportunity
n in to look at
case outcomes across actors (groups and across
individuals)
ourthouses.
c
It can then be
determined whether certain slopes vary
uses
across
as well.
courtho
For instance, the effect
familiarity on case outcomes may vary across courthouses, and as a result, d
factors may be able to explain this variation. The ability to extend the cur
different courthouses
andto have more cases nested within groups
opens up
of aactors
tremendous number of opportunities to learn more about
rt bureaucracies,
criminal trial cou
action systems, and the interconnection between the two. The current
study c
level model
cases as nested within individual
and cluster
actors
analyze the effect
.
of groups
In addition to the sample, future research may want to expand upon the
actor interaction and influence. While these measures are
rent
an advancement to
literature, they are not without limitations. First, the interaction and inf
on the
quantity
of interactions,quality
not of
theinteractions.
This means that the measures
cannot capture the nature of the interactions
er certain or
actors
whethwere good friends vers
mere acquaintances.
Second, the measures used do not assess attitudes of community
Eisenstein et al. (1988) noted that attitudes toward punishment and due proc
person to person and
on positi
to position. In her study, Pollitz Worden (1995) actual
for where judges fell on the due process/crime control scale, judges
wrongdoers, and judges
cynici
attitudes toward plea bargaining. While some of the
measuresd di
not emerge as significant, she did find that believing plea bargai
does lead judges to agree with prosecutors
sentencing
, whichrecommendations
is actually
contrary to what you would
. Itexpect
would be an important and necessary
ep to try next
to st
measure actor attitudes, if not
their
to
effect
determine
on case processing,
themout
to rule
as a
potential confounder
sin the relationship between courtroom workgroup interaction a
processing. Lastly, the measures used
for
only
twoaccounted
demographic characteristics
race
and gender. Hoskin Haynes et al. (2010) also considered law school similarit
and political party
y.similarit
Although the focus of this dissertation was on workgroup
to effects, it is
highlight the effects of race, gender, and age on the decision
cases
to plea. In a
82
involving
Blacks, males,
and older defendants were less likely to result in a plea o
contendere. This means that even after
for workgroup
controlling
characteristics, the effects
race, gender, and age were particularly strong. The data does not indicate w
defendant was offered a plea, and if so, what was included in that offer. Th
such that Blacks,
nstance,
for i may have been less likely to receive offers or may h
offers that were not of the same quality as offers received by Whites. This
within the study should be explored further in future research.
Along with
further cons
ideration of
independent
the
variables, the effect of courtro
workgroups on other case outcomes should be explored. One of the benefits of
is the focus on an earlier phase of case processing, but workgroups can have
phases of case processing as well, including the decision to incarcerate and
According to Flemming
et al. (1992), variations in sentencing
edby courtroom
can be explain
workgroups that differ in their stability and xt
familiarity.
step would Athen
logical
be toneuse
some of the same measures presented here to assess their impact on sentencin
Finally, Albonetti (1991) argues that judges manage uncertainties by de
patterned responses, and as a result, judges
ereotypes
may that
rely link
on stextralegal factor
the likelihood of future offending behavior. These stereotypes may then infl
processing decisions and case outcomes. In this circumstance, potential bias
and outcomes may exist
differe
across
nt races, genders, social
es, etc.
status
The processing of
cases through less formalizedcanalso
workgroups
introduce biases into the process. For
instance, Dixon (1995) found that race affects sentencing in counties with h
judicial
and prosecutorial
bureaucratization
. This finding is consistent with Eisenstein et
(1988) contention that greater division of labor and decentralized decision
bureaucratic courts creates unstable workgroups that on.
rely
It on
would
individual
be
disc
necessary to determine what workgroup interaction and influence characterist
instabilities that may then lead to stereotypes in case processing and case
Ultimately, the current study serves
uponwhat
to is
expand
wn
knoabout the effect of
workgroups in felony case and
processing
confirms some of the propositions of the court
community perspective, while refuting
The results
others.
indicate
amiliarity,
that f experience,
and influence among the actors matter
plea within
process
, with
the defense attorney familiarit
and influence having a particularly interesting and unexpected
up effect. These
83
new and interesting opportunities for future research. It was(a)
the goal of th
quantify court
room workgroup interaction and
(b)
influence,
focus on an earlier phase of case
processing,
(c)explain the predominance of the guilty
(c)test
pleasome
process,
of the key
propositions of the court community perspective,
(d)assess variation
and processing
in case
across courtroom workgroups, groups ofvidual
actors,
actors.
and indi
In achieving
, these goal
this dissertation
suggests
additional research
(a)in other courthouses,
(b)exploring different
court actor interaction and influence
s,(c)analyzing
measure
alternate outcomes,
case
and
(d)
assessing potential
related
issuesto discretion in case processing.
84
APPEND IX A
US SUPREME COURT CASES CITED
United States v., Booker
543 US 220 2005
Gall v. United, States
522 US _____ 2007
85
APPENDIX B
CHAPTER 5 MODEL COM PARISONS
Table B1
. Predicting
the Decision to Plea
Daysand
to Disposition:
Model Comparisons of
theEffects Judge
of -Prosecutor
Familiarity, Similarity, and Influence
GSEM
b
Direct Effects
he on
Decision
t
to Plea
Offense Seriousness
Decision to Plea
-.002***
Prior Record
Decision to Plea
-.018*
Multiple Counts
Decision to Plea
.045
Weapon Decision to Plea
-.090*
On ProbationDecision to Plea
.118***
Detained Decision to Plea
.028
Black Decision to Plea
-.106***
Male Decision to Plea
-.153***
A ge Decision to Plea
-.008***
Judge
-Prosecutor Familiarity
Decision to Plea
.006*
Judge
-Prosecutor ExperienceDecision
Gap
to Plea
-.009***
Judge
-Prosecutor Gender Similarity
Decision to Plea -.004
Judge
-Prosecutor Race Similarity
Decision to Plea
.167*
Prosecutor More Central
Decision to Plea
.069
Judge
-Prosecutor Average Experience
Decision to Ple .008***
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
-.451***
Offense Seriousness
Days to Disposition
.004***
Prior Record
Days to Disposition
-.009
Multiple Counts
Days to Disposition
-.010
Weapon Days to Disposition
.055
On ProbationDays to Disposition
-.062
Detained Days to Disposition
-.333***
Black Days to Disposition
.065
Male Days to Disposition
.074
Age Days to Disposition
.002
Judge
-Prosecutor Familiarity
Days to Disposition
-.009*
Judge
-Prosecutor ExperienceDays
Gap to Dispositio .003
Judge
-Prosecutor Gender Similarity
Days to Dispositio-.025
Judge
-Prosecutor Race Similarity
Days to Dispositio -.090
Prosecutor More Central
Days to Disposition
.018
Judge
-Prosecutor Average Experience
Days to Dispositi-.037***
Indirect Effects
Judge
-Prosecutor Familiarity
Days to Disposition
-.003
Judge
-Prosecutor ExperienceDays
Gap to Dispos
ition
.004***
Judge
-Prosecutor Gender Similarity
Days to Dispositio0.002
Judge
-Prosecutor Race Similarity
Days to Dispositio -.075*
Prosecutor More Central
Days to Disposition
-0.031
Judge
-Prosecutor Average Experience
Days to Dispositi-.003***
NOTES: n = 907.
ABBREVIATIONS: REG = Logistic and OLS regressions, b =
* p £.05; ** £.01;
p
*** £.001
p
86
SEM
b
WLSMV
b
REG
b
-.021***
-.126**
.360*
-.275
.620**
.384*
-.673***
-.868***
-.045***
.061*
-.055***
-.071
.807
.276
.060***
-.011***
-.075**
.203
-.193
.374**
.172
-.410***
-.518***
-.024***
.035*
-.033***
-.034
.498
.210
.036
**
-.021***
-.126**
.360*
-.275
.620**
.384*
-.673***
-.868***
-.045***
.061*
-.055***
-.071
.807
.276
.060***
-.451***
.004***
-.009
-.010
.055
-.062
-.333***
.065
.074
.002
-.009*
.003
-.025
-.090
.018
-.037***
-.251***
.002***
-.019
.021
.048
-.022
-.302***
.010
.013
-.001
-.003
-.001
-.032
-.041
.039
-.031***
-.451***
.004***
-.009
-.010
.055
-.062
-.333***
.065
.074
.002
-.009*
.003
-.025
-.090
.018
-.037***
-.027*
-.009*
.025*** .008***
.032
0.009
-.364
-0.125
-.125
-0.053
-.027*** -.009**
N/A
N/A
N/A
N/A
N/A
N/A
unstandardized coefficient
Table B2
. Predicting
the Decision toDays
Pleato Disposition:
Model Comparisons of the
Effects Judge
of -Defense
Familiarity, Similarity, and Influence
GSEM
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
-.002***
Prior Record
Decision to Plea
-.022**
Multiple Counts
Decision to Plea
.049
Weapon Decision to Plea
-.101*
On ProbationDecision to Plea
.123**
Detained Decision to Plea
.045
Black Decision to Plea
-.106**
Male Decision to Plea
-.165***
Age Decision to Plea
-.007***
Judge
-Defense
Familiarity
Decision to Plea
-.020***
Judge
-Defense Experience Gap
Decision to Plea
-.007***
Judge
-Defense Gender Similarity
Decision to Plea
.087*
Judge
-Defense Race Similarit
y Decision to Plea
-.020
Defense More Central
Decision to Plea
-.154**
Judge
-Defense Average Experience
Decision to Plea
.007**
Direct Effects on the Days to Disposition
Decision
o Plea
t
Days to Disposition
-.565***
Offense Seriousness
Days to Disposition
.004***
Prior Record
Days to Disposition
-.002
Multiple Counts
Days to Disposition
.006
Weapon Days to Disposition
.042
On ProbationDays to Disposition
-.052
Detained Days to Disposition
-.359***
Black Days to Disposition
.050
Male Days to Dispositio
n
.078
Age Days to Disposition
.001
Judge
-Defense Familiarity
Days to Disposition
-.012
Judge
-Defense Experience Gap
Days to Disposition -.005
Judge
-Defense Gender Similarity
Days to Dispositio -.100
Judge
-Defense Race Similarity
Days to Disposition
.010
Defense More Central
Days to Disposition
.060
Judge
-Defense Average Experience
Days to Dispositio-.024***
Indirect Effects
Judge
-Defense Familiarity
Days to Disposition
.011***
Judge
-Defense Experience Gap
Days to Disposition
.004**
Judge
-Defense Gender Similarity
Days to Dispositio -.049*
Judge
-Defense Race Similarity
Days to Disposition
.011
Defense More Central
Days to Disposition
.087**
Judge
-Defense Average Experience
Days to Dispositio-.004**
NOTES: n = 7.
90
ABBREVIATIONS: REG = Logistic and OLS regressions, b =
* p £.05; ** £.01;
p
*** £.001
p
87
SEM
b
WLSMV
b
REG
b
-.022***
-.158***
.314*
-.298
.641**
.486*
-.701***
-.832***
-.041***
-.104***
-.038***
.463*
-.123
-.905**
.044**
-.011***
-.093***
.181
-.224
.382**
.238*
-.416***
-.501***
-.022***
-.063***
-.022***
.258
-.075
-.526**
.026*
-.022***
-.158***
.314*
-.298
.641**
.486*
-.701***
-.832***
-.041***
-.104***
-.038***
.463*
-.123
-.905**
.044**
-.565***
.004***
-.002
.006
.042
-.052
-.359***
.050
.078
.001
-.012
-.005
-.100
.010
.060
-.024***
-.324***
.002***
-.019
.037
.026
.002
-.308***
-.025
.009
-.003
-.021*
-.009*
-.066
-.003
-.023
-.020***
-.565***
.004***
-.002
.006
.042
-.052
-.359***
.050
.078
.001
-.012
-.005
-.100
.010
.060
-.024***
.059***
.022**
-.261*
.069
.511**
-.025**
.020***
.007**
-.083
.024
.170**
-.008*
N/A
N/A
N/A
N/A
N/A
N/A
unstandardized coefficient
Table B3
. Predicting
the Decision to Plea
Daysand
to Disposition:
Model Comparisons of
theEffects Prosecutor
of
-Defense
Familiarit
y, Similarity, and Influence
GSEM
b
SEM
b
WLSMV
b
REG
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
-.023*** -.012*** -.023***
-.002***
Prior Record
Decision to Plea
-.017*
-.122**
-.071**
-.122**
Multiple unts
Co Decision to Plea
.054
.382*
.216*
.382*
Weapon Decision to Plea
-.083
-.181
-.143
-.181
On ProbationDecision to Plea
.119**
.629**
.371**
.629**
Detained Decision to Plea
.031
.410*
.180
.410*
Black Decision to Plea
-.102**
-.689*** -.415***
-.689***
Male Decision to Plea
-.155***
-.861*** -.525**
-.861***
Age Decision to Plea
-.007***
-.043*** -.024*** -.043***
Prosecutor
-Defense Familiarity
Decision to Plea
-.012*
-.092*
-.053
-.092*
Prosecutor
-Defense Experience Gap
Decisi
on to Plea
-.006***
-.052*** -.030*** -.052***
Prosecutor
-Defense Gender Similarity
Decision to Plea
.043
.219
.113
.219
Prosecutor
-Defense Race Similarity
Decision to Plea
-.001
-.032
-.026
-.032
Prosecutor
-Defense Average Experience
Decision Plea
to
.016***
.117*** .068*** .117***
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
-.451*** -.265*** -.451***
-.451***
Offense Seriousness
Days to Disposition
.004***
.004*** .002*** .004***
Prior Record
Daysto Disposition
-.005
-.005
-.016
-.005
Multiple Counts
Days to Disposition
-.019
-.019
.014
-.019
Weapon Days to Disposition
.017
.017
.016
.017
On ProbationDays to Disposition
-.093
-.093
-.049
-.093
Detained Days to Disposition
-.327***
-.32
7***
-.293*** -.327***
Black Days to Disposition
.062
.062
-.002
.062
Male Days to Disposition
.060
.060
-.009
.060
Age Days to Disposition
.002
.002
-.001
.002
Prosecutor
-Defense Familiarity
Days to Disposition
-.031***
-.031*** -.040**
-.031*
**
Prosecutor
-Defense Experience Gap
Days to Dispositio .011***
.011*** .006*
.011***
Prosecutor
-Defense Gender Similarity
Days to Dispositio -.114*
-.114*
-.103
-.114*
Prosecutor
-Defense Race Similarity
Days to Dispositio -.056
-.056
-.062
-.056
Prosecutor
-Defense Average Experience
Days to Dispositio
-.020***
-.020*** -.009*
-.020***
Indirect Effects
Prosecutor
-Defense Familiarity
Days to Disposition
.041*
.014
N/A
.005*
Prosecutor
-Defense Experience Gap
Days to Dispositio .003
***
.023*** .008***
N/A
Prosecutor
-Defense Gender Similarity
Days to Dispositio -.020
-.099
-.030
N/A
Prosecutor
-Defense Race Similarity
Days to Dispositio
.001
.014
.007
N/A
Prosecutor
-Defense Average Experience
Days to Dispositio
-.007***
-.053**
-.018***
N/A
NOTES: n = 907.
ABBREVIATIONS: REG = Logistic and OLS regressions, b = unstandardized coefficient
* p £.05; ** £.01;
p
*** £.001
p
88
Table B4
. Predicting
the Decision to Plea
Daysand
to Disposition:
Model Comparisons of
theEffec
ts of
WorkgroupFamiliarity, Similarity, and Influence
GSEM
b
SEM
b
WLSMV
b
Direct Effects on the Decision to Plea
Offense Seriousness
Decision to Plea
-.022*** -.011***
-.002***
Prior Record
Decision to Plea
-.019**
-.136*** -.080***
Multiple Counts
Decision to Plea
.042
.291
.167
Weapon Decision to Plea
-.095*
-.269
-.194
On ProbationDecision to Plea
.127***
.655**
.391**
Detained Decision to Plea
.043
.480*
.231*
Black Decisio
n to Plea
-.108***
-.688*** -.412***
Male Decision to Plea
-.161***
-.853*** -.497**
Age Decision to Plea
-.008***
-.043*** -.023***
Workgroup Familiarity
Decision to Plea
-.018
-.097
-.058
Workgroup Gender Simila
rity Decision to Plea
.073*
.349
.191
Workgroup Race Similarity
Decision to Plea
.012
.046
.016
Workgroup Average Experience
Decision to Plea
.014***
.082*** .050***
Direct Effects on the Days to Disposition
Decision to Plea
Days to Disposition
-.468*** -.268***
-.468***
Offense Seriousness
Days to Disposition
.004***
.004*** .002***
Prior Record
Days to Disposition
-.006
-.006
-.019
Multiple Counts
Days to Disposition
-.007
-.007
.018
Weapon Days to Disposition
.028
.028
.020
On ProbationDays to Disposition
-.071
-.071
-.025
Detained Days to Disposition
-.331***
-.331*** -.289***
Black Days to Disposition
.058
.058
-.002
Male Days to Disposition
.075
.075
.017
Age Days to Disposition
.001
.001
-.001
Workgroup Familiarity
Days to Disposition
-.041**
-.041**
-.048*
Workgroup Gender Similarity
Days to Disposition
-.166**
-.166**
-.149**
Workgroup Race Similarity
Days to Disposition
-.008
-.008
-.009
Workgroup Average Experience
Days to Disposition
-.029***
-.029*** -.022***
Indirect Effects
Workgroup Familiarity
Days to Disposition
.045
.016
.008
Workgroup Gender Similarity
Days to
Disposition
-.034
-.163
-.051
Workgroup Race Similarity
Days to Disposition
-.006
-.217
-.004
Workgroup Average Experience
Days to Disposition
-.006***
-.038*** -.014***
NOTES: n = 907.
ABBREVIATIONS: REG = Logistic and OLS regressions,
b = unstandardized coefficient
* p £.05; ** £.01;
p
*** £.001
p
89
REG
b
-.022***
-.136***
.291
-.269
.655**
.480*
-.688***
-.853***
-.043***
-.097
.349
.046
.082***
-.468***
.004***
-.006
-.007
.028
-.071
-.331***
.058
.075
.001
-.041**
-.166**
-.008
-.029***
N/A
N/A
N/A
N/A
APPENDIX C
IRB APPROVAL LETTER S
90
91
-
92
REFERENCES
Albonetti, C. A. (1990). Race and
itythe
of probabil
pleadingJournal
guilty.of Quantitative
Criminology,
315
6,
-334.
Albonetti, C. A. (1991). An integration of theories toSocial
explain judicial disc
Problems, 247
38,-266.
Anderson, A. L. & Spohn, C. (2010). Lawlessness
encing
in the
process:
federalA sent
test for
uniformity and consistency in sentence
Justice
outcomes.
Quarterly,
362-393.
27,
Blumberg, A. (1967). The practice of law as confidence game: Organizational
profession.
Law and Society Review,
1, 15
-40.
Borgatti,
. P.,
S Everrett, M. G., & Freeman,
Ucinet
L. C.for
(2002).
Windows: Software for
Social Network Analysis
. Harv
ard, MA: Analytic Technologies.
Borgatti, S. P. & Halgin, D. S. (2011). Analyzing affiliation networks. In J
Carrington (Eds.),
The SAGE Handbook of Social Network
(pp.
Analysis
417
-433).
London: SAGE Publications Inc.
Burstein, C. (1979). Criminal case processing from an organizational perspec
research trends.
The Justice System Journal,
5, 258
-273.
Bushway, S. D.
Forst,
&
B. (2013). Studying discretion in the processes that gen
justice sanctions.
Justice Quarterly,
199-222.
30,
Clynch, E. & Neubauer, D. W. (1981). Trial courts as organizations: A critiq
Law and Policy Quarterly,
3, 69
-94.
Cole, G. (1970). The Decision Law
to Prosecute.
and Society Review,
4, 331
-343.
Cole, G. (1976).
Criminal Justice: Law and
,Second
Politics
Edition
. North Scituate: Duxbury
Press.
Dixon, J. (1995). The organizational context of
Thecriminal
American
sentencing.
Journal of
Sociology, 1157
100,
-1198.
Eisenstein, J., Flemming, R. B., & Nardulli,
The Contours
P. F. (1988).
of Justice: Communities
and Their Courts
. Boston, Massachusetts: Little, Brown & Company Limited.
Eisenstein, J. & Jacob,
).Felony
H. (1977
Justice:
An Organizational Analysis of Criminal
Courts
. Boston: Little, Brown and Company, Inc.
Enders, C. K. & Tofighi, D. (2007). Centering predictor
-sectional
variables
multi
level in cross
models: A new look at an Psychological
old issue. thods,
Me
121
12,-138.
93
Engen, R. L. & Steen, S. (2000). The power to punish: Discretion and sentenc
war on drugs.
American Journal of Sociology,
1357-1395.
105,
Farrell, A., Ward, G., & Rousseau, D. (2009). Race effects
ng federal
of representation
court
workers: Does black workforce representation reduce The
sentencing
Annals disparit
of the American Academy of Political and Social
121-133.
Science, 623,
Feeley, M. (1973). Two models of the criminal justice system:
perspective.
An organizatio
Law & Society Review
,7, 407
-426.
Flemming, R. B., Nardulli, P. F., & Eisenstein,
The Craft
J. of
E. Justice:
(1992). Politics and
Work in Criminal Court Communities.
Philadelphia: University of Pennsylvania Pre
Frank, O. (1997). Composit
ion and structure of social
Mathematique,
networks. Informatique et
Sciences Humaines,
11-23.
35,
Frenzel, E. D. & Ball, J. D. (2008). Effects of individual characteristics o
under sentencing guidelines.
Journal of Ethnicity in
Justice,
Criminal
59-5,
82.
Galanter, M. (1974). Why the haves
change.
Law & Society Review
,9, 95
-160.
come out ahead: Speculations on the lim
Gertz, M. (1980). The impact of prosecutor/public defender interaction on se
exploratoryology.
typCriminal Justice Review,
43-54. 5,
Harris, J. & Jesilow, P. (2000). It s not the old ball game: Three strikes a
workgroup.
Justice Quarterly,
185-203.
17,
Hartley, R. D., Ventura Miller, H., & Spohn, C. (2010).
pay for?
Do you
Type
getof
what you
counsel and its effect on criminalJournal
court outcomes.
of Criminal Justice,
106338,
1070.
Hessick, III, F. A. & Saugani, R. M. (2002). Plea bargaining and convicting
role of the prosecutor, the defense
the judge.
counsel,
BYU Journal
and
of Public Law,
16,189-242.
Heumann, M. (1981).
Plea Bargaining: The Experiences of Prosecutors, Judges, and
Attorneys.
Chicago, IL: The University of Chicago Press.
Hinds, P. J., Carley, K. M., Krackhardt,(2000).
D., & Wholey,
ChoosingD.work group
members: Balancing similarity, competence,
Organizational
and familiarity.
Behavior
and Human Decision Processes,
226-251.
81,
Hoskins Haynes, S., Ruback, B., & Cusick, G. R. (2010). Courtroom workgroups
sentencing: The
fects
ef of similarity, proximity,
Crime
and &stability.
Delinquency, 56,
126-161.
94
Johnson, B. (2005). Contextual disparities in guidelines departures: Courtro
guidelines compliance, and extralegal disparitiesCrimi
in ncriminal
ology, 43,
sentencin
761-796.
Johnson, B. D. (2006). The multilevel context of criminalgesentencing:
-and
Integr
county
-level influences
.Criminology,259
44,
-298.
Johnson, B. D., Ulmer, J. T., & Kramer, J. H. (2008). The social context of
circumvention: The case of federal Criminology,
district courts.
737
46,
-783.
Kautt, P. (2002). Location, Location, Location: Interdistrict and Intercircu
Sentencing Outcomes for Federal
-Trafficking
Drug
Offenses.
Justice Quarterly,
633- 19,
671.
Kramer, J. H. & Ulmer, J. T. (2002). Downward departures for serious violent
court corrections to Pennsylvania s sentencing
Criminology,
guidelines.
897
40,
-932.
Louis, M. R. (1983). Organizati
ons as culture
-bearing milieux. In J. M. Shafritz and J. S.
(Eds.),
Classics of Organization Theory,(pp.
Fifth
509
-519).
Edition
Belmont:
Wadsworth, Inc.
Mears, D. P. (1998). The sociology of sentencing: Reconceptualizing decision
andoutcomes.
Law & Society Review,
667-724.
32,
Meyer, J. & Gray, T. (1997). Drunk drivers in the-legal
courts:
factors
legalaffecting
and extra
pleas and sentences.
Journal of Criminal Justice,
155-163. 25,
McCoy, C. (2005). Plea bargainingThe
as trial
coercion:
penalty and plea bargaining refor
Criminal Law Quarterly,
67-107.
50,
McGloin, J. M. & Piquero, A. (2010). On the relationship
-offending between
network co
redundancy and offending versatility.
Journal of Research in Crime and Delinquency,
63-90.
Miethe, T. D. & Moore, C. A. (1986). Racial differences in criminal processi
consequences of model selection on conclusions aboutThe
differential treat
Sociological Quarterly,
217-237.
27,
Nardulli, P. (1979).
The Study of Criminal
ourts:C Political Perspectives
. Cambridge: Ballinger
Publication Company.
Nardulli, P. F., Eisenstein, J., & Flemming,
The Tenor
R. B.
of (1988).
Justice: Criminal Courts
and the Guilty Plea Urbana:
Process.University of Illinois Press.
Neubauer, D. W.
Fradella,
&
H. F. America
(2011). s Courts and the Criminal Justice Syste
10thEdition.
California: Wadsworth, Cenage Learning.
95
Packer, H. L. (1964). Two models of the University
criminal process.
of Pennsylvania Law
Review, 113,
1-68.
Packer, H. L.8).
(196
The Limits of the Criminal
. Stanford:
Sanction Stanford University Press
Parsons, T. (1961).
Theories of Society: Foundations of Modern Sociological
. New York: Theory
Free Press.
Parsons, T. (1969).
Politics and Social .Structure
New York: Free
s.Pres
Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the c
for the equality of regression
Criminology,
coefficients.
859
36,
-866.
Pollitz Worden, A. (1995). The judge s role in plea bargaining:
udges agreement
An analysis
with prosecutors sentencing recommendations.
Justice Quarterly,
257-278.
12,
Sacks, M. & Ackerman, A. (2012). Pretrial detention and guilty pleas: If the
they must be guilty.
Criminal Justice Studies: A Critical
al of Crime,
JournLaw and
Society, 265
25,
-278.
Schanzenbach, M. (2005). Racial and sex disparities in prison sentences:
The
level judicial demographics.
Journal of Legal Studies,
57-88. 34,
Selznick, P. (1948). Foundations of
organization.
the theory
American
of Sociological, Review
13, 25
-35.
Seron, C. (1990). The impact of court organization
Law & Society
on litigation.
Review,
24, 451
465.
Skolnick, J. (1965). The sociology of law in America:
Social
Overview
Problems
,13,
and trends.
4-39.
Skolnick, J. (1967). Social control in the
The adversary
Journal ofsystem.
Conflict ,Resolution
11, 52
-70.
Smircich, L. (1983). Concepts of culture and organizational
Administrative
Analysis.
Science
Quarterly
,28, 339
-358.
Spohn, C. (1990).
entencing
The s
decisions of black and white judges: Expected and
similarities.
Law & Society Review,
1197-24,
1216.
Spohn, C. & Fomango, R. (2009). US attorneys and substantial assistance depa
interprosecutor disparity.
Criminolog
y, 47,
813-846.
Steffensmeier
, D. & Britt, C. (2001).
raceJudges
and judicial decision making: Do black j
sentence differently?
Social Science Quarterly,
749-764.
82,
96
Steffensmeier, D., Kramer, J., & Streifel. (1993). Gender
ons.
and imprisonment d
Criminology,411
31,
-446.
Sudnow, D. (1965). Normal crimes: Sociological features of a penal code in a
office.
Social Problems
,12, 255
-275.
Thompson, J. D. (1967).
Organizations in New
Action.
York: McGraw
-Hill.
Thompson Hephner,
S. (2002). Negotiation: The strength of the courtroom workgro
guilty plea process.
Dissertation.
Ulmer, J. (1995). The organization and consequences of social
The pasts in crimi
Sociological Quarterly,
587-605.
36,
Ulmer, J. T.7).
(199
Social Worlds of Sentencing: Court Communities Under Sentencin
Guidelines.
Albany: State University of New York Press.
Ulmer, J. T. (2012). Recent developments and new directions
Justice
in sentencing re
Quarterly,1-29,
40.
Ulmer, J. T.adley,
& Br M. S. (2006). Variation in trial penalties among serious
offenders.
Criminology,631
44,
-670.
Ulmer, J. T., Eisenstein, J., & Johnson, B. (2010). Trial penalties
in feder
guidelines factors and district
Justic
e variation.
Quarterly,
560
27,
-592.
Ulmer, J. & Johnson, B. (2004). Sentencing in context:
Criminology,
A multilevel
42,analysis
137-177.
Ulmer, J., Light, M. T., & Kramer, J. (2011). The liberation of federal ju
wake of the Booker/Fanfa
n decision: Is there increased disparity and divergen
courts?
Justice Quarterly,
799-837.
28,
Ward, G., Farrell, A., & Rousseau, D. (2009). Does racial balance in workfor
yield equal justice? Race relations of
ral
sentencing
court organizations
in .fede
Law &
Society Review,
, 757
43
805.
Williams, M. R. (2003). The effect of pretrial detentionCriminal
of imprisonment dec
Justice Review,
299-28,
316.
Wooldredge, J. D. (1989). An
-level
aggregate
examination of the
pressure
caseload
hypothesis.
Journal of Quantitative Criminology,
259-283.
5,
Wooldredge, J. (2010). Judges
Criminology,539
48,
-567.
unequal contributions to extralegal dispariti
97
BIOGRAPHICAL SKETCH
Christi Metcalfe, Ph.D., graduated
the College
fromof Criminology and Criminal Just
atFlorida State
University
in the summer of 2014 after completing her Bachelor s degr
Master s degree, and Ph.D. at the university. During her time at Florida Sta
an editorial nt
assista
for
Social Problems
, the internship coordinator
for the College of
Criminology and Criminal
, and
Justice
a teaching for
assistant
Dr. Marc Gertz. This
all,
coming f
Christi will begin her career as an in
assistant
the Department
professor
of Criminology
and
Criminal Justice
at the University of South Carolina. Her research interests incl
criminal justice systems, developmental/life course criminology, punitive at
quantitative methods. Her work has appeared in various
ingCrime outlets,
& Delinquency,
includ
Journal of Quantitative Criminology,
Journal of Research in Crime and Criminal
Delinquency,
Justice and Behavior, Youth Violence and
,andCriminal
Juvenile Justice
Justice Policy
. Review
98